The present disclosure provides method for preparing a personalized
cancer vaccine. The present disclosure also provides a method to train a
machine-learning HLA-peptide presentation prediction model.
| Inventors: |
Rooney; Michael Steven; (Boston, MA)
; Abelin; Jennifer Grace; (Boston, MA)
; Barthelme; Dominik; (Belmont, MA)
; Kamen; Robert; (Sudbury, MA)
|
| Applicant: | | Name | City | State | Country | Type |
Neon Therapeutics, Inc. |
Cambridge |
MA |
US | | |
|---|
| Family ID:
|
71101917
|
| Appl. No.:
|
16/824331
|
| Filed:
|
March 19, 2020 |
Related U.S. Patent Documents
| | | | |
|---|
|
| Application Number | Filing Date | Patent Number |
|---|
| | PCT/US2019/068084 | Dec 20, 2019 | |
| | 16824331 | | |
| | 62891101 | Aug 23, 2019 | |
| | 62855379 | May 31, 2019 | |
| | 62826827 | Mar 29, 2019 | |
| | 62783914 | Dec 21, 2018 | |
|
|
| Current U.S. Class: |
1/1 |
| Current CPC Class: |
C07K 16/2833 20130101; G16B 30/00 20190201; G16B 20/30 20190201; G16B 40/10 20190201; A61K 39/39 20130101; G16B 25/10 20190201; G16B 40/20 20190201; G16B 40/00 20190201; G16B 15/30 20190201; A61P 35/00 20180101 |
| International Class: |
G16B 30/00 20060101 G16B030/00; G16B 5/00 20060101 G16B005/00; G16B 40/00 20060101 G16B040/00; C07K 16/28 20060101 C07K016/28; A61K 39/39 20060101 A61K039/39 |
Claims
1. A method comprising: processing amino acid information of a plurality
of candidate peptide sequences using a machine learning HLA-peptide
presentation prediction model to generate a plurality of presentation
predictions, wherein each candidate peptide sequence of the plurality of
candidate peptide sequences is encoded by a genome, transcriptome, or
exome of a subject, or a pathogen or a virus in the subject; wherein the
plurality of presentation predictions comprises an HLA presentation
prediction for each of the plurality of candidate peptide sequences,
wherein each HLA presentation prediction is indicative of a likelihood
that one or more proteins encoded by a class II HLA allele of a cell of
the subject can present a given candidate peptide sequence of the
plurality of candidate peptide sequences, and wherein the machine
learning HLA-peptide presentation prediction model is trained using
training data comprising sequence information of sequences of training
peptides identified to bind to an HLA class II protein; and identifying,
based at least on the plurality of presentation predictions, a peptide
sequence of the plurality of peptide sequences as being presented by at
least one of the one or more proteins encoded by a class II HLA allele of
a cell of the subject; and wherein the machine learning HLA-peptide
presentation prediction model has a positive predictive value (PPV) of at
least 0.07 according to a presentation PPV determination method.
2. The method of claim 1, wherein the sequences of training peptides
identified to bind to an HLA class II protein have a length of at least 7
amino acids.
3. The method of claim 1, wherein the machine learning HLA-peptide
presentation prediction model is trained using training data comprising
sequence information of sequences of training peptides identified by mass
spectrometry to be presented by an HLA class II protein expressed in
training cells.
4. The method of claim 3, wherein: (i) the training cells comprise
training cells expressing a single MHC class II complex or a single
allelic variant for a class II HLA locus selected from the group
consisting of DR, DP, and DQ, where-in the single MHC class II complex or
a protein encoded by the single allelic variant is expressed by a cell of
the subject; or (ii) the training data comprises training data obtained
by deconvolution.
5. The method of claim 3, wherein the training cells express a protein
encoded by a class II HLA allele of a cell of the subject, wherein the
protein encoded by a class II HLA allele comprises an affinity tag.
6. The method of claim 3, wherein the machine learning HLA-peptide
presentation prediction model has a positive predictive value (PPV) of at
least 0.07 when amino acid information of a plurality of test peptide
sequences are processed to generate a plurality of test presentation
predictions, each test presentation prediction indicative of a likelihood
that the one or more proteins encoded by a class II HLA allele of a cell
of the subject can present a given test peptide sequence of the plurality
of test peptide sequences, wherein the plurality of test peptide
sequences comprises at least 500 test peptide sequences comprising: (i)
at least one hit peptide sequence identified by mass spectrometry to be
presented by an HLA protein expressed in cells, and (ii) at least 499
decoy peptide sequences contained within a protein encoded by a genome of
an organism, wherein the organism and the subject are the same species,
wherein the plurality of test peptide sequences comprises a ratio of
1:499 of the at least one hit pep-tide sequence to the at least 499 decoy
peptide sequences and a top 0.2% of the plurality of test pep-tide
sequences are predicted to be presented by the HLA protein expressed in
cells by the machine learning HLA-peptide presentation prediction model.
7. The method of claim 6, wherein: (i) the at least one hit peptide
sequence comprises at least 10 hit peptide sequences, (ii) the at least
499 decoy peptide sequences comprises at least 4990 decoy peptide
sequences, and (iii) the top percentage is a top 0.2%.
8. The method of claim 3, wherein any nine contiguous amino acid
sub-sequences of any of the at least one hit peptides does not overlap
with any nine contiguous amino acid sub-sequences of the at least 4990
decoy peptide sequences.
9. The method of claim 1, wherein each peptide sequence of the plurality
of candidate peptide sequences is associated with a cancer.
10. The method of claim 9, wherein each peptide sequence of the plurality
of candidate peptide sequences (i) comprises a mutation, (ii) is
expressed in a cancer cell of the subject, and (iii) is not encoded by a
genome of a non-cancer cell of the subject.
11. The method of claim 1, wherein the one or more proteins encoded by a
class II HLA allele is selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB5*01:01, HLA-DRB1*01:01,
HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02,
HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*09:01, HLA-DRB1*11:01,
HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*13:01,
HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01,
HLA-DRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:02, HLA-DRB3*01:01,
HLA-DRB3*02:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01,
HLA-DRB4*01:03, HLA-DRB5*01:01; HLA-DPB1*01:01, HLA-DPB1*02:01,
HLA-DPB1*02:02, HLA-DPB1*03:01, HLA-DPB1*04:01, HLA-DPB1*04:02,
HLA-DPB1*05:01, HLA-DPB1*06:01, HLA-DPB1*11:01, HLA-DPB1*13:01,
HLA-DPB1*17:01, HLA-DQA1*01:01/HLA-DQB1*05:01,
HLA-DQA1*01:02/HLA-DQB1*06:02, HLA-DQA1*01:02/HLA-DQB1*06:04,
HLA-DQA1*01:03/HLA-DQB1*06:03, HLA-DQA1*02:01/HLA-DQB1*02:02,
HLA-DQA1*02:01/HLA-DQB1*03:03, HLA-DQA1*03:01/HLA-DQB1*03:02,
HLA-DQA1*03:03/HLA-DQB1*03:01, HLA-DQA1*05:01/HLA-DQB1*02:01 and
HLA-DQA1*05:05/HLA-DQB1*03:01.
12. The method of claim 1, wherein the machine learning HLA-peptide
binding prediction model has a positive predictive value (PPV) of at
least 0.1 when amino acid information of a plurality of test peptide
sequences are processed to generate a plurality of test binding
predictions according to a binding PPV determination method, wherein each
test binding prediction is indicative of a likelihood that the one or
more proteins encoded by a class II HLA allele of a cell of the subject
binds to a given test peptide sequence of the plurality of test peptide
sequences, wherein the plurality of test peptide sequences comprises at
least 20 test peptide sequences comprising (i) at least one hit peptide
sequence identified by mass spectrometry to be presented by an HLA
protein expressed in cells and (ii) at least 19 decoy peptide sequences
contained within a protein comprising at least one peptide sequence
identified by mass spectrometry to be presented by an HLA protein
expressed in cells, such as a single HLA protein expressed in cells,
wherein the plurality of test peptide sequences comprises a ratio of 1:19
of the at least one hit peptide sequence to the at least 19 decoy peptide
sequences and a top 0.5% of the plurality of test peptide sequences are
predicted to bind to the HLA protein expressed in cells by the machine
learning HLA-peptide presentation prediction model.
13. The method of claim 1, wherein the method comprises obtaining a
plurality of polynucleotide sequences of the subject by genome,
transcriptome, or exome sequencing, wherein the plurality of
polynucleotide sequences encode the plurality of candidate peptide
sequences.
14. The method of claim 13, wherein the genome, transcriptome or exome
sequencing is whole genome sequencing, whole transcriptome, or whole
exome sequencing.
15. The method of claim 1, wherein the method comprises selecting one or
more epitope sequences of the plurality of candidate peptide sequences
for preparing a pharmaceutical composition, wherein the plurality of
candidate peptide sequences have been ranked based on the plurality of
presentation predictions.
16. The method of claim 15, wherein each of the one or more selected
epitope sequences binds to a protein encoded by a class II HLA allele of
a cell of the subject with an IC50 of 500 nM or less, or a predict-ed
IC50 of 500 nM or less.
17. The method of claim 15, wherein the method further comprises
preparing the pharmaceutical composition, wherein the pharmaceutical
composition comprises one or more polypeptides comprising at least two of
the selected epitope sequences or one or more polynucleotides encoding
the at least two of the selected epitope sequences.
18. The method of claim 15, wherein the method further comprises
administering the pharmaceutical composition to the subject, wherein the
pharmaceutical composition comprises one or more polypeptides comprising
at least one of the selected epitope sequences or one or more
polynucleotides encoding at least one of the selected epitope sequences.
19. The method of claim 18, wherein the pharmaceutical composition
further comprises an adjuvant.
20. The method of claim 18, wherein the pharmaceutical composition
elicits a CD4+ T cell response and/or a CD8+ T cell response in the
subject.
Description
CROSS-REFERENCE
[0001] This application is a continuation of International Application No.
PCT/US2019/068084 filed Dec. 20, 2019 which claims the benefit of U.S.
Provisional Application No. 62/891,101, filed on Aug. 23, 2019; U.S.
Provisional Application No. 62/855,379, filed on May 31, 2019; U.S.
Provisional Application No. 62/826,827, filed on Mar. 29, 2019; and
62/783,914, filed on Dec. 21, 2018; each of which is incorporated herein
by reference in its entirety.
SEQUENCE LISTING
[0002] The instant application contains a Sequence Listing which has been
submitted electronically in ASCII format and is hereby incorporated by
reference in its entirety. Said ASCII copy, created on Jan. 31, 2020, is
named 50401-735_301_SL.txt and is 27,415 bytes in size.
BACKGROUND
[0003] The major histocompatibility complex (MHC) is a gene complex
encoding human leukocyte antigen (HLA) genes. HLA genes are expressed as
protein heterodimers that are displayed on the surface of human cells to
circulating T cells. HLA genes are highly polymorphic, allowing them to
fine-tune the adaptive immune system. Adaptive immune responses rely, in
part, on the ability of T cells to identify and eliminate cells that
display disease-associated peptide antigens bound to human leukocyte
antigen (HLA) heterodimers.
[0004] In humans, endogenous and exogenous proteins can be processed into
peptides by the proteasome and by cytosolic and endosomal/lysosomal
proteases and peptidases and presented by two classes of cell surface
proteins encoded by MHC genes. These cell surface proteins are referred
to as human leukocyte antigens (HLA class I and class II), and the group
of peptides that bind them and elicit immune responses are termed HLA
epitopes. HLA epitopes are a key component that enables the immune system
to detect danger signals, such as pathogen infection and transformation
of self. CD4+ T cells recognize class II MHC (HLA-DR, HLA-DQ, and HLA-DP)
epitopes displayed on antigen presenting cells (APCs), such as dendritic
cells and macrophages. The endogenous processing and presentation of HLA
class II-ligands is a complex procedure and involves a variety of
chaperones and a subset of enzymes that are not all well characterized.
HLA class II-peptide presentation activates helper T cells, subsequently
promoting B cell differentiation and antibody production as well as CTL
responses. Activated helper T cells also secrete cytokines and chemokines
that activate and induce differentiation of other T cells.
[0005] Understanding the peptide-binding preferences of every HLA class II
heterodimer is the key to successfully predicting which cancer or
tumor-specific antigens are likely to elicit the cancer or tumor-specific
T cell responses. There is a need for methods of identifying and
isolating specific HLA class II-associated peptides (e.g., neoantigen
peptides). Such methodology and isolated molecules are useful, e.g., for
the development of therapeutics, including but not limited to, immune
based therapeutics.
SUMMARY
[0006] The methods and compositions described herein find uses in a wide
range of applications. For example, the methods and compositions
described herein can be used to identify immunogenic antigen peptides and
can be used to develop drugs, such as personalized medicine drugs, and
isolation and characterization of antigen-specific T cells.
[0007] CD4+ T cell responses may have anti-tumor activity. A high rate of
CD4+ T cell responses may be shown without using Class II prediction
(e.g., 60% of SLP epitopes in NeoVax study (49% in NT-001, see Ott et
al., Nature, 2017 Jul. 13; 547(7662):217-221), and 48% of mRNA epitopes
in Biontech study, see Sahin et al., Nature, 2017 Jul. 13;
547(7662):222-226). It may not be clear whether these epitopes are
typically presented natively (by tumor or by phagocytic DCs). It may be
desirable to translate high CD4+ T response rates into therapeutic
efficacy by improving identification of truly presented HLA class II
binding epitopes.
[0008] The roles of gene expression, enzymatic cleavage, and
pathway/localization bias may have not been robustly quantified. It may
be unclear whether autophagy (HLA class II presentation by tumor cells)
or phagocytosis (HLA class II presentation of tumor epitopes by APCs) is
the more relevant pathway, although most existing MS data may be presumed
to derive from autophagy. NetMHCIIpan may be the current prediction
standard, but it may not be regarded as accurate. Of the three HLA class
II loci (DR, DP, and DQ), data may only exist for certain common alleles
of HLA-DR.
[0009] There may be different data generation approaches for learning the
rules of HLA Class II presentation, including the field standard and the
proposed approach. The field standard may comprise affinity measurements,
which may be the basis for the NetMHCIIpan predictor, providing low
throughput and requiring radioactive reagents, and it misses the role of
processing. The proposed approach may comprise mass spectrometry, where
data from cell lines/tissues/tumors may help determine processing rules
for autophagy and mono-allelic MS may enable determination of
allele-specific binding rules (multi-allelic MS data is presumed overly
complex for efficient learning (Bassani-Sternberg. MCP. 2018)).
[0010] There may be different ways to validate the new HLA class II
predictors: validation on held-out MS data, which may be default setting;
retrospective of vaccine studies (e.g. NT-001), where immune monitoring
data may assess vaccine peptide loading on APCs rather than tumor
presentation and data may be thinly stretched across many different
alleles; biochemical affinity measurements, which may be configured to
get measurements for discordantly predicted peptides (only for 2-3
alleles); T cell inductions, which may be configured to test the rates at
which Neon-preferred and NetMHCIIpan-preferred epitopes induce ex vivo T
cell responses.
[0011] For validation through T cell inductions, the default approach may
comprise assessing neoORFs from TCGA that are discordantly predicted,
wherein induction materials may comprise healthy donor APCs and T cells
and induction and readout may be via SLP (.about.15mer peptides). Random
peptides may give a high rate of responses and SLP may insufficiently
address processing. Possible solutions may comprise induction via mRNA.
[0012] The methods disclosed herein may comprise generating LC-MS/MS
mono-allelic data for the training of allele-specific machine learning
methods for epitope prediction. Such methods may comprise increasing
LC-MS/MS data quality utilizing a set of quality metrics to stringently
remove false positives that increases the performance of a prediction
model; identifying allele-specific HLA class II binding cores from
HLA-ligandome LC-MS/MS datasets; utilizing machine learning algorithms to
improve HLA class II-ligand and epitope prediction; and/or identifying
biological variables that impact HLA class II-ligand presentation and
improve HLA class II epitope prediction, such as gene expression,
cleavability, gene bias, cellular localization, and secondary structure.
[0013] Provided herein is a method comprising: (a) processing amino acid
information of a plurality of candidate peptide sequences using a machine
learning HLA peptide presentation prediction model to generate a
plurality of presentation predictions, wherein each candidate peptide
sequence of the plurality of candidate peptide sequences is encoded by a
genome or exome of a subject, wherein the plurality of presentation
predictions comprises an HLA presentation prediction for each of the
plurality of candidate peptide sequences, wherein each HLA presentation
prediction is indicative of a likelihood that one or more proteins
encoded by a class II HLA allele of a cell of the subject can present a
given candidate peptide sequence of the plurality of candidate peptide
sequences, wherein the machine learning HLA peptide presentation
prediction model is trained using training data comprising sequence
information of sequences of training peptides identified by mass
spectrometry to be presented by an HLA protein expressed in training
cells; and (b) identifying, based at least on the plurality of
presentation predictions, a peptide sequence of the plurality of peptide
sequences as being presented by at least one of the one or more proteins
encoded by a class II HLA allele of a cell of the subject; wherein the
machine learning HLA peptide presentation prediction model has a positive
predictive value (PPV) of at least 0.07 according to a presentation PPV
determination method.
[0014] Provided herein is a method comprising: (a) processing amino acid
information of a plurality of peptide sequences of encoded by a genome or
exome of a subject using a machine learning HLA peptide binding
prediction model to generate a plurality of binding predictions, wherein
the plurality of binding predictions comprises an HLA binding prediction
for each of the plurality of candidate peptide sequences, each binding
prediction indicative of a likelihood that one or more proteins encoded
by a class II HLA allele of a cell of the subject binds to a given
candidate peptide sequence of the plurality of candidate peptide
sequences, wherein the machine learning HLA peptide binding prediction
model is trained using training data comprising sequence information of
sequences of peptides identified to bind to an HLA class II protein or an
HLA class II protein analog; and (b) identifying, based at least on the
plurality of binding predictions, a peptide sequence of the plurality of
peptide sequences that has a probability greater than a threshold binding
prediction probability value of binding to at least one of the one or
more proteins encoded by a class II HLA allele of a cell of the subject;
wherein the machine learning HLA peptide binding prediction model has a
positive predictive value (PPV) of at least 0.1 according to a binding
PPV determination method.
[0015] In some embodiments, the machine learning HLA peptide presentation
prediction model is trained using training data comprising sequence
information of sequences of training peptides identified by mass
spectrometry to be presented by an HLA protein expressed in training
cells.
[0016] In some embodiments, the method comprises ranking, based on the
presentation predictions, at least two peptides identified as being
presented by at least one of the one or more proteins encoded by a class
II HLA allele of a cell of the subject.
[0017] In some embodiments, the method comprises selecting one or more
peptides of the two or more ranked peptides.
[0018] In some embodiments, the method comprises selecting one or more
peptides of the plurality that were identified as being presented by at
least one of the one or more proteins encoded by a class II HLA allele of
a cell of the subject.
[0019] In some embodiments, the method comprises selecting one or more
peptides of two or more peptides ranked based on the presentation
predictions.
[0020] In some embodiments, the machine learning HLA peptide presentation
prediction model has a positive predictive value (PPV) of at least 0.07
when amino acid information of a plurality of test peptide sequences are
processed to generate a plurality of test presentation predictions, each
test presentation prediction indicative of a likelihood that the one or
more proteins encoded by a class II HLA allele of a cell of the subject
can present a given test peptide sequence of the plurality of test
peptide sequences, wherein the plurality of test peptide sequences
comprises at least 500 test peptide sequences comprising (i) at least one
hit peptide sequence identified by mass spectrometry to be presented by
an HLA protein expressed in cells and (ii) at least 499 decoy peptide
sequences contained within a protein encoded by a genome of an organism,
wherein the organism and the subject are the same species, wherein the
plurality of test peptide sequences comprises a ratio of 1:499 of the at
least one hit peptide sequence to the at least 499 decoy peptide
sequences and a top percentage of the plurality of test peptide sequences
are predicted to be presented by the HLA protein expressed in cells by
the machine learning HLA peptide presentation prediction model.
[0021] In some embodiments, the machine learning HLA peptide presentation
prediction model has a positive predictive value (PPV) of at least 0.1
when amino acid information of a plurality of test peptide sequences are
processed to generate a plurality of test binding predictions, each test
binding prediction indicative of a likelihood that the one or more
proteins encoded by a class II HLA allele of a cell of the subject binds
to a given test peptide sequence of the plurality of test peptide
sequences, wherein the plurality of test peptide sequences comprises at
least 20 test peptide sequences comprising (i) at least one hit peptide
sequence identified by mass spectrometry to be presented by an HLA
protein expressed in cells and (ii) at least 19 decoy peptide sequences
contained within a protein comprising at least one peptide sequence
identified by mass spectrometry to be presented by an HLA protein
expressed in cells, such as a single HLA protein expressed in cells
(e.g., mono-allelic cells), wherein the plurality of test peptide
sequences comprises a ratio of 1:19 of the at least one hit peptide
sequence to the at least 19 decoy peptide sequences and a top percentage
of the plurality of test peptide sequences are predicted to bind to the
HLA protein expressed in cells by the machine learning HLA peptide
presentation prediction model.
[0022] In some embodiments, no amino acid sequence overlap exist among the
at least one hit peptide sequence and the decoy peptide sequences.
[0023] In some embodiments, the machine learning HLA peptide presentation
prediction model has a positive predictive value (PPV) of at least 0.08,
0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2,
0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32,
0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44,
0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56,
0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68,
0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8,
0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92,
0.93, 0.94, 0.95, 0.96, 0.97, 0.98 or 0.99.
[0024] In some embodiments, the at least one hit peptide sequence
comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 hit peptide sequences.
[0025] In some embodiments, the at least 499 decoy peptide sequences
comprises at least 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400,
1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600,
2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800,
3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000,
5100, 5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200,
6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400,
7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600,
8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800,
9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000,
19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000, 47000, 48000,
49000, 50000, 52500, 55000, 57500, 60000, 62500, 65000, 67500, 70000,
72500, 75000, 77500, 80000, 82500, 85000, 87500, 90000, 92500, 95000,
97500, 100000, 125000, 150000, 175000, 200000, 225000, 250000, 275000,
300000, 325000, 350000, 375000, 400000, 425000, 450000, 475000, 500000,
600000, 700000, 800000, 900000 or 1000000 decoy peptide sequences. One of
skill in the art is able to recognize that changing the ratio of
hit:decoy changes the PPV.
[0026] In some embodiments, the at least 500 test peptide sequences
comprises at least 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400,
1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600,
2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800,
3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000,
5100, 5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200,
6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400,
7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600,
8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800,
9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000,
19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000, 47000, 48000,
49000, 50000, 52500, 55000, 57500, 60000, 62500, 65000, 67500, 70000,
72500, 75000, 77500, 80000, 82500, 85000, 87500, 90000, 92500, 95000,
97500, 100000, 125000, 150000, 175000, 200000, 225000, 250000, 275000,
300000, 325000, 350000, 375000, 400000, 425000, 450000, 475000, 500000,
600000, 700000, 800000, 900000 or 1000000 test peptide sequences.
[0027] In some embodiments, the top percentage is a top 0.20%, 0.30%,
0.40%, 0.50%, 0.60%, 0.70%, 0.80%, 0.90%, 1.00%, 1.10%, 1.20%, 1.30%,
1.40%, 1.50%, 1.60%, 1.70%, 1.80%, 1.90%, 2.00%, 2.10%, 2.20%, 2.30%,
2.40%, 2.50%, 2.60%, 2.70%, 2.80%, 2.90%, 3.00%, 3.10%, 3.20%, 3.30%,
3.40%, 3.50%, 3.60%, 3.70%, 3.80%, 3.90%, 4.00%, 4.10%, 4.20%, 4.30%,
4.40%, 4.50%, 4.60%, 4.70%, 4.80%, 4.90%, 5.00%, 5.10%, 5.20%, 5.30%,
5.40%, 5.50%, 5.60%, 5.70%, 5.80%, 5.90%, 6.00%, 6.10%, 6.20%, 6.30%,
6.40%, 6.50%, 6.60%, 6.70%, 6.80%, 6.90%, 7.00%, 7.10%, 7.20%, 7.30%,
7.40%, 7.50%, 7.60%, 7.70%, 7.80%, 7.90%, 8.00%, 8.10%, 8.20%, 8.30%,
8.40%, 8.50%, 8.60%, 8.70%, 8.80%, 8.90%, 9.00%, 9.10%, 9.20%, 9.30%,
9.40%, 9.50%, 9.60%, 9.70%, 9.80%, 9.90%, 10%, 11%, 12%, 13%, 14%, 15%,
16%, 17%, 18%, 19% or 20%.
[0028] In some embodiments, the at least one hit peptide sequence
comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 hit peptide sequences.
[0029] In some embodiments, the at least 19 decoy peptide sequences
comprises at least 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,
150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280,
290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420,
430, 440, 450, 460, 470, 480, 490, 500, 600, 700, 800, 900, 1000, 1100,
1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300,
2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500,
3600, 3700, 3800, 3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700,
4800, 4900, 5000, 5100, 5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900,
6000, 6100, 6200, 6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100,
7200, 7300, 7400, 7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300,
8400, 8500, 8600, 8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500,
9600, 9700, 9800, 9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000,
17000, 18000, 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000,
27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000,
37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000,
47000, 48000, 49000, 50000, 52500, 55000, 57500, 60000, 62500, 65000,
67500, 70000, 72500, 75000, 77500, 80000, 82500, 85000, 87500, 90000,
92500, 95000, 97500, 100000, 125000, 150000, 175000, 200000, 225000,
250000, 275000, 300000, 325000, 350000, 375000, 400000, 425000, 450000,
475000, 500000, 600000, 700000, 800000, 900000 or 1000000 decoy peptide
sequences.
[0030] In some embodiments, the at least 20 test peptide sequences
comprises at least wherein the at least 500 test peptide sequences
comprises at least 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,
150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280,
290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420,
430, 440, 450, 460, 470, 480, 490, 500, 600, 700, 800, 900, 1000, 1100,
1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300,
2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500,
3600, 3700, 3800, 3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700,
4800, 4900, 5000, 5100, 5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900,
6000, 6100, 6200, 6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100,
7200, 7300, 7400, 7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300,
8400, 8500, 8600, 8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500,
9600, 9700, 9800, 9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000,
17000, 18000, 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000,
27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000,
37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000,
47000, 48000, 49000, 50000, 52500, 55000, 57500, 60000, 62500, 65000,
67500, 70000, 72500, 75000, 77500, 80000, 82500, 85000, 87500, 90000,
92500, 95000, 97500, 100000, 125000, 150000, 175000, 200000, 225000,
250000, 275000, 300000, 325000, 350000, 375000, 400000, 425000, 450000,
475000, 500000, 600000, 700000, 800000, 900000 or 1000000 test peptide
sequences test peptide sequences.
[0031] In some embodiments, the top percentage is a top 5%, 6%, 7%, 8%,
9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%,
24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%,
38%, 39%, or 40%.
[0032] In some embodiments, the PPV is greater than the respective PPV of
column 2 of Table 11 for the protein encoded by the corresponding HLA
allele of Table 11. In some embodiments, the PPV is at least equal to the
respective PPV of column 3 of Table 11 for the protein encoded by the
corresponding HLA allele of Table 11.
[0033] In some embodiments, the PPV is equal to or greater than the
respective PPV of column 2 of Table 12 for the protein encoded by an HLA
class II allele.
[0034] In some embodiments, the PPV is greater than the respective PPV of
column 2 of Table 16 for the protein encoded by an HLA class II allele.
[0035] In some embodiments, the subject is a single subject.
[0036] In some embodiments, the subject is a mammal.
[0037] In some embodiments, the subject is a human.
[0038] In some embodiments, the training cells are cells expressing a
single protein encoded by a class II HLA allele of a cell of the subject.
[0039] In some embodiments, the training cells are monoallelic HLA cells,
or cells expressing an HLA allele with an affinity tag.
[0040] In some embodiments, the cell of the subject comprises cancer
cells.
[0041] In some embodiments, the method is for identifying peptide
sequences.
[0042] In some embodiments, the method is for selecting peptide sequences.
[0043] In some embodiments, the method is for preparing a cancer therapy.
[0044] In some embodiments, the method is for preparing a subject-specific
cancer therapy.
[0045] In some embodiments, the method is for preparing a cancer
cell-specific cancer therapy.
[0046] In some embodiments, each peptide sequence of the plurality of
peptide sequences is associated with a cancer.
[0047] In some embodiments, at least one peptide sequence of the plurality
of peptide sequences is overexpressed by a cancer cell of the subject.
[0048] In some embodiments, each peptide sequence of the plurality of
peptide sequences is overexpressed by a cancer cell of the subject.
[0049] In some embodiments, at least one peptide sequence of the plurality
of peptide sequences is a cancer cell-specific peptide.
[0050] In some embodiments, each peptide sequence of the plurality of
peptide sequences is a cancer cell-specific peptide.
[0051] In some embodiments, each peptide sequence of the plurality of
peptide sequences is expressed by a cancer cell of the subject.
[0052] In some embodiments, at least one peptide sequence of the plurality
of peptide sequences is not encoded by a non-cancer cell of the subject.
[0053] In some embodiments, each peptide sequence of the plurality of
peptide sequences is not encoded by a non-cancer cell of the subject.
[0054] In some embodiments, at least one peptide sequence of the plurality
of peptide sequences is not expressed by a non-cancer cell of the subject
[0055] In some embodiments, each peptide sequence of the plurality of
peptide sequences is not expressed by a non-cancer cell of the subject.
[0056] In some embodiments, the method comprises obtaining the plurality
of peptide sequences of the subject.
[0057] In some embodiments, the method comprises obtaining a plurality of
polynucleotide sequences of the subject.
[0058] In some embodiments, the method comprises obtaining a plurality of
polynucleotide sequences of the subject that encodes the plurality of
peptide sequences encoded by a genome or exome of a subject, or by a
pathogen or virus in the subject.
[0059] In some embodiments, the method comprises obtaining a plurality of
polynucleotide sequences of the subject that encodes the plurality of
peptide sequences encoded by a genome or exome of a subject by a computer
processor.
[0060] In some embodiments, the method comprises obtaining a plurality of
polynucleotide sequences of the subject by genomic or exomic sequencing.
[0061] In some embodiments, the method comprises obtaining a plurality of
polynucleotide sequences of the subject by whole genome sequencing or
whole exome sequencing.
[0062] In some embodiments, processing comprises processing by a computer
processor
[0063] In some embodiments, processing comprises generating a plurality of
predictor variables based at least on the amino acid information of the
plurality of peptide sequences
[0064] In some embodiments, processing the plurality of predictor
variables using the machine-learning HLA-peptide presentation prediction
model.
[0065] In some embodiments, the that one or more proteins encoded by a
class II HLA allele of a cell of the subject are one or more proteins
encoded by a class II HLA allele that are expressed by the subject.
[0066] In some embodiments, the that one or more proteins encoded by a
class II HLA allele of a cell of the subject are one or more proteins
encoded by a class II HLA allele that are expressed by cancer cells of
the subject.
[0067] In some embodiments, the that one or more proteins encoded by a
class II HLA allele of a cell of the subject is a single protein encoded
by a class II HLA allele of a cell of the subject.
[0068] In some embodiments, the that one or more proteins encoded by a
class II HLA allele of a cell of the subject is two, three, four, five or
six or more proteins encoded by a class II HLA allele of a cell of the
subject.
[0069] In some embodiments, the that one or more proteins encoded by a
class II HLA allele of a cell of the subject is each protein encoded by a
class II HLA allele of a cell of the subject.
[0070] In some embodiments, the method further comprises administering to
the subject a composition comprising one or more of the selected sub-set
of peptide sequences.
[0071] In some embodiments, identifying the plurality of peptide sequences
comprises comparing DNA, RNA, or protein sequences from cancer cells of
the subject to DNA, RNA, or protein sequences from normal cells of the
subject, wherein each of the plurality of the peptides comprise at least
one mutation, which is present in the cancer cell of the subject, and not
present in the normal cell of the subject.
[0072] In some embodiments, the machine-learning HLA-peptide presentation
prediction model comprises a plurality of predictor variables identified
at least based on the training data, wherein the training data comprises
training peptide sequence information comprising amino acid position
information, wherein the training peptide sequence information is
associated with the HLA protein expressed in cells; and a function
representing a relation between the amino acid position information and
the presentation likelihood generated as output based on the amino acid
position information and the plurality of predictor variables.
[0073] In some embodiments, identifying comprises identifying, based at
least on the plurality of presentation predictions, a peptide sequence of
the plurality of peptide sequences that has a probability greater than a
threshold presentation prediction probability value of being presented by
at least one of the one or more proteins encoded by a class II HLA allele
of a cell of the subject.
[0074] In some embodiments, one or more of the 0.2% of the plurality of
test peptide sequences predicted to be presented by the by the machine
learning HLA peptide presentation prediction model has a probability
greater than the threshold presentation prediction probability value of
being presented by at least one of the one or more proteins encoded by a
class II HLA allele of a cell of the subject.
[0075] In some embodiments, each of the 0.2% of the plurality of test
peptide sequences predicted to be presented by the by the machine
learning HLA peptide presentation prediction model has a probability
greater than the threshold presentation prediction probability value of
being presented by at least one of the one or more proteins encoded by a
class II HLA allele of a cell of the subject.
[0076] In some embodiments, the number of positives is constrained to be
equal to the number of hits.
[0077] In some embodiments, the mass spectrometry is mono-allelic mass
spectrometry.
[0078] In some embodiments, the peptides are presented by a HLA protein
expressed in cells through autophagy.
[0079] In some embodiments, the peptides are presented by a HLA protein
expressed in cells through phagocytosis.
[0080] In some embodiments, the plurality of predictor variables comprises
expression level predictor of the source protein comprising the peptide.
[0081] In some embodiments, the plurality of predictor variables comprises
stability predictor of the source protein comprising the peptide.
[0082] In some embodiments, the plurality of predictor variables comprises
degradation rate predictor of the source protein comprising the peptide.
[0083] In some embodiments, the plurality of predictor variables comprises
protein cleavability predictor of the source protein comprising the
peptide.
[0084] In some embodiments, the plurality of predictor variables comprises
cellular or tissue localization predictor of the source protein
comprising the peptide.
[0085] In some embodiments, the plurality of predictor variables comprises
a predictor for the intracellular processing mode of the source protein
comprising the peptide, wherein processing mode of the source protein
comprises predictor for whether the source protein is subject to
autophagy, phagocytosis, and intracellular transport, among others.
[0086] In some embodiments, quality of the training data is increased by
using a plurality of quality metrics.
[0087] In some embodiments, the plurality of quality metrics comprises
common contaminant peptide removal, high scored peak intensity, high
score, and high mass accuracy.
[0088] In some embodiments, a scored peak intensity is at least 50%.
[0089] In some embodiments, the scored peak intensity is at least 60%.
[0090] In some embodiments, a score is at least 7.
[0091] In some embodiments, a mass accuracy is at most 5 ppm.
[0092] In some embodiments, the peptides presented by an HLA protein
expressed in cells are peptides presented by a single immunoprecipitated
HLA protein expressed in cells.
[0093] In some embodiments, the peptides presented by an HLA protein
expressed in cells are peptides presented by a single exogenous HLA
protein expressed in cells.
[0094] In some embodiments, the peptides presented by an HLA protein
expressed in cells are peptides presented by a single recombinant HLA
protein expressed in cells.
[0095] In some embodiments, the plurality of predictor variables comprises
a peptide-HLA affinity predictor variable.
[0096] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by searching a no-enzyme specificity without
modification peptide database.
[0097] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by searching a peptide database using a
reversed-database search strategy.
[0098] In some embodiments, the HLA protein comprises an HLA-DR, HLA-DQ,
or an HLA-DP protein.
[0099] In some embodiments, the HLA protein comprises an HLA class II
protein selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DQB1*02:01/HLA-DQA1*05:01, HLA-DQB1*02:02/HLA-DQA1*02:01,
HLA-DQB1*06:02/HLA-DQA1*01:02, HLA-DQB1*06:04/HLA-DQA1*01:02,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB5*01:01.
[0100] In some embodiments, the HLA-DR is paired with paired with
DRA*01:01.
[0101] In some embodiments, the HLA protein is a HLA class II protein
selected from the group consisting of: DPA*01:03/DPB*04:01, DRB1*01:01,
DRB1*01:02, DRB1*03:01, DRB1*04:01, DRB1*04:02, DRB1*04:04, DRB1*04:05,
DRB1*07:01, DRB1*08:01, DRB1*08:02, DRB1*08:03, DRB1*09:01, DRB1*11:01,
DRB1*11:02, DRB1*11:04, DRB1*12:01, DRB1*13:01, DRB1*13:02, DRB1*13:03,
DRB1*14:01, DRB1*15:01, DRB1*15:02, DRB1*15:03, DRB1*16:02, DRB3*01:01,
DRB3*02:01, DRB3*02:02, DRB3*03:01, DRB4*01:01, DRB4*01:03 and
DRB5*01:01.
[0102] In some embodiments, the HLA-DR protein comprises a DRA*01:01 in
the dimer.
[0103] In some embodiments, the HLA protein comprises an HLA-DP protein
selected from the group consisting of: DPB1*01:01, DPB1*02:01,
DPB1*02:02, DPB1*03:01, DPB1*04:01, DPB1*04:02, DPB1*05:01, DPB1*06:01,
DPB1*11:01, DPB1*13:01, DPB1*17:01.
[0104] In some embodiments, the HLA-DP protein is paired comprising
DPA1*01:03.
[0105] In some embodiments, the HLA protein comprises an HLA-DQ protein
complex selected from the group consisting of: A1*01:01+B1*05:01,
A1*01:02+B1*06:02, A1*01:02+B1*06:04, A1*01:03+B1*06:03,
A1*02:01+B1*02:02, A1*02:01+B1*03:03, A1*03:01+B1*03:02,
A1*03:03+B1*03:01, A1*05:01+B1*02:01 and A1*05:05+B1*03:01.
[0106] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by comparing a MS/MS spectra of the
HLA-peptides with MS/MS spectra of one or more peptides or proteins in a
peptide or protein database.
[0107] In some embodiments, the mutation is selected from the group
consisting of a point mutation, a splice site mutation, a frameshift
mutation, a read-through mutation, and a gene fusion mutation.
[0108] In some embodiments, the peptides presented by the HLA protein have
a length of from 15-40 amino acids.
[0109] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by identifying peptides presented by an HLA
protein by comparing a MS/MS spectra of the HLA-peptides with MS/MS
spectra of one or more peptides or proteins in a peptide or protein
database.
[0110] In some embodiments, the personalized cancer therapy further
comprises an adjuvant.
[0111] In some embodiments, the personalized cancer therapy further
comprises an immune checkpoint inhibitor.
[0112] In some embodiments, the training data comprises structured data,
time-series data, unstructured data, relational data, or any combination
thereof.
[0113] In some embodiments, the unstructured data comprises image data.
[0114] In some embodiments, the relational data comprises data from a
customer system, an enterprise system, an operational system, a website,
web accessible application program interface (API), or any combination
thereof.
[0115] In some embodiments, the training data is uploaded to a cloud-based
database.
[0116] In some embodiments, the training is performed using convolutional
neural networks.
[0117] In some embodiments, the convolutional neural networks comprise at
least two convolutional layers.
[0118] In some embodiments, the convolutional neural networks comprise at
least one batch normalization step.
[0119] In some embodiments, the convolutional neural networks comprise at
least one spatial dropout step.
[0120] In some embodiments, the convolutional neural networks comprise at
least one global max pooling step.
[0121] In some embodiments, the convolutional neural networks comprise at
least one dense layer.
[0122] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences with a mutation expressed in cancer cells
of a subject.
[0123] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences not expressed in normal cells of a subject.
[0124] In some embodiments, identifying peptide sequences comprises
identifying viral peptide sequences.
[0125] In some embodiments, identifying peptide sequences comprises
identifying overexpressed peptide sequences.
[0126] Provided herein is a method for identifying HLA class II specific
peptides for immunotherapy for a subject, comprising: obtaining, by a
computer processor, a candidate peptide comprising an epitope, and a
plurality of peptide sequences, each comprising the epitope; processing,
by a computer processor, amino acid information of the plurality of
peptide sequences using a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of the
plurality of peptide sequences to an immune cell, each presentation
prediction indicative of a likelihood that one or more proteins encoded
by an HLA class II allele can present a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data comprising
sequence information of sequences of peptides presented by an HLA protein
expressed in cells and identified by mass spectrometry; selecting a
protein from the one or more proteins encoded by the HLA class II allele
of a cell of the subject, predicted to bind to the candidate peptide by
the machine-learning HLA-peptide presentation prediction model, wherein
the protein has a probability greater than a threshold presentation
prediction probability value for presenting the candidate peptide to an
immune cell; contacting the candidate peptide with the selected protein,
such that the candidate peptide competes with a placeholder peptide
associated with the selected protein; and identifying the candidate
peptide as a peptide for immunotherapy specific for the selected protein
based on whether the candidate peptide displaces the placeholder
[0127] In some embodiments, obtaining comprises identifying the candidate
peptide, wherein identifying the candidate peptide comprises comparing
DNA, RNA, or protein sequences from cancer cells of the subject to DNA,
RNA, or protein sequences from normal cells of the subject.
[0128] In some embodiments, processing comprises identifying a plurality
of predictor variables based at least on the amino acid information of
the plurality of peptide sequences, and processing the plurality of
predictor variables using the machine-learning HLA-peptide presentation
prediction model.
[0129] In some embodiments, the machine-learning HLA-peptide presentation
prediction model comprises a plurality of predictor variables identified
at least based on the training data, wherein the training data comprises:
training peptide sequence information comprising amino acid position
information, wherein the training peptide sequence information is
associated with the HLA protein expressed in cells; and a function
representing a relation between the amino acid position information and
the presentation likelihood generated as output based on the amino acid
position information and the plurality of predictor variables.
[0130] In some embodiments, the number of positives is constrained to be
equal to the number of hits.
[0131] In some embodiments, the mass spectrometry is mono-allelic mass
spectrometry.
[0132] In some embodiments, the plurality of predictor variables comprises
any one or more of: expression level predictor, stability predictor,
degradation rate predictor, cleavability predictor, cellular or tissue
localization predictor, and intracellular processing mode comprising
autophagy, phagocytosis, and intracellular transport predictor, of the
source protein comprising the peptide.
[0133] In some embodiments, quality of the training data is increased by
using a plurality of quality metrics.
[0134] In some embodiments, the plurality of quality metrics comprises
common contaminant peptide removal, high scored peak intensity, high
score, and high mass accuracy.
[0135] In some embodiments, a scored peak intensity is at least 50%.
[0136] In some embodiments, the scored peak intensity is at least 60%.
[0137] In some embodiments, the placeholder peptide is a CLIP peptide.
[0138] In some embodiments, the placeholder peptide is a CMV peptide.
[0139] In some embodiments, the method further comprises measuring the
IC50 of displacement of the placeholder peptide by the target peptide.
[0140] In some embodiments, the IC50 of displacement of the placeholder
peptide by the target peptide is less than 500 nM.
[0141] In some embodiments, the at least one protein from the one or more
proteins encoded by the HLA class II allele of a cell of the subject is
an HLA class II tetramer or multimer.
[0142] In some embodiments, the target peptide is further identified by
mass spectrometry.
[0143] In some embodiments, the at least one protein encoded by the HLA
class II allele of a cell of the subject is a recombinant protein.
[0144] In some embodiments, the at least one protein encoded by the HLA
class II allele of a cell of the subject is expressed in a eukaryotic
cell.
[0145] In some embodiments, the peptides are presented by a HLA protein
expressed in cells through autophagy.
[0146] In some embodiments, the peptides are presented by a HLA protein
expressed in cells through phagocytosis.
[0147] In some embodiments, the peptides presented by a HLA protein
expressed in cells are peptides presented by a single immunoprecipitated
HLA protein expressed in cells.
[0148] In some embodiments, the peptides presented by a HLA protein
expressed in cells are peptides presented by a single exogenous HLA
protein expressed in cells.
[0149] In some embodiments, the peptides presented by a HLA protein
expressed in cells are peptides presented by a single recombinant HLA
protein expressed in cells.
[0150] In some embodiments, the plurality of predictor variables comprises
a peptide-HLA affinity predictor variable.
[0151] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by searching a no-enzyme specificity without
modification peptide database.
[0152] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by searching a peptide database using a
reversed-database search strategy.
[0153] In some embodiments, the HLA protein comprises an HLA-DR, HLA-DQ,
or an HLA-DP protein.
[0154] In some embodiments, the immunotherapy is cancer immunotherapy.
[0155] In some embodiments, the epitope is a cancer specific epitope.
[0156] In some embodiments, the at least one protein encoded by the HLA
class II allele comprises at least an alpha 1 subunit and a beta 1
subunit of the HLA protein, present in dimer form.
[0157] In some embodiments, the identity of the peptide is known.
[0158] In some embodiments, the identity of the peptide is not known.
[0159] In some embodiments, the identity of the peptide is determined by
mass spectrometry.
[0160] In some embodiments, peptide exchange assay comprises detection of
peptide fluorescent probes or tags.
[0161] In some embodiments, in the placeholder peptide is a CLIP peptide.
In some embodiments, the placeholder peptide has an amino acid sequence
of PVSKMRMATPLLMQA (SEQ ID NO: 1).
[0162] In some embodiments, the polynucleic acid construct comprises an
expression vector, further comprising one or more of: a promoter, a
secretion signal, dimerization factors, ribosomal skipping sequence, one
or more tags for purification and/or detection.
[0163] In some embodiments, the placeholder peptide sequence is encoded by
a nucleic acid sequence within the vector.
[0164] In some embodiments, a sequence encoding a cleavable domain is
placed in between the sequence encoding the placeholder peptide and the
HLA beta1 peptide.
[0165] Provided herein is a method for assaying immunogenicity of a MHC
class II binding peptide, comprising: selecting a protein encoded by an
HLA class II allele predicted by a machine-learning HLA-peptide
presentation prediction model to bind to the MHC class II binding
peptide, wherein the machine-learning HLA-peptide presentation prediction
model is configured to generate a presentation prediction for a given
peptide sequence, the presentation prediction indicative of a likelihood
that one or more proteins encoded by the HLA class II allele can present
the given peptide sequence, and wherein the protein has a probability
greater than a threshold presentation prediction probability value for
presenting the MHC class II binding peptide; contacting the peptide with
the selected protein such that the peptide competes with a placeholder
peptide associated with the selected protein, and displaces the
placeholder peptide, thereby forming a complex comprising the HLA class
II protein and the MHC class II binding peptide; contacting the complex
with a CD4+ T cell, and assaying for one or more of activation parameters
of the CD4+ T cell, selected from the group consisting of: induction of a
cytokine, induction of a chemokine, and expression of a cell surface
marker.
[0166] In some embodiments, the HLA class II allele is a tetramer or
multimer.
[0167] In some embodiments, the cytokine is IL-2.
[0168] Provided herein is a method for inducing a CD4+ T cells activation
in a subject for cancer immunotherapy, the method comprising: identifying
a peptide sequence associated with cancer and comprising a cancer
mutation, wherein identifying the peptide sequence comprises comparing
DNA, RNA, or protein sequences from cancer cells of the subject to DNA,
RNA, or protein sequences from normal cells of the subject; selecting a
protein encoded by an HLA class II allele that is normally expressed by a
cell of the subject, and predicted by a machine-learning HLA-peptide
presentation prediction model to bind to the peptide; wherein the
prediction model has a positive predictive value of at least 0.1 at a
recall rate of at least 0.1%, from 0.1%-50% or at most 50%. and wherein
the protein has a probability greater than a threshold presentation
prediction probability value for presenting the identified peptide
sequence; contacting the identified peptide with the selected protein
encoded by the HLA class II allele to verify whether the identified
peptide competes with a placeholder peptide associated with the selected
protein encoded by the HLA class II allele to displace the placeholder
peptide with an IC50 value of less than 500 nM; optionally, purifying the
identified peptide; and administering an effective amount of a
polypeptide comprising a sequence of the identified peptide or a
polynucleotide encoding the polypeptide to the subject.
[0169] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining, by a computer processor, a plurality of peptide sequences of
the polypeptide sequence; processing, by a computer processor, amino acid
information of the plurality of peptide sequences using a
machine-learning HLA-peptide presentation prediction model to generate a
presentation prediction for each of the plurality of peptide sequences,
each presentation prediction indicative of a likelihood that one or more
proteins encoded by a class I or II MHC allele of a cell of the subject
can present an epitope sequence of a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data comprising
sequence information associated with the HLA protein expressed in cells;
determining or predicting that each of the plurality of peptide sequences
of the polypeptide sequence would not be immunogenic to the subject based
on the plurality of presentation predictions; and administering to the
subject a composition comprising the drug.
[0170] Provided herein is a method for manufacturing HLA class II
tetramers or multimers by conjugation of four individual HLA protein
alpha1 and beta1 heterodimers, the method comprising: expressing in a
eukaryotic cell, a vector comprising a nucleic acid sequence encoding an
alpha chain and a beta chain of HLA protein, a secretion signal, a
biotinylation motif and at least one tag for identification or for
purification, such that each HLA protein alpha 1 and beta1 heterodimers
is secreted in dimerized state, wherein the heterodimer is associated
with a placeholder peptide, purifying the secreted heterodimer from cell
medium, validating the peptide binding activity using peptide exchange
assay, adding streptavidin thereby conjugating heterodimers into
tetramers, purifying the tetramers and having a yield of greater than 1
mg/L. Multimers, for example pentamers, hexamers or octamers can also be
likewise generated, which are equally contemplated herein.
[0171] In some embodiments, the vector comprises a CMV promoter.
[0172] In some embodiments, the vector comprises a sequence encoding a
placeholder peptide linked via a cleavable site to the beta 1 chain.
[0173] In some embodiments, peptide exchange assay involves prior cleavage
of the placeholder peptide from the beta chain.
[0174] In some embodiments, the cleavable site is a thrombin cleavage
site.
[0175] In some embodiments, peptide exchange assay is a FRET assay.
[0176] In some embodiments, the purification is by any one of: column
chromatography, ion exchange chromatography, size exclusion
chromatography, affinity chromatography, or LC-MS.
[0177] Provided herein is an HLA class II tetramer or multimer comprising
either HLA-DR, or HLA-DP, or HLA-DQ heterodimers, each heterodimer
comprising an alpha and a beta chain, wherein the heterodimer is purified
and present at a concentration of greater than 1 mg/L.
[0178] In some embodiments, the HLA class II tetramers are selected from
Table 8A-8C.
[0179] In some embodiments, the HLA class II tetramer comprises
heterodimer pairs selected from the group consisting of: an HLA-DR, an
HLA-DP, and an HLA-DQ protein.
[0180] In some embodiments, the HLA protein is an HLA class II protein
selected from the group consisting of: HLA-DPB1*01:01/HLA-DPA1*01:03,
HLA-DPB1*02:01/HLA-DPA1*01:03, HLA-DPB1*03:01/HLA-DPA1*01:03,
HLA-DPB1*04:01/HLA-DPA1*01:03, HLA-DPB1*04:02/HLA-DPA1*01:03,
HLA-DPB1*06:01/HLA-DPA1*01:03, HLA-DQB1*02:01/HLA-DQA1*05:01,
HLA-DQB1*02:02/HLA-DQA1*02:01, HLA-DQB1*06:02/HLA-DQA1*01:02,
HLA-DQB1*06:04/HLA-DQA1*01:02, HLA-DRB1*01:01, HLA-DRB1*01:02,
HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA-DRB1*04:02,
HLA-DRB1*04:03, HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*04:07,
HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA-DRB1*08:03,
HLA-DRB1*08:04, HLA-DRB1*09:01, HLA-DRB1*10:01, HLA-DRB1*11:01,
HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*12:02,
HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01,
HLA-DRB1*15:01, HLA-DRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:01,
HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01, and
HLA-DRB5*01:01.
[0181] In some embodiments, the heterodimer pair is expressed in a
eukaryotic cell.
[0182] In some embodiments, the heterodimer pairs are encoded by a vector.
[0183] Provided herein is a vector, wherein the vector comprises a nucleic
acid sequence encoding an alpha chain and a beta chain of HLA protein
described herein, a secretion signal, a biotinylation motif and at least
one tag for identification or for purification, such that each HLA
protein alpha 1 and beta1 heterodimers is secreted in dimerized state,
wherein the secreted heterodimer is optionally associated with a
placeholder peptide.
[0184] Provided herein is a cell, comprising a vector described herein.
[0185] In some embodiments, the HLA class II heterodimers are secreted
from eukaryotic cells into cell culture medium, which is further purified
by any one of: column chromatography, ion exchange chromatography, size
exclusion chromatography, affinity chromatography or LC-MS.
[0186] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining, by a computer processor, a plurality of peptide sequences of
the polypeptide sequence; processing, by a computer processor, amino acid
information of the plurality of peptide sequences using a
machine-learning HLA-peptide presentation prediction model to generate a
presentation prediction for each of the plurality of peptide sequences,
each presentation prediction indicative of a likelihood that one or more
proteins encoded by a class I or II MHC allele of a cell of the subject
can present an epitope sequence of a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data comprising
sequence information of sequences of peptides presented by a HLA protein
expressed in cells and identified by mass spectrometry; and determining
or predicting that at least one of the plurality of peptide sequences of
the polypeptide sequence would be immunogenic to the subject based on the
plurality of presentation predictions.
[0187] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, the method
comprising: inputting amino acid information of peptide sequences of the
polypeptide sequence, using a computer processor, into a machine-learning
HLA-peptide presentation prediction model to generate a set of
presentation predictions for the peptide sequences, each presentation
prediction representing a probability that one or more proteins encoded
by a class I or II MHC allele of a cell of the subject will present an
epitope sequence of a given peptide sequence; wherein the
machine-learning HLA-peptide presentation prediction model comprises: a
plurality of predictor variables identified at least based on training
data, wherein the training data comprises: sequence information of
sequences of peptides presented by a HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence information
comprising amino acid position information, wherein the training peptide
sequence information is associated with the HLA protein expressed in
cells; and a function representing a relation between the amino acid
position information received as input and the presentation likelihood
generated as output based on the amino acid position information and the
predictor variables; determining or predicting that each of the peptide
sequences of the polypeptide sequence would not be immunogenic to the
subject based on the set of presentation predictions; and administering
to the subject a composition comprising the drug.
[0188] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, the method
comprising: inputting amino acid information of peptide sequences of the
polypeptide sequence, using a computer processor, into a machine-learning
HLA-peptide presentation prediction model to generate a set of
presentation predictions for the peptide sequences, each presentation
prediction representing a probability that one or more proteins encoded
by a class I or II MHC allele of a cell of the subject will present an
epitope sequence of a given peptide sequence; wherein the
machine-learning HLA-peptide presentation prediction model comprises: a
plurality of predictor variables identified at least based on training
data; wherein the training data comprises: sequence information of
sequences of peptides presented by a HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence information
comprising amino acid position information, wherein the training peptide
sequence information is associated with the HLA protein expressed in
cells; and a function representing a relation between the amino acid
position information received as input and the presentation likelihood
generated as output based on the amino acid position information and the
predictor variables; determining or predicting that at least one of the
peptide sequences of the polypeptide sequence would be immunogenic to the
subject based on the set of presentation predictions.
[0189] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining, by a computer processor, a plurality of peptide sequences of
the polypeptide sequence; processing, by a computer processor, amino acid
information of the plurality of peptide sequences using a
machine-learning HLA-peptide presentation prediction model to generate a
presentation prediction for each of the plurality of peptide sequences,
each presentation prediction indicative of a likelihood that one or more
proteins encoded by a class I or II MHC allele of a cell of the subject
can present an epitope sequence of a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data comprising
sequence information associated with the HLA protein expressed in cells;
determining or predicting that each of the plurality of peptide sequences
of the polypeptide sequence would not be immunogenic to the subject based
on the plurality of presentation predictions; and administering to the
subject a composition comprising the drug.
[0190] In some embodiments, the method further comprises deciding not to
administer the drug to the subject.
[0191] In some embodiments, the drug comprises an antibody or binding
fragment thereof.
[0192] In some embodiments, the peptide sequences of the polypeptide
sequence have a length of 8, 9, 10, 11, or 12 amino acids, and wherein
the protein encoded by a class I or II MHC allele of a cell of the
subject is a protein encoded by a class I MHC allele of a cell of the
subject.
[0193] In some embodiments, the peptide sequences of the polypeptide
sequence have a length of 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25
amino acids, and wherein the protein encoded by a class I or II MHC
allele of a cell of the subject is a protein encoded by a class II MHC
allele of a cell of the subject.
[0194] Provided herein is a method of treating a subject with an
autoimmune disease or condition comprising: (a) identifying or predicting
an epitope of an expressed protein presented by a class I or II MHC of a
cell of the subject, wherein a complex comprising the identified or
predicted epitope and the class I or II MHC is targeted by a CD8 or CD4 T
cell of the subject; (b) identifying a T cell receptor (TCR) that binds
to the complex; (c) expressing the TCR in a regulatory T cell from the
subject or an allogeneic regulatory T cell; and (d) administering the
regulatory T cell expressing the TCR to the subject.
[0195] In some embodiments, the autoimmune disease or condition is
diabetes.
[0196] In some embodiments, the cell is an islet cell.
[0197] Provided herein is a method of treating a subject with an
autoimmune disease or condition, comprising administering to the subject
a regulatory T cell expressing a T cell receptor (TCR) that binds to a
complex comprising: (i) an epitope of an expressed protein identified or
predicted to be presented by a class I or II MHC of a cell of the
subject, and (ii) the class I or II MHC, wherein the complex is targeted
by a CD8 or CD4 T cell of the subject.
[0198] Provided herein is a computer system for identifying peptide
sequences for a personalized cancer therapy of a subject, comprising: a
database that is configured to store a plurality of peptide sequences of
the subject; and one or more computer processors operatively coupled to
said database, wherein said one or more computer processors are
individually collectively programmed to: process amino acid information
of the plurality of peptide sequences using a machine-learning
HLA-peptide presentation prediction model to generate a presentation
prediction for each of the plurality of peptide sequences, each
presentation prediction indicative of a likelihood that one or more
proteins encoded by a class II MHC allele of a cell of the subject can
present a given peptide sequence of the plurality of peptide sequences,
wherein the machine-learning HLA-peptide presentation prediction model is
trained using training data comprising sequence information of sequences
of peptides presented by an HLA protein expressed in cells and identified
by mass spectrometry; and select a subset of the plurality of peptide
sequences for the personalized cancer therapy of the subject based at
least on the plurality of presentation predictions.
[0199] Provided herein is a computer system for identifying HLA class II
specific peptides for immunotherapy for a subject, comprising: a database
that is configured to store a candidate peptide comprising an epitope,
and a plurality of peptide sequences, each comprising the epitope; and
one or more computer processors operatively coupled to said database,
wherein said one or more computer processors are individually
collectively programmed to: process amino acid information of the
plurality of peptide sequences a machine-learning HLA-peptide
presentation prediction model to generate a presentation prediction for
each of the plurality of peptide sequences to an immune cell, each
presentation prediction indicative of a likelihood that one or more
proteins encoded by an HLA class II allele can present a given peptide
sequence of the plurality of peptide sequences, wherein the
machine-learning HLA-peptide presentation prediction model is trained
using training data comprising sequence information of sequences of
peptides presented by an HLA protein expressed in cells and identified by
mass spectrometry; select a protein from the one or more proteins encoded
by the HLA class II allele of a cell of the subject, predicted to bind to
the candidate peptide by the machine-learning HLA-peptide presentation
prediction model, wherein the protein has a probability greater than a
threshold presentation prediction probability value for presenting the
candidate peptide to an immune cell; and identify the candidate peptide
as a peptide for immunotherapy specific for the selected protein based on
whether the candidate peptide displaces the placeholder peptide, upon
contacting the candidate peptide with the selected protein, such that the
candidate peptide competes with a placeholder peptide associated with the
selected protein.
[0200] Provided herein is a computer system for screening a drug
comprising a polypeptide sequence for immunogenicity in a subject,
comprising: a database that is configured to store a plurality of peptide
sequences of the polypeptide sequence; and one or more computer
processors operatively coupled to said database, wherein said one or more
computer processors are individually collectively programmed to: process
amino acid information of the plurality of peptide sequences using a
machine-learning HLA-peptide presentation prediction model to generate a
presentation prediction for each of the plurality of peptide sequences,
each presentation prediction indicative of a likelihood that one or more
proteins encoded by a class I or II MHC allele of a cell of the subject
can present an epitope sequence of a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data comprising
sequence information associated with the HLA protein expressed in cells;
and determine or predict that each of the plurality of peptide sequences
of the polypeptide sequence would not be immunogenic to the subject based
on the plurality of presentation predictions, wherein a composition
comprising the drug is administered to the subject.
[0201] Provided herein is a computer system for screening a drug
comprising a polypeptide sequence for immunogenicity in a subject,
comprising: a database that is configured to store a plurality of peptide
sequences of the polypeptide sequence; and one or more computer
processors operatively coupled to said database, wherein said one or more
computer processors are individually collectively programmed to: process
amino acid information of the plurality of peptide sequences using a
machine-learning HLA-peptide presentation prediction model to generate a
presentation prediction for each of the plurality of peptide sequences,
each presentation prediction indicative of a likelihood that one or more
proteins encoded by a class I or II MHC allele of a cell of the subject
can present an epitope sequence of a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data comprising
sequence information of sequences of peptides presented by a HLA protein
expressed in cells and identified by mass spectrometry; and determine or
predict that at least one of the plurality of peptide sequences of the
polypeptide sequence would be immunogenic to the subject based on the
plurality of presentation predictions.
[0202] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or more
computer processors, implements a method for identifying peptide
sequences for a personalized cancer therapy of a subject, said method
comprising: obtaining a plurality of peptide sequences of the subject;
processing amino acid information of the plurality of peptide sequences
using a machine-learning HLA-peptide presentation prediction model to
generate a presentation prediction for each of the plurality of peptide
sequences, each presentation prediction indicative of a likelihood that
one or more proteins encoded by a class II MHC allele of a cell of the
subject can present a given peptide sequence of the plurality of peptide
sequences, wherein the machine-learning HLA-peptide presentation
prediction model is trained using training data comprising sequence
information of sequences of peptides presented by an HLA protein
expressed in cells and identified by mass spectrometry; and selecting a
subset of the plurality of peptide sequences for the personalized cancer
therapy of the subject based at least on the plurality of presentation
predictions.
[0203] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or more
computer processors, implements a method for identifying HLA class II
specific peptides for immunotherapy for a subject, comprising: obtaining
a candidate peptide comprising an epitope, and a plurality of peptide
sequences, each comprising the epitope; processing amino acid information
of the plurality of peptide sequences a machine-learning HLA-peptide
presentation prediction model to generate a presentation prediction for
each of the plurality of peptide sequences to an immune cell, each
presentation prediction indicative of a likelihood that one or more
proteins encoded by an HLA class II allele can present a given peptide
sequence of the plurality of peptide sequences, wherein the
machine-learning HLA-peptide presentation prediction model is trained
using training data comprising sequence information of sequences of
peptides presented by an HLA protein expressed in cells and identified by
mass spectrometry; selecting a protein from the one or more proteins
encoded by the HLA class II allele of a cell of the subject, predicted to
bind to the candidate peptide by the machine-learning HLA-peptide
presentation prediction model, wherein the protein has a probability
greater than a threshold presentation prediction probability value for
presenting the candidate peptide to an immune cell; and identifying the
candidate peptide as a peptide for immunotherapy specific for the
selected protein based on whether the candidate peptide displaces the
placeholder peptide, upon contacting the candidate peptide with the
selected protein, such that the candidate peptide competes with a
placeholder peptide
[0204] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or more
computer processors, implements a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining a plurality of peptide sequences of the polypeptide sequence;
processing amino acid information of the plurality of peptide sequences
using a machine-learning HLA-peptide presentation prediction model to
generate a presentation prediction for each of the plurality of peptide
sequences, each presentation prediction indicative of a likelihood that
one or more proteins encoded by a class I or II MHC allele of a cell of
the subject can present an epitope sequence of a given peptide sequence
of the plurality of peptide sequences, wherein the machine-learning
HLA-peptide presentation prediction model is trained using training data
comprising sequence information associated with the HLA protein expressed
in cells; and determining or predicting that each of the plurality of
peptide sequences of the polypeptide sequence would not be immunogenic to
the subject based on the plurality of presentation predictions, wherein a
composition comprising the drug is administered to the subject.
[0205] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or more
computer processors, implements a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining a plurality of peptide sequences of the polypeptide sequence;
processing amino acid information of the plurality of peptide sequences
using a machine-learning HLA-peptide presentation prediction model to
generate a presentation prediction for each of the plurality of peptide
sequences, each presentation prediction indicative of a likelihood that
one or more proteins encoded by a class I or II MHC allele of a cell of
the subject can present an epitope sequence of a given peptide sequence
of the plurality of peptide sequences, wherein the machine-learning
HLA-peptide presentation prediction model is trained using training data
comprising sequence information of sequences of peptides presented by a
HLA protein expressed in cells and identified by mass spectrometry; and
determining or predicting that at least one of the plurality of peptide
sequences of the polypeptide sequence would be immunogenic to the subject
based on the plurality of presentation predictions.
[0206] Provided herein is a method comprising: processing amino acid
information of a plurality of candidate peptide sequences using a machine
learning HLA peptide presentation prediction model to generate a
plurality of presentation predictions, wherein each candidate peptide
sequences of the plurality is encoded by a genome or exome of a subject,
wherein the plurality of presentation predictions comprises an HLA
presentation prediction for each of the plurality of candidate peptide
sequences, wherein each presentation prediction indicative of a
likelihood that one or more proteins encoded by a class II HLA allele of
a cell of the subject can present a given candidate peptide sequence of
the plurality, wherein the machine learning HLA peptide presentation
prediction model is trained using training data comprising sequence
information of sequences of training peptides identified by mass
spectrometry to be presented by an HLA protein expressed in training
cells; and identifying, based at least on the plurality of presentation
predictions, a peptide sequence of the plurality of peptide sequences
that has a probability greater than a threshold presentation prediction
probability value of being presented by at least one of the one or more
proteins encoded by a class II HLA allele of a cell of the subject;
wherein the machine learning HLA peptide presentation prediction model
has a positive predictive value (PPV) of at least 0.07 when amino acid
information of a plurality of test peptide sequences are processed to
generate a plurality of test presentation predictions, each test
presentation prediction indicative of a likelihood that the one or more
proteins encoded by a class II HLA allele of a cell of the subject can
present a given test peptide sequence of the plurality of test peptide
sequences, wherein the plurality of test peptide sequences comprises at
least 500 test peptide sequences comprising (i) at least one hit peptide
sequence identified by mass spectrometry to be presented by an HLA
protein expressed in cells and (ii) at least 499 decoy peptide sequences
contained within a protein encoded by a genome of an organism, wherein
the organism and the subject are the same species, wherein the plurality
of test peptide sequences comprises a ratio of 1:499 of the at least one
hit peptide sequence to the at least 499 decoy peptide sequences and 0.2%
of the plurality of test peptide sequences are predicted to be presented
by the HLA protein expressed in cells by the machine learning HLA peptide
presentation prediction model.
[0207] Provided herein is a method comprising: processing amino acid
information of a plurality of peptide sequences of encoded by a genome or
exome of a subject using a machine-learning HLA-peptide binding
prediction model to generate a plurality of binding predictions, wherein
the plurality of binding predictions comprises an HLA binding prediction
for each of the plurality of candidate peptide sequences, each binding
prediction indicative of a likelihood that one or more proteins encoded
by a class II HLA allele of a cell of the subject binds to a given
candidate peptide sequence of the plurality of candidate peptide
sequences, wherein the machine learning HLA peptide binding prediction
model is trained using training data comprising sequence information of
sequences of peptides identified to bind to an HLA class II protein or an
HLA class II protein analog; and identifying, based at least on the
plurality of binding predictions, a peptide sequence of the plurality of
peptide sequences that has a probability greater than a threshold binding
prediction probability value of binding to at least one of the one or
more proteins encoded by a class II HLA allele of a cell of the subject;
wherein the machine learning HLA peptide binding prediction model has a
positive predictive value (PPV) of at least 0.1 when amino acid
information of a plurality of test peptide sequences are processed to
generate a plurality of test binding predictions, each test binding
prediction indicative of a likelihood that the one or more proteins
encoded by a class II HLA allele of a cell of the subject binds to a
given test peptide sequence of the plurality of test peptide sequences,
wherein the plurality of test peptide sequences comprises at least 50
test peptide sequences comprising (i) at least one hit peptide sequence
identified by mass spectrometry to be presented by an HLA protein
expressed in cells and (ii) at least 19 decoy peptide sequences contained
within a protein comprising a peptide sequence identified by mass
spectrometry to be presented by an HLA protein expressed in cells,
wherein the organism and the subject are the same species, wherein the
plurality of test peptide sequences comprises a ratio of 1:19 of the at
least one hit peptide sequence to the at least 19 decoy peptide sequences
and 5% of the plurality of test peptide sequences are predicted to bind
to the HLA protein expressed in cells by the machine learning HLA peptide
presentation prediction model.
[0208] In some embodiments, the machine learning HLA peptide presentation
prediction model is trained using training data comprising sequence
information of sequences of training peptides identified by mass
spectrometry to be presented by an HLA protein expressed in training
cells
[0209] In some embodiments, one or more of the 0.2% of the plurality of
test peptide sequences predicted to be presented by the by the machine
learning HLA peptide presentation prediction model has a probability
greater than the threshold presentation prediction probability value of
being presented by at least one of the one or more proteins encoded by a
class II HLA allele of a cell of the subject.
[0210] In some embodiments, each of the 0.2% of the plurality of test
peptide sequences predicted to be presented by the by the machine
learning HLA peptide presentation prediction model has a probability
greater than the threshold presentation prediction probability value of
being presented by at least one of the one or more proteins encoded by a
class II HLA allele of a cell of the subject.
[0211] In some embodiments, the PPV is greater than the respective PPV of
column 2 of Table 11 for the protein encoded by the corresponding HLA
allele of Table 13. In some embodiments, the PPV is at least equal to the
respective PPV of column 3 of Table 11 for the protein encoded by the
corresponding HLA allele of Table 11.
[0212] In some embodiments, the PPV is greater than the respective PPV of
column 2 of Table 12 for the protein encoded by an HLA class II allele.
[0213] In some embodiments, the PPV is at least equal to the respective
PPV of column 2 of Table 16 for the protein encoded by the corresponding
HLA allele of Table 16.
[0214] Provided herein is a method for preparing a personalized cancer
therapy, the method comprising: identifying peptide sequences, wherein
the peptide sequences are associated with cancer, wherein identifying
comprises comparing DNA, RNA or protein sequences from the cancer cells
of the subject to DNA, RNA or protein sequences from the normal cells of
the subject; inputting amino acid position information of the peptide
sequences identified, using a computer processor, into a machine-learning
HLA-peptide presentation prediction model to generate a set of
presentation predictions for the peptide sequences identified, each
presentation prediction representing a probability that one or more
proteins encoded by an HLA class II allele of a cell of the subject will
present a given sequence of a peptide sequence identified; wherein the
machine-learning HLA-peptide presentation prediction model comprises: a
plurality of predictor variables identified at least based on training
data wherein the training data comprises: sequence information of
sequences of peptides presented by an HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence information
comprising amino acid position information, wherein the training peptide
sequence information is associated with the HLA protein expressed in
cells; and a function representing a relation between the amino acid
position information received as input and the presentation likelihood
generated as output based on the amino acid position information and the
predictor variables; and selecting a subset of the peptide sequences
identified based on the set of presentation predictions for preparing the
personalized cancer therapy; wherein the prediction model has a positive
predictive value of at least 0.1 at a recall rate of at least 0.1%, from
0.1%-50% or at the most 50%.
[0215] Provided herein is a method comprising training a machine-learning
HLA-peptide presentation prediction model, wherein training comprises
inputting amino acid position information sequences of HLA-peptides
isolated from one or more HLA-peptide complexes from a cell expressing an
HLA class II allele into the HLA-peptide presentation prediction model
using a computer processor; the machine-learning HLA-peptide presentation
prediction model comprising: a plurality of predictor variables
identified at least based on training data that comprises: sequence
information of sequences of peptides presented by an HLA protein
expressed in cells and identified by mass spectrometry; training peptide
sequence information comprising amino acid position information of
training peptides, wherein the training peptide sequence information is
associated with the HLA protein expressed in cells; and a function
representing a relation between the amino acid position information
received as input and a presentation likelihood generated as output based
on the amino acid position information and the predictor variables.
[0216] In some embodiments, the presentation model has a positive
predictive value of at least 0.25 at a recall rate at least 0.1%, from
0.1%-50% or at the most 50%.
[0217] In some embodiments, the presentation model has a positive
predictive value of at least 0.4 at a recall rate of at least 0.1%, from
0.1%-50% or at the most 50%.
[0218] In some embodiments, the presentation model has a positive
predictive value of at least 0.6 at a recall rate of at least 0.1%, from
0.1%-50% or at the most 50%.
[0219] In some embodiments, the mass spectrometry is mono-allelic mass
spectrometry.
[0220] In some embodiments, the peptides are presented by an HLA protein
expressed in cells through autophagy.
[0221] In some embodiments, the peptides are presented by an HLA protein
expressed in cells through phagocytosis.
[0222] In some embodiments, quality of the training data is increased by
using a plurality of quality metrics.
[0223] In some embodiments, the plurality of quality metrics comprises
common contaminant peptide removal, high scored peak intensity, high
score, and high mass accuracy.
[0224] In some embodiments, the scored peak intensity is at least 50%.
[0225] In some embodiments, the scored peak intensity is at least 60%.
[0226] In some embodiments, a score is at least 7.
[0227] In some embodiments, a mass accuracy is at most 5 ppm.
[0228] In some embodiments, a mass accuracy is at most 2 ppm.
[0229] In some embodiments, a backbone cleavage score is at least 5.
[0230] In some embodiments, a backbone cleavage score is at least 8.
[0231] In some embodiments, the peptides presented by an HLA protein
expressed in cells are peptides presented by a single immunoprecipitated
HLA protein expressed in cells.
[0232] In some embodiments, the peptides presented by an HLA protein
expressed in cells are peptides presented by a single exogenous HLA
protein expressed in cells.
[0233] In some embodiments, the peptides presented by an HLA protein
expressed in cells are peptides presented by a single recombinant HLA
protein expressed in cells.
[0234] In some embodiments, the plurality of predictor variables comprises
a peptide-HLA affinity predictor variable.
[0235] In some embodiments, the plurality of predictor variables comprises
a source protein expression level predictor variable.
[0236] In some embodiments, the plurality of predictor variables comprises
a peptide cleavability predictor variable.
[0237] In some embodiments, the training peptide sequence information
comprises sequences from the peptides presented by the HLA protein, which
comprise peptides identified by searching a no-enzyme specificity without
modification to a peptide database. In some embodiments, the peptides
presented by the HLA protein comprise peptides identified by searching
the de novo peptide sequencing tools.
[0238] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by searching a peptide database using a
reversed-database search strategy.
[0239] In some embodiments, the HLA protein comprises an HLA-DR, and
HLA-DP or an HLA-DQ protein.
[0240] In some embodiments, the HLA protein comprises an HLA-DR protein
selected from the group consisting of an HLA-DR, and HLA-DP or an HLA-DQ
protein. In some embodiments, the HLA protein comprises an HLA-DR protein
selected from the group consisting of: HLA-DPB1*01:01/HLA-DPA1*01:03,
HLA-DPB1*02:01/HLA-DPA1*01:03, HLA-DPB1*03:01/HLA-DPA1*01:03,
HLA-DPB1*04:01/HLA-DPA1*01:03, HLA-DPB1*04:02/HLA-DPA1*01:03,
HLA-DPB1*06:01/HLA-DPA1*01:03, HLA-DQB1*02:01/HLA-DQA1*05:01,
HLA-DQB1*02:02/HLA-DQA1*02:01, HLA-DQB1*06:02/HLA-DQA1*01:02,
HLA-DQB1*06:04/HLA-DQA1*01:02, HLA-DRB1*01:01, HLA-DRB1*01:02,
HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA-DRB1*04:02,
HLA-DRB1*04:03, HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*04:07,
HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA-DRB1*08:03,
HLA-DRB1*08:04, HLA-DRB1*09:01, HLA-DRB1*10:01, HLA-DRB1*11:01,
HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*12:02,
HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01,
HLA-DRB1*15:01, HLA-DRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:01,
HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01, and
HLA-DRB5*01:01.
[0241] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by comparing MS/MS spectra of the
HLA-peptides with MS/MS spectra of one or more HLA-peptides in a peptide
database.
[0242] In some embodiments, the mutation is selected from the group
consisting of a point mutation, a splice site mutation, a frameshift
mutation, a read-through mutation, and a gene fusion mutation.
[0243] In some embodiments, the peptides presented by the HLA protein have
a length of 15-40 amino acids.
[0244] In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by (a) isolating one or more HLA complexes
from a cell line expressing a single HLA class II allele; (b) isolating
one or more HLA-peptides from the one or more isolated HLA complexes; (c)
obtaining MS/MS spectra for the one or more isolated HLA-peptides; and
(d) obtaining a peptide sequence that corresponds to the MS/MS spectra of
the one or more isolated HLA-peptides from a peptide database; wherein
one or more sequences obtained from step (d) identifies the sequence of
the one or more isolated HLA-peptides.
[0245] In some embodiments, the personalized cancer therapy further
comprises an adjuvant.
[0246] In some embodiments, the personalized cancer therapy further
comprises an immune checkpoint inhibitor.
[0247] In some embodiments, the training data comprises structured data,
time-series data, unstructured data, relational data, or any combination
thereof.
[0248] In some embodiments, the unstructured data comprises image data.
[0249] In some embodiments, the relational data comprises data from a
customer system, an enterprise system, an operational system, a website,
web accessible application program interface (API), or any combination
thereof.
[0250] In some embodiments, the training data is uploaded to a cloud-based
database.
[0251] In some embodiments, the training is performed using convolutional
neural networks.
[0252] In some embodiments, the convolutional neural networks comprise at
least two convolutional layers.
[0253] In some embodiments, the convolutional neural networks (CNN)
comprise at least one batch normalization step.
[0254] In some embodiments, the convolutional neural networks comprise at
least one spatial dropout step.
[0255] In some embodiments, the convolutional neural networks comprise at
least one global max pooling step.
[0256] In some embodiments, the convolutional neural networks comprise at
least one dense layer.
[0257] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences with a mutation expressed in cancer cells
of a subject.
[0258] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences not expressed in normal cells of a subject.
[0259] In some embodiments, identifying peptide sequences comprises
identifying overexpressed peptide sequences.
[0260] In some embodiments, identifying peptide sequences comprises
identifying viral peptide sequences. In one aspect, provided herein is a
method for identifying HLA class II specific peptides for immunotherapy
specific for a subject, the method comprising: identifying a candidate
peptide comprising an epitope; inputting amino acid information of a
plurality of peptide sequences, each comprising an epitope, using a
computer processor, into a machine-learning HLA-peptide presentation
prediction model to generate a set of HLA presentation predictions for
the peptide sequence to an immune cell, each presentation prediction
representing a probability that one or more proteins encoded by an HLA
class II allele of a cell of the subject will present a given peptide
sequence comprising the epitope; wherein the prediction model has a
positive predictive value of at least 0.1 at a recall rate of at least
0.1%, from 0.1%-50% or at the most 50%, selecting a protein from the one
or more proteins encoded by the HLA class II allele of a cell of the
subject, predicted to bind to the candidate peptide by the prediction
model, wherein the protein has a probability greater than a threshold
presentation prediction probability value for presenting the candidate
peptide to an immune cell; contacting the candidate peptide with the
protein encoded by the HLA class II allele, such that the candidate
peptide competes with a placeholder peptide associated with the protein
encoded by the HLA class II allele; and, identifying the candidate
peptide as a peptide for immunotherapy specific for the protein encoded
by an HLA class II allele based on whether the candidate peptide
displaces the placeholder peptide.
[0261] In some embodiments, the immunotherapy is cancer immunotherapy.
[0262] In some embodiments, identifying comprises comparing DNA, RNA or
protein sequences from the cancer cells of the subject to DNA, RNA or
protein sequences from the normal cells of the subject. In some
embodiments, the epitope is a cancer specific epitope.
[0263] In some embodiments, the at least one protein encoded by the HLA
class II allele comprises at least an alpha 1 subunit and a beta 1
subunit of the HLA protein, or fragments thereof, present in dimer form.
In some embodiments, the placeholder peptide is a CLIP peptide. In some
embodiments, the placeholder peptide is a CMV peptide. In some
embodiments, the method further comprises measuring the IC50 of
displacement of the placeholder peptide by the target peptide. In some
embodiments, the IC50 of displacement of the placeholder peptide by the
target peptide is less than 500 nM. In some embodiments, the at least one
protein from the one or more proteins encoded by the HLA class II allele
of a cell of the subject is an HLA class II tetramer or multimer. In some
embodiments, the target peptide is further identified by mass
spectrometry. In some embodiments, the at least one protein encoded by
the HLA class II allele of a cell of the subject is a recombinant
protein. In some embodiments, the at least one protein encoded by the HLA
class II allele of a cell of the subject is expressed in a eukaryotic
cell.
[0264] In one aspect, provided herein is assay method for verifying the
specificity of a candidate peptide for binding an HLA class II protein,
the method comprising: expressing in a eukaryotic cell, a polynucleic
acid construct comprising a nucleic acid sequence encoding an HLA class
II protein comprising an alpha chain and beta chain or portions thereof,
capable of binding a peptide comprising an MHC-II-binding epitope, and
wherein the expressed HLA class II protein or portions thereof remains
associated with a placeholder peptide; isolating the HLA class II protein
or portions thereof expressed in the eukaryotic cell; performing a
peptide exchange assay by (a) adding increasing amount of the candidate
peptide to determine whether the candidate peptide displaces the
placeholder peptide associated with the HLA class II protein or portions
thereof; and (b) calculating the IC50 of the displacement reaction to
determine the affinity of the candidate peptide to the HLA class II
protein or portions thereof relative to the placeholder peptide, thereby
verifying the specificity of the candidate peptide for binding an HLA
class II protein.
[0265] In some embodiments, the identity of the peptide is known. In some
embodiments, the identity of the peptide is not known. In some
embodiments, the identity of the peptide is determined by mass
spectrometry.
[0266] In some embodiments, the peptide exchange assay comprises detection
of peptide fluorescent probes or tags. In some embodiments, the
placeholder peptide is a CLIP peptide.
[0267] In some embodiments, the polynucleic acid construct comprises an
expression vector, further comprising one or more of: a promoter, a
linker, one or more protease cleavage sites, a secretion signal,
dimerization factors, ribosomal skipping sequence, one or more tags for
purification and or detection.
[0268] In one aspect, provided herein is a method for assaying
immunogenicity of a MHC class II binding peptide, the method comprising:
selecting a protein encoded by an HLA class II allele predicted by a
machine-learning HLA-peptide presentation prediction model to bind to the
peptide; wherein the prediction model has a positive predictive value of
at least 0.1 at a recall rate of at least 0.1%, from 0.1%-50% or at the
most 50% and wherein the protein has a probability greater than a
threshold presentation prediction probability value for presenting the
identified peptide sequence; contacting the peptide with the selected
protein encoded by the HLA class II allele such that the peptide competes
with a placeholder peptide associated with the selected protein encoded
by the HLA class II allele, and displaces the placeholder peptide,
thereby forming a complex comprising the HLA class II protein and the
identified peptide; contacting the HLA class II protein and the
identified peptide complex with a CD4+ T cell, assaying for one or more
of activation parameters of the CD4+ T cell, selected from induction of a
cytokine, induction of a chemokine and expression of a cell surface
marker.
[0269] In some embodiments, the HLA class II allele is a tetramer or
multimer. In some embodiments, the cytokine is IL-2. In some embodiments,
the cytokine is IFN-gamma.
[0270] In one aspect, provided herein is a method for inducing a CD4+ T
cells activation in a subject for cancer immunotherapy, the method
comprising: identifying a peptide sequence associated with cancer and
comprising a cancer mutation, wherein identifying comprises comparing
DNA, RNA or protein sequences from the cancer cells of the subject to
DNA, RNA or protein sequences from the normal cells of the subject;
selecting a protein encoded by an HLA class II allele that is normally
expressed by a cell of the subject, and predicted by a machine-learning
HLA-peptide presentation prediction model to bind to the peptide; wherein
the prediction model has a positive predictive value of at least 0.1 at a
recall rate of at least 0.1%, from 0.1%-50% or at the most 50% and
wherein the protein has a probability greater than a threshold
presentation prediction probability value for presenting the identified
peptide sequence; contacting the identified peptide with the selected
protein encoded by the HLA class II allele to verify whether the
identified peptide competes with a placeholder peptide associated with
the selected protein encoded by the HLA class II allele to displace the
placeholder peptide with an IC50 value of less than 500 nM; purifying the
identified peptide; and administer an effective amount of the identified
peptide to the subject.
[0271] In one aspect, provided herein is a method of manufacturing HLA
class II tetramers or multimers, the method comprising: expressing in a
eukaryotic cell, a vector comprising a nucleic acid sequence encoding an
alpha chain and a beta chain of HLA protein, a linker, one or more
protease cleavage sites, a secretion signal, a biotinylation motif and at
least one tag for identification or for purification, such that each HLA
protein alpha 1 and beta 1 heterodimers is secreted in dimerized state,
wherein the heterodimer is associated with a placeholder peptide,
purifying the secreted heterodimer from cell medium, validating the
peptide binding activity using peptide exchange assay, adding
streptavidin thereby conjugating heterodimers into tetramers, purifying
the tetramers and having an yield of greater than 1 mg/L.
[0272] In some embodiments, the vector comprises a CMV promoter. In some
embodiments, the vector comprises a sequence encoding a placeholder
peptide linked via a cleavable site to the beta1 chain. In some
embodiments, peptide exchange assay involves prior cleavage of the
placeholder peptide from the beta chain. In some embodiments, the
cleavable site is a thrombin cleavage site. In some embodiments, peptide
exchange assay is a FRET assay. In some embodiments, the purification is
by any one of: column chromatography, batch chromatography, ion exchange
chromatography, size exclusion chromatography, affinity chromatography or
LC-MS.
[0273] In one aspect, provided herein is a composition comprising HLA
class II tetramers comprising either HLA-DR, or HLA-DP, or HLA-DQ
heterodimers, each heterodimer comprising an alpha and a beta chain,
purified and present at a concentration of greater than 0.25 mg/L. In
some embodiments, the HLA class II tetramer comprises heterodimer pairs
selected from a group consisting of: protein may be selected from the
group consisting of an HLA-DR, and HLA-DP or an HLA-DQ protein. In some
embodiments, the HLA protein is selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DQB1*02:01/HLA-DQA1*05:01, HLA-DQB1*02:02/HLA-DQA1*02:01,
HLA-DQB1*06:02/HLA-DQA1*01:02, HLA-DQB1*06:04/HLA-DQA1*01:02,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB5*01:01).
[0274] In some embodiments, the heterodimer pairs are expressed in a
eukaryotic cell. In some embodiments, the heterodimer pair is encoded by
a vector. In some embodiments, the vector comprises: a nucleic acid
sequence encoding an alpha chain and a beta chain of HLA protein, a
secretion signal, a biotinylation motif and at least one tag for
identification or for purification, such that each HLA protein alpha 1
and beta1 heterodimers is secreted in dimerized state, wherein the
secreted heterodimer is associated with a placeholder peptide. In some
embodiments, the vector comprises: a nucleic acid sequence encoding an
alpha chain and a beta chain of HLA protein, a secretion signal, a
biotinylation motif and at least one tag for identification or for
purification, such that each HLA protein alpha 1 and beta1 heterodimers
is secreted in dimerized state, wherein the secreted heterodimer is
associated with a placeholder peptide.
[0275] In some embodiments, HLA class II heterodimers secreted from
eukaryotic cells into cell culture medium, and is purified by any one of:
column or batch chromatography, ion exchange chromatography, size
exclusion chromatography, affinity chromatography or LC-MS.
[0276] In one aspect, provided herein is a method of screening a drug
comprising a polypeptide sequence for immunogenicity in a subject, the
method comprising: inputting amino acid information of peptide sequences
of the polypeptide sequence, using a computer processor, into a
machine-learning HLA-peptide presentation prediction model to generate a
set of presentation predictions for the peptide sequences, each
presentation prediction representing a probability that one or more
proteins encoded by an HLA class I or II allele of a cell of the subject
will present an epitope sequence of a given peptide sequence; wherein the
machine-learning HLA-peptide presentation prediction model comprises: a
plurality of predictor variables identified at least based on training
data wherein the training data comprises: sequence information of
sequences of peptides presented by an HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence information
comprising amino acid position information, wherein the training peptide
sequence information is associated with the HLA protein expressed in
cells; and a function representing a relation between the amino acid
position information received as input and the presentation likelihood
generated as output based on the amino acid position information and the
predictor variables; (b) determining or predicting that each of the
peptide sequences of the polypeptide sequence would not be immunogenic to
the subject based on the set of presentation predictions; and (c)
administering to the subject a composition comprising the drug.
[0277] In one aspect, provided herein is a method of screening a drug
comprising a polypeptide sequence for immunogenicity in a subject, the
method comprising: (a) inputting amino acid information of peptide
sequences of the polypeptide sequence, using a computer processor, into a
machine-learning HLA-peptide presentation prediction model to generate a
set of presentation predictions for the peptide sequences, each
presentation prediction representing a probability that one or more
proteins encoded by an HLA class I or II allele of a cell of the subject
will present an epitope sequence of a given peptide sequence; wherein the
machine-learning HLA-peptide presentation prediction model comprises: a
plurality of predictor variables identified at least based on training
data; wherein the training data comprises: sequence information of
sequences of peptides presented by an HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence information
comprising amino acid position information, wherein the training peptide
sequence information is associated with the HLA protein expressed in
cells; and a function representing a relation between the amino acid
position information received as input and the presentation likelihood
generated as output based on the amino acid position information and the
predictor variables; (b) determining or predicting that at least one of
the peptide sequences of the polypeptide sequence would be immunogenic to
the subject based on the set of presentation predictions.
[0278] In one embodiment, the method further comprises deciding not to
administer the drug to the subject.
[0279] In one embodiment, the drug comprises and antibody or binding
fragment thereof
[0280] In one embodiment, the peptide sequences of the polypeptide
sequences comprise each contiguous peptide sequence of the polypeptide
sequence that has a length of 8, 9, 10, 11 or 12 amino acids, and wherein
the protein encoded by an HLA class I or II allele of a cell of the
subject is a protein encoded by an HLA class I allele of a cell of the
subject.
[0281] In one embodiment, the peptide sequences of the polypeptide
sequences comprise each contiguous peptide sequence of the polypeptide
sequence that has a length of 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or
25 amino acids, and wherein the protein encoded by an HLA class I or II
allele of a cell of the subject is a protein encoded by a class II MHC
allele of a cell of the subject.
[0282] In one aspect, provided herein is a method of treating a subject
with an autoimmune disease or condition comprising: (a) identifying or
predicting an epitope of an expressed protein presented by an HLA class I
or II of a cell of the subject, wherein a complex comprising the
identified or predicted epitope and the HLA class I or II is targeted by
a CD8 or CD4 T cell of the subject; (b) identifying a T cell receptor
(TCR) that binds to the complex; (c) expressing the TCR in a regulatory T
cell from the subject or an allogeneic regulatory T cell; and (d)
administering the regulatory T cell expressing the TCR to the subject.
[0283] In one embodiment, the autoimmune disease or condition is diabetes.
[0284] In one embodiment, the cell is an islet cell.
[0285] In one aspect, provided herein is a method of treating a subject
with an autoimmune disease or condition comprising administering to the
subject a regulatory T cell expressing a T cell receptor (TCR) that binds
to a complex comprising (i) an epitope of an expressed protein identified
or predicted to be presented by an HLA class I or II of a cell of the
subject and (ii) the HLA class I or II, wherein the complex is targeted
by a CD8 or CD4 T cell of the subject.
[0286] Additional aspects and advantages of the present disclosure will
become readily apparent to those skilled in this art from the following
detailed description, wherein only illustrative embodiments of the
present disclosure are shown and described. As will be realized, the
present disclosure is capable of other and different embodiments, and its
several details are capable of modifications in various obvious respects,
all without departing from the disclosure. Accordingly, the drawings and
description are to be regarded as illustrative in nature, and not as
restrictive.
[0287] MAPTAC.TM. can be used for high-throughput peptide binding assays
where peptides bound to HLA class II are measured after isolation with
MAPTAC.TM. constructs at different time points and under different
conditions, such as heating at 37.degree. C., to obtain the sequences of
populations of peptides with different stabilities using LC-MS/MS.
[0288] In one aspect, provided herein is a method for treating a cancer in
a subject the method comprising: identifying peptide sequences, wherein
the peptide sequences are associated with cancer, wherein identifying
comprises comparing DNA, RNA or protein sequences from the cancer cells
of the subject to DNA, RNA or protein sequences from the normal cells of
the subject; inputting amino acid information of the peptide sequences
identified, using a computer processor, into a machine-learning
HLA-peptide presentation prediction model to generate a set of
presentation predictions for the peptide sequences identified, each
presentation prediction representing a probability that one or more
proteins encoded by an HLA class II allele of a cell of the subject will
present a given sequence of a peptide sequence identified; wherein the
machine-learning HLA-peptide presentation prediction model comprises: a
plurality of predictor variables identified at least based on training
data wherein the training data comprises: sequence information of
sequences of peptides presented by an HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence information
comprising amino acid position information, wherein the training peptide
sequence information is associated with the HLA protein expressed in
cells; and a function representing a relation between the amino acid
position information received as input and the presentation likelihood
generated as output based on the amino acid position information and the
predictor variables; and selecting a subset of the peptide sequences
identified based on the set of presentation predictions for preparing the
personalized cancer therapy; and administering to the subject a
composition comprising one or more of the peptides, wherein the
prediction model has a positive predictive value of at least 0.1 at a
recall rate of at least 0.1%, from 0.1%-50% or at most 50%.
[0289] In some embodiments, the machine-learning HLA-peptide presentation
prediction model comprises sequence information of sequences of peptides
presented by an HLA protein expressed in cells and identified by mass
spectrometry after performing reverse phase offline fractionation.
[0290] In some embodiments, the prediction model exhibits a 1.1.times. to
100.times. fold improvement compared to NetMHCIIpan. In some embodiments,
the prediction model exhibits a 1.1, 2, 3, 4, 5, 6, 7, 7.4, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 50, 55,
60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
78, 79, 80, 81, 8, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,
96, 97, 98, 99, 100-fold or more improvement compared to NetMHCIIpan.
INCORPORATION BY REFERENCE
[0291] All publications, patents, and patent applications mentioned in
this specification are herein incorporated by reference to the same
extent as if each individual publication, patent, or patent application
was specifically and individually indicated to be incorporated by
reference. To the extent publications and patents or patent applications
incorporated by reference contradict the disclosure contained in the
specification, the specification is intended to supersede and/or take
precedence over any such contradictory material.
BRIEF DESCRIPTION OF THE DRAWINGS
[0292] The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained by
reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention are
utilized, and the accompanying drawings (also "FIG." herein), of which:
[0293] FIG. 1A diagram representing a peptide docked onto MHC Class I
protein. Figure discloses SEQ ID NO: 36.
[0294] FIG. 1B depicts an exemplary diagram representing a peptide docked
onto MHC Class II protein. Figure discloses SEQ ID NO: 37.
[0295] FIG. 2 depicts an exemplary experimental approach for generating
mono-allelic HLA class II binding peptide data. HLA class II peptides are
introduced into any cell, including a cell not expressing HLA class II so
that specific HLA class II allele(s) are expressed in the cell.
Populations of genetically engineered HLA expressing cells are harvested,
lysed, and their HLA-peptide complexes are tagged (e.g., biotinylated)
and immunopurified (e.g., using the biotin-streptavidin interaction).
HLA-associated peptides specific to a single HLA can be eluted from their
tagged (e.g., biotinylated) complexes and evaluated (e.g., sequenced
using high resolution LC-MS/MS).
[0296] FIG. 3 depicts an exemplary sequence logo representation of HLA
class II-DRB1*11:01-associated peptides across Neon BAP, Expi293 cell
line; Neon BAP, A375 cell line; IEDB, Affinity <50 nM; and Pan-HLA
Class II Ab, Homozygous LCL. FIG. 3 shows that examples of MS-derived
motifs match known patterns and show consistency across transfected cell
lines.
[0297] FIG. 4 is an exemplary depiction of the HLA class II binding
predictor performance. FIG. 4 is a bar plot showing the performance of
the binding predictor (neonmhc2) and NetMHCIIpan applied to a validation
dataset consisting of observed mass spec peptides and decoy peptides
which are generated at a ratio of 1:19 (hits:decoys) by randomly
shuffling the hit peptides. For the NEON binding predictor neonmhc2, a
separate model is built for each MHC II allele shown. The height of the
bars shows the positive predictive value (PPV), defined as the fraction
of predicted binders in the validation set which were indeed hit
peptides. The alleles are sorted by the model's performance when
predicting for that allele.
[0298] FIG. 5 depicts an exemplary effect of scored peak intensity (SPI)
thresholds on binding predictor validation. FIG. 5 shows the performance
of the HLA class II binding predictor when trained/validated on sets of
peptides with different scored peak intensity (SPI) cutoffs. For each
allele-specific model that is trained, shown is the model's performance
in 3 settings: trained and evaluated on datasets using observed MS hit
peptides of larger than or equal to 70 SPI, trained on peptides with
larger than or equal to 50 SPI and validated on peptides with larger than
or equal to 70 SPI, and trained and validated on peptides with larger
than or equal to 50 SPI.
[0299] FIG. 6 depicts an exemplary bar plot showing representative data
from number of observed peptides by allele profiling by LC-MS/MS with
larger than or equal to 70 scored peak intensity (SPI) cutoffs. Each bar
represents the total number of observed peptides of an allele. There are
collected data for 35 HLA-DR alleles. The collected data for 35 HLA-DR
alleles have >95% population coverage for HLA-DR (USA allele
frequencies).
[0300] FIG. 7A shows the PPV of the model when applied to test partition
of data for the indicated HLA class II alleles. The decoy peptides used
were scrambled sequences of the positive (hit) peptide sequences at a hit
to decoy ratio of 1:19. PPV was determined by identifying the top-scoring
5% of peptides in the test partition and determining the fraction of them
that were positive for binding to the protein encoded by the respective
HLA class II allele.
[0301] FIG. 7B depicts exemplary prediction performance as a function of
training set size (curves obtained by artificially down-sampling the
training set). FIG. 7B shows that, generally, for the 35 HLA-DR alleles
collected, when the training set size increases, the value of PPV
increases.
[0302] FIG. 8 depicts an exemplary graph, demonstrating that
processing-related variables can improve prediction further. Distinguish
MS-observed peptides random sequences selected from protein-coding exome
may be distinguished. On the training data partition, a logistic
regression may be fit to predict HLA class II presentation using binding
strength (NetMHCIIpan or Neon's predictor) and processing features
(RNA-Seq expression and a derived gene-level bias term). On a separate
evaluation partition, exonic positions overlapping MS-observed MHC II
peptides ("hits") may be scored alongside random exonic positions not
observed in MS (1:499 ratio). The top 0.2% (1/500) may be called as
positives, and positive predictive value may be assessed this threshold.
[0303] FIG. 9 depicts an exemplary neural network architecture. Input
peptides are represented as 20mers, with shorter peptides being filled in
with "missing" characters. Each peptide has a 31-dimensional embedding,
so the input into the neural network is a 20.times.31 matrix. Before
being processed by the neural network, feature normalization on the
20.times.31 matrix is performed based on feature value means and standard
deviations in the training set. The first convolutional layer has a
kernel of 9 amino acids and 50 filters (also called channels) with a
Rectified Linear Unit (ReLU) activation function. This is followed by
batch normalization then spatial dropout with a dropout rate of 20%. This
is followed by another convolutional layer with a kernel of 3 amino acids
and 20 filters with a ReLU activation function and then again followed by
batch normalization and spatial dropout with a dropout rate of 20%.
Global max pooling is then applied, taking the maximally-activated neuron
in each of the 20 filters; then these 20 values are passed into a fully
connected (dense) layer with a single neuron using a Sigmoid activation
function. The output of this layer is treated as the binding/non-binding
prediction. L2 regularization is applied to the weights of the first
convolutional layer, second convolutional layer, and dense layer with
weights of 0.05, 0.1, and 0.01, respectively. Additional models used have
varied the number of convolutional layers and the kernel size of each
layer.
[0304] FIG. 10 depicts an exemplary computer control system that is
programmed or otherwise configured to implement methods provided herein.
[0305] FIG. 11A depicts an exemplary overview of the MAPTAC.TM.
experimental workflow. Figure discloses SEQ ID NO: 38.
[0306] FIG. 11B depicts exemplary per-allele peptide counts, merged across
replicates.
[0307] FIG. 11C depicts exemplary peptide length distributions for HLA
class I and HLA class II alleles profiled by MAPTAC.TM..
[0308] FIG. 11D depicts exemplary per-residue cysteine frequencies
observed for MAPTAC.TM. and IEDB (alleles DRB1*01:01, DRB1*03:01,
DRB1*09:01, and DRB1*11:01), the human proteome, and multi-allelic MS
data from previous publications.
[0309] FIG. 12A depicts Caucasian frequencies for HLA-DR, -DP, and -DQ
alleles present in >1% of individuals and counts of peptides from the
indicated sources measured as strong binders (<50 nM).
[0310] FIG. 12B depicts exemplary length distributions of IEDB peptides
with associated HLA class II affinity measurements.
[0311] FIG. 12C depicts exemplary Western blots of (1) Expi293, (2) HeLa,
and (3) A375 cell lines individually transfected with two HLA class I and
two HLA class II alleles: HLA-A*02:01, HLA-B*45:01, HLA-DRB1*01:01, and
HLA-DRB1*11:01. Membranes were blotted with anti-biotin ligase epitope
tag to visualize biotin acceptor peptide (BAP) and anti-beta-tubulin as a
loading control. Lanes correspond to the following fractions collected
during the MAPTAC.TM. protocol: lane 1 input, lane 2 biotinylated input,
and lane 3 input after pull-down.
[0312] FIG. 12D depicts exemplary per-residue amino acid frequencies
observed for MAPTAC.TM. and IEDB (alleles DRB1*01:01, DRB1*03:01,
DRB1*09:01, and DRB1*11:01), the human proteome, and multi-allelic MS
data from previous publications.
[0313] FIG. 12E depicts Caucasian frequencies for HLA-DR, -DP, and -DQ
alleles present in >1% of individuals and counts of peptides from the
indicated sources measured as strong binders (<50 nM). This figure
includes additional data relative to FIG. 12A. The additional data were
taken from: tools.iedb.org/main/datasets/.
[0314] FIG. 12F depicts exemplary per-residue amino acid frequencies
observed for MAPTAC.TM. (reduced and alkylated), MAPTAC.TM. (no
treatment) and IEDB (alleles DRB1*01:01, DRB1*03:01, DRB1*09:01, and
DRB1*11:01), the human proteome, and multi-allelic MS data from previous
publications
[0315] FIG. 13 depicts an exemplary representation of core binding
sequence logos for MHC II alleles per MAPTAC.TM. and IEDB. Sequence logos
are graphical representations wherein the height of each amino acid is
proportional to its frequency of occurrence in a peptide that binds to
the MHC protein encoded by the allele. Positions with lowest entropy are
represented in color, where colors correspond to amino acid properties.
Peptides are derived from the indicated data sets and are aligned
according to a CNN-based predictor (Methods). Logos represent all
peptides including those that did not closely match the overall motif
(e.g., no peptides are sequestered in a "trash" cluster).
[0316] FIG. 14A depicts exemplary sequence logos for HLA-A*02:01 binding
peptides (ligands) analyzed using different HLA-ligand profiling
technologies including binding assays, stability assays, soluble HLA
(sHLA) mass spectrometry, mono-allelic mass spectrometry, and MAPTAC.TM.
in two different cell lines (A375 & expi293).
[0317] FIG. 14B depicts an exemplary fraction of MAPTAC.TM. peptides
exhibiting 0, 1, 2, 3, and 4 of the heuristically defined anchors.
[0318] FIG. 14C depicts an exemplary distribution of NetMHCIIpan-predicted
binding affinities for MAPTAC.TM.-observed peptides (20 peptides per
allele, each with SPI>70 and a nested set of size >=2) and
length-matched decoys sampled from the proteome.
[0319] FIG. 15A depicts an exemplary architecture of a convolutional
neural network (CNN) trained to distinguish mono-allelic MHC peptides
from scrambled length-matched decoys. The schematic indicates the usage
of an amino acid feature embedding, 2 convolutional layers with different
filter sizes, and the usage of global max pooling as input to a final
logistic output node.
[0320] FIG. 15B is an exemplary result that shows Kendall Tau statistics
for the correlation of measured IEDB affinities with binding predictions
either from neonmhc2 or NetMHCIIpan. Evaluated peptides include only
those posted to IEDB the year after NetMHCIIpan was released.
[0321] FIG. 16 is an exemplary depiction of the performance of neonmhc2 as
a function of training data set size.
[0322] FIG. 17A depicts exemplary cluster assignments for MAPTAC.TM.
peptides (20 per allele) spiked into pan-DR and pan-class II MHC MS
datasets. Datasets were deconvolved using GibbsCluster. Each box
represents one MAPTAC.TM. peptide. The color of the box indicates which
cluster it was assigned to, and gray bars indicate which allele the
peptide actually came from. The total number of clusters in the Gibbs
cluster solution (right side) was selected using a mutual information
(MI) metric. The MI score also determines how the samples are sorted;
samples with high-MI solutions appear at the top.
[0323] FIG. 17B depicts exemplary core-binding sequence logos for
multi-allelic MS data deconvolved by GibbsCluster. Each set of peptides
corresponds to the cluster that aligned best with the MAPTAC.TM.
spike-ins.
[0324] FIG. 17C depicts representative performance of models using either
MAPTAC.TM. data or deconvolved multi-allelic data to predict hold-out
MAPTAC.TM. peptides. For each allele, the larger of the two data sources
(usually MAPTAC.TM.) was down-sampled so that the predictors would be
based on an equal number of training examples. NetMHCIIpan performance is
shown as an additional comparison.
[0325] FIG. 17D depicts exemplary core binding sequence logos derived from
multi-allelic MS data from the indicated sources.
[0326] FIG. 18A depicts an exemplary graph of fraction of peptides vs
source gene expression (transcripts per million (TPM)) for MS-observed
peptides and random proteome decoys (data replotted from Schuster et al.
2017).
[0327] FIG. 18B depicts exemplary observed vs. expected number of Class II
peptides per gene as determined by a joint analysis of colorectal cancer,
melanoma, and ovarian cancer datasets (Loffler et al., 2018, and Schuster
et al., 2017). The expected count is derived by multiplying gene length
by expression level. Expected and observed counts were summed across
relevant samples. Genes with known presence in plasma are marked
according to their concentration (Inset).
[0328] FIG. 18C depicts exemplary distribution of enrichment scores (ratio
of observed to expected observations, as in FIG. 18B) for genes
associated with autophagy.
[0329] FIG. 18D depicts exemplary distribution of enrichment scores
according to the localization of each source gene. Source gene
localization was determined using Uniprot (uniprot_sprot.dat).
[0330] FIG. 18E depicts exemplary data representing comparison of the
expected versus observed frequency of fraction of total number of
peptides having MHC-II binding affinity, segregated based on their
cellular localization properties.
[0331] FIG. 18F depicts exemplary representative data of relative
concordance of peptides in observations with respect to two different
gene expression profiles. For each sample, gene-level peptide counts were
modeled as a linear combination of a bulk tumor gene expression and
professional APC (macrophage) gene expression profile. The ratio of the
coefficients determines the relative concordance of each expression
profile with the peptide repertoire. Error bars correspond to a 95%
confidence interval computed by bootstrap resampling.
[0332] FIG. 19A depicts exemplary representative data of expression levels
of HLA-DRB1 in the five example studies. Each dot represents expression
in an individual cell type in an individual patient, averaged over cells.
[0333] FIG. 19B depicts exemplary representative data of tumor and stromal
derived HLA-DRB1 expression as inputted from RNA-Seq of TCGA patients.
Horizontal bars correspond to individual patients and are grouped by
tumor type. Patients were included if they had a mutation in HLA class II
pathway gene (CIITA, CD74 or CTSSS) as determined by DNA-based mutation
calls. For each patient, the fraction of HLA-DRB1 expression attributable
to the tumor estimated as min(1,2f), where f is the fraction of RNA-Seq
reads in CIITA, CD74, or CTSS exhibiting a mutation.
[0334] FIG. 19C depicts exemplary representative data of additional
single-cell RNA-Seq studies that include biopsies pre- and
post-checkpoint blockade immunotherapy.
[0335] FIG. 20 depicts exemplary representative experimental data
assessing prediction overall performance on natural donor tissues.
[0336] FIG. 21A depicts exemplary representative data, showing that the
integrated presentation model predicts cellular HLA class II ligandomes.
It represents. PPV at a 1:499 hit-to-decoy ratio for pan-DR datasets
(also analyzed in FIG. 30B and FIG. 32E). Predictors use binding
prediction (NetMHCIIpan or neonmhc2) and optionally employ gene
expression, gene bias (per FIG. 32A), and overlap with previously
observed HLA-DQ peptides. For each candidate peptide, the binding score
was calculated as the maximum across the HLA-DR alleles in the sample
genotype.
[0337] FIG. 21B depicts exemplary representative data, showing prediction
performance for tumor-derived peptides as identified using SILAC,
presented by dendritic cells (analyzed from cell lysates) using the same
hit:decoy ratio and performance metrics as in FIG. 21A, with and without
use of processing features.
[0338] FIG. 21C depicts exemplary expression and gene bias scores for
heavy-labeled peptides observed in an UV treatment experiment (red dots,
plotted according to K562 expression) as compared to light-labeled
peptides (gray dots, plotted according to DC expression).
[0339] FIG. 21D depicts an exemplary diagram representing overlap of
heavy-labeled peptide source genes according to the lysate and
UV-treatment experiments. Gene names are colored by functional class.
[0340] FIG. 22A depicts an exemplary flow diagram representing an assay
protocol disclosed herein, to validate HLA class II-driven CD4+ T cells
and T cell responses.
[0341] FIG. 22B depicts an exemplary HLA protein dimer construct design
for peptide exchange assay (upper panel) and a graphical representation
of an exemplary assay workflow (lower panel). Figure discloses
"10.times.His" as SEQ ID NO: 20.
[0342] FIG. 23 depicts an exemplary graphical illustration of an exemplary
vector design for MHC-II expression for screening new binding peptides,
and a representation of the expressed protein product. Figure discloses
SEQ ID NO: 39 and discloses "10.times.His" as SEQ ID NO: 20.
[0343] FIG. 24 depicts an exemplary flow diagram of transfection,
purification and cleavage of placeholder peptide from beta chain.
[0344] FIG. 25A depicts an exemplary graphical illustration showing vector
encoding CLIP peptides that are associated with increased secretion of
expressed MHC-II peptides. Figure discloses SEQ ID NO: 21.
[0345] FIG. 25B depicts an exemplary graphical representation with the
shorter and longer forms of the nucleic acids encoding CLIP0 and CLIP1
respectively. Figure discloses SEQ ID NOS 1 and 21, respectively, in
order of appearance.
[0346] FIG. 25C depicts an exemplary representative result of a Coomassie
gel analysis of the alpha and beta chains with or without the longer
clip.
[0347] FIG. 26A depicts an exemplary graphical illustration of the TR-FRET
assay.
[0348] FIG. 26B depicts exemplary representative polarization data from an
HLA class II peptide binding assay using Fluorescence Resonance Energy
Transfer (FRET) assay using specific peptides.
[0349] FIG. 26C depicts exemplary representative polarization data from an
HLA class II peptide binding assay using Fluorescence Resonance Energy
Transfer (FRET) assay using specific peptides.
[0350] FIG. 26D depicts an exemplary percent displacement of MHC-construct
bound peptide that was calculated from increase in fluorescence.
[0351] FIG. 26E depicts an exemplary percent displacement of MHC-construct
bound peptide that was calculated from increase in fluorescence.
[0352] FIG. 26F depicts an exemplary peptide exchange using assay using
differential scanning fluorometry (DSF). A graphical representation is
depicted showing an exemplary mechanism of detecting peptide dissociation
from MHC class II with heat which also dissociates the MHC class II
heterodimer, resulting in binding of the fluorophore and high
fluorescence. An exemplary schematic of placeholder peptide dislodgement
by epitope peptide is also depicted. Exemplary melting curves plotted
over temperature are also depicted.
[0353] FIG. 26G depicts an exemplary soluble HLA-DM construct and its use
for the performance of MHC Class II peptide exchange. The construct
depicted contains a CMV promoter, a coding sequence for HLA-DM beta chain
and a coding sequence for a HLA-DM alpha chain downstream of a secretion
sequence (leader) and a BAP sequence at the 3' end of the beta chain
coding sequence; a His tag at the 3' end of the alpha chain coding
sequence. The two chains are be separated by an intervening ribosomal
skipping sequence. The construct was expressed in Expi-CHO cells and the
protein secreted into the medium culture medium was purified. Figure
discloses "10.times.His" as SEQ ID NO: 20.
[0354] FIG. 26H shows exemplary size exclusion chromatography data using
HLA-sDM to perform peptide exchange.
[0355] FIG. 27A depicts an exemplary graphical illustration of an
exemplary DRB tetramer repertoire build.
[0356] FIG. 27B depicts an exemplary graphical illustration of an
exemplary class II tetramer repertoire build.
[0357] FIG. 27C depicts an exemplary graphical illustration of a summary
of DRB tetramer repertoire coverage for the DRB1 allele for peptide
exchange.
[0358] FIG. 27D depicts exemplary coverage of human MHC class II allele
production.
[0359] FIG. 27E shows an exemplary result from tetramer staining of
samples induced with Flu epitopes (memory response) or HIV epitopes
(naive response).
[0360] FIG. 28A depicts an exemplary graphical representation of a method
of evaluation of peptides for HLA class II restriction by fluorescence
polarization assay that enables a screening method to rapidly identify
allele restriction for epitope peptides. The assay principle depicted in
FIG. 28A allows for affinity measurements, and an unambiguous measurement
of peptide exchange.
[0361] FIG. 28B depicts an exemplary summary of the multiple assay
conditions explored (upper panel) in the fluorescence polarization assay
with DRB1*01:01. Also depicted is an illustration of a soluble MHC class
II allele and a full-length MHC class II allele with the transmembrane
domain in a detergent micelle (lower panel), both of which were
constructed with placeholder peptide with the cleavable linker for use in
the assay.
[0362] FIG. 28C depicts an exemplary graphical representation of the
assays for investigating the full length and the soluble allele
previously shown in FIG. 28B lower panel. In short, both the full length
and the soluble alleles are expressed in cells. The membrane bound full
length allele form is harvested by permeabilizing the membrane, while the
secreted form is harvested from the cell supernatant. The harvested Class
II HLA allele proteins are purified by passing through nickel (Ni.sup.2+)
columns.
[0363] FIG. 28D depicts exemplary data showing that purification method
does not affect peptide potency. Shown on the left are average IC50
values from experiments using L243 purified full length HLA-DR1 and
Ni.sup.2+ purified full-length HLA-DR1.
[0364] FIG. 28E depicts exemplary data showing choice of the soluble form
(sDR1) or the full-length form (fDR1) does not affect the peptide
potency. Shown on the left are average IC50 values from experiments using
sDR1 form or fDR1. FP, fluorescence polarization.
[0365] FIG. 28F depicts an exemplary graphical view of an exemplary
evaluation of neonmhc2 and NetMHCIIpan predicted peptides in binding
assay and identification of discordant peptides.
[0366] FIG. 28G depicts exemplary fluorescence polarization binding screen
data for evaluation of neonmhc2 predicted peptides; shown as heat map as
also the percent inhibition of probe binding indicated for each
concentration of the peptide used. Green depicts good binding which is
proportionate to the color intensity. Yellow depicts intermediate binding
and red depicts poor binding, as also indicated by the corresponding
percent inhibition values.
[0367] FIG. 28H depicts a summary of an evaluation of neonmhc2 predicted
peptides in an exemplary binding assay.
[0368] FIG. 29 depicts an exemplary average count of peptides from an
average MAPTAC.TM. experimental replicate (50 million cells), per each
HLA allele.
[0369] FIGS. 30A-30C depict an exemplary binding core analysis for HLA
class II MAPTAC.TM. alleles+/- HLA-DM and multi-allelic deconvolution
fidelity. FIG. 30A depicts exemplary sequence logos for one
representative HLA-DR, -DQ, and -DP allele according to MAPTAC.TM. with
and without HLA-DM co-transfection (expi293 cell line) and IEDB wherein
the height of each amino acid is proportional to its frequency Amino
acids with frequency greater than 10% are shown in color according to
chemical properties; all others are shown in gray. Peptides were aligned
according to the GibbsCluster tool (Supplemental Methods), and logos
represent all peptides, including those that did not closely match the
overall motif (e.g. no peptides are sequestered in a "trash" cluster).
FIG. 30B depicts an exemplary description of cluster assignments for
MAPTAC.TM. peptides (20 per allele) spiked into pan-DR MS datasets.
Datasets were deconvolved using GibbsCluster. Each colored box represents
one MAPTAC.TM. peptide. The color of the box indicates which cluster it
was assigned to, and gray bars indicate which allele the peptide came
from. FIG. 30C depicts an exemplary graph showing that the share of
peptides exhibiting 0, 1, 2, 3, or 4 expected residues in anchor
positions, for alleles shown in FIG. 30B. Anchor positions were defined
as the four positions with lowest entropy, and the "expected" residues
were defined as those with .gtoreq.10% frequency in those positions.
[0370] FIGS. 31A-31F depict an exemplary architecture and benchmarking of
the neonmhc2 binding prediction algorithm. FIG. 31A depicts an exemplary
architecture of a convolutional neural network (CNN) trained to
distinguish mono-allelic HLA class II peptides from scrambled
length-matched decoys. The schematic indicates the usage of an amino acid
feature embedding layer, 2 convolutional layers of width 6, the presence
of skip-to-end connections, and a combination of average- and max-pooling
operations as input to a final logistic output node. FIG. 31B depicts an
exemplary positive predictive value (PPV) for NetMHCIIpan and neonmhc2 as
evaluated on a partition of MAPTAC.TM. data that was not used for
training or hyper-parameter optimization. For each allele, n MS-observed
peptides were scored in conjunction with 19n length-matched decoys
sampled from the same set of source genes, and each predictor's n
top-ranked peptides (e.g. the top 5%) were called as positives. According
to this evaluation protocol, PPV is identical to recall because the
number of false positives and false negatives is necessarily equal. FIG.
31C depicts an exemplary PPV for NetMHCIIpan and neonmhc2 on the TGEM
data set. For each allele, the n top-ranked peptides were called
positives, where n is the number of confirmed immunogenic epitopes in the
evaluated set. FIG. 31D depicts exemplary ex vivo T cell induction
results for neoantigen peptides. Peptides were selected based on high
neonmhc2 scores and weak NetMHCIIpan scores for HLA-DRB1*11:01. Figure
discloses SEQ ID NOS 87-89, 91, 90, 2, 92-94, 3, and 95-96, respectively,
in order of appearance. FIG. 31E depicts comparison of models trained on
monoallelic MAPTAC data versus deconvolved multiallelic data as evaluated
on hold-out monoallelic data. Values are as shown for neonmhc2 where the
training dataset is down-sampled to match the size of the deconvolution
training set. FIG. 31F shows PPV on the TGEM dataset for
NetMHCIIpan-v3.1, the deconvolution-trained predictor, and neonmhc2 (with
and without down-sampling). For each allele, the n top-ranked peptides
were called positives, where n is the number of confirmed immunogenic
epitopes in the evaluated set.
[0371] FIGS. 32A-32E depict exemplary gene representation and protein
processing in HLA class II tumor peptidomes. FIG. 32A depicts exemplary
results of observed vs. expected number of HLA class II peptides per gene
as determined by a joint analysis of colorectal cancer, melanoma, and
ovarian cancer datasets. The expected count is derived by multiplying
gene length by expression level. Expected and observed counts were summed
across relevant samples. Genes with known presence in plasma are marked
according to their concentration. FIG. 32B depicts exemplary results of
expected vs. observed frequency of peptides per cellular localization.
FIG. 32C depicts exemplary results of distribution of enrichment scores
(ratio of observed to expected observations, as in part FIG. 32B) for
genes regulated by the proteasome. Gene sets include those with known
ubiquitination sites and those that increase in abundance upon
application of a proteasome inhibitor. FIG. 32D depicts a diagram
presenting three exemplary working models for how HLA class II peptides
are processed, according to which i) cathepsins and other enzyme break
cleave proteins into peptide fragments that are subsequently bound by
HLA, ii) proteins or unfolded polypeptides bind HLA and are subsequently
cleaved to peptide length iii) proteins are partially digested before
binding and further trimmed after binding. Each model corresponds to a
different prediction approach. FIG. 32E depicts absolute increase in PPV
observed for logistic regression models that included processing-related
variables and neonmhc2 binding predictions as compared to models that
only used binding predictions. Evaluation was conducted on eleven samples
that were profiled by HLA-DR antibody (the same samples analyzed in FIG.
30B); each point corresponds to one sample. Asterisks mark significant
improvements (*: p<0.01, **: p<0.001, ***: p<0.0001) according
to two-tailed paired t-tests. The same analysis is shown in FIG. 40B but
instead using NetMHCIIpan as the base predictor. Methods for decoy
selection and PPV calculation are identical to those used in FIG. 31B.
[0372] FIGS. 33A-33G depict exemplary results of identification and
prediction tumor antigens presented by dendritic cells. FIG. 33A depicts
an exemplary graphical representation of experimental workflow for
identifying DC-presented HLA-II ligands that originate from cancer cells
(K562). Cancer cells were grown in SILAC media to full incorporation,
either lysed or irradiated, and then plated with monocyte-derived
dendritic cells. Presented peptides were isolated by pan-DR antibody and
sequenced by LC-MS/MS. FIG. 33B depicts exemplary data representing
prediction performance for tumor-derived peptides presented by dendritic
cells using the same hit-to-decoy ratio and performance metrics as in
FIG. 21A. Performance is shown for NetMHCIIpan- and neonmhc2-based models
with and without use of processing features. FIG. 33C depicts exemplary
gene expression distribution for source genes of heavy-labeled peptides
observed in the UV-treatment experiment (red curve, plotted according to
K562 expression) as compared to the source genes of light-labeled
peptides (gray curve, plotted according to DC expression). FIG. 33D shows
an exemplary graph of PPV at a 1:499 hit-to-decoy ratio for predicting
presented tumor antigens using NetMHCIIpan- and neonmhc2-based models
with and without processing features. Data points from left to right
represent samples: Donor 1 HOC1 treated cells: NetMHCIIpan continuous
expression; NetMHCIIpan continuous expression+gene bias; NetMHCIIpan
continuous expression+gene bias+DQ overlap, full processing mode; Donor
1, UV-treated: neonmhc2; neonmhc2+threshold expression;
neonmhc2+continuous expression; neonmhc2+continuous expression+gene bias;
neonmhc2+continuous expression+gene bias+DQ overlap. FIG. 33E depicts
significance of various gene localizations and functional classes in
predicting heavy (K562-derived and light (DC-derived) peptides
respectively. P-values are calculated according to logistic regression
that controls for neonmhc2 binding score and source gene expression. Bar
colors indicate sign associated with coefficient in the regression. FIG.
33F depicts an exemplary graphical representation of results showing
overlap of tumor cell-derived peptide source genes (colored by functional
class) in the UV- and HOC1-treated experiments. FIG. 33G depicts
exemplary data showing PPV for predicting presented tumor antigens in a
second donor using logistic models fit on heavy-labeled peptides observed
in the first donor. Models were fit using neonmhc2 binding alone; binding
and expression; or binding, expression, and a binary variable indicating
if a peptide was from a mitochondria gene.
[0373] FIGS. 34A-34B depict exemplary characterization of MAPTAC.TM. data
related to FIG. 29. FIG. 34A depicts an exemplary HLA cell surface
analysis by FACS of Expi293 cell lines transfected with MAPTAC.TM.
constructs coding for affinity-tagged HLA-A*02:01-BAP FIG. 34B depicts an
exemplary HLA cell surface analysis by FACS of Expi293 cell lines
transfected with MAPTAC.TM. constructs coding for affinity-tagged
HLA-DRB1*11:01-BAP (bottom). HLA cell surface expression of transfected
Expi293 cells (orange) were compared with stained untransfected Expi293
(blue), unstained untransfected Expi293 (red), stained PBMCs (dark
green), and unstained PBMCs (light green). All HLA class I stains
utilized W6/32 (pan-HLA class I), while HLA class II stains utilized
REA332 (pan-HLA class II).
[0374] FIG. 35 depicts an exemplary comparison of MAPTAC.TM. and IEDB
logos, related to FIG. 30A. Measured and NetMHCIIpan-predicted affinities
for MS-observed peptides that did not exhibit good NetMHCIIpan scores but
were well supported by MS (scored peak intensity >70 and nested set
size .gtoreq.1).
[0375] FIGS. 36A-36C depict an exemplary analysis of HLA-DR1 MAPTAC.TM.
data fidelity, related to FIG. 30A-30C. FIG. 36A depicts exemplary
NetMHCIIpan3.1 scores for HLA-DR1 MAPTAC.TM. peptides (green) (lengths
12-23) as compared to 50,000 length-matched decoy peptides randomly
sampled from the proteome (blue), for common alleles. FIG. 36B depicts
exemplary measured and NetMHCIIpan-predicted affinities for exemplary
MS-observed peptides that did not exhibit good NetMHCIIpan scores but
were well-supported by MS (scored peak intensity>70 and nested set
size .gtoreq.1). Figure discloses SEQ ID NOS 40-86, top to bottom, left
to right, respectively, in order of appearance. FIG. 36C depicts
exemplary HLA class II sequence logos for HLA-DRB1 alleles as determined
by MAPTAC.TM. in different cell types.
[0376] FIGS. 37A-37C depict an additional exemplary analysis of MAPTAC.TM.
motifs, related to FIGS. 30A-30C. FIG. 37A depicts MAPTAC.TM.-derived
sequence logos for experiments with and without HLA-DM co-transfection
(expi293 cell line). FIG. 37B depicts sequence logos for several HLA
class I alleles according to MAPTAC.TM. and IEDB. Note that A*32:01 does
not show a high frequency Q at P2 and C*03:03 does not show a high
frequency Y at P9, differing with previous studies that used
multi-allelic deconvolution; the logo for B*52:01 is previously
unpublished. FIG. 37C depicts an exemplary alignment of
MAPAC.TM.-observed peptides to the gene sequence of CD74.
[0377] FIGS. 38Ai-38D depict exemplary neonmhc2 performance statistics and
T cell flow staining, related to FIGS. 31A-31D. FIG. 38Ai depicts an
exemplary performance of neonmhc2 as a function of training data set
size. PPV was evaluated in the same manner and using the same evaluation
peptides as in FIG. 31B; however, the training data was randomly
down-sampled to mimic smaller training data sets. FIG. 38Aii depicts
exemplary sequence logos for peptide clusters derived from multi-allelic
HLA-DR ligandome using GibbsCluster (default settings; "trash cluster
allowed). FIG. 38B depicts exemplary representative flow cytometry plots
of IFN-.gamma. expression by CD4+ cells from induction samples recalled
with neoantigen peptides predicted with neonmhc2. Delta values were
calculated by subtracting the percent of CD4+ cells expressing
IFN-.gamma. when recalled with neoantigen (+Peptide) from the percent of
CD4+ expressing IFN-.gamma. when recalled in the presence of no
neoantigen (No Peptide). The left two flow plots are representative of a
neoantigen that induced a CD4+ T cell T cell response (PEASLYGALSKGSGG
(SEQ ID NO: 2)) and a neoantigen that did not induce a T cell response
(PATYILILKEFCLVG (SEQ ID NO: 3)). FIG. 38C depicts exemplary delta values
from wells recalled with single neonmch2 neoantigen peptides. Peptides
were considered an induction hit if they had a positive response (delta
response above 3%, highlighted). Figure discloses SEQ ID NOS 87-91, 2,
92-94, 3, and 95-96, respectively, in order of appearance. FIG. 38D shows
exemplary sequence logos for peptide clusters derived for multi-allelic
HLA-DR ligandomes using GibbsCluster (default settings; "trash" cluster
allowed).
[0378] FIGS. 39A-39C depict an additional exemplary cell-of-origin
analysis for HLA class II, related to FIGS. 32A-32E. FIG. 39A depicts
exemplary percent-rank neonmhc2 scores for HLA class II peptides observed
in 4 PBMC samples profiled by pan-DR antibody (RG1248, RG1104, RG1095,
and HDSC from FIG. 30B), according to whether the peptide source gene is
present in human plasma. For each peptide, the best (lowest) percent rank
was used across the alleles present in the donor. Scores for random
length-matched proteome decoys are shown for comparison. Box plots mark
the 5th, 25th, 50th, 75th, and 95th percentiles. FIG. 39B depicts
exemplary counts of observed vs. expected peptides per gene for HLA class
I, using the same methodology as in FIG. 32A. Data correspond to the same
tumor types (colorectal, ovarian, and melanoma). Genes present in human
plasma are highlighted in blue and sized according to their
concentration. FIG. 39C depicts an exemplary relative concordance of
peptide observations with respect to two different gene expression
profiles. For each sample, gene-level peptide counts were modeled as a
linear combination of a bulk tumor gene expression and professional APC
gene expression profile. The ratio of the coefficients determines the
relative concordance of each expression profile with the peptide
repertoire. Error bars correspond to a 95% confidence interval computed
by bootstrap resampling.
[0379] FIGS. 40A-40B depict an additional exemplary analysis of processing
motifs related to FIGS. 32A-32E. FIG. 40A depicts exemplary amino acid
frequencies near N-terminal and C-terminal peptide cut sites relative to
average proteome frequencies (applies for upstream positions U3-U1 and
downstream positions D1-D3) or relative to average peptide frequencies
(applies for internal positions N1-C1) as observed in donor PBMC,
monocyte-derived dendritic cells, colorectal cancer, melanoma, ovarian
cancer, and the expi293 cell line (used for most MAPTAC.TM. data
generation). FIG. 40B depicts the same analysis as FIG. 32E but using
NetMHCIIpan as the base predictor. Absolute increase in PPV observed for
logistic regression models that included processing-related variables in
addition to NetMHCIIpan predictions (as compared to NetMHCIIpan-only
models) for eight samples profiled by HLA-DR antibody (the same samples
analyzed in FIG. 31B). Asterisks mark significant improvements (*:
p<0.01, **: p<0.001, ***: p<0.0001) according to two-tailed
paired t-tests.
[0380] FIG. 41 depicts an exemplary naming system used to refer to
positions upstream of peptides, within peptides, and downstream of
peptides.
[0381] FIG. 42A depicts a diagram representing an exemplary workflow for
analysis of endogenously processed and HLA-1 and HLA class II presented
peptides by nLC-MS/MS.
[0382] FIG. 42B depicts a graph showing exemplary experimental results
from nLC-MS/MS analysis of tryptic peptides with or without FAIMS.
Representative overlap in the detections of HLA-1 and HLA class II
peptides by nLC-MS/MS analysis with or without FAIMS at the analysis
scale as indicated are also depicted.
[0383] FIG. 43A depicts exemplary HLA class I acidic and basic reverse
phase fractionated peptide detections with or without FAIMS.
[0384] FIG. 43B depicts exemplary experimental results showing detection
of HLA class I bound unique peptides plotted over retention time.
[0385] FIG. 44A depicts exemplary HLA class II acidic and basic reverse
phase fractionated peptide detections with or without FAIMS.
[0386] FIG. 44B depicts exemplary experimental results showing detection
of HLA class II bound unique peptides plotted over retention time.
[0387] FIG. 45 depicts an exemplary graph of intersection size of HLA
class I binding peptides detected using the methods indicated (left) and
a Venn diagram of an exemplary standard workflow and an optimized
workflow for LC-MS/MS detection of HLA class I binding peptides (right).
[0388] FIG. 46 depicts an exemplary graph of intersection size of HLA
class II binding peptides detected using the methods indicated (left) and
a Venn diagram of an exemplary standard workflow and an optimized
workflow for LC-MS/MS detection of HLA class II binding peptides (right).
DETAILED DESCRIPTION
[0389] All terms are intended to be understood as they would be understood
by a person skilled in the art. Unless defined otherwise, all technical
and scientific terms used herein have the same meaning as commonly
understood by one of ordinary skill in the art to which the disclosure
pertains.
[0390] The section headings used herein are for organizational purposes
only and are not to be construed as limiting the subject matter
described.
[0391] Although various features of the present disclosure can be
described in the context of a single embodiment, the features can also be
provided separately or in any suitable combination. Conversely, although
the present disclosure can be described herein in the context of separate
embodiments for clarity, the disclosure can also be implemented in a
single embodiment.
[0392] The present disclosure is based on the important finding that the
presentation of antigens, specifically cancer antigens by specific HLA
class II alpha and beta chain pairs can be predicted with high degree of
confidence using a new computer-based machine-learning HLA-peptide
presentation prediction model which allows use of HLA class II specific
peptides for improved immunotherapy.
[0393] In one aspect, the present disclosure provides method for
predicting peptides that can accurately pair with, or bind to, a specific
HLA class II alpha and beta chain heterodimer, such that the high
fidelity binding of the peptide to HLA class II protein (comprising the
alpha and beta chain heterodimer) ensures presentation of the specific
peptide to the T lymphocytes, thereby eliciting a specific immune
response and avoid any cross-reactivity or immune promiscuity. Several
recent studies have shown that CD4+ T cells can also recognize HLA class
II presented ligands and contribute to tumor control. Cancer vaccines and
other immunotherapies would ideally take advantage of directing CD4+ T
cell responses, but current efforts have forgone HLA class II antigen
prediction entirely because the accuracy of current prediction tools is
inadequate.
[0394] In one aspect, the present disclosure provides method for
predicting peptides that can accurately bind to a specific HLA class II
protein, such that a more sustained and robust immune response can be
activated with the peptide, when the peptide is administered
therapeutically to a subject expressing the specific cognate HLA class II
protein, by means of the ability of HLA class II protein's activation of
CD4+ T cells and stimulate immunological memory. In some embodiments, the
method provided herein exhibits an improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a
1.1-fold improvement in a specific HLA class II protein prediction over
currently available predictor. In some embodiments, the method provided
herein exhibits at least about a 2-fold improvement in a specific HLA
class II protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a 3-fold
improvement in a specific HLA class II protein prediction over currently
available predictor. In some embodiments, the method provided herein
exhibits at least about a 4-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a 5-fold
improvement in a specific HLA class II protein prediction over currently
available predictor. In some embodiments, the method provided herein
exhibits at least about a 6-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a 7-fold
improvement in a specific HLA class II protein prediction over currently
available predictor. In some embodiments, the method provided herein
exhibits at least about a 8-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a 9-fold
improvement in a specific HLA class II protein prediction over currently
available predictor. In some embodiments, the method provided herein
exhibits at least about a 10-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a 15-fold
improvement in a specific HLA class II protein prediction over currently
available predictor. In some embodiments, the method provided herein
exhibits at least about a 20-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a 30-fold
improvement in a specific HLA class II protein prediction over currently
available predictor. In some embodiments, the method provided herein
exhibits at least about a 40-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a 50-fold
improvement in a specific HLA class II protein prediction over currently
available predictor. In some embodiments, the method provided herein
exhibits at least about a 60-fold improvement in a specific HLA class II
protein prediction over currently available predictor.
[0395] In one aspect, presented herein are methods of immunotherapy
tailored or personalized for a specific subject. Every subject or patient
expresses a specific array of HLA class I and HLA class II proteins. HLA
typing is a well-known technique that allows determination of the
specific repertoire of HLA proteins expressed by the subject. Once the
HLA heterodimers expressed by a specific subject is known, having an
improved, sophisticated and reliable method as described herein for
predicting peptides that can bind to a specific HLA class II alpha and
beta chain heterodimer, with high fidelity can ensure that a specific
immune response can be generated tailored specifically for the subject.
[0396] In this application, the use of the singular includes the plural
unless specifically stated otherwise. It must be noted that, as used in
the specification, the singular forms "a," "an" and "the" include plural
referents unless the context clearly dictates otherwise. In this
application, the use of "or" means "and/or" unless stated otherwise.
Furthermore, use of the term "including" as well as other forms, such as
"include", "includes," and "included," is not limiting. The terms "one or
more" or "at least one," such as one or more or at least one member(s) of
a group of members, is clear per se, by means of further exemplification,
the term encompasses inter alia a reference to any one of said members,
or to any two or more of said members, such as, e.g., any .gtoreq.3,
.gtoreq.4, .gtoreq.5, .gtoreq.6 or .gtoreq.7 etc. of said members, and up
to all said members.
[0397] Reference in the specification to "some embodiments," "an
embodiment," "one embodiment" or "other embodiments" means that a
feature, structure, or characteristic described in connection with the
embodiments is included in at least some embodiments, but not necessarily
all embodiments, of the present disclosure.
[0398] As used in this specification and claim(s), the words "comprising"
(and any form of comprising, such as "comprise" and "comprises"),
"having" (and any form of having, such as "have" and "has"), "including"
(and any form of including, such as "includes" and "include") or
"containing" (and any form of containing, such as "contains" and
"contain") are inclusive or open-ended and do not exclude additional,
unrecited elements or method steps. It is contemplated that any
embodiment discussed in this specification can be implemented with
respect to any method or composition of the disclosure, and vice versa.
Furthermore, compositions of the disclosure can be used to achieve
methods of the disclosure.
[0399] The term "about" or "approximately" as used herein when referring
to a measurable value such as a parameter, an amount, a temporal
duration, and the like, is meant to encompass variations of +/-20% or
less, +/-10% or less, +/-5% or less, or +/-1% or less of and from the
specified value, insofar such variations are appropriate to perform in
the present disclosure. It is to be understood that the value to which
the modifier "about" or "approximately" refers is itself also
specifically disclosed.
[0400] The term "immune response" includes T cell mediated and/or B cell
mediated immune responses that are influenced by modulation of T cell
costimulation. Exemplary immune responses include T cell responses, e.g.,
cytokine production, and cellular cytotoxicity. In addition, the term
immune response includes immune responses that are indirectly affected by
T cell activation, e.g., antibody production (humoral responses) and
activation of cytokine responsive cells, e.g., macrophages.
[0401] A "receptor" is to be understood as meaning a biological molecule
or a molecule grouping capable of binding a ligand. A receptor can serve
to transmit information in a cell, a cell formation or an organism. The
receptor comprises at least one receptor unit and can contain two or more
receptor units, where each receptor unit can consist of a protein
molecule, e.g., a glycoprotein molecule. The receptor has a structure
that complements the structure of a ligand and can complex the ligand as
a binding partner. Signaling information can be transmitted by
conformational changes of the receptor following binding with the ligand
on the surface of a cell. According to the present disclosure, a receptor
can refer to proteins of MHC classes I and II capable of forming a
receptor/ligand complex with a ligand, e.g., a peptide or peptide
fragment of suitable length. The class I and class II MHC peptides that
are encoded by HLA class I and class II alleles are often referred to
here as HLA class I and HLA class II peptides respectively, or HLA class
I and HLA class II peptides, or HLA class I class II proteins, or HLA
class I and HLA class II proteins, or HLA class I and class II molecules,
or such common variants thereof, as is well understood within the context
of the discussion by one of ordinary skill in the art.
[0402] A "ligand" is a molecule which is capable of forming a complex with
a receptor. According to the present disclosure, a ligand is to be
understood as meaning, for example, a peptide or peptide fragment which
has a suitable length and suitable binding motifs in its amino acid
sequence, so that the peptide or peptide fragment is capable of binding
to and forming a complex with proteins of MHC class I or MHC class II
(i.e., HLA class I and HLA class II proteins).
[0403] An "antigen" is a molecule capable of stimulating an immune
response, and can be produced by cancer cells or infectious agents or an
autoimmune disease. Antigens recognized by T cells, whether helper T
lymphocytes (T helper (TH) cells) or cytotoxic T lymphocytes (CTLs), are
not recognized as intact proteins, but rather as small peptides in
association with HLA class I or class II proteins on the surface of
cells. During the course of a naturally occurring immune response,
antigens that are recognized in association with HLA class II molecules
on antigen presenting cells (APCs) are acquired from outside the cell,
internalized, and processed into small peptides that associate with the
HLA class II molecules. APCs can also cross-present peptide antigens by
processing exogenous antigens and presenting the processed antigens on
HLA class I molecules. Antigens that give rise to peptides that are
recognized in association with HLA class I MHC molecules are generally
peptides that are produced within the cells, and these antigens are
processed and associated with class I MHC molecules. It is now understood
that the peptides that associate with given HLA class I or class II
molecules are characterized as having a common binding motif, and the
binding motifs for a large number of different HLA class I and II
molecules have been determined. Synthetic peptides that correspond to the
amino acid sequence of a given antigen and that contain a binding motif
for a given HLA class I or II molecule can also be synthesized. These
peptides can then be added to appropriate APCs, and the APCs can be used
to stimulate a T helper cell or CTL response either in vitro or in vivo.
The binding motifs, methods for synthesizing the peptides, and methods
for stimulating a T helper cell or CTL response are all known and readily
available to one of ordinary skill in the art.
[0404] The term "peptide" is used interchangeably with "mutant peptide"
and "neoantigenic peptide" in the present specification. Similarly, the
term "polypeptide" is used interchangeably with "mutant polypeptide" and
"neoantigenic polypeptide" in the present specification. By "neoantigen"
or "neoepitope" is meant a class of tumor antigens or tumor epitopes
which arises from tumor-specific mutations in expressed protein. The
present disclosure further includes peptides that comprise tumor specific
mutations, peptides that comprise known tumor specific mutations, and
mutant polypeptides or fragments thereof identified by the method of the
present disclosure. These peptides and polypeptides are referred to
herein as "neoantigenic peptides" or "neoantigenic polypeptides." The
polypeptides or peptides can be a variety of lengths, either in their
neutral (uncharged) forms or in forms which are salts, and either free of
modifications such as glycosylation, side chain oxidation,
phosphorylation, or any post-translational modification or containing
these modifications, subject to the condition that the modification not
destroy the biological activity of the polypeptides as herein described.
In some embodiments, the neoantigenic peptides of the present disclosure
can include: for HLA class I, 22 residues or less in length, e.g., from
about 8 to about 22 residues, from about 8 to about 15 residues, or 9 or
10 residues; for HLA Class II, 40 residues or less in length, e.g., from
about 8 to about 40 residues in length, from about 8 to about 24 residues
in length, from about 12 to about 19 residues, or from about 14 to about
18 residues. In some embodiments, a neoantigenic peptide or neoantigenic
polypeptide comprises a neoepitope.
[0405] The term "epitope" includes any protein determinant capable of
specific binding to an antibody, antibody peptide, and/or antibody-like
molecule (including but not limited to a T cell receptor) as defined
herein. Epitopic determinants typically consist of chemically active
surface groups of molecules such as amino acids or sugar side chains and
generally have specific three-dimensional structural characteristics as
well as specific charge characteristics.
[0406] A "T cell epitope" is a peptide sequence which can be bound by the
MHC molecules of class I or II in the form of a peptide-presenting MHC
molecule or MHC complex and then, in this form, be recognized and bound
by cytotoxic T-lymphocytes or T-helper cells, respectively.
[0407] The term "antibody" as used herein includes IgG (including IgG1,
IgG2, IgG3, and IgG4), IgA (including IgA1 and IgA2), IgD, IgE, IgM, and
IgY, and is meant to include whole antibodies, including single-chain
whole antibodies, and antigen-binding (Fab) fragments thereof.
Antigen-binding antibody fragments include, but are not limited to, Fab,
Fab' and F(ab')2, Fd (consisting of VH and CH1), single-chain variable
fragment (scFv), single-chain antibodies, disulfide-linked variable
fragment (dsFv) and fragments comprising either a VL or VH domain. The
antibodies can be from any animal origin. Antigen-binding antibody
fragments, including single-chain antibodies, can comprise the variable
region(s) alone or in combination with the entire or partial of the
following: hinge region, CH1, CH2, and CH3 domains. Also included are any
combinations of variable region(s) and hinge region, CH1, CH2, and CH3
domains. Antibodies can be monoclonal, polyclonal, chimeric, humanized,
and human monoclonal and polyclonal antibodies which, e.g., specifically
bind an HLA-associated polypeptide or an HLA-HLA binding peptide
(HLA-peptide) complex. A person of skill in the art will recognize that a
variety of immunoaffinity techniques are suitable to enrich soluble
proteins, such as soluble HLA-peptide complexes or membrane bound
HLA-associated polypeptides, e.g., which have been proteolytically
cleaved from the membrane. These include techniques in which (1) one or
more antibodies capable of specifically binding to the soluble protein
are immobilized to a fixed or mobile substrate (e.g., plastic wells or
resin, latex or paramagnetic beads), and (2) a solution containing the
soluble protein from a biological sample is passed over the antibody
coated substrate, allowing the soluble protein to bind to the antibodies.
The substrate with the antibody and bound soluble protein is separated
from the solution, and optionally the antibody and soluble protein are
disassociated, for example by varying the pH and/or the ionic strength
and/or ionic composition of the solution bathing the antibodies.
Alternatively, immunoprecipitation techniques in which the antibody and
soluble protein are combined and allowed to form macromolecular
aggregates can be used. The macromolecular aggregates can be separated
from the solution by size exclusion techniques or by centrifugation.
[0408] The term "immunopurification (IP)" (or immunoaffinity purification
or immunoprecipitation) is a process well known in the art and is widely
used for the isolation of a desired antigen from a sample. In general,
the process involves contacting a sample containing a desired antigen
with an affinity matrix comprising an antibody to the antigen covalently
attached to a solid phase. The antigen in the sample becomes bound to the
affinity matrix through an immunochemical bond. The affinity matrix is
then washed to remove any unbound species. The antigen is removed from
the affinity matrix by altering the chemical composition of a solution in
contact with the affinity matrix. The immunopurification can be conducted
on a column containing the affinity matrix, in which case the solution is
an eluent. Alternatively, the immunopurification can be in a batch
process, in which case the affinity matrix is maintained as a suspension
in the solution. An important step in the process is the removal of
antigen from the matrix. This is commonly achieved by increasing the
ionic strength of the solution in contact with the affinity matrix, for
example, by the addition of an inorganic salt. An alteration of pH can
also be effective to dissociate the immunochemical bond between antigen
and the affinity matrix.
[0409] An "agent" is any small molecule chemical compound, antibody,
nucleic acid molecule, or polypeptide, or fragments thereof.
[0410] An "alteration" or "change" is an increase or decrease. An
alteration can be by as little as 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, or
by 40%, 50%, 60%, or even by as much as 70%, 75%, 80%, 90%, or 100%.
[0411] A "biologic sample" is any tissue, cell, fluid, or other material
derived from an organism. As used herein, the term "sample" includes a
biologic sample such as any tissue, cell, fluid, or other material
derived from an organism. "Specifically binds" refers to a compound
(e.g., peptide) that recognizes and binds a molecule (e.g., polypeptide),
but does not substantially recognize and bind other molecules in a
sample, for example, a biological sample.
[0412] "Capture reagent" refers to a reagent that specifically binds a
molecule (e.g., a nucleic acid molecule or polypeptide) to select or
isolate the molecule (e.g., a nucleic acid molecule or polypeptide).
[0413] As used herein, the terms "determining", "assessing", "assaying",
"measuring", "detecting" and their grammatical equivalents refer to both
quantitative and qualitative determinations, and as such, the term
"determining" is used interchangeably herein with "assaying,"
"measuring," and the like. Where a quantitative determination is
intended, the phrase "determining an amount" of an analyte and the like
is used. Where a qualitative and/or quantitative determination is
intended, the phrase "determining a level" of an analyte or "detecting"
an analyte is used.
[0414] A "fragment" is a portion of a protein or nucleic acid that is
substantially identical to a reference protein or nucleic acid. In some
embodiments, the portion retains at least 50%, 75%, or 80%, or 90%, 95%,
or even 99% of the biological activity of the reference protein or
nucleic acid described herein.
[0415] The terms "isolated," "purified", "biologically pure" and their
grammatical equivalents refer to material that is free to varying degrees
from components which normally accompany it as found in its native state.
"Isolate" denotes a degree of separation from original source or
surroundings. "Purify" denotes a degree of separation that is higher than
isolation. A "purified" or "biologically pure" protein is sufficiently
free of other materials such that any impurities do not materially affect
the biological properties of the protein or cause other adverse
consequences. That is, a nucleic acid or peptide of the present
disclosure is purified if it is substantially free of cellular material,
viral material, or culture medium when produced by recombinant DNA
techniques, or chemical precursors or other chemicals when chemically
synthesized. Purity and homogeneity are typically determined using
analytical chemistry techniques, for example, polyacrylamide gel
electrophoresis or high performance liquid chromatography. The term
"purified" can denote that a nucleic acid or protein gives rise to
essentially one band in an electrophoretic gel. For a protein that can be
subjected to modifications, for example, phosphorylation or
glycosylation, different modifications can give rise to different
isolated proteins, which can be separately purified.
[0416] An "isolated" polypeptide (e.g., a peptide from an HLA-peptide
complex) or polypeptide complex (e.g., an HLA-peptide complex) is a
polypeptide or polypeptide complex of the present disclosure that has
been separated from components that naturally accompany it. Typically,
the polypeptide or polypeptide complex is isolated when it is at least
60%, by weight, free from the proteins and naturally-occurring organic
molecules with which it is naturally associated. The preparation can be
at least 75%, at least 90%, or at least 99%, by weight, a polypeptide or
polypeptide complex of the present disclosure. An isolated polypeptide or
polypeptide complex of the present disclosure can be obtained, for
example, by extraction from a natural source, by expression of a
recombinant nucleic acid encoding such a polypeptide or one or more
components of a polypeptide complex, or by chemically synthesizing the
polypeptide or one or more components of the polypeptide complex. Purity
can be measured by any appropriate method, for example, column
chromatography, polyacrylamide gel electrophoresis, or by HPLC analysis.
In some cases, an HLA allele-encoded MHC Class II protein (i.e., an MHC
class II peptide) is interchangeably referred to within this document as
an HLA class II protein (or HLA class II peptide).
[0417] The term "vectors" refers to a nucleic acid molecule capable of
transporting or mediating expression of a heterologous nucleic acid. A
plasmid is a species of the genus encompassed by the term "vector." A
vector typically refers to a nucleic acid sequence containing an origin
of replication and other entities necessary for replication and/or
maintenance in a host cell. Vectors capable of directing the expression
of genes and/or nucleic acid sequence to which they are operatively
linked are referred to herein as "expression vectors". In general,
expression vectors of utility are often in the form of "plasmids" which
refer to circular double stranded DNA molecules which, in their vector
form are not bound to the chromosome, and typically comprise entities for
stable or transient expression or the encoded DNA. Other expression
vectors that can be used in the methods as disclosed herein include, but
are not limited to plasmids, episomes, bacterial artificial chromosomes,
yeast artificial chromosomes, bacteriophages or viral vectors, and such
vectors can integrate into the host's genome or replicate autonomously in
the cell. A vector can be a DNA or RNA vector. Other forms of expression
vectors known by those skilled in the art which serve the equivalent
functions can also be used, for example, self-replicating
extrachromosomal vectors or vectors capable of integrating into a host
genome. Exemplary vectors are those capable of autonomous replication
and/or expression of nucleic acids to which they are linked.
[0418] The terms "spacer" or "linker" as used in reference to a fusion
protein refers to a peptide that joins the proteins comprising a fusion
protein. Generally, a spacer has no specific biological activity other
than to join or to preserve some minimum distance or other spatial
relationship between the proteins or RNA sequences. However, in some
embodiments, the constituent amino acids of a spacer can be selected to
influence some property of the molecule such as the folding, net charge,
or hydrophobicity of the molecule. Suitable linkers for use in an
embodiment of the present disclosure are well known to those of skill in
the art and include, but are not limited to, straight or branched-chain
carbon linkers, heterocyclic carbon linkers, or peptide linkers. The
linker is used to separate two antigenic peptides by a distance
sufficient to ensure that, in some embodiments, each antigenic peptide
properly folds. Exemplary peptide linker sequences adopt a flexible
extended conformation and do not exhibit a propensity for developing an
ordered secondary structure. Typical amino acids in flexible protein
regions include Gly, Asn and Ser. Virtually any permutation of amino acid
sequences containing Gly, Asn and Ser would be expected to satisfy the
above criteria for a linker sequence. Other near neutral amino acids,
such as Thr and Ala, also can be used in the linker sequence. Still other
amino acid sequences that can be used as linkers are disclosed in Maratea
et al. (1985), Gene 40: 39-46; Murphy et al. (1986) Proc. Nat'l. Acad.
Sci. USA 83: 8258-62; U.S. Pat. Nos. 4,935,233; and 4,751,180.
[0419] The term "neoplasia" refers to any disease that is caused by or
results in inappropriately high levels of cell division, inappropriately
low levels of apoptosis, or both. Glioblastoma is one non-limiting
example of a neoplasia or cancer. The terms "cancer" or "tumor" or
"hyperproliferative disorder" refer to the presence of cells possessing
characteristics typical of cancer-causing cells, such as uncontrolled
proliferation, immortality, metastatic potential, rapid growth and
proliferation rate, and certain characteristic morphological features.
Cancer cells are often in the form of a tumor, but such cells can exist
alone within an animal, or can be a non-tumorigenic cancer cell, such as
a leukemia cell. Cancers include, but are not limited to, B cell cancer
(e.g., multiple myeloma, Waldenstrom's macroglobulinemia), the heavy
chain diseases (such as, for example, alpha chain disease, gamma chain
disease, and mu chain disease), benign monoclonal gammopathy, and
immunocytic amyloidosis, melanomas, breast cancer, lung cancer, bronchus
cancer, colorectal cancer, prostate cancer (e.g., metastatic, hormone
refractory prostate cancer), pancreatic cancer, stomach cancer, ovarian
cancer, urinary bladder cancer, brain or central nervous system cancer,
peripheral nervous system cancer, esophageal cancer, cervical cancer,
uterine or endometrial cancer, cancer of the oral cavity or pharynx,
liver cancer, kidney cancer, testicular cancer, biliary tract cancer,
small bowel or appendix cancer, salivary gland cancer, thyroid gland
cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of
hematological tissues, and the like. Other non-limiting examples of types
of cancers applicable to the methods encompassed by the present
disclosure include human sarcomas and carcinomas, e.g., fibrosarcoma,
myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma,
angiosarcoma, endotheliosarcoma, lymphangiosarcoma,
lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor,
leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, colorectal cancer,
pancreatic cancer, breast cancer, ovarian cancer, squamous cell
carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma,
sebaceous gland carcinoma, papillary carcinoma, papillary
adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic
carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, liver
cancer, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor,
cervical cancer, bone cancer, brain tumor, testicular cancer, lung
carcinoma, small cell lung carcinoma, bladder carcinoma, epithelial
carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma,
ependymoma, pinealoma, hemangioblastoma, acoustic neuroma,
oligodendroglioma, meningioma, melanoma, neuroblastoma, retinoblastoma;
leukemias, e.g., acute lymphocytic leukemia and acute myelocytic leukemia
(myeloblastic, promyelocytic, myelomonocytic, monocytic and
erythroleukemia); chronic leukemia (chronic myelocytic (granulocytic)
leukemia and chronic lymphocytic leukemia); and polycythemia vera,
lymphoma (Hodgkin's disease and non-Hodgkin's disease), multiple myeloma,
Waldenstrom's macroglobulinemia, and heavy chain disease. In some
embodiments, the cancer is an epithelial cancer such as, but not limited
to, bladder cancer, breast cancer, cervical cancer, colon cancer,
gynecologic cancers, renal cancer, laryngeal cancer, lung cancer, oral
cancer, head and neck cancer, ovarian cancer, pancreatic cancer, prostate
cancer, or skin cancer. In other embodiments, the cancer is breast
cancer, prostate cancer, lung cancer, or colon cancer. In still other
embodiments, the epithelial cancer is non-small-cell lung cancer,
nonpapillary renal cell carcinoma, cervical carcinoma, ovarian carcinoma
(e.g., serous ovarian carcinoma), or breast carcinoma. The epithelial
cancers can be characterized in various other ways including, but not
limited to, serous, endometrioid, mucinous, clear cell, brenner, or
undifferentiated. In some embodiments, the present disclosure is used in
the treatment, diagnosis, and/or prognosis of lymphoma or its subtypes,
including, but not limited to, mantle cell lymphoma. Lymphoproliferative
disorders are also considered to be proliferative diseases.
[0420] The term "vaccine" is to be understood as meaning a composition for
generating immunity for the prophylaxis and/or treatment of diseases
(e.g., neoplasia/tumor/infectious agents/autoimmune diseases).
Accordingly, vaccines are medicaments which comprise antigens and are
intended to be used in humans or animals for generating specific defense
and protective substance by vaccination. A "vaccine composition" can
include a pharmaceutically acceptable excipient, carrier or diluent.
Aspects of the present disclosure relate to use of the technology in
preparing an antigen-based vaccine. In these embodiments, vaccine is
meant to refer one or more disease-specific antigenic peptides (or
corresponding nucleic acids encoding them). In some embodiments, the
antigen-based vaccine contains at least two, at least three, at least
four, at least five, at least six, at least seven, at least eight, at
least nine, at least 10, at least 11, at least 12, at least 13, at least
14, at least 15, at least 16, at least 17, at least 18, at least 19, at
least 20, at least 21, at least 22, at least 23, at least 24, at least
25, at least 26, at least 27, at least 28, at least 29, at least 30, or
more antigenic peptides. In some embodiments, the antigen-based vaccine
contains from 2 to 100, 2 to 75, 2 to 50, 2 to 25, 2 to 20, 2 to 19, 2 to
18, 2 to 17, 2 to 16, 2 to 15, 2 to 14, 2 to 13, 2 to 12, 2 to 10, 2 to
9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 3 to 100, 3 to 75, 3 to 50, 3
to 25, 3 to 20, 3 to 19, 3 to 18, 3 to 17, 3 to 16, 3 to 15, 3 to 14, 3
to 13, 3 to 12, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 4 to
100, 4 to 75, 4 to 50, 4 to 25, 4 to 20, 4 to 19, 4 to 18, 4 to 17, 4 to
16, 4 to 15, 4 to 14, 4 to 13, 4 to 12, 4 to 10, 4 to 9, 4 to 8, 4 to 7,
4 to 6, 5 to 100, 5 to 75, 5 to 50, 5 to 25, 5 to 20, 5 to 19, 5 to 18, 5
to 17, 5 to 16, 5 to 15, 5 to 14, 5 to 13, 5 to 12, 5 to 10, 5 to 9, 5 to
8, or 5 to 7 antigenic peptides. In some embodiments, the antigen-based
vaccine contains 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, or 20 antigenic peptides. In some cases, the antigenic peptides
are neoantigenic peptides. In some cases, the antigenic peptides comprise
one or more neoepitopes.
[0421] The term "pharmaceutically acceptable" refers to approved or
approvable by a regulatory agency of the Federal or a state government or
listed in the U.S. Pharmacopeia or other generally recognized
pharmacopeia for use in animals, including humans. A "pharmaceutically
acceptable excipient, carrier or diluent" refers to an excipient, carrier
or diluent that can be administered to a subject, together with an agent,
and which does not destroy the pharmacological activity thereof and is
nontoxic when administered in doses sufficient to deliver a therapeutic
amount of the agent. A "pharmaceutically acceptable salt" of pooled
disease specific antigens as recited herein can be an acid or base salt
that is generally considered in the art to be suitable for use in contact
with the tissues of human beings or animals without excessive toxicity,
irritation, allergic response, or other problem or complication. Such
salts include mineral and organic acid salts of basic residues such as
amines, as well as alkali or organic salts of acidic residues such as
carboxylic acids. Specific pharmaceutical salts include, but are not
limited to, salts of acids such as hydrochloric, phosphoric, hydrobromic,
malic, glycolic, fumaric, sulfuric, sulfamic, sulfanilic, formic, toluene
sulfonic, methane sulfonic, benzene sulfonic, ethane disulfonic,
2-hydroxyethylsulfonic, nitric, benzoic, 2-acetoxybenzoic, citric,
tartaric, lactic, stearic, salicylic, glutamic, ascorbic, pamoic,
succinic, fumaric, maleic, propionic, hydroxymaleic, hydroiodic,
phenylacetic, alkanoic such as acetic, HOOC--(CH2)n-COOH where n is 0-4,
and the like. Similarly, pharmaceutically acceptable cations include, but
are not limited to sodium, potassium, calcium, aluminum, lithium and
ammonium. Those of ordinary skill in the art will recognize from this
disclosure and the knowledge in the art that further pharmaceutically
acceptable salts for the pooled disease specific antigens provided
herein, including those listed by Remington's Pharmaceutical Sciences,
17th ed., Mack Publishing Company, Easton, Pa., p. 1418 (1985). In
general, a pharmaceutically acceptable acid or base salt can be
synthesized from a parent compound that contains a basic or acidic moiety
by any conventional chemical method. Briefly, such salts can be prepared
by reacting the free acid or base forms of these compounds with a
stoichiometric amount of the appropriate base or acid in an appropriate
solvent.
[0422] Nucleic acid molecules useful in the methods of the disclosure
include any nucleic acid molecule that encodes a polypeptide of the
disclosure or a fragment thereof. Such nucleic acid molecules need not be
100% identical with an endogenous nucleic acid sequence, but will
typically exhibit substantial identity. Polynucleotides having
substantial identity to an endogenous sequence are typically capable of
hybridizing with at least one strand of a double-stranded nucleic acid
molecule. "Hybridize" refers to when nucleic acid molecules pair to form
a double-stranded molecule between complementary polynucleotide
sequences, or portions thereof, under various conditions of stringency.
(See, e.g., Wahl, G. M. and S. L. Berger (1987) Methods Enzymol. 152:399;
Kimmel, A. R. (1987) Methods Enzymol. 152:507). For example, stringent
salt concentration can ordinarily be less than about 750 mM NaCl and 75
mM trisodium citrate, less than about 500 mM NaCl and 50 mM trisodium
citrate, or less than about 250 mM NaCl and 25 mM trisodium citrate. Low
stringency hybridization can be obtained in the absence of organic
solvent, e.g., formamide, while high stringency hybridization can be
obtained in the presence of at least about 35% formamide, or at least
about 50% formamide Stringent temperature conditions can ordinarily
include temperatures of at least about 30.degree. C., at least about
37.degree. C., or at least about 42.degree. C. Varying additional
parameters, such as hybridization time, the concentration of detergent,
e.g., sodium dodecyl sulfate (SDS), and the inclusion or exclusion of
carrier DNA, are well known to those skilled in the art. Various levels
of stringency are accomplished by combining these various conditions as
needed. In an exemplary embodiment, hybridization can occur at 30.degree.
C. in 750 mM NaCl, 75 mM trisodium citrate, and 1% SDS. In another
exemplary embodiment, hybridization can occur at 37.degree. C. in 500 mM
NaCl, 50 mM trisodium citrate, 1% SDS, 35% formamide, and 100 .mu.g/ml
denatured salmon sperm DNA (ssDNA). In another exemplary embodiment,
hybridization can occur at 42.degree. C. in 250 mM NaCl, 25 mM trisodium
citrate, 1% SDS, 50% formamide, and 200 .mu.g/ml ssDNA. Useful variations
on these conditions will be readily apparent to those skilled in the art.
For most applications, washing steps that follow hybridization can also
vary in stringency. Wash stringency conditions can be defined by salt
concentration and by temperature. As above, wash stringency can be
increased by decreasing salt concentration or by increasing temperature.
For example, stringent salt concentration for the wash steps can be less
than about 30 mM NaCl and 3 mM trisodium citrate, or less than about 15
mM NaCl and 1.5 mM trisodium citrate. Stringent temperature conditions
for the wash steps can include a temperature of at least about 25.degree.
C., of at least about 42.degree. C., or at least about 68.degree. C. In
exemplary embodiments, wash steps can occur at 25.degree. C. in 30 mM
NaCl, 3 mM trisodium citrate, and 0.1% SDS. In other exemplary
embodiments, wash steps can occur at 42.degree. C. in 15 mM NaCl, 1.5 mM
trisodium citrate, and 0.1% SDS. In another exemplary embodiment, wash
steps can occur at 68.degree. C. in 15 mM NaCl, 1.5 mM trisodium citrate,
and 0.1% SDS. Additional variations on these conditions will be readily
apparent to those skilled in the art. Hybridization techniques are well
known to those skilled in the art and are described, for example, in
Benton and Davis (Science 196:180, 1977); Grunstein and Hogness (Proc.
Natl. Acad. Sci., USA 72:3961, 1975); Ausubel et al. (Current Protocols
in Molecular Biology, Wiley Interscience, New York, 2001); Berger and
Kimmel (Guide to Molecular Cloning Techniques, 1987, Academic Press, New
York); and Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold
Spring Harbor Laboratory Press, New York.
[0423] "Substantially identical" refers to a polypeptide or nucleic acid
molecule exhibiting at least 50% identity to a reference amino acid
sequence (for example, any one of the amino acid sequences described
herein) or nucleic acid sequence (for example, any one of the nucleic
acid sequences described herein). Such a sequence can be at least 60%,
80% or 85%, 90%, 95%, 96%, 97%, 98%, or even 99% or more identical at the
amino acid level or nucleic acid to the sequence used for comparison.
Sequence identity is typically measured using sequence analysis software
(for example, Sequence Analysis Software Package of the Genetics Computer
Group, University of Wisconsin Biotechnology Center, 1710 University
Avenue, Madison, Wis. 53705, BLAST, BESTFIT, GAP, or PILEUP/PRETTYBOX
programs). Such software matches identical or similar sequences by
assigning degrees of homology to various substitutions, deletions, and/or
other modifications. Conservative substitutions typically include
substitutions within the following groups: glycine, alanine; valine,
isoleucine, leucine; aspartic acid, glutamic acid, asparagine, glutamine;
serine, threonine; lysine, arginine; and phenylalanine, tyrosine. In an
exemplary approach to determining the degree of identity, a BLAST program
can be used, with a probability score between e-3 and e-m.degree.
indicating a closely related sequence. A "reference" is a standard of
comparison.
[0424] The term "subject" or "patient" refers to an animal which is the
object of treatment, observation, or experiment. By way of example only,
a subject includes, but is not limited to, a mammal, including, but not
limited to, a human or a non-human mammal, such as a non-human primate,
murine, bovine, equine, canine, ovine, or feline.
[0425] The terms "treat," "treated," "treating," "treatment," and the like
are meant to refer to reducing, preventing, or ameliorating a disorder
and/or symptoms associated therewith (e.g., a neoplasia or tumor or
infectious agent or an autoimmune disease). "Treating" can refer to
administration of the therapy to a subject after the onset, or suspected
onset, of a disease (e.g., cancer or infection by an infectious agent or
an autoimmune disease). "Treating" includes the concepts of
"alleviating", which refers to lessening the frequency of occurrence or
recurrence, or the severity, of any symptoms or other ill effects related
to the disease and/or the side effects associated with therapy. The term
"treating" also encompasses the concept of "managing" which refers to
reducing the severity of a disease or disorder in a patient, e.g.,
extending the life or prolonging the survivability of a patient with the
disease, or delaying its recurrence, e.g., lengthening the period of
remission in a patient who had suffered from the disease. It is
appreciated that, although not precluded, treating a disorder or
condition does not require that the disorder, condition, or symptoms
associated therewith be completely eliminated.
[0426] The term "prevent", "preventing", "prevention" and their
grammatical equivalents as used herein, means avoiding or delaying the
onset of symptoms associated with a disease or condition in a subject
that has not developed such symptoms at the time the administering of an
agent or compound commences.
[0427] The term "therapeutic effect" refers to some extent of relief of
one or more of the symptoms of a disorder (e.g., a neoplasia, tumor, or
infection by an infectious agent or an autoimmune disease) or its
associated pathology. "Therapeutically effective amount" as used herein
refers to an amount of an agent which is effective, upon single or
multiple dose administration to the cell or subject, in prolonging the
survivability of the patient with such a disorder, reducing one or more
signs or symptoms of the disorder, preventing or delaying, and the like
beyond that expected in the absence of such treatment. "Therapeutically
effective amount" is intended to qualify the amount required to achieve a
therapeutic effect. A physician or veterinarian having ordinary skill in
the art can readily determine and prescribe the "therapeutically
effective amount" (e.g., ED50) of the pharmaceutical composition
required. For example, the physician or veterinarian can start doses of
the compounds of the present disclosure employed in a pharmaceutical
composition at levels lower than that required in order to achieve the
desired therapeutic effect and gradually increase the dosage until the
desired effect is achieved. Disease, condition, and disorder are used
interchangeably herein.
[0428] Those of ordinary skill in the art will recognize that the terms
"peptide tag," "affinity tag," "epitope tag," or "affinity acceptor tag"
are used interchangeably herein. As used herein, the term "affinity
acceptor tag" refers to an amino acid sequence that permits the tagged
protein to be readily detected or purified, for example, by affinity
purification. An affinity acceptor tag is generally (but need not be)
placed at or near the N- or C-terminus of an HLA allele. Various peptide
tags are well known in the art. Non-limiting examples include
poly-histidine tag (e.g., 4 to 15 consecutive His residues (SEQ ID NO:
4), such as 8 consecutive His residues (SEQ ID NO: 5));
poly-histidine-glycine tag; HA tag (e.g., Field et al., Mol. Cell. Biol.,
8:2159, 1988); c-myc tag (e.g., Evans et al., Mol. Cell. Biol., 5:3610,
1985); Herpes simplex virus glycoprotein D (gD) tag (e.g., Paborsky et
al., Protein Engineering, 3:547, 1990); FLAG tag (e.g., Hopp et al.,
BioTechnology, 6:1204, 1988; U.S. Pat. Nos. 4,703,004 and 4,851,341); KT3
epitope tag (e.g., Martine et al., Science, 255:192, 1992); tubulin
epitope tag (e.g., Skinner, Biol. Chem., 266:15173, 1991); T7 gene 10
protein peptide tag (e.g., Lutz-Freyemuth et al., Proc. Natl. Acad. Sci.
USA, 87:6393, 1990); streptavidin tag (StrepTag.TM. or StrepTagII.TM.;
see, e.g., Schmidt et al., J. Mol. Biol., 255(5):753-766, 1996 or U.S.
Pat. No. 5,506,121; also commercially available from Sigma-Genosys); or a
VSV-G epitope tag derived from the Vesicular Stomatis viral glycoprotein;
or a V5 tag derived from a small epitope (Pk) found on the P and V
proteins of the paramyxovirus of simian virus 5 (SV5). In some
embodiments, the affinity acceptor tag is an "epitope tag," which is a
type of peptide tag that adds a recognizable epitope (antibody binding
site) to the HLA-protein to provide binding of corresponding antibody,
thereby allowing identification or affinity purification of the tagged
protein. Non-limiting example of an epitope tag is protein A or protein
G, which binds to IgG. In some embodiments, the matrix of IgG Sepharose 6
Fast Flow chromatography resin is covalently coupled to human IgG. This
resin allows high flow rates, for rapid and convenient purification of a
protein tagged with protein A. Numerous other tag moieties are known to,
and can be envisioned by, the ordinarily skilled artisan, and are
contemplated herein. Any peptide tag can be used as long as it is capable
of being expressed as an element of an affinity acceptor tagged
HLA-peptide complex.
[0429] As used herein, the term "affinity molecule" refers to a molecule
or a ligand that binds with chemical specificity to an affinity acceptor
peptide. Chemical specificity is the ability of a protein's binding site
to bind specific ligands. The fewer ligands a protein can bind, the
greater its specificity. Specificity describes the strength of binding
between a given protein and ligand. This relationship can be described by
a dissociation constant (KD), which characterizes the balance between
bound and unbound states for the protein-ligand system.
[0430] The term "affinity acceptor tagged HLA-peptide complex" refers to a
complex comprising an HLA class I or class II-associated peptide or a
portion thereof specifically bound to a single allelic recombinant HLA
class I or class II peptide comprising an affinity acceptor peptide.
[0431] The terms "specific binding" or "specifically binding" when used in
reference to the interaction of an affinity molecule and an affinity
acceptor tag or an epitope and an HLA peptide mean that the interaction
is dependent upon the presence of a particular structure (e.g., the
antigenic determinant or epitope) on the protein; in other words, the
affinity molecule is recognizing and binding to a specific affinity
acceptor peptide structure rather than to proteins in general.
[0432] As used herein, the term "affinity" refers to a measure of the
strength of binding between two members of a binding pair, for example,
an "affinity acceptor tag" and an "affinity molecule" and an HLA-binding
peptide and an HLA class I or II molecule. KD is the dissociation
constant and has units of molarity. The affinity constant is the inverse
of the dissociation constant. An affinity constant is sometimes used as a
generic term to describe this chemical entity. It is a direct measure of
the energy of binding. Affinity can be determined experimentally, for
example by surface plasmon resonance (SPR) using commercially available
Biacore SPR units. Affinity can also be expressed as the inhibitory
concentration 50 (IC50), that concentration at which 50% of the peptide
is displaced. Likewise, ln IC50 refers to the natural log of the IC50.
K.sub.off refers to the off-rate constant, for example, for dissociation
of an affinity molecule from the affinity acceptor tagged HLA-peptide
complex.
[0433] In some embodiments, an affinity acceptor tagged HLA-peptide
complex comprises biotin acceptor peptide (BAP) and is immunopurified
from complex cellular mixtures using streptavidin/NeutrAvidin beads. The
biotin-avidin/streptavidin binding is the strongest non-covalent
interaction known in nature. This property is exploited as a biological
tool for a wide range of applications, such as immunopurification of a
protein to which biotin is covalently attached. In an exemplary
embodiment, the nucleic acid sequence encoding the HLA allele implements
biotin acceptor peptide (BAP) as an affinity acceptor tag for
immunopurification. BAP can be specifically biotinylated in vivo or in
vitro at a single lysine residue within the tag (e.g., U.S. Pat. Nos.
5,723,584; 5,874,239; and 5,932,433; and U.K Pat. No. GB2370039). BAP is
typically 15 amino acids long and contains a single lysine as a biotin
acceptor residue. In some embodiments, BAP is placed at or near the N- or
C-terminus of a single allele HLA peptide. In some embodiments, BAP is
placed in between a heavy chain domain and .beta.2 microglobulin domain
of an HLA class I peptide. In some embodiments, BAP is placed in between
.beta.-chain domain and .alpha.-chain domain of an HLA class II peptide.
In some embodiments, BAP is placed in loop regions between .alpha.1,
.alpha.2, and .alpha.3 domains of the heavy chain of HLA class I, or
between .alpha.1 and .alpha.2 and .beta.1 and .beta.2 domains of the
.alpha.-chain and .beta.-chain, respectively of HLA class II. Exemplary
constructs designed for HLA class I and II expression implementing BAP
for biotinylation and immunopurification are described in FIG. 2.
[0434] As used herein, the term "biotin" refers to the compound biotin
itself and analogues, derivatives and variants thereof. Thus, the term
"biotin" includes biotin (cis-hexahydro-2-oxo-1H-thieno
[3,4]imidazole-4-pentanoic acid) and any derivatives and analogs thereof,
including biotin-like compounds. Such compounds include, for example,
biotin-e-N-lysine, biocytin hydrazide, amino or sulfhydryl derivatives of
2-iminobiotin and biotinyl-E-aminocaproic acid-N-hydroxysuccinimide
ester, sulfosuccinimideiminobiotin, biotinbromoacetylhydrazide,
p-diazobenzoyl biocytin, 3-(N-maleimidopropionyl)biocytin, desthiobiotin,
and the like. The term "biotin" also comprises biotin variants that can
specifically bind to one or more of a Rhizavidin, avidin, streptavidin,
tamavidin moiety, or other avidin-like peptides.
[0435] As used herein, a "PPV determination method" can refer to a
presentation PPV determination method. For example, a "PPV determination
method" can refer to a method comprising (a) processing amino acid
information of a plurality of test peptide sequences using an HLA peptide
presentation prediction model, such as a machine learning HLA peptide
presentation prediction model, to generate a plurality of test
presentation predictions, each test presentation prediction indicative of
a likelihood that one or more proteins encoded by a class II HLA allele
of a cell, such as a class II HLA allele of a cell of a subject, can
present a given test peptide sequence of the plurality of test peptide
sequences, wherein the plurality of test peptide sequences comprises at
least 500 test peptide sequences comprising (i) at least one hit peptide
sequence identified by mass spectrometry to be presented by an HLA
protein expressed in cells and (ii) at least 499 decoy peptide sequences
contained within a protein encoded by a genome of an organism, such as an
organism that is the same species as the subject, wherein the plurality
of test peptide sequences comprises a ratio of less than one of the
number of hit peptide sequences to the number of decoy peptide sequences,
such as a ratio of 1:499 of the at least one hit peptide sequences to the
at least 499 decoy peptide sequences; (b) identifying or calling a top
percentage of the plurality of test peptide sequences, such as a top 0.2%
of the plurality of test peptide sequences, as being presented by the
class II HLA allele of a cell; and (c) calculating a PPV of the HLA
peptide presentation prediction model, wherein the PPV is the fraction of
the test peptide sequences of the plurality that were identified or
called as being presented by the class II HLA allele of a cell that are
peptides observed by mass spectrometry as being presented by the class II
HLA allele of a cell. In some embodiments, a decoy peptide is of the same
length, i.e., comprises the same number of amino acids as a hit peptide.
In some embodiments, a decoy peptide may comprise one more or one less
amino acid as compared to the hit peptide. In some embodiments the decoy
peptide is a peptide that is an endogenous peptide. In some embodiments a
decoy peptide is a synthetic peptide. In some embodiments the decoy
peptide is an endogenous peptide that has been identified by mass
spectrometry to bind to a first MHC class I or class II protein, wherein
the first MHC class I or class II protein is distinct from a second MHC
class I or class II protein that binds to a hit peptide. In some
embodiments, the decoy peptide may be a scrambled peptide, e.g., the
decoy peptide may comprise an amino acid sequence in which the amino acid
positions are rearranged relative to that of the hit peptide within the
length of the peptide. In some embodiments, the PPV determination method
can be a presentation PPV determination method. In some embodiments, the
ratio of the number of hit peptide sequences to the number of decoy
peptide sequences is about 1:10, 1:20, 1:50, 1:100, 1:250, 1:500, 1:1000,
1:1500, 1:2000, 1:2500, 1:5000, 1:7500, 1:10000, 1:25000, 1:50000 or
1:100000. In some embodiments, the at least one hit peptide sequence
comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 hit peptide sequences.
In some embodiments, the at least 499 decoy peptide sequences comprises
at least 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500,
1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700,
2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900,
4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100,
5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200, 6300,
6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500,
7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600, 8700,
8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800, 9900,
10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000,
20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,
30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000, 39000,
40000, 41000, 42000, 43000, 44000, 45000, 46000, 47000, 48000, 49000,
50000, 52500, 55000, 57500, 60000, 62500, 65000, 67500, 70000, 72500,
75000, 77500, 80000, 82500, 85000, 87500, 90000, 92500, 95000, 97500,
100000, 125000, 150000, 175000, 200000, 225000, 250000, 275000, 300000,
325000, 350000, 375000, 400000, 425000, 450000, 475000, 500000, 600000,
700000, 800000, 900000 or 1000000 decoy peptide sequences. In some
embodiments, the at least 500 test peptide sequences comprises at least
600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,
1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000,
3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000, 4100, 4200,
4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100, 5200, 5300, 5400,
5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200, 6300, 6400, 6500, 6600,
6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500, 7600, 7700, 7800,
7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600, 8700, 8800, 8900, 9000,
9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800, 9900, 10000, 11000,
12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000, 21000,
22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000, 30000, 31000,
32000, 33000, 34000, 35000, 36000, 37000, 38000, 39000, 40000, 41000,
42000, 43000, 44000, 45000, 46000, 47000, 48000, 49000, 50000, 52500,
55000, 57500, 60000, 62500, 65000, 67500, 70000, 72500, 75000, 77500,
80000, 82500, 85000, 87500, 90000, 92500, 95000, 97500, 100000, 125000,
150000, 175000, 200000, 225000, 250000, 275000, 300000, 325000, 350000,
375000, 400000, 425000, 450000, 475000, 500000, 600000, 700000, 800000,
900000 or 1000000 test peptide sequences. In some embodiments,
identifying or calling a top percentage of the plurality of test peptide
sequences as being presented by the class II HLA allele of a cell
comprises identifying or calling a top 0.20%, 0.30%, 0.40%, 0.50%, 0.60%,
0.70%, 0.80%, 0.90%, 1.00%, 1.10%, 1.20%, 1.30%, 1.40%, 1.50%, 1.60%,
1.70%, 1.80%, 1.90%, 2.00%, 2.10%, 2.20%, 2.30%, 2.40%, 2.50%, 2.60%,
2.70%, 2.80%, 2.90%, 3.00%, 3.10%, 3.20%, 3.30%, 3.40%, 3.50%, 3.60%,
3.70%, 3.80%, 3.90%, 4.00%, 4.10%, 4.20%, 4.30%, 4.40%, 4.50%, 4.60%,
4.70%, 4.80%, 4.90%, 5.00%, 5.10%, 5.20%, 5.30%, 5.40%, 5.50%, 5.60%,
5.70%, 5.80%, 5.90%, 6.00%, 6.10%, 6.20%, 6.30%, 6.40%, 6.50%, 6.60%,
6.70%, 6.80%, 6.90%, 7.00%, 7.10%, 7.20%, 7.30%, 7.40%, 7.50%, 7.60%,
7.70%, 7.80%, 7.90%, 8.00%, 8.10%, 8.20%, 8.30%, 8.40%, 8.50%, 8.60%,
8.70%, 8.80%, 8.90%, 9.00%, 9.10%, 9.20%, 9.30%, 9.40%, 9.50%, 9.60%,
9.70%, 9.80%, 9.90%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19% or
20% as being presented by the class II HLA allele of a cell. In some
embodiments, the cell is a mono-allelic cell.
[0436] As used herein, a "PPV determination method" can refer to a binding
PPV determination method. For example, a "PPV determination method" can
refer to a method comprising (a) processing amino acid information of a
plurality of test peptide sequences using an HLA peptide binding
prediction model, such as a machine learning HLA peptide binding
prediction model, to generate a plurality of test binding predictions,
each test binding prediction indicative of a likelihood that the one or
more proteins encoded by a class II HLA allele of a cell, such as a class
II HLA allele of a cell of a subject, binds to a given test peptide
sequence of the plurality of test peptide sequences, wherein the
plurality of test peptide sequences comprises at least 20 test peptide
sequences comprising (i) at least one hit peptide sequence identified by
mass spectrometry to be presented by an HLA protein expressed in cells
and (ii) at least 19 decoy peptide sequences contained within a protein
comprising at least one peptide sequence identified by mass spectrometry
to be presented by an HLA protein expressed in cells, wherein the
plurality of test peptide sequences comprises a ratio of less than one of
the number of hit peptide sequences to the number of decoy peptide
sequences, such as a ratio of 1:19 of the at least one hit peptide
sequences to the at least 19 decoy peptide sequences; (b) identifying or
calling a top percentage of the plurality of test peptide sequences, such
as a top 5% of the plurality of test peptide sequences, as binding to the
HLA protein; and (c) calculating a PPV of the HLA peptide binding
prediction model, wherein the PPV is the fraction of the test peptide
sequences of the plurality that were identified or called as binding to
the class II HLA allele of a cell that are peptides observed by mass
spectrometry as being presented by the class II HLA allele of a cell. In
some embodiments, the ratio of the number of hit peptide sequences to the
number of decoy peptide sequences is about 1:2, 1:3, 1:4, 1:5, 1:10,
1:20, 1:25, 1:30, 1:40, 1:50, 1:75, 1:100, 1:200, 1:250, 1:500 or 1:1000.
In some embodiments, the at least one hit peptide sequence comprises at
least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,
75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, 99 or 100 hit peptide sequences. In some
embodiments, the at least 19 decoy peptide sequences comprises at least
30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,
190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320,
330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460,
470, 480, 490, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400,
1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600,
2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800,
3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000,
5100, 5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200,
6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400,
7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600,
8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800,
9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000,
19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000, 47000, 48000,
49000, 50000, 52500, 55000, 57500, 60000, 62500, 65000, 67500, 70000,
72500, 75000, 77500, 80000, 82500, 85000, 87500, 90000, 92500, 95000,
97500, 100000, 125000, 150000, 175000, 200000, 225000, 250000, 275000,
300000, 325000, 350000, 375000, 400000, 425000, 450000, 475000, 500000,
600000, 700000, 800000, 900000 or 1000000 decoy peptide sequences. In
some embodiments, the at least 20 test peptide sequences comprises at
least 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170,
180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310,
320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450,
460, 470, 480, 490, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300,
1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500,
2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700,
3800, 3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900,
5000, 5100, 5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100,
6200, 6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300,
7400, 7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500,
8600, 8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700,
9800, 9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000,
18000, 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000,
28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000,
38000, 39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000, 47000,
48000, 49000, 50000, 52500, 55000, 57500, 60000, 62500, 65000, 67500,
70000, 72500, 75000, 77500, 80000, 82500, 85000, 87500, 90000, 92500,
95000, 97500, 100000, 125000, 150000, 175000, 200000, 225000, 250000,
275000, 300000, 325000, 350000, 375000, 400000, 425000, 450000, 475000,
500000, 600000, 700000, 800000, 900000 or 1000000 test peptide sequences.
In some embodiments, identifying or calling a top percentage of the
plurality of test peptide sequences as being presented by the class II
HLA allele of a cell comprises identifying or calling a top 5%, 6%, 7%,
8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%,
23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%,
37%, 38%, 39%, or 40% as being presented by the class II HLA allele of a
cell. In some embodiments, the cell is a mono-allelic cell.
Human Leukocyte Antigen (HLA) System
[0437] The immune system can be classified into two functional subsystems:
the innate and the adaptive immune system. The innate immune system is
the first line of defense against infections, and most potential
pathogens are rapidly neutralized by this system before they can cause,
for example, a noticeable infection. The adaptive immune system reacts to
molecular structures, referred to as antigens, of the intruding organism.
Unlike the innate immune system, the adaptive immune system is highly
specific to a pathogen. Adaptive immunity can also provide long-lasting
protection; for example, someone who recovers from measles is now
protected against measles for their lifetime. There are two types of
adaptive immune reactions, which include the humoral immune reaction and
the cell-mediated immune reaction. In the humoral immune reaction,
antibodies secreted by B cells into bodily fluids bind to
pathogen-derived antigens, leading to the elimination of the pathogen
through a variety of mechanisms, e.g. complement-mediated lysis. In the
cell-mediated immune reaction, T cells capable of destroying other cells
are activated. For example, if proteins associated with a disease are
present in a cell, they are fragmented proteolytically to peptides within
the cell. Specific cell proteins then attach themselves to the antigen or
peptide formed in this manner and transport them to the surface of the
cell, where they are presented to the molecular defense mechanisms, in T
cells, of the body. Cytotoxic T cells recognize these antigens and kill
the cells that harbor the antigens.
[0438] The term "major histocompatibility complex (MHC)", "MHC molecules",
or "MHC proteins" refers to proteins capable of binding peptides
resulting from the proteolytic cleavage of protein antigens and
representing potential T cell epitopes, transporting them to the cell
surface and presenting the peptides to specific cells, e.g., in cytotoxic
T-lymphocytes or T-helper cells. The human MHC is also called the HLA
complex. Thus, the term "human leukocyte antigen (HLA) system", "HLA
molecules" or "HLA proteins" refers to a gene complex encoding the MHC
proteins in humans. The term MHC is referred as the "H-2" complex in
murine species. Those of ordinary skill in the art will recognize that
the terms "major histocompatibility complex (MHC)", "MHC molecules", "MHC
proteins" and "human leukocyte antigen (HLA) system", "HLA molecules",
"HLA proteins" are used interchangeably herein.
[0439] HLA proteins are classified into two types, referred to as HLA
class I and HLA class II. The structures of the proteins of the two HLA
classes are very similar; however, they have very different functions.
HLA class I proteins are present on the surface of almost all cells of
the body, including most tumor cells. HLA class I proteins are loaded
with antigens that usually originate from endogenous proteins or from
pathogens present inside cells and are then presented to naive or
cytotoxic T-lymphocytes (CTLs). HLA class II proteins are present on
antigen presenting cells (APCs), including but not limited to dendritic
cells, B cells, and macrophages. They mainly present peptides, which are
processed from external antigen sources, e.g. outside of the cells, to
helper T cells. Most of the peptides bound by the HLA class I proteins
originate from cytoplasmic proteins produced in the healthy host cells of
an organism itself, and do not normally stimulate an immune reaction.
[0440] HLA class I molecules (FIG. 1) consist of two non-covalently linked
polypeptide chains, an HLA-encoded .alpha. chain (heavy chain, 44 to 47
kD) and a non-HLA encoded subunit called .beta.2 microglobulin (or,
.beta.2m), (12 kD). The .alpha. chain has three extracellular domains,
.alpha.1, .alpha.2 and .alpha.3 and a transmembrane region, of which the
.alpha.1 and .alpha.2 regions are capable of binding a peptide of about 7
to 13 amino acids (e.g., about 8 to 11 amino acids, or 9 or 10 amino
acids). An HLA class 1 molecule binds to a peptide that has the suitable
binding motifs, and presents it to cytotoxic T-lymphocytes. HLA class 1
heavy chains can be the protein product of an HLA-A allele, also termed
as an HLA-A monomer, or the protein product of HLA-B allele (likewise, an
HLA-B monomer) or the protein product of HLA-C allele (an HLA-C monomer),
each of which complexes with a .beta.-2-microglobulin. The .alpha.1 rests
upon the non-HLA protein .beta.2m; .beta.2m is encoded by
beta-2-microglobulin gene located on human chromosome 15. The .alpha.3
domain is connected to the transmembrane region, anchoring the HLA class
I molecule to the cell membrane. The peptide being presented is held by
the floor of the peptide-binding groove, in the central region of the
.alpha.1/.alpha.2 heterodimer (a molecule composed of two non-identical
subunits). HLA class I-A, HLA class I-B or HLA class I-C are highly
polymorphic. Each of a HLA class 1-A gene (termed HLA-A gene), a HLA
class 1-B gene (termed HLA-B gene) and a HLA class 1-C gene (termed HLA-C
gene) contains 8 exons, exon 1 encodes the leader peptide, exons 2 and 3
encode the .alpha.1 and .alpha.2 domains, exon 5 encodes the
transmembrane region and exons 6 and 7 encode the cytoplasmic tail.
Polymorphisms of exon 2 and exon 3 are responsible for the peptide
binding specificity of each class 1 molecule. HLA class I-B gene (HLA-B)
has many possible variations, expression patterns and presented antigens.
This group is subdivided into a group encoded within HLA loci, e.g.,
HLA-E, HLA-F, HLA-G, as well as those not, e.g., stress ligands such as
ULBPs, Rac1 and H60. The antigen/ligand for many of these molecules
remains unknown, but they can interact with each of CD8+ T cells, NKT
cells, and NK cells.
[0441] In some embodiments, the present disclosure utilizes a
non-classical HLA class I-E allele. HLA-E molecules are recognized by
natural killer (NK) cells and CD8+ T cells. HLA-E is expressed in almost
all tissues including lung, liver, skin and placental cells. HLA-E
expression is also detected in solid tumors (e.g., osteosarcoma and
melanoma). HLA-E molecule binds to TCR expressed on CD8+ T cells,
resulting in T cell activation. HLA-E is also known to bind CD94/NKG2
receptor expressed on NK cells and CD8+ T cells. CD94 can pair with
several different isoforms of NKG2 to form receptors with potential to
either inhibit (NKG2A, NKG2B) or promote (NKG2C) cellular activation.
HLA-E can bind to a peptide derived from amino acid residues 3-11 of the
leader sequences of most HLA-A, -B, -C, and -G molecules, but cannot bind
to its own leader peptide. HLA-E has also been shown to present peptides
derived from endogenous proteins similar to HLA-A, -B, and -C alleles.
Under physiological conditions, the engagement of CD94/NKG2A with HLA-E,
loaded with peptides from the HLA class I leader sequences, usually
induces inhibitory signals. Cytomegalovirus (CMV) utilizes the mechanism
for escape from NK cell immune surveillance via expression of the UL40
glycoprotein, mimicking the HLA-A leader. However, it is also reported
that CD8+ T cells can recognize HLA-E loaded with the UL40 peptide
derived from CMV Toledo strain and play a role in defense against CMV. A
number of studies revealed several important functions of HLA-E in
infectious disease and cancer.
[0442] The peptide antigens attach themselves to the molecules of HLA
class I by competitive affinity binding within the endoplasmic reticulum
before they are presented on the cell surface. Here, the affinity of an
individual peptide antigen is directly linked to its amino acid sequence
and the presence of specific binding motifs in defined positions within
the amino acid sequence. If the sequence of such a peptide is known, it
is possible to manipulate the immune system against diseased cells using,
for example, peptide vaccines.
[0443] MHC molecules are highly polymorphic, that is, there are many MHC
variants. Each variant is encoded by a variation of the gene encoding the
protein, and each such variant gene is called an allele. For human
beings, MHC is known as Human Leukocyte Antigens (HLA), which involves
three types of HLA class II molecules: DP, DQ and DR. HLA class II
peptides (FIG. 1) have two chains, .alpha. and .beta., each having two
domains--.alpha.1 and .alpha.2 and .beta.1 and .beta.2--each chain having
a transmembrane domain, .alpha.2 and .beta.2, respectively, anchoring the
HLA class II molecule to the cell membrane. The peptide-binding groove is
formed from the heterodimer of .alpha.1 and .beta.1. The most widely
studied HLA-DR molecules have DRA and DRB, corresponding to .alpha. and
.beta. domains, respectively. The DRB is diverse, DRA is almost
identical. Thus, the binding specificity of a DRB allele indicates that
of the corresponding HLA-DR. Each MHC protein has its own binding
specificity, meaning that a set of peptides binding to an MHC molecule
can be different from those to another MHC molecule. Classic molecules
present peptides to CD4+ lymphocytes. Nonclassic molecules, accessories,
with intracellular functions, are not exposed on cell membranes but in
internal membranes in lysosomes, normally loading the antigenic peptides
onto classic HLA class II molecules.
[0444] In HLA class II system, phagocytes such as macrophages and immature
dendritic cells take up entities by phagocytosis into phagosomes--though
B cells exhibit the more general endocytosis into endosomes--which fuse
with lysosomes whose acidic enzymes cleave the uptaken protein into many
different peptides. Autophagy is another source of HLA class II peptides.
Via physicochemical dynamics in molecular interaction with the HLA class
II variants borne by the host, encoded in the host's genome, a particular
peptide exhibits immunodominance and loads onto HLA class II molecules.
These are trafficked to and externalized on the cell surface. The most
studied subclasses of HLA class II genes are: HLA-DPA1, HLA-DPB1,
HLA-DQA1, HLA-DQB1, HLA-DRA, and HLA-DRB1.
[0445] Presentation of peptides by HLA class II molecules to CD4+ helper T
cells is required for immune responses to foreign antigens (Roche and
Furuta, 2015). Once activated, CD4+ T cells promote B cell
differentiation and antibody production, as well as CD8+ T cell (CTL)
responses. CD4+ T cells also secrete cytokines and chemokines that
activate and induce differentiation of other immune cells. HLA class II
molecules are heterodimers of .alpha.- and .beta.-chains that interact to
form a peptide-binding groove that is more open than HLA class I
peptide-binding grooves (Unanue et al., 2016). Peptides bound to HLA
class II molecules are believed to have a 9-amino acid binding core with
flanking residues on either N- or C-terminal side that overhang from the
groove (Jardetzky et al., 1996; Stern et al., 1994). These peptides are
usually 12-16 amino acids in length and often contain 3-4 anchor residues
at positions P1, P4, P6/7 and P9 of the binding register (Rossjohn et
al., 2015).
[0446] HLA alleles are expressed in codominant fashion, meaning that the
alleles (variants) inherited from both parents are expressed equally. For
example, each person carries 2 alleles of each of the 3 class I genes,
(HLA-A, HLA-B and HLA-C) and so can express six different types of HLA
class II. In the HLA class II locus, each person inherits a pair of
HLA-DP genes (DPA1 and DPB1, which encode .alpha. and .beta. chains),
HLA-DQ (DQA1 and DQB1, for .alpha. and .beta. chains), one gene
HLA-DR.alpha. (DRA1), and one or more genes HLA-DR.beta. (DRB1 and DRB3,
-4 or -5). HLA-DRB1, for example, has more than nearly 400 known alleles.
That means that one heterozygous individual can inherit six or eight
functioning HLA class II alleles: three or more from each parent. Thus,
the HLA genes are highly polymorphic; many different alleles exist in the
different individuals inside a population. Genes encoding HLA proteins
have many possible variations, allowing each person's immune system to
react to a wide range of foreign invaders. Some HLA genes have hundreds
of identified versions (alleles), each of which is given a particular
number. In some embodiments, the HLA class I alleles are HLA-A*02:01,
HLA-B*14:02, HLA-A*23:01, HLA-E*01:01 (non-classical). In some
embodiments, HLA class II alleles are HLA-DRB*01:01, HLA-DRB*01:02,
HLA-DRB*11:01, HLA-DRB*15:01, and HLA-DRB*07:01.
[0447] Subject specific HLA alleles or HLA genotype of a subject can be
determined by any method known in the art. In exemplary embodiments, HLA
genotypes are determined by any method described in International Patent
Application number PCT/US2014/068746, published Jun. 11, 2015 as
WO2015085147, which is incorporated herein by reference in its entirety.
Briefly, the methods include determining polymorphic gene types that can
comprise generating an alignment of reads extracted from a sequencing
data set to a gene reference set comprising allele variants of the
polymorphic gene, determining a first posterior probability or a
posterior probability derived score for each allele variant in the
alignment, identifying the allele variant with a maximum first posterior
probability or posterior probability derived score as a first allele
variant, identifying one or more overlapping reads that aligned with the
first allele variant and one or more other allele variants, determining a
second posterior probability or posterior probability derived score for
the one or more other allele variants using a weighting factor,
identifying a second allele variant by selecting the allele variant with
a maximum second posterior probability or posterior probability derived
score, the first and second allele variant defining the gene type for the
polymorphic gene, and providing an output of the first and second allele
variant.
[0448] In some embodiments the MHC class II peptide:antigenic peptide
binding and presenting prediction methods described herein have the
capacity to predict binders from a large repertoire MHC class II peptides
encoded by individual HLA alleles. In some embodiments, the MAPTAC
technology is trained with a large database of mass spectrometry
validated HLA-matched peptides. In some embodiments, the large database
of mass spectrometry validated HLA-matched peptides comprise greater than
1.2.times.10{circumflex over ( )}6 such HLA-matched peptides. In some
embodiments, the large database of mass spectrometry validated
HLA-matched peptides cover greater than 150 HLA alleles including both
MHC Class I and Class II allelic subtypes. In some embodiments, the
database covers at least 95% of US population for HLA-I and HLA-II (DR
subtype).
[0449] As described herein, there is a large body of evidence in both
animals and humans that mutated epitopes are effective in inducing an
immune response and that cases of spontaneous tumor regression or long
term survival correlate with CD8+ T cell responses to mutated epitopes
and that "immunoediting" can be tracked to alterations in expression of
dominant mutated antigens in mice and man.
[0450] Sequencing technology has revealed that each tumor contains
multiple, patient-specific mutations that alter the protein coding
content of a gene. Such mutations create altered proteins, ranging from
single amino acid changes (caused by missense mutations) to additions of
long regions of novel amino acid sequences due to frame shifts,
read-through of termination codons or translation of intron regions
(novel open reading frame mutations; neoORFs). These mutated proteins are
valuable targets for the host's immune response to the tumor as, unlike
native proteins, they are not subject to the immune-dampening effects of
self-tolerance. Therefore, mutated proteins are more likely to be
immunogenic and are also more specific for the tumor cells compared to
normal cells of the patient. In essence, short peptides (8-24 amino acids
long) containing a cancer associated mutation are candidates for cancer
immunotherapy.
[0451] In some embodiments the algorithm driving the prediction method can
be further utilized for mutation calling on a peptide. In some
embodiments, the prediction method may be used for determining driver
mutation status, and/or RNA expression status, and/or cleavage prediction
within the peptide.
[0452] The term "T cell" includes CD4+ T cells and CD8+ T cells. The term
T cell also includes both T helper 1 type T cells and T helper 2 type T
cells. T cells as used herein are generally classified by function and
cell surface antigens (cluster differentiation antigens, or CDs), which
also facilitate T cell receptor binding to antigen, into two major
classes: helper T (TH) cells and cytotoxic T-lymphocytes (CTLs).
[0453] Mature helper T (TH) cells express the surface protein CD4 and are
referred as CD4+ T cells. Following T cell development, matured, naive T
cells leave the thymus and begin to spread throughout the body, including
the lymph nodes. Naive T cells are those T cells that have never been
exposed to the antigen that they are programmed to respond to. Like all T
cells, they express the T cell receptor-CD3 complex. The T cell receptor
(TCR) consists of both constant and variable regions. The variable region
determines what antigen the T cell can respond to. CD4+ T cells have TCRs
with an affinity for MHC class II, proteins and CD4 are involved in
determining MHC affinity during maturation in the thymus. MHC class II
proteins are generally only found on the surface of specialized
antigen-presenting cells (APCs). Specialized antigen presenting cells
(APCs) are primarily dendritic cells, macrophages and B cells, although
dendritic cells are the only cell group that expresses MHC Class II
constitutively (at all times). Some APCs also bind native (or
unprocessed) antigens to their surface, such as follicular dendritic
cells, but unprocessed antigens do not interact with T cells and are not
involved in their activation. The peptide antigens that bind to HLA class
I proteins are typically shorter than peptide antigens that bind to HLA
class II proteins.
[0454] Cytotoxic T-lymphocytes (CTLs), also known as cytotoxic T cells,
cytolytic T cells, CD8+ T cells, or killer T cells, refer to lymphocytes
which induce apoptosis in targeted cells. CTLs form antigen-specific
conjugates with target cells via interaction of TCRs with processed
antigen (Ag) on target cell surfaces, resulting in apoptosis of the
targeted cell. Apoptotic bodies are eliminated by macrophages. The term
"CTL response" is used to refer to the primary immune response mediated
by CTL cells. Cytotoxic T-lymphocytes have both T cell receptors (TCR)
and CD8 molecules on their surface. T cell receptors are capable of
recognizing and binding peptides complexed with the molecules of HLA
class I. Each cytotoxic T-lymphocyte expresses a unique T cell receptor
which is capable of binding specific MHC/peptide complexes. Most
cytotoxic T cells express T cell receptors (TCRs) that can recognize a
specific antigen. In order for the TCR to bind to the HLA class I
molecule, the former must be accompanied by a glycoprotein called CD8,
which binds to the constant portion of the HLA class I molecule.
Therefore, these T cells are called CD8+ T cells. The affinity between
CD8 and the MHC molecule keeps the T cell and the target cell bound
closely together during antigen-specific activation. CD8+ T cells are
recognized as T cells once they become activated and are generally
classified as having a pre-defined cytotoxic role within the immune
system. However, CD8+ T cells also have the ability to make some
cytokines.
[0455] "T cell receptors (TCR)" are cell surface receptors that
participate in the activation of T cells in response to the presentation
of antigen. The TCR is generally made from two chains, alpha and beta,
which assemble to form a heterodimer and associates with the
CD3-transducing subunits to form the T cell receptor complex present on
the cell surface. Each alpha and beta chain of the TCR consists of an
immunoglobulin-like N-terminal variable (V) and constant (C) region, a
hydrophobic transmembrane domain, and a short cytoplasmic region. As for
immunoglobulin molecules, the variable regions of the alpha and beta
chains are generated by V(D)J recombination, creating a large diversity
of antigen specificities within the population of T cells. However, in
contrast to immunoglobulins that recognize intact antigen, T cells are
activated by processed peptide fragments in association with an MHC
molecule, introducing an extra dimension to antigen recognition by T
cells, known as MHC restriction. Recognition of MHC disparities between
the donor and recipient through the T cell receptor leads to T cell
proliferation and the potential development of GVHD. It has been shown
that normal surface expression of the TCR depends on the coordinated
synthesis and assembly of all seven components of the complex (Ashwell
and Klusner 1990). The inactivation of TCR.alpha. or TCR.beta. can result
in the elimination of the TCR from the surface of T cells preventing
recognition of alloantigen and thus GVHD. However, TCR disruption
generally results in the elimination of the CD3 signaling component and
alters the means of further T cell expansion.
[0456] The term "HLA peptidome" refers to a pool of peptides which
specifically interacts with a particular HLA class and can encompass
thousands of different sequences. HLA peptidomes include a diversity of
peptides, derived from both normal and abnormal proteins expressed in the
cells. Thus, the HLA peptidomes can be studied to identify cancer
specific peptides, for development of tumor immunotherapeutics and as a
source of information about protein synthesis and degradation schemes
within the cancer cells. In some embodiments, HLA peptidome is a pool of
soluble HLA peptides (sHLA). In some embodiments, HLA peptidome is a pool
of membrane associated HLA (mHLA).
[0457] "Antigen presenting cell" or "APC" includes professional antigen
presenting cells (e.g., B lymphocytes, macrophages, monocytes, dendritic
cells, Langerhans cells), as well as other antigen presenting cells
(e.g., keratinocytes, endothelial cells, astrocytes, fibroblasts,
oligodendrocytes, thymic epithelial cells, thyroid epithelial cells,
glial cells (brain), pancreatic beta cells, and vascular endothelial
cells). An "antigen presenting cell" or "APC" is a cell that expresses
the Major Histocompatibility complex (MHC) molecules and can display
foreign antigen complexed with MHC on its surface.
Mono-Allelic HLA Cell Lines
[0458] A mono-allelic cell line expressing either a single HLA class I
allele, a single pair of HLA class II alleles, or a single HLA class I
allele and a single pair of HLA class II alleles can be generated by
transducing or transfecting a suitable cell population with a polynucleic
acid, e.g., a vector, coding a single HLA allele (FIG. 2). Suitable cell
populations include, e.g., HLA class I deficient cells lines in which a
single HLA class I allele is exogenously expressed, HLA class II
deficient cell lines in which a single exogenous pair of HLA class II
alleles are expressed, or class I and class II deficient cell lines in
which a single HLA class I and/or single pair of class II alleles are
exogenously expressed. As an exemplary embodiment, the HLA class I
deficient B cell line is B721.221. However, it is clear to a skilled
person that other cell populations can be generated which are HLA class I
and/or HLA class II deficient. An exemplary method for
deleting/inactivating endogenous HLA class I or HLA class II genes
includes CRISPR-Cas9 mediated genome editing in, for example, THP-1
cells. In some embodiments, the populations of cells are professional
antigen presenting cells, such as macrophages, B cells, and dendritic
cells. The cells can be B cells or dendritic cells. In some embodiments,
the cells are tumor cells or cells from a tumor cell line. In some
embodiments, the cells are isolated from a patient. In some embodiments,
the cells contain an infectious agent or a portion thereof. In some
embodiments, the population of cells comprises at least 107 cells. In
some embodiments, the population of cells are further modified, such as
by increasing or decreasing the expression and/or activity of at least
one gene. In some embodiments, the gene encodes a member of the
immunoproteasome. The immunoproteasome is known to be involved in the
processing of HLA class I binding peptides and includes the LMP2
(.beta.1i), MECL-1 (.beta.2i), and LMP7 (.beta.5i) subunits. The
immunoproteasome can also be induced by interferon-gamma. Accordingly, in
some embodiments, the population of cells can be contacted with one or
more cytokines, growth factors, or other proteins. The cells can be
stimulated with inflammatory cytokines such as interferon-gamma, IL-10,
IL-6, and/or TNF-.alpha.. The population of cells can also be subjected
to various environmental conditions, such as stress (heat stress, oxygen
deprivation, glucose starvation, DNA damaging agents, etc.). In some
embodiments, the cells are contacted with one or more of a chemotherapy
drug, radiation, targeted therapies, or immunotherapy. The methods
disclosed herein can therefore be used to study the effect of various
genes or conditions on HLA peptide processing and presentation. In some
embodiments, the conditions used are selected so as to match the
condition of the patient for which the population of HLA-peptides is to
be identified.
[0459] A single HLA-allele of the present disclosure can be encoded and
expressed using a viral based system (e.g., an adenovirus system, an
adeno associated virus (AAV) vector, a poxvirus, or a lentivirus).
Plasmids that can be used for adeno associated virus, adenovirus, and
lentivirus delivery have been described previously (see e.g., U.S. Pat.
Nos. 6,955,808 and 6,943,019, and U.S. Patent application No.
20080254008, hereby incorporated by reference). Among vectors that can be
used in the practice of the present disclosure, integration in the host
genome of a cell is possible with retrovirus gene transfer methods, often
resulting in long term expression of the inserted transgene. In an
exemplary embodiment, the retrovirus is a lentivirus. Additionally, high
transduction efficiencies have been observed in many different cell types
and target tissues. The tropism of a retrovirus can be altered by
incorporating foreign envelope proteins, expanding the potential target
population of target cells. A retrovirus can also be engineered to allow
for conditional expression of the inserted transgene, such that only
certain cell types are infected by the lentivirus. Cell type specific
promoters can be used to target expression in specific cell types.
Lentiviral vectors are retroviral vectors (and hence both lentiviral and
retroviral vectors can be used in the practice of the present
disclosure). Moreover, lentiviral vectors are able to transduce or infect
non-dividing cells and typically produce high viral titers.
[0460] Selection of a retroviral gene transfer system can depend on the
target tissue. Retroviral vectors are comprised of cis-acting long
terminal repeats with packaging capacity for up to 6-10 kb of foreign
sequence. The minimum cis-acting LTRs are sufficient for replication and
packaging of the vectors, which are then used to integrate the desired
nucleic acid into the target cell to provide permanent expression. Widely
used retroviral vectors that can be used in the practice of the present
disclosure include those based upon murine leukemia virus (MuLV), gibbon
ape leukemia virus (GaLV), Simian Immunodeficiency virus (SIV), human
immunodeficiency virus (HIV), and combinations thereof (see, e.g.,
Buchscher et al., (1992) J. Virol. 66:2731-2739; Johann et al., (1992) J.
Virol. 66:1635-1640; Sommnerfelt et al., (1990) Virol. 176:58-59; Wilson
et al., (1998) J. Virol. 63:2374-2378; Miller et al., (1991) J. Virol.
65:2220-2224; PCT/US94/05700). Also, useful in the practice of the
present disclosure is a minimal non-primate lentiviral vector, such as a
lentiviral vector based on the equine infectious anemia virus (EIAV)
(see, e.g., Balagaan, (2006) J Gene Med; 8: 275-285, Published online 21
Nov. 2005 in Wiley InterScience DOI: 10.1002/jgm.845). The vectors can
have cytomegalovirus (CMV) promoter driving expression of the target
gene. Accordingly, the present disclosure contemplates amongst vector(s)
useful in the practice of the present disclosure: viral vectors,
including retroviral vectors and lentiviral vectors.
[0461] Any HLA allele can be expressed in the cell population. In an
exemplary embodiment, the HLA allele is an HLA class I allele. In some
embodiments, the HLA class I allele is an HLA-A allele or an HLA-B
allele. In some embodiments, the HLA allele is an HLA class II allele.
Sequences of HLA class I and class II alleles can be found in the
IPD-IMGT/HLA Database. Exemplary HLA alleles include, but are not limited
to, HLA-A*02:01, HLA-B*14:02, HLA-A*23:01, HLA-E*01:01, HLA-DRB*01:01,
HLA-DRB*01:02, HLA-DRB*11:01, HLA-DRB*15:01, and HLA-DRB*07:01.
[0462] In some embodiments, the HLA allele is selected so as to correspond
to a genotype of interest. In some embodiments, the HLA allele is a
mutated HLA allele, which can be non-naturally occurring allele or a
naturally occurring allele in an afflicted patient. The methods disclosed
herein have the further advantage of identifying HLA binding peptides for
HLA alleles associated with various disorders as well as alleles which
are present at low frequency. Accordingly, in some embodiments, the
method provided herein can identify the HLA allele even if it is present
at a frequency of less than 1% within a population, such as within the
Caucasian population.
[0463] In some embodiments, the nucleic acid sequence encoding the HLA
allele further comprises an affinity acceptor tag which can be used to
immunopurify the HLA-protein. Suitable tags are well-known in the art. In
some embodiments, an affinity acceptor tag is poly-histidine tag,
poly-histidine-glycine tag, poly-arginine tag, poly-aspartate tag,
poly-cysteine tag, poly-phenylalanine, c-myc tag, Herpes simplex virus
glycoprotein D (gD) tag, FLAG tag, KT3 epitope tag, tubulin epitope tag,
T7 gene 10 protein peptide tag, streptavidin tag, streptavidin binding
peptide (SPB) tag, Strep-tag, Strep-tag II, albumin-binding protein (ABP)
tag, alkaline phosphatase (AP) tag, bluetongue virus tag (B-tag),
calmodulin binding peptide (CBP) tag, chloramphenicol acetyl transferase
(CAT) tag, choline-binding domain (CBD) tag, chitin binding domain (CBD)
tag, cellulose binding domain (CBP) tag, dihydrofolate reductase (DHFR)
tag, galactose-binding protein (GBP) tag, maltose binding protein (MBP),
glutathione-S-transferase (GST), Glu-Glu (EE) tag, human influenza
hemagglutinin (HA) tag, horseradish peroxidase (HRP) tag, NE-tag, HSV
tag, ketosteroid isomerase (KSI) tag, KT3 tag, LacZ tag, luciferase tag,
NusA tag, PDZ domain tag, AviTag, Calmodulin-tag, E-tag, S-tag, SBP-tag,
Softag 1, Softag 3, TC tag, VSV-tag, Xpress tag, Isopeptag, SpyTag,
SnoopTag, Profinity eXact tag, Protein C tag, S1-tag, S-tag,
biotin-carboxy carrier protein (BCCP) tag, green fluorescent protein
(GFP) tag, small ubiquitin-like modifier (SUMO) tag, tandem affinity
purification (TAP) tag, HaloTag, Nus-tag, Thioredoxin-tag, Fc-tag, CYD
tag, HPC tag, TrpE tag, ubiquitin tag, a VSV-G epitope tag derived from
the Vescular Stomatis viral glycoprotein, or a V5 tag derived from a
small epitope (Pk) found on the P and V proteins of the paramyxovirus of
simian virus 5 (SV5). In some embodiments, the affinity acceptor tag is
an "epitope tag," which is a type of peptide tag that adds a recognizable
epitope (antibody binding site) to the HLA-protein to provide binding of
corresponding antibody, thereby allowing identification or affinity
purification of the tagged protein. Non-limiting example of an epitope
tag is protein A or protein G, which binds to IgG. In some embodiments,
affinity acceptor tags include the biotin acceptor peptide (BAP) or Human
influenza hemagglutinin (HA) peptide sequence. Numerous other tag
moieties are known to, and can be envisioned by, the ordinarily skilled
artisan, and are contemplated herein. Any peptide tag can be used as long
as it is capable of being expressed as an element of an affinity acceptor
tagged HLA-peptide complex.
[0464] The methods provided herein comprise isolating HLA-peptide
complexes from the cells transfected or transduced with affinity pulldown
of HLA constructs (FIG. 3). In some embodiments, the complexes can be
isolated using standard immunoprecipitation techniques known in the art
with commercially available antibodies. The cells can be first lysed. HLA
class I-peptide complexes can be isolated using HLA class I specific
antibodies such as the W6/32 antibody, while HLA class II-peptide
complexes can be isolated using HLA class II specific antibodies such as
the M5/114.15.2 monoclonal antibody. In some embodiments, the single (or
pair of) HLA alleles are expressed as a fusion protein with a peptide tag
and the HLA-peptide complexes are isolated using binding molecules that
recognize the peptide tags.
[0465] The methods further comprise isolating peptides from said
HLA-peptide complexes and sequencing the peptides. The peptides are
isolated from the complex by any method known to one of skill in the art,
such as acid elution. While any sequencing method can be used, methods
employing mass spectrometry, such as liquid chromatography--mass
spectrometry (LC-MS or LC-MS/MS, or alternatively HPLC-MS or HPLC-MS/MS)
are utilized in some embodiments. These sequencing methods are well-known
to a skilled person and are reviewed in Medzihradszky K F and Chalkley R
J. Mass Spectrom Rev. 2015 January-February; 34(1):43-63.
[0466] In some embodiments, the population of cells expresses one or more
endogenous HLA alleles. In some embodiments, the population of cells is
an engineered population of cells lacking one or more endogenous HLA
class I alleles. In some embodiments, the population of cells is an
engineered population of cells lacking endogenous HLA class I alleles. In
some embodiments, the population of cells is an engineered population of
cells lacking one or more endogenous HLA class II alleles. In some
embodiments, the population of cells is an engineered population of cells
lacking endogenous HLA class II alleles or an engineered population of
cells lacking endogenous HLA class I alleles and endogenous HLA class II
alleles. In some embodiments, the population of cells comprises cells
that have been enriched or sorted, such as by fluorescence activated cell
sorting (FACS). In some embodiments, fluorescence activated cell sorting
(FACS) is used to sort the population of cells. In some embodiments, the
population of cells is previously FACS sorted for cell surface expression
of either HLA class I or class II or both HLA class I and class II. For
example, FACS can be used to sort the population of cells for cell
surface expression of an HLA class I allele, an HLA class II allele, or a
combination thereof
Methods for Preparing a Personalized Cancer Vaccine
[0467] Once a mutation specific for a cancer is identified, such that the
mutation exists in the DNA in cancer cells but not in the normal cells of
the same human subject, and the mutation leads to a change in one or more
amino acids in the protein encoded by the DNA, the mutation can be a
target for the host immune response. A natural immune response can be
directed against the mutated protein leading to the destruction of cancer
cells expressing the protein. Because of the natural tolerance response
and immunocompromised environment in the cancerous tissue, immunotherapy
is a clinical path that attempts augmenting such immune response to
override the body's tolerance and immunosuppressive effects. A protein or
a peptide comprising the mutation as described above is therefore a
suitable candidate for immunotherapy.
[0468] A mutated protein is ingested by professional phagocytes acting as
antigen presenting cells (APCs), chopped and displayed as antigens on the
cell surface for T cell activation in an antigen presentation complex
comprising a Major Histocompatibility Complex (MHC) protein. Human MHC
proteins are called Human Leukocytic antigens, HLAs. The MHC protein can
be a MHC-class I or a class II protein, and while several functional
distinctions are attributed to the presentation of peptides by either
class I or class II MHC proteins (HLA class I and HLA class II proteins),
one salient distinction lies in the fact that HLA class I-peptide
complexes present antigens to cytotoxic CD8+ T cells, whereas the HLA
class II peptide complexes are also capable of activating CD4+ T cell
leading to prolonged immune response. CD8+ T cells are indispensable in
the task of cell-by-cell elimination of a diseased cell, such as an
infected cell or a tumor cell. CD4+ T cells have a more sustained effects
upon activation, the most important of those being generation of
immunological memory. CD4 subsets are differentially recruited according
to the type of immunologic threat, and multiple subsets with overlapping
or disparate functions may be co-recruited. This helps in balancing the
immunological response with respect to the pathogenic threat. In these
respects, HLA class II peptide mediated antigen presentation effects a
sustained and tailored immune response. On the other hand, HLA class II
binding to peptides may be promiscuous and therefore non-specific peptide
binding and presentation to the immune system leads to aberrant immune
response, such as autoimmunity.
[0469] In one aspect, the present disclosure provides method for
predicting peptides that can accurately pair with, or bind to, a specific
HLA class II alpha and beta chain heterodimer, such that the high
fidelity binding of the peptide to HLA class II protein (comprising the
alpha and beta chain heterodimer) ensures presentation of the specific
peptide to the T lymphocytes, thereby eliciting a specific immune
response and avoid any cross-reactivity or immune promiscuity.
[0470] In one aspect, the present disclosure provides method for
predicting peptides that can accurately bind to a specific HLA class II
protein, such that a more sustained and robust immune response can be
activated with the peptide, when the peptide is administered
therapeutically to a subject expressing the specific cognate HLA class II
protein, by dint of the ability of HLA class II protein's activation of
CD4+ T cells and stimulate immunological memory. In some embodiments, the
given peptide that is predicted to bind to a HLA class II protein with
high specificity is a peptide comprising a mutation, wherein the mutation
is prevalent in a cancer or a tumor cell of a subject; whereas the same
HLA class II protein predicted to bind the mutated peptide either (a)
does not bind, or (b) binds with distinctly lower affinity to the
corresponding non-mutated wild type peptide compared to the affinity for
binding to the mutated peptide of the subject. The preferential binding
of the HLA to the mutated peptide is advantageous in the development of
an immunotherapeutic, since the cells expressing the wild type peptide
will be spared from the immune attack by the T cells reactive to the
HLA-presented peptide. In some embodiments, predicted peptides that bind
specifically to the HLA class II proteins are peptides that have
post-translation modifications. Exemplary post-translational
modifications include but are not limited to: phosphorylation,
ubiquitylation, dephosphorylation, glycosylation, methylation, or,
acetylation. In some embodiments, the predicted peptides are subjected to
post-translational modifications prior for use in immunotherapy.
[0471] In some embodiments, the immunotherapy methods and strategies
disclosed herein could also be applicable in suppressing unwanted immune
activation, such as, in an autoimmune reaction. Specifically, peptides
identified as potential binders for specific HLA subtypes could be
tailored to bind to the specific HLA molecule and induces tolerance
rather than cause immunogenic response.
[0472] In one aspect, presented herein are methods of immunotherapy
tailored or personalized for a specific subject. Every subject or patient
expresses a specific array of HLA class I and HLA class II proteins. HLA
typing is a well-known technique that allows determination of the
specific repertoire of HLA proteins expressed by the subject. Once the
HLA heterodimers expressed by a specific subject is known, having an
improved, sophisticated and reliable method as described herein for
predicting peptides that can bind to a specific HLA class II alpha and
beta chain heterodimer, with high fidelity can ensure that a specific
immune response can be generated tailored specifically for the subject.
[0473] The genes coding for HLA heterodimers are highly polymorphic, with
more 4,000 HLA class II allele variants identified across the human
population. From maternal and paternal HLA haplotypes, an individual can
inherit different alleles for each of the HLA class II loci, and each HLA
class II heterodimer is made of an .alpha.- and .beta.-chain Because of
the large number of .alpha.- and .beta.-chain pairing combinations,
especially for HLA-DP and HLA-DQ alleles, the population of possible HLA
heterodimers is highly complex. HLA class II heterodimers are translated
in the endoplasmic reticulum (ER) and assembled into a stable complex
with the invariant chain (Ii) derived from the protein CD74. The Ii
stabilizes the class II complex by allowing proper protein folding and
enables the export of HLA class II heterodimers into endosomal/lysosomal
compartments. Inside these HLA class II loading compartments, the Ii is
proteolytically cleaved by cathepsins into a placeholder peptide called
CLIP. CLIP is then exchanged for higher-affinity peptides in a low pH
environment by the chaperone HLA-DM, a non-classical HLA class II
heterodimer. High affinity peptide-loaded HLA class II complexes are then
to the trans-Golgi and finally to the cell surface for display for CD4+ T
cells.
[0474] Each HLA heterodimer is estimated to bind thousands of peptides
with allele-specific binding preferences. In fact, each HLA allele is
estimated to bind and present .about.1,000-10,000 unique peptides to T
cells. Given such diversity in HLA binding, accurate prediction of
whether a peptide is likely to bind to a specific HLA allele is highly
challenging. Less is known about allele-specific peptide-binding
characteristics of HLA class II molecules because of the heterogeneity of
.alpha.- and .beta.-chain pairing, complexity of data limiting the
ability to confidently assign core binding epitopes, and the lack of
immunoprecipitation grade, allele-specific antibodies required for
high-resolution biochemical analyses. Furthermore, analyzing peptide
epitopes derived from a given HLA allele raises ambiguity when multiple
HLA alleles are presented on a cell surface.
[0475] Predictions for candidate neoantigens are predominantly made for
HLA class I epitopes (given the availability of experimental data for
class I prediction algorithms compared to class II), yet CD4+ T cell
responses are often observed in both pre-clinical and clinical
personalized neoantigen vaccination studies. These observations
demonstrate that HLA class II epitope processing and presentation may
also play a critical role in cancer treatment. Although HLA class II
prediction algorithms exist, they are inaccurate because the open-ended
peptide-binding groove on HLA class II heterodimers allows for longer
peptides (generally 15-40 amino acids) to bind, which increases the
heterogeneity and complexity of epitope presentation. Further work to
better understand the characteristics of HLA class II peptide-binding
cores and the cellular processes involved in class II epitope processing
and presentation is therefore required. The proteomics field is currently
limited by the complexity of HLA class II heterodimer formation and the
availability of immunoprecipitation grade antibodies for HLA class
II-peptide complex isolation. To overcome these challenges, a
mono-allelic HLA profiling workflow was developed that relies on LC-MS/MS
for the characterization of allele-specific HLA class II-ligandomes to
class II epitope prediction methods. The following definitions supplement
those in the art and are directed to the current application and are not
to be imputed to any related or unrelated case, e.g., to any commonly
owned patent or application. Although any methods and materials similar
or equivalent to those described herein can be used in the practice for
testing of the present disclosure, exemplary materials and methods are
described herein. Accordingly, the terminology used herein is for the
purpose of describing particular embodiments only, and is not intended to
be limiting.
[0476] Disclosed herein are methods to preparing a personalized cancer
vaccine. The method for preparing a personalized cancer vaccine may
comprise identifying peptide sequences with a mutation expressed in
cancer cells of a subject; inputting amino acid position information of
the peptide sequences identified, using a computer processor, into a
machine-learning HLA-peptide presentation prediction model to generate a
set of presentation predictions for the peptide sequences identified,
each presentation prediction representing a probability that one or more
proteins encoded by a class II MHC allele of a cancer cell of the subject
will present a given sequence of a peptide sequence identified; and
selecting a subset of the peptide sequences identified based on the set
of presentation predictions for preparing the personalized cancer
vaccine.
[0477] In some embodiments, one or more results obtained from a method
described herein may provide a quantitative value or values indicative of
one or more of the following: a likelihood of diagnostic accuracy, a
likelihood of a presence of a condition in a subject, a likelihood of a
subject developing a condition, a likelihood of success of a particular
treatment, or any combination thereof. In some embodiments, a method as
described herein may predict a risk or likelihood of developing a
condition. In some embodiments, a method as described herein may be an
early diagnostic indicator of developing a condition. In some
embodiments, a method as described herein may confirm a diagnosis or a
presence of a condition. In some embodiments, a method as described
herein may monitor the progression of a condition. In some embodiments, a
method as described herein may monitor the efficacy of a treatment for a
condition in a subject.
Method for Identification of MHC-II Peptides
[0478] In one aspect, presented herein is a method of identifying one or
more peptides that are presented by MHC-II proteins for immune
activation. In some embodiments, the one r more peptides comprise an
epitope. In some embodiments, the method involves computational
prediction of the likelihood that specific epitopes are presented by an
MHC-II protein. In some embodiments, the method involves computational
prediction of the specificity of an epitope for MHC-II presentation. In
some embodiments, the computational prediction methods involve an
assessment of peptide-MHC interactions. In some embodiments, the
computational prediction methods involve an prediction of the allelic
specificity of a peptide for antigen presentation. In some embodiments,
the computational prediction methods involve integration of
bioinformatics information, for example, nucleotide sequences, structural
motifs of biomolecules, protein-protein interaction features and
functional potency such as immunogenicity. In some embodiments, the
computational prediction methods involve machine learning. Many
immunoinformatics methods for prediction of peptide-MHC interactions have
been developed for both MHC class I and II, based on machine learning
approaches such as simple pattern motif, support vector machine (SVM),
hidden Markov model (HMM), neural network (NN) models, quantitative
structure-activity relationship (QSAR) analysis, structure-based methods,
and biophysical methods. These methods can be divided into two
categories, namely, intra-allele (allele-specific) and trans-allele
(pan-specific) methods. Intra-allelic methods are trained for a specific
MHC molecule on a limited set of experimental peptide-binding data and
applied for prediction of peptides binding to that molecule. Because of
the extreme polymorphism of MHC molecules, the existence of thousands of
allele variants, combined with the lack of sufficient experimental
binding data, it is impossible to build a prediction model for each
allele. Thus, trans-allele and general purpose methods such as
NetMHCIIpan (Karosiene E et al., NetMHCIIpan-3.0, a common pan-specific
MHC class II prediction method including all three human MHC class II
isotypes, HLA-DR, HLA-DP and HLADQ. Immunogenetics (2013) 65(10):711-24),
and TEPITOPEpan (Zhang L, et al., TEPITOPEpan: extending TEPITOPE for
peptide binding prediction covering over 700 HLA-DR molecules. PLoS One
(2012) 7(2):e30483) have been developed using peptide-binding data
expanding over many alleles or across species Similar methods for MHC-I
are also available such as NetMHCpan and KISS.
[0479] In some embodiments, the peptide sequences may not be expressed in
normal cells of the subject. In some embodiments, each and every cell of
the subject may not be cancer cells. The cancer cells may be produced
through different cancers, including, but not limited to, thyroid cancer,
adrenal cortical cancer, anal cancer, aplastic anemia, bile duct cancer,
bladder cancer, bone cancer, bone metastasis, central nervous system
(CNS) cancers, peripheral nervous system (PNS) cancers, breast cancer,
Castleman's disease, cervical cancer, childhood Non-Hodgkin's lymphoma,
lymphoma, colon and rectum cancer, endometrial cancer, esophagus cancer,
Ewing's family of tumors (e.g. Ewing's sarcoma), eye cancer, gallbladder
cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal
tumors, gestational trophoblastic disease, hairy cell leukemia, Hodgkin's
disease, Kaposi's sarcoma, kidney cancer, laryngeal and hypopharyngeal
cancer, acute lymphocytic leukemia, acute myeloid leukemia, children's
leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, liver
cancer, lung cancer, lung carcinoid tumors, Non-Hodgkin's lymphoma, male
breast cancer, malignant mesothelioma, multiple myeloma, myelodysplastic
syndrome, myeloproliferative disorders, nasal cavity and paranasal
cancer, nasopharyngeal cancer, neuroblastoma, oral cavity and
oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer,
penile cancer, pituitary tumor, prostate cancer, retinoblastoma,
rhabdomyosarcoma, salivary gland cancer, sarcoma (adult soft tissue
cancer), melanoma skin cancer, non-melanoma skin cancer, stomach cancer,
testicular cancer, thymus cancer, uterine cancer (e.g. uterine sarcoma),
vaginal cancer, vulvar cancer, or Waldenstrom's macroglobulinemia.
[0480] The identifying may comprise comparing DNA, RNA or protein
sequences from the cancer cells of the subject to DNA, RNA or protein
sequences from the normal cells of the subject. The DNA, RNA or protein
sequences from the cancer cells of the subject may be different from the
DNA, RNA or protein sequences from the normal cells of the subject. The
identifying may identify nucleic acid variants with high sensitivity.
[0481] The machine-learning HLA-peptide presentation prediction model may
comprise a plurality of predictor variables identified at least based on
training data. The training data may comprises sequence information of
sequences of peptides presented by an HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence information
comprising amino acid position information, wherein the training peptide
sequence information is associated with the HLA protein expressed in
cells; and a function representing a relation between the amino acid
position information received as input and the presentation likelihood
generated as output based on the amino acid position information and the
predictor variables.
[0482] In some embodiments, the training data may further comprise
structured data, time-series data, unstructured data, and relational
data. Unstructured data may comprise audio data, image data, video,
mechanical data, electrical data, chemical data, and any combination
thereof, for use in accurately simulating or training robotics or
simulations. Time-series data may comprise data from one or more of a
smart meter, a smart appliance, a smart device, a monitoring system, a
telemetry device, or a sensor. Relational data comprises data from a
customer system, an enterprise system, an operational system, a website,
web accessible application program interface (API), or any combination
thereof. This may be done by a user through any method of inputting files
or other data formats into software or systems.
[0483] In some embodiments, the training data may be stored in a database.
A database can be stored in computer readable format. A computer
processor may be configured to access the data stored in the computer
readable memory. In some embodiments, the computer system may be used to
analyze the data to obtain a result. The result may be stored remotely or
internally on storage medium, and communicated to personnel such as
medication professionals. In some embodiments, the computer system may be
operatively coupled with components for transmitting the result.
Components for transmitting can include wired and wireless components.
Examples of wired communication components can include a Universal Serial
Bus (USB) connection, a coaxial cable connection, an Ethernet cable such
as a Cat5 or Cat6 cable, a fiber optic cable, or a telephone line.
Examples or wireless communication components can include a Wi-Fi
receiver, a component for accessing a mobile data standard such as a 3G
or 4G LTE data signal, or a Bluetooth receiver. In some embodiments, all
these data in the storage medium is collected and archived to build a
data warehouse.
[0484] In some embodiments, the database comprises an external database.
The external database may be a medical database, for example, but not
limited to, Adverse Drug Effects Database, AHFS Supplemental File,
Allergen Picklist File, Average WAC Pricing File, Brand Probability File,
Canadian Drug File v2, Comprehensive Price History, Controlled Substances
File, Drug Allergy Cross-Reference File, Drug Application File, Drug
Dosing & Administration Database, Drug Image Database v2.0/Drug Imprint
Database v2.0, Drug Inactive Date File, Drug Indications Database, Drug
Lab Conflict Database, Drug Therapy Monitoring System (DTMS) v2.2/DTMS
Consumer Monographs, Duplicate Therapy Database, Federal Government
Pricing File, Healthcare Common Procedure Coding System Codes (HCPCS)
Database, ICD-10 Mapping Files, Immunization Cross-Reference File,
Integrated A to Z Drug Facts Module, Integrated Patient Education, Master
Parameters Database, Medi-Span Electronic Drug File (MED-File) v2,
Medicaid Rebate File, Medicare Plans File, Medical Condition Picklist
File, Medical Conditions Master Database, Medication Order Management
Database (MOMD), Parameters to Monitor Database, Patient Safety Programs
File, Payment Allowance Limit-Part B (PAL-B) v2.0, Precautions Database,
RxNorm Cross-Reference File, Standard Drug Identifiers Database,
Substitution Groups File, Supplemental Names File, Uniform System of
Classification Cross-Reference File, or Warning Label Database.
[0485] In some embodiments, the training data may also be obtained through
other data sources. The data sources may include sensors or smart
devices, such as appliances, smart meters, wearables, monitoring systems,
data stores, customer systems, billing systems, financial systems, crowd
source data, weather data, social networks, or any other sensor,
enterprise system or data store. Example of smart meters or sensors may
include meters or sensors located at a customer site, or meters or
sensors located between customers and a generation or source location. By
incorporating data from a broad array of sources, the system may be
capable of performing complex and detailed analyses. In some embodiments,
the data sources may include sensors or databases for other medical
platforms without limitation.
[0486] HLA-typing is conventionally carried out by either serological
methods using antibodies or by PCR-based methods such as Sequence
Specific Oligonucleotide Probe Hybridization (SSOP), or Sequence Based
Typing (SBT). While the first is hampered by the potentially high degree
of cross reactivity and limited resolution capabilities, the second
suffers from difficulties associated with the efficiency of the PCR due
to very limited possibilities for positioning primers because of
polymorphic positions.
[0487] In some embodiments, the sequence information is identified by
either sequencing methods or methods employing mass spectrometry, such as
liquid chromatography-mass spectrometry (LC-MS or LC-MS/MS, or
alternatively HPLC-MS or HPLC-MS/MS). These sequencing methods may be
well-known to a skilled person and are reviewed in Medzihradszky K F and
Chalkley R J. Mass Spectrom Rev. 2015 January-February; 34(1):43-63. In
some embodiments, the mass spectrometry is mono-allelic mass
spectrometry. In some embodiments, the mass spectrometry may be MS
analysis, MS/MS analysis, LC-MS/MS analysis, or a combination thereof. In
some embodiments, MS analysis may be used to determine a mass of an
intact peptide. For example, the determining can comprise determining a
mass of an intact peptide (e.g., MS analysis). In some embodiments, MS/MS
analysis may be used to determine a mass of peptide fragments. For
example, the determining can comprise determining a mass of peptide
fragments, which can be used to determine an amino acid sequence of a
peptide or portion thereof (e.g., MS/MS analysis). In some embodiments,
the mass of peptide fragments may be used to determine a sequence of
amino acids within the peptide. In some embodiments, LC-MS/MS analysis
may be used to separate complex peptide mixtures. For example, the
determining can comprise separating complex peptide mixtures, such as by
liquid chromatography, and determining a mass of an intact peptide, a
mass of peptide fragments, or a combination thereof (e.g., LC-MS/MS
analysis). This data can be used, e.g., for peptide sequencing.
[0488] In some embodiments, the training peptide sequence information
comprises amino acid position information of training peptides. In some
embodiments, the training peptide sequence information comprises at most
about 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10% or less sequence
information of sequences of peptides presented by an HLA protein
expressed in cells and identified by mass spectrometry. In some
embodiments, the training peptide sequence information may comprise at
least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more sequence
information of sequences of peptides presented by an HLA protein
expressed in cells and identified by mass spectrometry.
[0489] Any information and data may be paired with a subject who is the
source of the information and data. The subject or medical professional
can retrieve the information and data from a storage or a server through
a subject identity. A subject identity may comprise patient's photo,
name, address, social security number, birthday, telephone number, zip
code, or any combination thereof. A subject identity may be encrypted and
encoded in a visual graphical code. A visual graphical code may be a
one-time barcode that can be uniquely associated with a subject identity.
A barcode may be a UPC barcode, EAN barcode, Code 39 barcode, Code 128
barcode, ITF barcode, CodaBar barcode, GS1 DataBar barcode, MSI Plessey
barcode, QR barcode, Datamatrix code, PDF417 code, or an Aztec barcode. A
visual graphical code may be configured to be displayed on a display
screen. A barcode may comprise QR that can be optically captured and read
by a machine. A barcode may define an element such as a version, format,
position, alignment, or timing of the barcode to enable reading and
decoding of the barcode. A barcode can encode various types of
information in any type of suitable format, such as binary or
alphanumeric information. A QR code can have various symbol sizes as long
as the QR code can be scanned from a reasonable distance by an imaging
device. A QR code can be of any image file format (e.g. EPS or SVG vector
graphs, PNG, TIF, GIF, or JPEG raster graphics format).
[0490] In some embodiments, the function representing a relation between
the amino acid position information received as input and the
presentation likelihood generated as output based on the amino acid
position information and the predictor variables comprises a linear or
non-linear function. The function may be, for example, a rectified linear
unit (ReLU) activation function, a Leaky ReLu activation function, or
other function such as a saturating hyperbolic tangent, identity, binary
step, logistic, arcTan, softsign, parameteric rectified linear unit,
exponential linear unit, softPlus, bent identity, softExponential,
Sinusoid, Sinc, Gaussian, or sigmoid function, or any combination
thereof.
[0491] In some embodiments, the linear function is obtained through linear
regression. In some embodiments, the linear regression is a method to
predict a target variable by fitting the best linear relationship between
the dependent and independent variable. The best fit may mean that the
sum of all the distances between the shape and the actual observations at
each point is the least. Linear regression may comprise simple linear
regression or multiple linear regression. The simple linear regression
may use a single independent variable to predict a dependent variable.
The multiple linear regressions may use more than one independent
variables to predict a dependent variable by fitting a best linear
relationship. The non-linear function may be obtained through non-linear
regression. The nonlinear regression may be a form of regression analysis
in which observational data are modeled by a function which is a
nonlinear combination of the model parameters and depends on one or more
independent variables. The nonlinear regression may comprise a step
function, piecewise function, spline, and generalized additive model.
[0492] In some embodiments, the presentation likelihood is presented by
one-dimensional values (e.g., probabilities). In some embodiments, the
probability is configured to measure the likelihood that an event may
occur. In some embodiments, the probability ranges from about 0 and 1,
0.1 to 0.9, 0.2 to 0.8, 0.3 to 0.7, or 0.4 to 0.6. The higher the
probability of an event, the more likely the event may occur. In some
embodiments, the event comprises any type of situation, including, by way
of non-limiting examples, whether the HLA-peptide will present some
peptide with certain amino acid position information, and whether a
person will be sick based on amino acid position information. In some
embodiments, the likelihood may be presented by multi-dimensional values.
The multi-dimensional values may be presented by multi-dimensional space,
heatmap, or spreadsheet.
[0493] In one embodiment, selecting a subset of the peptide sequences
identified based on the set of presentation predictions is configured to
prepare the personalized cancer vaccine. In some embodiments, the subset
comprises at most about 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10% or
less of the peptide sequences identified based on the set of presentation
predictions. In other cases, the subset may comprise at least about 10%,
20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more of the peptide sequences
identified based on the set of presentation predictions. A cancer vaccine
may be a vaccine that either treats existing cancer or prevents
development of a cancer. Vaccines may be prepared from samples taken from
the patient, and may be specific to that patient.
[0494] In some embodiments, a Poxvirus is used in the disease (e.g.,
cancer) vaccine or immunogenic composition. These include orthopoxvirus,
avipox, vaccinia, MVA, NYVAC, canarypox, ALVAC, fowlpox, TROVAC, etc.
Advantages of the vectors may include simple construction, ability to
accommodate large amounts of foreign DNA and high expression levels.
Information concerning poxviruses that can be used in the practice of the
disclosure, such as Chordopoxvirinae subfamily poxviruses (poxviruses of
vertebrates), for instance, orthopoxviruses and avipoxviruses, e.g.,
vaccinia virus (e.g., Wyeth Strain, WR Strain (e.g., ATCC.RTM. VR-1354),
Copenhagen Strain, NYVAC, NYVAC.1, NYVAC.2, MVA, MVA-BN), canarypox virus
(e.g., Wheatley C93 Strain, ALVAC), fowlpox virus (e.g., FP9 Strain,
Webster Strain, TROVAC), dovepox, pigeonpox, quailpox, and raccoon pox,
inter alia, synthetic or non-naturally occurring recombinants thereof,
uses thereof, and methods for making and using such recombinants can be
found in scientific and patent literature.
[0495] In some embodiments, a vaccinia virus is used in the disease
vaccine or immunogenic composition to express an antigen. The recombinant
vaccinia virus may be able to replicate within the cytoplasm of the
infected host cell and the polypeptide of interest may therefore induce
an immune response.
[0496] In some embodiments, ALVAC is used as a vector in a disease vaccine
or immunogenic composition. ALVAC may be a canarypox virus that can be
modified to express foreign transgenes and has been used as a method for
vaccination against both prokaryotic and eukaryotic antigens.
[0497] In some embodiments, a Modified Vaccinia Ankara (MVA) virus is used
as a viral vector for an antigen vaccine or immunogenic composition. MVA
may be a member of the Orthopoxvirus family and has been generated by
about 570 serial passages on chicken embryo fibroblasts of the Ankara
strain of Vaccinia virus (CVA). As a consequence of these passages, the
resulting MVA virus may comprise 31 kilobases fewer genomic information
compared to CVA, and is highly host-cell restricted. MVA may be
characterized by its extreme attenuation, namely, by a diminished
virulence or infectious ability, but still holds an excellent
immunogenicity. When tested in a variety of animal models, MVA may be
proven to be avirulent, even in immuno-suppressed individuals. Moreover,
MVA-BN.RTM.-HER2 may be a candidate immunotherapy designed for the
treatment of HER-2-positive breast cancer and is currently in clinical
trials.
[0498] In some embodiments, a positive predictive value (PPV) is used as
part of the prediction model. A PPV, also known as a precision
measurement, is the probability that an individual diagnosed with a
disease or condition through, for example, a test or model, actually has
the disease or condition. It can be calculated by dividing the number of
true positive results by the total number of results that returned
positive (results that include false positives). PPV=True Positives/(True
positives+False positives). For example, if in a set of 100 patients, the
model identified a positive result in 50 patients, of which 25 were true
positives, the PPV would be 25/50=0.5. A PPV closer to 1 represents a
more accurate diagnosis method, such as a test or model. A PPV may be
used to determine the accuracy of the prediction model. A PPV may be used
to adjust the prediction model to accommodate for false positive results
that may be generated by the model.
[0499] A recall rate may be used as part of the prediction model. A recall
rate may be considered as the percentage of true positive results out of
the total number of positives in the sample set. Recall=True
Positives/(True positives+False Negatives). For example, if in a set of
100 patients, the model identified a positive result in 50 patients, of
which 25 were true positives, and there were a total of 75 positives in
the set of patients, the recall rate would be {25/(25+25)}.times.100=50%.
A recall rate may be used to determine the accuracy of the prediction
model. A recall rate may be used to adjust the prediction model to
accommodate for false positive results or false negative results that may
be generated by the model.
[0500] In some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of from 0.1%-10%. In some embodiments, the
prediction model may have a positive predictive value of at most 0.9,
0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1 or less at a recall rate of from
0.1%-10%. The prediction model may have a positive predictive value of at
least 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a
recall rate less than 0.1%. In some embodiments, the prediction model may
have a positive predictive value of at most 0.9, 0.8, 0.7, 0.6, 0.5, 0.4,
0.3, 0.2, 0.1 or less at a recall rate less than 0.1%. The prediction
model may have a positive predictive value of at least 0.05, 0.1, 0.2,
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate more than
10%. In some embodiments, the prediction model may have a positive
predictive value of at most 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1
or less at a recall rate more than 10%.
[0501] In some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of 0.1% to 10%. In some embodiments, the
prediction model has a positive predictive value of at least 0.05, 0.1,
0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate
of 0.1% to 0.5%, 0.1% to 1%, 0.1% to 2%, 0.1% to 3%, 0.1% to 4%, 0.1% to
5%, 0.1% to 6%, 0.1% to 7%, 0.1% to 8%, 0.1% to 9%, 0.1% to 10%, 0.5% to
1%, 0.5% to 2%, 0.5% to 3%, 0.5% to 4%, 0.5% to 5%, 0.5% to 6%, 0.5% to
7%, 0.5% to 8%, 0.5% to 9%, 0.5% to 10%, 1% to 2%, 1% to 3%, 1% to 4%, 1%
to 5%, 1% to 6%, 1% to 7%, 1% to 8%, 1% to 9%, 1% to 10%, 2% to 3%, 2% to
4%, 2% to 5%, 2% to 6%, 2% to 7%, 2% to 8%, 2% to 9%, 2% to 10%, 3% to
4%, 3% to 5%, 3% to 6%, 3% to 7%, 3% to 8%, 3% to 9%, 3% to 10%, 4% to
5%, 4% to 6%, 4% to 7%, 4% to 8%, 4% to 9%, 4% to 10%, 5% to 6%, 5% to
7%, 5% to 8%, 5% to 9%, 5% to 10%, 6% to 7%, 6% to 8%, 6% to 9%, 6% to
10%, 7% to 8%, 7% to 9%, 7% to 10%, 8% to 9%, 8% to 10%, or 9% to 10%. In
some embodiments, the prediction model has a positive predictive value of
at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or
greater at a recall rate of 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%,
9%, or 10%. In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6,
0.7, 0.8, 0.9 or greater at a recall rate of at least 0.1%, 0.5%, 1%, 2%,
3%, 4%, 5%, 6%, 7%, 8%, or 9%. In some embodiments, the prediction model
has a positive predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3,
0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate of at most 0.5%,
1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, or 10%.
[0502] In some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of 10% to 20%. In some embodiments, the
prediction model has a positive predictive value of at least 0.05, 0.1,
0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate
of 10% to 11%, 10% to 12%, 10% to 13%, 10% to 14%, 10% to 15%, 10% to
16%, 10% to 17%, 10% to 18%, 10% to 19%, 10% to 20%, 11% to 12%, 11% to
13%, 11% to 14%, 11% to 15%, 11% to 16%, 11% to 17%, 11% to 18%, 11% to
19%, 11% to 20%, 12% to 13%, 12% to 14%, 12% to 15%, 12% to 16%, 12% to
17%, 12% to 18%, 12% to 19%, 12% to 20%, 13% to 14%, 13% to 15%, 13% to
16%, 13% to 17%, 13% to 18%, 13% to 19%, 13% to 20%, 14% to 15%, 14% to
16%, 14% to 17%, 14% to 18%, 14% to 19%, 14% to 20%, 15% to 16%, 15% to
17%, 15% to 18%, 15% to 19%, 15% to 20%, 16% to 17%, 16% to 18%, 16% to
19%, 16% to 20%, 17% to 18%, 17% to 19%, 17% to 20%, 18% to 19%, 18% to
20%, or 19% to 20%. In some embodiments, the prediction model has a
positive predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate of 10%, 11%, 12%,
13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%. In some embodiments, the
prediction model has a positive predictive value of at least 0.05, 0.1,
0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate
of at least 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, or 19%. In some
embodiments, the prediction model has a positive predictive value of at
least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater
at a recall rate of at most 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%,
or 20%.
[0503] In some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of at least 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%,
6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%.
For example, prediction model may have a positive predictive value of at
least 0.1 at a recall rate of at least 10%. For example, prediction model
may have a positive predictive value of at least 0.2 at a recall rate of
at least 10%. For example, prediction model may have a positive
predictive value of at least 0.3 at a recall rate of at least 10%. For
example, prediction model may have a positive predictive value of at
least 0.4 at a recall rate of at least 10%. For example, prediction model
may have a positive predictive value of at least 0.5 at a recall rate of
at least 10%. For example, prediction model may have a positive
predictive value of at least 0.6 at a recall rate of at least 10%. For
example, prediction model may have a positive predictive value of at
least 0.7 at a recall rate of at least 10%. For example, prediction model
may have a positive predictive value of at least 0.8 at a recall rate of
at least 10%. For example, prediction model may have a positive
predictive value of at least 0.9 at a recall rate of at least 10%. For
example, prediction model may have a positive predictive value of at
least 0.1 at a recall rate of at least 5%. For example, prediction model
may have a positive predictive value of at least 0.2 at a recall rate of
at least 5%. For example, prediction model may have a positive predictive
value of at least 0.3 at a recall rate of at least 5%. For example,
prediction model may have a positive predictive value of at least 0.4 at
a recall rate of at least 5%. For example, prediction model may have a
positive predictive value of at least 0.5 at a recall rate of at least
5%. For example, prediction model may have a positive predictive value of
at least 0.6 at a recall rate of at least 5%. For example, prediction
model may have a positive predictive value of at least 0.7 at a recall
rate of at least 5%. For example, prediction model may have a positive
predictive value of at least 0.8 at a recall rate of at least 5%. For
example, prediction model may have a positive predictive value of at
least 0.9 at a recall rate of at least 5%. For example, prediction model
may have a positive predictive value of at least 0.1 at a recall rate of
at least 20%. For example, prediction model may have a positive
predictive value of at least 0.2 at a recall rate of at least 20%. For
example, prediction model may have a positive predictive value of at
least 0.3 at a recall rate of at least 20%. For example, prediction model
may have a positive predictive value of at least 0.4 at a recall rate of
at least 20%. For example, prediction model may have a positive
predictive value of at least 0.5 at a recall rate of at least 20%. For
example, prediction model may have a positive predictive value of at
least 0.6 at a recall rate of at least 20%. For example, prediction model
may have a positive predictive value of at least 0.7 at a recall rate of
at least 20%. For example, prediction model may have a positive
predictive value of at least 0.8 at a recall rate of at least 20%. For
example, prediction model may have a positive predictive value of at
least 0.9 at a recall rate of at least 20%.
[0504] In some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%,
7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%. For
example, prediction model may have a positive predictive value of at
least 0.1 at a recall rate of about 10%. For example, prediction model
may have a positive predictive value of at least 0.2 at a recall rate of
about 10%. For example, prediction model may have a positive predictive
value of at least 0.3 at a recall rate of about 10%. For example,
prediction model may have a positive predictive value of at least 0.4 at
a recall rate of about 10%. For example, prediction model may have a
positive predictive value of at least 0.5 at a recall rate of about 10%.
For example, prediction model may have a positive predictive value of at
least 0.6 at a recall rate of about 10%. For example, prediction model
may have a positive predictive value of at least 0.7 at a recall rate of
about 10%. For example, prediction model may have a positive predictive
value of at least 0.8 at a recall rate of about 10%. For example,
prediction model may have a positive predictive value of at least 0.9 at
a recall rate of about 10%. For example, prediction model may have a
positive predictive value of at least 0.1 at a recall rate of about 5%.
For example, prediction model may have a positive predictive value of at
least 0.2 at a recall rate of about 5%. For example, prediction model may
have a positive predictive value of at least 0.3 at a recall rate of
about 5%. For example, prediction model may have a positive predictive
value of at least 0.4 at a recall rate of about 5%. For example,
prediction model may have a positive predictive value of at least 0.5 at
a recall rate of about 5%. For example, prediction model may have a
positive predictive value of at least 0.6 at a recall rate of about 5%.
For example, prediction model may have a positive predictive value of at
least 0.7 at a recall rate of about 5%. For example, prediction model may
have a positive predictive value of at least 0.8 at a recall rate of
about 5%. For example, prediction model may have a positive predictive
value of at least 0.9 at a recall rate of about 5%. For example,
prediction model may have a positive predictive value of at least 0.1 at
a recall rate of about 20%. For example, prediction model may have a
positive predictive value of at least 0.2 at a recall rate of about 20%.
For example, prediction model may have a positive predictive value of at
least 0.3 at a recall rate of about 20%. For example, prediction model
may have a positive predictive value of at least 0.4 at a recall rate of
about 20%. For example, prediction model may have a positive predictive
value of at least 0.5 at a recall rate of about 20%. For example,
prediction model may have a positive predictive value of at least 0.6 at
a recall rate of about 20%. For example, prediction model may have a
positive predictive value of at least 0.7 at a recall rate of about 20%.
For example, prediction model may have a positive predictive value of at
least 0.8 at a recall rate of about 20%. For example, prediction model
may have a positive predictive value of at least 0.9 at a recall rate of
about 20%.
[0505] In some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of less than 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%,
6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%.
For example, prediction model may have a positive predictive value of at
least 0.1 at a recall rate of at most 10%. For example, prediction model
may have a positive predictive value of at least 0.2 at a recall rate of
at most 10%. For example, prediction model may have a positive predictive
value of at least 0.3 at a recall rate of at most 10%. For example,
prediction model may have a positive predictive value of at least 0.4 at
a recall rate of at most 10%. For example, prediction model may have a
positive predictive value of at least 0.5 at a recall rate of at most
10%. For example, prediction model may have a positive predictive value
of at least 0.6 at a recall rate of at most 10%. For example, prediction
model may have a positive predictive value of at least 0.7 at a recall
rate of at most 10%. For example, prediction model may have a positive
predictive value of at least 0.8 at a recall rate of at most 10%. For
example, prediction model may have a positive predictive value of at
least 0.9 at a recall rate of at most 10%. For example, prediction model
may have a positive predictive value of at least 0.1 at a recall rate of
at most 5%. For example, prediction model may have a positive predictive
value of at least 0.2 at a recall rate of at most 5%. For example,
prediction model may have a positive predictive value of at least 0.3 at
a recall rate of at most 5%. For example, prediction model may have a
positive predictive value of at least 0.4 at a recall rate of at most 5%.
For example, prediction model may have a positive predictive value of at
least 0.5 at a recall rate of at most 5%. For example, prediction model
may have a positive predictive value of at least 0.6 at a recall rate of
at most 5%. For example, prediction model may have a positive predictive
value of at least 0.7 at a recall rate of at most 5%. For example,
prediction model may have a positive predictive value of at least 0.8 at
a recall rate of at most 5%. For example, prediction model may have a
positive predictive value of at least 0.9 at a recall rate of at most 5%.
For example, prediction model may have a positive predictive value of at
least 0.1 at a recall rate of at most 20%. For example, prediction model
may have a positive predictive value of at least 0.2 at a recall rate of
at most 20%. For example, prediction model may have a positive predictive
value of at least 0.3 at a recall rate of at most 20%. For example,
prediction model may have a positive predictive value of at least 0.4 at
a recall rate of at most 20%. For example, prediction model may have a
positive predictive value of at least 0.5 at a recall rate of at most
20%. For example, prediction model may have a positive predictive value
of at least 0.6 at a recall rate of at most 20%. For example, prediction
model may have a positive predictive value of at least 0.7 at a recall
rate of at most 20%. For example, prediction model may have a positive
predictive value of at least 0.8 at a recall rate of at most 20%. For
example, prediction model may have a positive predictive value of at
least 0.9 at a recall rate of at most 20%.
[0506] In some embodiments, at a recall rate of about 0.1%, 0.5%, 1%, 2%,
3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%,
19%, or 20% the prediction model has a positive predictive value of 0.05%
to 0.6%. At a recall rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%,
7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% the
prediction model may have a positive predictive value of 0.05% to 0.1%,
0.05% to 0.15%, 0.05% to 0.2%, 0.05% to 0.25%, 0.05% to 0.3%, 0.05% to
0.35%, 0.05% to 0.4%, 0.05% to 0.45%, 0.05% to 0.5%, 0.05% to 0.55%,
0.05% to 0.6%, 0.1% to 0.15%, 0.1% to 0.2%, 0.1% to 0.25%, 0.1% to 0.3%,
0.1% to 0.35%, 0.1% to 0.4%, 0.1% to 0.45%, 0.1% to 0.5%, 0.1% to 0.55%,
0.1% to 0.6%, 0.15% to 0.2%, 0.15% to 0.25%, 0.15% to 0.3%, 0.15% to
0.35%, 0.15% to 0.4%, 0.15% to 0.45%, 0.15% to 0.5%, 0.15% to 0.55%,
0.15% to 0.6%, 0.2% to 0.25%, 0.2% to 0.3%, 0.2% to 0.35%, 0.2% to 0.4%,
0.2% to 0.45%, 0.2% to 0.5%, 0.2% to 0.55%, 0.2% to 0.6%, 0.25% to 0.3%,
0.25% to 0.35%, 0.25% to 0.4%, 0.25% to 0.45%, 0.25% to 0.5%, 0.25% to
0.55%, 0.25% to 0.6%, 0.3% to 0.35%, 0.3% to 0.4%, 0.3% to 0.45%, 0.3% to
0.5%, 0.3% to 0.55%, 0.3% to 0.6%, 0.35% to 0.4%, 0.35% to 0.45%, 0.35%
to 0.5%, 0.35% to 0.55%, 0.35% to 0.6%, 0.4% to 0.45%, 0.4% to 0.5%, 0.4%
to 0.55%, 0.4% to 0.6%, 0.45% to 0.5%, 0.45% to 0.55%, 0.45% to 0.6%,
0.5% to 0.55%, 0.5% to 0.6%, or 0.55% to 0.6%. At a recall rate of about
0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%,
15%, 16%, 17%, 18%, 19%, or 20% the prediction model may have a positive
predictive value of 0.05%, 0.1%, 0.15%, 0.2%, 0.25%, 0.3%, 0.35%, 0.4%,
0.45%, 0.5%, 0.55%, or 0.6%. At a recall rate of about 0.1%, 0.5%, 1%,
2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%,
18%, 19%, or 20% the prediction model may have a positive predictive
value of at least 0.05%, 0.1%, 0.15%, 0.2%, 0.25%, 0.3%, 0.35%, 0.4%,
0.45%, 0.5%, or 0.55%. At a recall rate of about 0.1%, 0.5%, 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%,
or 20% the prediction model may have a positive predictive value of at
most 0.1%, 0.15%, 0.2%, 0.25%, 0.3%, 0.35%, 0.4%, 0.45%, 0.5%, 0.55%, or
0.6%.
[0507] At a recall rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%,
8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% the
prediction model may have a positive predictive value of 0.45% to 0.98%.
At a recall rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%,
10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% the prediction
model may have a positive predictive value of 0.45% to 0.5%, 0.45% to
0.55%, 0.45% to 0.6%, 0.45% to 0.65%, 0.45% to 0.7%, 0.45% to 0.75%,
0.45% to 0.8%, 0.45% to 0.85%, 0.45% to 0.9%, 0.45% to 0.96%, 0.45% to
0.98%, 0.5% to 0.55%, 0.5% to 0.6%, 0.5% to 0.65%, 0.5% to 0.7%, 0.5% to
0.75%, 0.5% to 0.8%, 0.5% to 0.85%, 0.5% to 0.9%, 0.5% to 0.96%, 0.5% to
0.98%, 0.55% to 0.6%, 0.55% to 0.65%, 0.55% to 0.7%, 0.55% to 0.75%,
0.55% to 0.8%, 0.55% to 0.85%, 0.55% to 0.9%, 0.55% to 0.96%, 0.55% to
0.98%, 0.6% to 0.65%, 0.6% to 0.7%, 0.6% to 0.75%, 0.6% to 0.8%, 0.6% to
0.85%, 0.6% to 0.9%, 0.6% to 0.96%, 0.6% to 0.98%, 0.65% to 0.7%, 0.65%
to 0.75%, 0.65% to 0.8%, 0.65% to 0.85%, 0.65% to 0.9%, 0.65% to 0.96%,
0.65% to 0.98%, 0.7% to 0.75%, 0.7% to 0.8%, 0.7% to 0.85%, 0.7% to 0.9%,
0.7% to 0.96%, 0.7% to 0.98%, 0.75% to 0.8%, 0.75% to 0.85%, 0.75% to
0.9%, 0.75% to 0.96%, 0.75% to 0.98%, 0.8% to 0.85%, 0.8% to 0.9%, 0.8%
to 0.96%, 0.8% to 0.98%, 0.85% to 0.9%, 0.85% to 0.96%, 0.85% to 0.98%,
0.9% to 0.96%, 0.9% to 0.98%, or 0.96% to 0.98%. At a recall rate of
about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%,
14%, 15%, 16%, 17%, 18%, 19%, or 20% the prediction model may have a
positive predictive value of 0.45%, 0.5%, 0.55%, 0.6%, 0.65%, 0.7%,
0.75%, 0.8%, 0.85%, 0.9%, 0.96%, or 0.98%. At a recall rate of about
0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%,
15%, 16%, 17%, 18%, 19%, or 20% the prediction model may have a positive
predictive value of at least 0.45%, 0.5%, 0.55%, 0.6%, 0.65%, 0.7%,
0.75%, 0.8%, 0.85%, 0.9%, or 0.96%. At a recall rate of about 0.1%, 0.5%,
1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%,
17%, 18%, 19%, or 20% the prediction model may have a positive predictive
value of at most 0.5%, 0.55%, 0.6%, 0.65%, 0.7%, 0.75%, 0.8%, 0.85%,
0.9%, 0.96%, or 0.98%.
Methods of Training a Machine-Learning HLA-Peptide Presentation Prediction
Model
[0508] In an aspect, a method of training a machine-learning HLA-peptide
presentation prediction model may comprise inputting amino acid position
information sequences of HLA-peptides isolated from one or more
HLA-peptide complexes from a cell expressing an HLA class II allele into
the HLA-peptide presentation prediction model using a computer processor;
training the machine-learning HLA-peptide presentation prediction model
may comprise adjusting weighted values on nodes of a neural network to
best match the provided training data.
[0509] The training data may comprise sequence information of sequences of
peptides presented by an HLA protein expressed in cells and identified by
mass spectrometry; training peptide sequence information comprising amino
acid position information of training peptides, wherein the training
peptide sequence information is associated with the HLA protein expressed
in cells; and a function representing a relation between the amino acid
position information received as input and a presentation likelihood
generated as output based on the amino acid position information and the
predictor variables. The training data, training peptide sequence
information, function, and presentation likelihood are disclosed
elsewhere herein.
[0510] The trained algorithm may comprise one or more neural networks. A
neural network may be a type of computing system based upon a graph of
several connected neurons (or nodes) in a series of layers. A neural
network may comprise an input layer, to which data is presented; one or
more internal, and/or "hidden," layers; and an output layer, from which
results are presented. A neural network may learn the relationships
between an input data set and a target data set by adjusting a series of
connection weights. A neuron may be connected to neurons in other layers
via connections that have weights, which are parameters that control the
strength of a connection. The number of neurons in each layer may be
related to the complexity of a problem to be solved. The minimum number
of neurons required in a layer may be determined by the problem
complexity, and the maximum number may be limited by the ability of a
neural network to generalize. Input neurons may receive data being
presented and then transmit that data to a node in the first hidden layer
through connection weights, which are modified during training. The
result node may sum up the products of all pairs of inputs and their
associated weights. The weighted sum may be offset with a bias to adjust
the value of the result node. The output of a node or neuron may be gated
using a threshold or activation function. An activation function may be a
linear or non-linear function. An activation function may be, for
example, a rectified linear unit (ReLU) activation function, a Leaky ReLu
activation function, or other function such as a saturating hyperbolic
tangent, identity, binary step, logistic, arcTan, softsign, parameteric
rectified linear unit, exponential linear unit, softPlus, bent identity,
softExponential, Sinusoid, Sinc, Gaussian, or sigmoid function, or any
combination thereof.
[0511] A hidden layer in the neural network may process data and transmit
its result to the next layer through a second set of weighted
connections. Each subsequent layer may "pool" results from previous
layers into more complex relationships. Neural networks may be trained
with a known sample set of training data (data collected from one or more
sensors) by allowing them to modify themselves during (and after)
training so as to provide a desired output from a given set of inputs,
such as an output value. A trained algorithm may comprise convolutional
neural networks, recurrent neural networks, dilated convolutional neural
networks, fully connected neural networks, deep generative models, and
Boltzmann machines.
[0512] Weighing factors, bias values, and threshold values, or other
computational parameters of a neural network, may be "taught" or
"learned" in a training phase using one or more sets of training data.
For example, parameters may be trained using input data from a training
data set and a gradient descent or backward propagation method so that
output value(s) from a neural network are consistent with examples
included in a training data set.
[0513] The number of nodes used in an input layer of a neural network may
be at least about 10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900,
1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 20,000,
30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000 or
greater. In other instances, the number of node used in an input layer
may be at most about 100,000, 90,000, 80,000, 70,000, 60,000, 50,000,
40,000, 30,000, 20,000, 10,000, 9000, 8000, 7000, 6000, 5000, 4000, 3000,
2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, or 10 or
smaller. In some instance, the total number of layers used in a neural
network (including input and output layers) may be at least about 3, 4,
5, 10, 15, 20, or greater. In other instances, the total number of layers
may be at most about 20, 15, 10, 5, 4, 3 or less.
[0514] In some instances, the total number of learnable or trainable
parameters, e.g., weighting factors, biases, or threshold values, used in
a neural network may be at least about 10, 50, 100, 200, 300, 400, 500,
600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000,
10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000,
100,000 or greater. In other instances, the number of learnable
parameters may be at most about 100,000, 90,000, 80,000, 70,000, 60,000,
50,000, 40,000, 30,000, 20,000, 10,000, 9000, 8000, 7000, 6000, 5000,
4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50,
or 10 or smaller.
[0515] A neural network may comprise a convolutional neural network. A
convolutional neural network may comprise one or more convolutional
layers, dilated layers or fully connected layers. The number of
convolutional layers may be between 1-10 and dilated layers between 0-10.
The total number of convolutional layers (including input and output
layers) may be at least about 1, 2, 3, 4, 5, 10, 15, 20, or greater, and
the total number of dilated layers may be at least about 1, 2, 3, 4, 5,
10, 15, 20, or greater. The total number of convolutional layers may be
at most about 20, 15, 10, 5, 4, 3 or less, and the total number of
dilated layers may be at most about 20, 15, 10, 5, 4, 3 or less. In some
embodiments, the number of convolutional layers is between 1-10 and fully
connected layers between 0-10. The total number of convolutional layers
(including input and output layers) may be at least about 1, 2, 3, 4, 5,
10, 15, 20, or greater, and the total number of fully connected layers
may be at least about 1, 2, 3, 4, 5, 10, 15, 20, or greater. The total
number of convolutional layers may be at most about 20, 15, 10, 5, 4, 3
or less, and the total number of fully connected layers may be at most
about 20, 15, 10, 5, 4, 3 or less.
[0516] A convolutional neural network (CNN) may be a deep and feed-forward
artificial neural network. A CNN may be applicable to analyzing visual
imagery. A CNN may comprise an input, an output layer, and multiple
hidden layers. Hidden layers of a CNN may comprise convolutional layers,
pooling layers, fully connected layers and normalization layers. Layers
may be organized in 3 dimensions: width, height and depth.
[0517] Convolutional layers may apply a convolution operation to an input
and pass results of a convolution operation to a next layer. For
processing images, a convolution operation may reduce the number of free
parameters, allowing a network to be deeper with fewer parameters. In a
convolutional layer, neurons may receive input from only a restricted
subarea of a previous layer. Convolutional layer's parameters may
comprise a set of learnable filters (or kernels). Learnable filters may
have a small receptive field and extend through the full depth of an
input volume. During a forward pass, each filter may be convolved across
the width and height of an input volume, compute a dot product between
entries of a filter and an input, and produce a 2-dimensional activation
map of that filter. As a result, a network may learn filters that
activate when it detects some specific type of feature at some spatial
position in an input.
[0518] Pooling layers may comprise global pooling layers. Global pooling
layers may combine outputs of neuron clusters at one layer into a single
neuron in the next layer. For example, max pooling layers may use the
maximum value from each of a cluster of neurons at a prior layer; and
average pooling layers may use an average value from each of a cluster of
neurons at the prior layer. Fully connected layers may connect every
neuron in one layer to every neuron in another layer. In a
fully-connected layer, each neuron may receive input from every element
of a previous layer. A normalization layer may be a batch normalization
layer. A batch normalization layer may improve performance and stability
of neural networks. A batch normalization layer may provide any layer in
a neural network with inputs that are zero mean/unit variance. Advantages
of using batch normalization layer may include faster trained networks,
higher learning rates, easier to initialize weights, more activation
functions viable, and simpler process of creating deep networks.
[0519] A neural network may comprise a recurrent neural network. A
recurrent neural network may be configured to receive sequential data as
an input, such as consecutive data inputs, and a recurrent neural network
software module may update an internal state at every time step. A
recurrent neural network can use internal state (memory) to process
sequences of inputs. A recurrent neural network may be applicable to
tasks such as handwriting recognition or speech recognition, next word
prediction, music composition, image captioning, time series anomaly
detection, machine translation, scene labeling, and stock market
prediction. A recurrent neural network may comprise fully recurrent
neural network, independently recurrent neural network, Elman networks,
Jordan networks, Echo state, neural history compressor, long short-term
memory, gated recurrent unit, multiple timescales model, neural Turing
machines, differentiable neural computer, neural network pushdown
automata, or any combination thereof.
[0520] A trained algorithm may comprise a supervised or unsupervised
learning method such as, for example, SVM, random forests, clustering
algorithm (or software module), gradient boosting, logistic regression,
and/or decision trees. Supervised learning algorithms may be algorithms
that rely on the use of a set of labeled, paired training data examples
to infer the relationship between an input data and output data.
Unsupervised learning algorithms may be algorithms used to draw
inferences from training data sets to output data. Unsupervised learning
algorithms may comprise cluster analysis, which may be used for
exploratory data analysis to find hidden patterns or groupings in process
data. One example of an unsupervised learning method may comprise
principal component analysis. Principal component analysis may comprise
reducing the dimensionality of one or more variables. The dimensionality
of a given variables may be at least 1, 5, 10, 50, 100, 200, 300, 400,
500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700,
1800, or greater. The dimensionality of a given variables may be at most
1800, 1600, 1500, 1400, 1300, 1200, 1100, 1000, 900, 800, 700, 600, 500,
400, 300, 200, 100, 50, 10 or less.
[0521] A training algorithm may be obtained through statistical
techniques. In some embodiments, statistical techniques may comprise
linear regression, classification, resampling methods, subset selection,
shrinkage, dimension reduction, nonlinear models, tree-based methods,
support vector machines, unsupervised learning, or any combination
thereof.
[0522] A linear regression may be a method to predict a target variable by
fitting the best linear relationship between a dependent and independent
variable. The best fit may mean that the sum of all distances between a
shape and actual observations at each point is the least. Linear
regression may comprise simple linear regression and multiple linear
regression. A simple linear regression may use a single independent
variable to predict a dependent variable. A multiple linear regression
may use more than one independent variable to predict a dependent
variable by fitting a best linear relationship.
[0523] A classification may be a data mining technique that assigns
categories to a collection of data in order to achieve accurate
predictions and analysis. Classification techniques may comprise logistic
regression and discriminant analysis. Logistic regression may be used
when a dependent variable is dichotomous (binary). Logistic regression
may be used to discover and describe a relationship between one dependent
binary variable and one or more nominal, ordinal, interval or ratio-level
independent variables. A resampling may be a method comprising drawing
repeated samples from original data samples. A resampling may not involve
a utilization of a generic distribution tables in order to compute
approximate probability values. A resampling may generate a unique
sampling distribution on a basis of an actual data. In some embodiments,
a resampling may use experimental methods, rather than analytical
methods, to generate a unique sampling distribution. Resampling
techniques may comprise bootstrapping and cross-validation. Bootstrapping
may be performed by sampling with replacement from original data, and
take "not chosen" data points as test cases. Cross validation may be
performed by split training data into a plurality of parts.
[0524] A subset selection may identify a subset of predictors related to a
response. A subset selection may comprise best-subset selection, forward
stepwise selection, backward stepwise selection, hybrid method, or any
combination thereof. In some embodiments, shrinkage fits a model
involving all predictors, but estimated coefficients are shrunken towards
zero relative to the least squares estimates. This shrinkage may reduce
variance. A shrinkage may comprise ridge regression and a lasso. A
dimension reduction may reduce a problem of estimating n+1 coefficients
to a simpler problem of m+1 coefficients, where m<n. It may be
attained by computing n different linear combinations, or projections, of
variables. Then these n projections are used as predictors to fit a
linear regression model by least squares. Dimension reduction may
comprise principal component regression and partial least squares. A
principal component regression may be used to derive a low-dimensional
set of features from a large set of variables. A principal component used
in a principal component regression may capture the most variance in data
using linear combinations of data in subsequently orthogonal directions.
The partial least squares may be a supervised alternative to principal
component regression because partial least squares may make use of a
response variable in order to identify new features.
[0525] A nonlinear regression may be a form of regression analysis in
which observational data are modeled by a function which is a nonlinear
combination of model parameters and depends on one or more independent
variables. A nonlinear regression may comprise a step function, piecewise
function, spline, generalized additive model, or any combination thereof.
[0526] Tree-based methods may be used for both regression and
classification problems. Regression and classification problems may
involve stratifying or segmenting the predictor space into a number of
simple regions. Tree-based methods may comprise bagging, boosting, random
forest, or any combination thereof. Bagging may decrease a variance of
prediction by generating additional data for training from the original
dataset using combinations with repetitions to produce multistep of the
same carnality/size as original data. Boosting may calculate an output
using several different models and then average a result using a weighted
average approach. A random forest algorithm may draw random bootstrap
samples of a training set. Support vector machines may be classification
techniques. Support vector machines may comprise finding a hyperplane
that best separates two classes of points with the maximum margin.
Support vector machines may constrain an optimization problem such that a
margin is maximized subject to a constraint that it perfectly classifies
data.
[0527] Unsupervised methods may be methods to draw inferences from
datasets comprising input data without labeled responses. Unsupervised
methods may comprise clustering, principal component analysis, k-Mean
clustering, hierarchical clustering, or any combination thereof.
[0528] The mass spectrometry may be mono-allelic mass spectrometry. In
some embodiments, the mass spectrometry may be MS analysis, MS/MS
analysis, LC-MS/MS analysis, or a combination thereof. In some
embodiments, MS analysis may be used to determine a mass of an intact
peptide. For example, the determining can comprise determining a mass of
an intact peptide (e.g., MS analysis). In some embodiments, MS/MS
analysis may be used to determine a mass of peptide fragments. For
example, the determining can comprise determining a mass of peptide
fragments, which can be used to determine an amino acid sequence of a
peptide or portion thereof (e.g., MS/MS analysis). In some embodiments,
the mass of peptide fragments may be used to determine a sequence of
amino acids within the peptide. In some embodiments, LC-MS/MS analysis
may be used to separate complex peptide mixtures. For example, the
determining can comprise separating complex peptide mixtures, such as by
liquid chromatography, and determining a mass of an intact peptide, a
mass of peptide fragments, or a combination thereof (e.g., LC-MS/MS
analysis). This data can be used, e.g., for peptide sequencing.
[0529] The peptides may be presented by an HLA protein expressed in cells
through autophagy. Autophagy may allow the orderly degradation and
recycling of cellular components. The autophagy may comprise
macroautophagy, microautophagy and Chaperone mediated autophagy. The
peptides may be presented by an HLA protein expressed in cells through
phagocytosis. The phagocytosis may be a major mechanism used to remove
pathogens and cell debris. For example, when a macrophage ingests a
pathogenic microorganism, the pathogen becomes trapped in a phagosome
which then fuses with a lysosome to form a phagolysosome. In HLA class
II, phagocytes such as macrophages and immature dendritic cells may take
up entities by phagocytosis into phagosomes--though B cells exhibit the
more general endocytosis into endosomes--which fuse with lysosomes whose
acidic enzymes cleave the uptaken protein into many different peptides.
[0530] The quality of the training data may be increased by using a
plurality of quality metrics. The plurality of quality metrics may
comprise common contaminant peptide removal, high scored peak intensity,
high score, and high mass accuracy. The scored peak intensity may be used
prior to performing scoring. The MS/MS Search first screens the MS/MS
spectrum against candidate sequences using a simple filter. This filter
may be minimum scored peak intensity. Using the scored peak intensity may
enhance search speed by allowing candidate sequences to be rapidly and
summarily rejected once a sufficient number of spectral peaks are
examined and found not to meet the threshold established by this filter.
The scored peak intensity may be at least 50%. The scored peak intensity
may be at least 70%. The scored peak intensity may be at least 10%, 20%,
30%, 40%, 50%, 60%, 70%, 80%, 90% or greater. In some cases, the scored
peak intensity may be at most 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%
or less. The score may be at least 7. The score may be at least 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 15, 20 or greater. In some cases, the score may be
at most about 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 or less. The mass
accuracy may be at most 5 ppm. The mass accuracy may be at most 10 ppm, 9
ppm, 8 ppm, 7 ppm, 6 ppm, 5 ppm, 4 ppm, 3 ppm, 2 ppm, 1 ppm or less. The
mass accuracy may be at least 1 ppm, 2 ppm, 3 ppm, 4 ppm, 5 ppm, 6 ppm, 7
ppm, 8 ppm, 9 ppm, 10 ppm or greater.
[0531] In some embodiments, a mass accuracy is at most 2 ppm. In some
embodiments, a backbone cleavage score is at least 5. In some
embodiments, a backbone cleavage score is at least 8.
[0532] The peptides presented by an HLA protein expressed in cells may be
peptides presented by a single immunoprecipitated HLA protein expressed
in cells Immunoprecipitation (IP) may be the technique of precipitating a
protein antigen out of solution using an antibody that specifically binds
to that particular protein. This process can be used to isolate and
concentrate a particular protein from a sample containing many thousands
of different proteins Immunoprecipitation may require that the antibody
be coupled to a solid substrate at some point in the procedure.
[0533] The peptides presented by an HLA protein expressed in cells may be
peptides presented by a single exogenous HLA protein expressed in cells.
The single exogenous HLA protein may be created by introducing one or
more exogenous peptides to the population of cells. In some embodiments,
the introducing comprises contacting the population of cells with the one
or more exogenous peptides or expressing the one or more exogenous
peptides in the population of cells. In some embodiments, the introducing
comprises contacting the population of cells with one or more nucleic
acids encoding the one or more exogenous peptides. In some embodiments,
the one or more nucleic acids encoding the one or more peptides is DNA.
In some embodiments, the one or more nucleic acids encoding the one or
more peptides is RNA, optionally wherein the RNA is mRNA. In some
embodiments, the enriching does not comprise use of a tetramer (or
multimer) reagent.
[0534] The peptides presented by an HLA protein expressed in cells may be
peptides presented by a single recombinant HLA protein expressed in
cells. The recombinant HLA protein may be encoded by a recombinant HLA
class I or HLA class II allele. The HLA class I may be selected from the
group consisting of HLA-A, HLA-B, HLA-C. The HLA class I may be a
non-classical class-I-b group. The HLA class I may be selected from the
group consisting of HLA-E, HLA-F, and HLA-G. The HLA class I may be a
non-classical class-I-b group selected from the group consisting of
HLA-E, HLA-F, and HLA-G. In some embodiments, the HLA class II comprises
an HLA class II .alpha.-chain, an HLA class II I3-chain, or a combination
thereof.
[0535] The plurality of predictor variables may comprise a peptide-HLA
affinity predictor variable. The plurality of predictor variables may
comprise a source protein expression level predictor variable. The source
protein expression level may be the expression level of the source
protein of the peptide within a cell. In some embodiments, the expression
level may be determined by measuring the amount of source protein or the
amount of RNA encoding the source protein. The plurality of predictor
variables may comprise peptide sequence, amino acid physical properties,
peptide physical properties, expression level of the source protein of a
peptide within a cell, protein stability, protein translation rate,
ubiquitination sites, protein degradation rate, translational
efficiencies from ribosomal profiling, protein cleavability, protein
localization, motifs of host protein that facilitate TAP transport, host
protein is subject to autophagy, motifs that favor ribosomal stalling
(e.g., polyproline or polylysine stretches), protein features that favor
NMD (e.g., long 3' UTR, stop codon >50 nt upstream of last exon:exon
junction and peptide cleavability).
[0536] The plurality of predictor variables may comprise a peptide
cleavability predictor variable. The peptide cleavability may be
associated with a cleavable linker or a cleavage sequence. In some
embodiments, the cleavable linker is a ribosomal skipping site or an
internal ribosomal entry site (IRES) element. In some embodiments, the
ribosomal skipping site or IRES is cleaved when expressed in the cells.
In some embodiments, the ribosomal skipping site is selected from the
group consisting of F2A, T2A, P2A, and E2A. In some embodiments, the IRES
element is selected from common cellular or viral IRES sequences. A
cleavage sequence, such as F2A, or an internal ribosome entry site (IRES)
can be placed between the .alpha.-chain and .beta.2-microglobulin (HLA
class I) or between the .alpha.-chain and .beta.-chain (HLA class II). In
some embodiments, a single HLA class I allele is HLA-A*02:01, HLA-A*23:01
and HLA-B*14:02, or HLA-E*01:01, and HLA class II allele is
HLA-DRB*01:01, HLA-DRB*01:02 and HLA-DRB*11:01, HLA-DRB*15:01, or
HLA-DRB*07:01. In some embodiments, the cleavage sequence is a T2A, P2A,
E2A, or F2A sequence. For example, the cleavage sequence can be
EGRGSLTCGDVENPGP (SEQ ID NO: 6) (T2A), ATNFSLKQAGDVENPGP (SEQ ID NO: 7)
(P2A), QCTNYALKLAGDVESNPGP (SEQ ID NO: 8) (E2A), or VKQTLNFDLKLAGDVESNPGP
(SEQ ID NO: 9) (F2A).
[0537] In some embodiments, the cleavage sequence may be a thrombin
cleavage site CLIP.
[0538] The peptides presented by the HLA protein may comprise peptides
that are identified by searching a no-enzyme specificity without
modification peptide database. The peptide database may be a no-enzyme
specificity peptide database, such as a without modification database or
a with modification (e.g., phosphorylation or cysteinylation) database.
In some embodiments, the peptide database is a polypeptide database. In
some embodiments, the polypeptide database may be a protein database. In
some embodiments, the method further comprises searching the peptide
database using a reversed-database search strategy. In some embodiments,
the method further comprises searching a protein database using a
reversed-database search strategy. In some embodiments, a de novo search
is performed, e.g., to discover new peptides that are not included in a
normal peptide or protein database. The peptide database may be generated
by providing a first and a second population of cells each comprising one
or more cells comprising an affinity acceptor tagged HLA, wherein the
sequence affinity acceptor tagged HLA comprises a different recombinant
polypeptide encoded by a different HLA allele operatively linked to an
affinity acceptor peptide; enriching for affinity acceptor tagged
HLA-peptide complexes; characterizing a peptide or a portion thereof
bound to an affinity acceptor tagged HLA-peptide complex from the
enriching; and generating an HLA-allele specific peptide database.
[0539] The peptides presented by the HLA protein may comprise peptides
identified by comparing a MS/MS spectra of the HLA-peptides with MS/MS
spectra of one or more HLA-peptides in a peptide database.
[0540] There may be mutation on either peptides or nucleic acid that
encodes peptides. The mutation may be selected from the group consisting
of a point mutation, a splice site mutation, a frameshift mutation, a
read-through mutation, and a gene fusion mutation. The point mutation may
be a genetic mutation where a single nucleotide base is changed, inserted
or deleted from a sequence of DNA or RNA. The splice site mutation may be
a genetic mutation that inserts, deletes or changes a number of
nucleotides in the specific site at which splicing takes place during the
processing of precursor messenger RNA into mature messenger RNA. The
frameshift mutation may be a genetic mutation caused by indels
(insertions or deletions) of a number of nucleotides in a DNA sequence
that is not divisible by three. The mutation may also comprise
insertions, deletions, substitution mutations, gene duplications,
chromosomal translocations, and chromosomal inversions.
[0541] In some embodiments, the HLA class II protein comprises an HLA-DR
protein.
[0542] In some embodiments, the HLA class II protein comprises an HLA-DP
protein.
[0543] In some embodiments, the HLA class II protein comprises an HLA-DQ
protein.
[0544] In some embodiments, the HLA class II protein may be selected from
the group consisting an HLA-DR, and HLA-DP or an HLA-DQ protein. In some
embodiments, the HLA protein is an HLA class II protein selected from the
group consisting of: HLA-DPB1*01:01/HLA-DPA1*01:03,
HLA-DPB1*02:01/HLA-DPA1*01:03, HLA-DPB1*03:01/HLA-DPA1*01:03,
HLA-DPB1*04:01/HLA-DPA1*01:03, HLA-DPB1*04:02/HLA-DPA1*01:03,
HLA-DPB1*06:01/HLA-DPA1*01:03, HLA-DQB1*02:01/HLA-DQA1*05:01,
HLA-DQB1*02:02/HLA-DQA1*02:01, HLA-DQB1*06:02/HLA-DQA1*01:02,
HLA-DQB1*06:04/HLA-DQA1*01:02, HLA-DRB1*01:01, HLA-DRB1*01:02,
HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA-DRB1*04:02,
HLA-DRB1*04:03, HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*04:07,
HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA-DRB1*08:03,
HLA-DRB1*08:04, HLA-DRB1*09:01, HLA-DRB1*10:01, HLA-DRB1*11:01,
HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*12:02,
HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01,
HLA-DRB1*15:01, HLA-DRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:01,
HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01,
HLA-DRB5*01:01). The peptides presented by the HLA protein may have a
length of from 15-40 amino acids. The peptides presented by the HLA
protein may have a length of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or
greater amino acids. In some embodiments, the peptides presented by the
HLA protein may have a length of at most 30, 29, 28, 27, 26, 25, 24, 23,
22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, or less amino acids.
[0545] The peptides presented by the HLA protein may comprise peptides
identified by (a) isolating one or more HLA complexes from a cell line
expressing a single HLA class II allele; (b) isolating one or more
HLA-peptides from the one or more isolated HLA complexes; (c) obtaining
MS/MS spectra for the one or more isolated HLA-peptides; and (d)
obtaining a peptide sequence that corresponds to the MS/MS spectra of the
one or more isolated HLA-peptides from a peptide database; wherein one or
more sequences obtained from steps (a, b, c) and (d) identifies the
sequence of the one or more isolated HLA-peptides.
[0546] The isolating may comprise isolating HLA-peptide complexes from the
cells transfected or transduced with affinity tagged HLA constructs. In
some embodiments, the complexes can be isolated using standard
immunoprecipitation techniques known in the art with commercially
available antibodies. The cells can be first lysed. HLA class II-peptide
complexes can be isolated using HLA class II specific antibodies such as
the M5/114.15.2 monoclonal antibody. In some embodiments, the single (or
pair of) HLA alleles are expressed as a fusion protein with a peptide tag
and the HLA-peptide complexes are isolated using binding molecules that
recognize the peptide tags.
[0547] The isolating may comprise isolating peptides from the HLA-peptide
complexes and sequencing the peptides. The peptides are isolated from the
complex by any method known to one of skill in the art, such as acid
elution. While any sequencing method can be used, methods employing mass
spectrometry, such as liquid chromatography-mass spectrometry (LC-MS or
LC-MS/MS, or alternatively HPLC-MS or HPLC-MS/MS) are utilized in some
embodiments. These sequencing methods may be well-known to a skilled
person and are reviewed in Medzihradszky K F and Chalkley R J. Mass
Spectrom Rev. 2015 January-February; 34(1):43-63.
[0548] Additional candidate components and molecules suitable for
isolation or purification may comprise binding molecules, such as biotin
(biotin-avidin specific binding pair), an antibody, a receptor, a ligand,
a lectin, or molecules that comprise a solid support, including, for
example, plastic or polystyrene beads, plates or beads, magnetic beads,
test strips, and membranes. Purification methods such as cation exchange
chromatography can be used to separate conjugates by charge difference,
which effectively separates conjugates into their various molecular
weights. The content of the fractions obtained by cation exchange
chromatography can be identified by molecular weight using conventional
methods, for example, mass spectroscopy, SDS-PAGE, or other known methods
for separating molecular entities by molecular weight.
[0549] In some embodiments, the method further comprises isolating
peptides from the affinity acceptor tagged HLA-peptide complexes before
the characterizing. In some embodiments, an HLA-peptide complex is
isolated using an anti-HLA antibody. In some cases, an HLA-peptide
complex with or without an affinity tag is isolated using an anti-HLA
antibody. In some cases, a soluble HLA (sHLA) with or without an affinity
tag is isolated from media of a cell culture. In some cases, a soluble
HLA (sHLA) with or without an affinity tag is isolated using an anti-HLA
antibody. For example, an HLA, such as a soluble HLA (sHLA) with or
without an affinity tag, can be isolated using a bead or column
containing an anti-HLA antibody. In some embodiments, the peptides are
isolated using anti-HLA antibodies. In some cases, a soluble HLA (sHLA)
with or without an affinity tag is isolated using an anti-HLA antibody.
In some cases, a soluble HLA (sHLA) with or without an affinity tag is
isolated using a column containing an anti-HLA antibody. In some
embodiments, the method further comprises removing one or more amino
acids from a terminus of a peptide bound to an affinity acceptor tagged
HLA-peptide complex.
[0550] The personalized cancer vaccine may further comprise an adjuvant.
For example, poly-ICLC, an agonist of TLR3 and the RNA helicase-domains
of MDAS and RIG3, has shown several desirable properties for a vaccine
adjuvant. These properties may include the induction of local and
systemic activation of immune cells in vivo, production of stimulatory
chemokines and cytokines, and stimulation of antigen-presentation by DCs.
Furthermore, poly-ICLC can induce durable CD4+ and CD8+ responses in
humans Importantly, striking similarities in the upregulation of
transcriptional and signal transduction pathways may be seen in subjects
vaccinated with poly-ICLC and in volunteers who had received the highly
effective, replication-competent yellow fever vaccine. Furthermore,
>90% of ovarian carcinoma patients immunized with poly-ICLC in
combination with a NYESO-1 peptide vaccine (in addition to Montanide)
showed induction of CD4+ and CD8+ T cell, as well as antibody responses
to the peptide in a recent phase 1 study.
[0551] The personalized cancer vaccine may further comprise an immune
checkpoint inhibitor. The immune checkpoint inhibitor may comprise a type
of drug that blocks certain proteins made by some types of immune system
cells, such as T cells, and some cancer cells. These proteins help keep
immune responses in check and can keep T cells from killing cancer cells.
When these proteins are blocked, the "brakes" on the immune system are
released and T cells are able to kill cancer cells better. Examples of
checkpoint proteins found on T cells or cancer cells include PD-1/PD-L1
and CTLA-4/B7-1/B7-2. Some immune checkpoint inhibitors are used to treat
cancer.
[0552] The training data may further comprise structured data, time-series
data, unstructured data, and relational data. Unstructured data may
comprise audio data, image data, video, mechanical data, electrical data,
chemical data, and any combination thereof, for use in accurately
simulating or training robotics or simulations. Time-series data may
comprise data from one or more of a smart meter, a smart appliance, a
smart device, a monitoring system, a telemetry device, or a sensor.
Relational data comprises data from a customer system, an enterprise
system, an operational system, a website, web accessible application
program interface (API), or any combination thereof. This may be done by
a user through any method of inputting files or other data formats into
software or systems.
[0553] The training data may be uploaded to a cloud-based database. The
cloud-based database may be accessible from local and/or remote computer
systems on which the machine learning-based sensor signal processing
algorithms are running. The cloud-based database and associated software
may be used for archiving electronic data, sharing electronic data, and
analyzing electronic data. The data or datasets generated locally may be
uploaded to a cloud-based database, from which it may be accessed and
used to train other machine learning-based detection systems at the same
site or a different site. Sensor device and system test results generated
locally may be uploaded to a cloud-based database and used to update the
training data set in real time for continuous improvement of sensor
device and detection system test performance.
[0554] The training may be performed using convolutional neural networks.
The convolutional neural network (CNN) is described elsewhere herein. The
convolutional neural networks may comprise at least two convolutional
layers. The number of convolutional layers may be between 1-10 and the
dilated layers between 0-10. The total number of convolutional layers
(including input and output layers) may be at least about 1, 2, 3, 4, 5,
10, 15, 20, or greater, and the total number of dilated layers may be at
least about 1, 2, 3, 4, 5, 10, 15, 20, or greater. The total number of
convolutional layers may be at most about 20, 15, 10, 5, 4, 3 or less,
and the total number of dilated layers may be at most about 20, 15, 10,
5, 4, 3 or less. In some embodiments, the number of convolutional layers
is between 1-10 and the fully connected layers between 0-10. The total
number of convolutional layers (including input and output layers) may be
at least about 1, 2, 3, 4, 5, 10, 15, 20, or greater, and the total
number of fully connected layers may be at least about 1, 2, 3, 4, 5, 10,
15, 20, or greater. The total number of convolutional layers may be at
most about 20, 15, 10, 5, 4, 3 or less, and the total number of fully
connected layers may be at most about 20, 15, 10, 5, 4, 3 or less.
[0555] The convolutional neural networks may comprise at least one batch
normalization step. The batch normalization layer may improve the
performance and stability of neural networks. The batch normalization
layer may provide any layer in a neural network with inputs that are zero
mean/unit variance. The total number of batch normalization layers may be
at least about 3, 4, 5, 10, 15, 20 or more. The total number of batch
normalization layers may be at most about 20, 15, 10, 5, 4, 3 or less
[0556] The convolutional neural networks may comprise at least one spatial
dropout step. The total number of spatial dropout steps may be at least
about 3, 4, 5, 10, 15, 20 or more, and the total number of spatial
dropout steps may be at most about 20, 15, 10, 5, 4, 3 or less.
[0557] The convolutional neural networks may comprise at least one global
max pooling step. The global pooling layers may combine the outputs of
neuron clusters at one layer into a single neuron in the next layer. For
example, max pooling layers may use the maximum value from each of a
cluster of neurons at the prior layer; and average pooling layers may use
the average value from each of a cluster of neurons at the prior layer.
The convolutional neural networks may comprise at least about 1, 2, 3, 4,
5, 10, 15, 20, or greater global max pooling steps. The convolutional
neural networks may comprise at most about 20, 15, 10, 5, 4, 3 or less
global max pooling steps.
[0558] The convolutional neural networks may comprise at least one dense
layer. The convolutional neural networks may comprise at least about 1,
2, 3, 4, 5, 10, 15, 20, or greater dense layers. The convolutional neural
networks may comprise at most about 20, 15, 10, 5, 4, 3 or less dense
layers.
Therapeutic Methods
[0559] Personalized immunotherapy using tumor-specific peptides has been
described. Tumor neoantigens, which arise as a result of genetic change
(e.g., inversions, translocations, deletions, missense mutations, splice
site mutations, etc.) within malignant cells, represent the most
tumor-specific class of antigens. Neoantigens have rarely been used in
cancer vaccine or immunogenic compositions due to technical difficulties
in identifying them, selecting optimized antigens, and producing
neoantigens for use in a vaccine or immunogenic composition. Efficiently
choosing which particular peptides to utilize as an immunogen requires
the ability to predict which tumor-specific peptides would efficiently
bind to the HLA alleles present in a patient and would be effectively
presented to the patient's immune system for inducing anti-tumor
immunity. One of the critical barriers to developing curative and
tumor-specific immunotherapy is the identification and selection of
highly specific and restricted tumor antigens to avoid autoimmunity. This
is particularly important in case of candidate tumor specific peptides
for immunotherapy that are presented by MHC class II antigens, because
there is a certain level of promiscuity in MHC class II-peptide binding
and presentation to the immune system. At the same time, MHC class II
presented peptides are required for activation of not only cytotoxic
cells but also CD4+ve memory T cells. MHC class II mediated immunogenic
response is therefore needed for a robust, offer long term immunogenicity
for greater effectiveness in tumor protection. These problems can be
addressed by: having a reliable peptide-MHC predicting algorithm and
having a reliable system for assaying and validating the peptide-MHC
interaction and immunogenicity. Therefore, in some embodiments, a highly
efficient and immunogenic cancer vaccine may be produced by identifying
candidate mutations in neoplasias/tumors which are present at the DNA
level in tumor but not in matched germline samples from a high proportion
of subjects having cancer; analyzing the identified mutations with one or
more peptide-MHC binding prediction algorithms to identify which MHC
(human leukocytic antigen or HLA in case of humans) bind to a high
proportion of patient HLA alleles; and synthesizing the plurality of
neoantigenic peptides selected from the sets of all neoantigen peptides
and predicted binding peptides for use in a cancer vaccine or immunogenic
composition suitable for treating a high proportion of subjects having
cancer.
[0560] For example, translating peptide sequencing information into a
therapeutic vaccine can include prediction of mutated peptides that can
bind to HLA peptides of a high proportion of individuals. Efficiently
choosing which particular mutations to utilize as immunogen requires the
ability to predict which mutated peptides would efficiently bind to a
high proportion of patient's HLA alleles. Recently, neural network based
learning approaches with validated binding and non-binding peptides have
advanced the accuracy of prediction algorithms for the major HLA-A and -B
alleles. However, although using advanced neural network-based algorithms
has helped to encode HLA-peptide binding rules, several factors limit the
power to predict peptides presented on HLA alleles.
[0561] For example, translating peptide sequencing information into a
therapeutic vaccine can include formulating the drug as a multi-epitope
vaccine of long peptides. Targeting as many mutated epitopes as
practically possible takes advantage of the enormous capacity of the
immune system, prevents the opportunity for immunological escape by
down-modulation of an immune targeted gene product, and compensates for
the known inaccuracy of epitope prediction approaches. Synthetic peptides
provide a useful means to prepare multiple immunogens efficiently and to
rapidly translate identification of mutant epitopes to an effective
vaccine. Peptides can be readily synthesized chemically and easily
purified utilizing reagents free of contaminating bacteria or animal
substances. The small size allows a clear focus on the mutated region of
the protein and also reduces irrelevant antigenic competition from other
components (unmutated protein or viral vector antigens).
[0562] For example, translating peptide sequencing information into a
therapeutic vaccine can include a combination with a strong vaccine
adjuvant. Effective vaccines can require a strong adjuvant to initiate an
immune response. For example, poly-ICLC, an agonist of TLR3 and the RNA
helicase-domains of MDA5 and RIG3, has shown several desirable properties
for a vaccine adjuvant. These properties include the induction of local
and systemic activation of immune cells in vivo, production of
stimulatory chemokines and cytokines, and stimulation of
antigen-presentation by DCs. Furthermore, poly-ICLC can induce durable
CD4+ and CD8+ responses in humans. Importantly, striking similarities in
the upregulation of transcriptional and signal transduction pathways were
seen in subjects vaccinated with poly-ICLC and in volunteers who had
received the highly effective, replication-competent yellow fever
vaccine. Furthermore, >90% of ovarian carcinoma patients immunized
with poly-ICLC in combination with a NYESO-1 peptide vaccine (in addition
to Montanide) showed induction of CD4+ and CD8+ T cell, as well as
antibody responses to the peptide in a recent phase 1 study. At the same
time, poly-ICLC has been extensively tested in more than 25 clinical
trials to date and exhibited a relatively benign toxicity profile.
[0563] In some embodiments, immunogenic peptides can be identified from
cells from a subject with a disease or condition. In some embodiments,
immunogenic peptides can be specific to a subject with a disease or
condition. In some embodiments, immunogenic peptides can bind to an HLA
that is matched to an HLA haplotype of a subject with a disease or
condition.
[0564] In some embodiments, a library of peptides can be expressed in the
cells. In some embodiments, the cells comprise the peptides to be
identified or characterized. In some embodiments, the peptides to be
identified or characterized are endogenous peptides. In some embodiments,
the peptides are exogenous peptides. For example, the peptides to be
identified or characterized can be expressed from a plurality of
sequences encoding a library of peptides.
[0565] Prior to disclosure of the instant specification, the majority of
LC-MS/MS studies of the HLA peptidome have used cells expressing multiple
HLA peptides, which requires peptides to be assigned to 1 of up to 6 HLA
class I alleles using pre-existing bioinformatic predictors or
"deconvolution" (Bassani-Sternberg and Gfeller, 2016). Thus, peptides
that do not closely match known motifs could not confidently be reported
as binders to a given HLA allele.
[0566] Provided herein are methods of prediction of peptides, such as
mutated peptides, that can bind to HLA peptides of individuals. In some
embodiments, the application provides methods of identifying from a given
set of antigen comprising peptides the most suitable peptides for
preparing an immunogenic composition for a subject, said method
comprising selecting from a given set of peptides the plurality of
peptides capable of binding an HLA protein of the subject, wherein said
ability to bind an HLA protein is determined by analyzing the sequence of
peptides with a machine which has been trained with peptide sequence
databases corresponding to the specific HLA-binding peptides for each of
the HLA-alleles of said subject. Provided herein are methods of
identifying from a given set of antigen comprising peptides the most
suitable peptides for preparing an immunogenic composition for a subject,
said method comprising selecting from a given set of peptides the
plurality of peptides determined as capable of binding an HLA protein of
the subject, ability to bind an HLA protein is determined by analyzing
the sequence of peptides with a machine which has been trained with a
peptide sequence database obtained by carrying out the methods described
herein above. Thus, in some embodiments, the present disclosure provides
methods of identifying a plurality of subject-specific peptides for
preparing a subject-specific immunogenic composition, wherein the subject
has a tumor and the subject-specific peptides are specific to the subject
and the subject's tumor, said method comprising: sequencing a sample of
the subject's tumor and a non-tumor sample of the subject; determining
based on the nucleic acid sequencing: non-silent mutations present in the
genome of cancer cells of the subject but not in normal tissue from the
subject, and the HLA genotype of the subject; and selecting from the
identified non-silent mutations the plurality of subject-specific
peptides, each having a different tumor epitope that is specific to the
tumor of the subject and each being identified as capable of binding an
HLA protein of the subject, as determined by analyzing the sequence of
peptides derived from the non-silent mutations in the methods for
predicting HLA binding described herein.
[0567] In some embodiments, disclosed herein, is a method of
characterizing HLA-peptide complexes specific to an individual.
[0568] In some embodiments, a method of characterizing HLA-peptide
complexes specific to an individual is used to develop an
immunotherapeutic in an individual in need thereof, such as a subject
with a condition or disease.
[0569] Provided herein is a method of providing an anti-tumor immunity in
a mammal comprising administering to the mammal a polynucleic acid
comprising a sequence encoding a peptide identified according to a method
described. Provided herein is a method of providing an anti-tumor
immunity in a mammal comprising administering to the mammal an effective
amount of a peptide with a sequence of a peptide identified according to
a method described herein. Provided herein is a method of providing an
anti-tumor immunity in a mammal comprising administering to the mammal a
cell comprising a peptide comprising the sequence of a peptide identified
according to a method described herein. Provided herein is a method of
providing an anti-tumor immunity in a mammal comprising administering to
the mammal a cell comprising a polynucleic acid comprising a sequence
encoding a peptide comprising the sequence of peptide identified
according to a method described herein. In some embodiments, the cell
presents the peptide as an HLA-peptide complex.
[0570] Provided herein is a method of treating a disease or disorder in a
subject, the method comprising administering to the subject a polynucleic
acid comprising a sequence encoding a peptide identified according to a
method described herein. Provided herein is a method of treating a
disease or disorder in a subject, the method comprising administering to
the subject an effective amount of a peptide comprising the sequence of a
peptide identified according to a method described herein. Provided
herein is a method of treating a disease or disorder in a subject, the
method comprising administering to the subject a cell comprising a
peptide comprising the sequence of a peptide identified according to a
method described herein. Provided herein is a method of treating a
disease or disorder in a subject, the method comprising administering to
the subject a cell comprising a polynucleic acid comprising a sequence
encoding a peptide comprising the sequence of a peptide identified
according to a method described herein. In some embodiments, the disease
or disorder is cancer. In some embodiments, the method further comprises
administering an immune checkpoint inhibitor to the subject.
[0571] Disclosed herein, in some embodiments, are methods of developing an
immunotherapeutic for an individual in need thereof by characterizing
HLA-peptide complexes comprising: a) providing a population of cells
derived from the individual in need thereof wherein one or more cells of
the population of cells comprise a polynucleic acid comprising a sequence
encoding an affinity acceptor tagged HLA class I or HLA class II allele,
wherein the sequence encoding an affinity acceptor tagged HLA comprises:
i) a sequence encoding a recombinant HLA class I or HLA class II allele
operatively linked to ii) a sequence encoding an affinity acceptor
peptide; b) expressing the affinity acceptor tagged HLA in at least one
cell of the one or more cells of the population of cells, thereby forming
affinity acceptor tagged HLA-peptide complexes in the at least one cell;
c) enriching for the affinity acceptor tagged HLA-peptide complexes,
characterizing HLA-peptide complexes specific to the individual in need
thereof; and d) developing the immunotherapeutic based on an HLA-peptide
complex specific to the individual in need thereof; wherein the
individual has a disease or condition.
[0572] In some embodiments, the immunotherapeutic is a nucleic acid or a
peptide therapeutic.
[0573] In some embodiments, the method comprises introducing one or more
peptides to the population of cells. In some embodiments, the method
comprises contacting the population of cells with the one or more
peptides or expressing the one or more peptides in the population of
cells. In some embodiments, the method comprises contacting the
population of cells with one or more nucleic acids encoding the one or
more peptides.
[0574] In some embodiments, the method comprises developing an
immunotherapeutic based on peptides identified in connection with the
patient-specific HLAs. In some embodiments, the population of cells is
derived from the individual in need thereof.
[0575] In some embodiments, the method comprises expressing a library of
peptides in the population of cells. In some embodiments, the method
comprises expressing a library of affinity acceptor tagged HLA-peptide
complexes. In some embodiments, the library comprises a library of
peptides associated with the disease or condition. In some embodiments,
the disease or condition is cancer or an infection with an infectious
agent or an autoimmune disease. In some embodiments, the method comprises
introducing the infectious agent or portions thereof into one or more
cells of the population of cells. In some embodiments, the method
comprises characterizing one or more peptides from the HLA-peptide
complexes specific to the individual in need thereof, optionally wherein
the peptides are from one or more target proteins of the infectious agent
or the autoimmune disease. In some embodiments, the method comprises
characterizing one or more regions of the peptides from the one or more
target proteins of the infectious agent or autoimmune disease. In some
embodiments, the method comprises identifying peptides from the
HLA-peptide complexes derived from an infectious agent or an autoimmune
disease.
[0576] In some embodiments, the infectious agent is a pathogen. In some
embodiments, the pathogen is a virus, bacteria, or a parasite.
[0577] In some embodiments, the virus is selected from the group
consisting of: BK virus (BKV), Dengue viruses (DENV-1, DENV-2, DENV-3,
DENV-4, DENV-5), cytomegalovirus (CMV), Hepatitis B virus (HBV),
Hepatitis C virus (HCV), Epstein-Barr virus (EBV), an adenovirus, human
immunodeficiency virus (HIV), human T cell lymphotrophic virus (HTLV-1),
an influenza virus, RSV, HPV, rabies, mumps rubella virus, poliovirus,
yellow fever, hepatitis A, hepatitis B, Rotavirus, varicella virus, human
papillomavirus (HPV), smallpox, zoster, and combinations thereof.
[0578] In some embodiments, the bacteria is selected from the group
consisting of: Klebsiella spp., Tropheryma whipplei, Mycobacterium
leprae, Mycobacterium lepromatosis, and Mycobacterium tuberculosis. In
some embodiments, the bacteria is selected from the group consisting of:
typhoid, pneumococcal, meningococcal, haemophilus B, anthrax, tetanus
toxoid, meningococcal group B, bcg, cholera, and combinations thereof.
[0579] In some embodiments, the parasite is a helminth or a protozoan. In
some embodiments, the parasite is selected from the group consisting of:
Leishmania spp. (e.g. L. major, L. infantum, L. braziliensis, L.
donovani, L. chagasi, L. mexicana), Plasmodium spp. (e.g. P. falciparum,
P. vivax, P. ovale, P. malariae), Trypanosoma cruzi, Ascaris
lumbricoides, Trichuris trichiura, Necator americanus, and Schistosoma
spp. (S. mansoni, S. haematobium, S. japonicum).
[0580] In some embodiments, the immunotherapeutic is an engineered
receptor. In some embodiments, the engineered receptor is a chimeric
antigen receptor (CAR), a T cell receptor (TCR), or a B cell receptor
(BCR), an adoptive T cell therapy (ACT), or a derivative thereof. In
other aspects, the engineered receptor is a chimeric antigen receptor
(CAR). In some aspects, the CAR is a first generation CAR. In other
aspects, the CAR is a second generation CAR. In still other aspects, the
CAR is a third generation CAR.
[0581] In some aspects, the CAR comprises an extracellular portion, a
transmembrane portion, and an intracellular portion. In some aspects, the
intracellular portion comprises at least one T cell co-stimulatory
domain. In some aspects, the T cell co-stimulatory domain is selected
from the group consisting of CD27, CD28, TNFRS9 (4-1BB), TNFRSF4 (OX40),
TNFRSF8 (CD30), CD40LG (CD40L), ICOS, ITGB2 (LFA-1), CD2, CD7, KLRC2
(NKG2C), TNFRS18 (GITR), TNFRSF14 (HVEM), or any combination thereof.
[0582] In some aspects, the engineered receptor binds a target. In some
aspects, the binding is specific to a peptide identified from the method
of characterizing HLA-peptide complexes specific to an individual
suffering from a disease or condition.
[0583] In some aspects, the immunotherapeutic is a cell as described in
detail herein. In some aspects, the immunotherapeutic is a cell
comprising a receptor that specifically binds a peptide identified from
the method characterizing HLA-peptide complexes specific to an individual
suffering from a disease or condition. In some aspects, the
immunotherapeutic is a cell used in combination with the peptides/nucleic
acids of this invention. In some embodiments, the cell is a patient cell.
In some embodiments, the cell is a T cell. In some embodiments, the cell
is tumor infiltrating lymphocyte.
[0584] In some aspects, a subject with a condition or disease is treated
based on a T cell receptor repertoire of the subject. In some
embodiments, an antigen vaccine is selected based on a T cell receptor
repertoire of the subject. In some embodiments, a subject is treated with
T cells expressing TCRs specific to an antigen or peptide identified
using the methods described herein. In some embodiments, a subject is
treated with an antigen or peptide identified using the methods described
herein specific to TCRs, e.g., subject specific TCRs. In some
embodiments, a subject is treated with an antigen or peptide identified
using the methods described herein specific to T cells expressing TCRs,
e.g., subject specific TCRs. In some embodiments, a subject is treated
with an antigen or peptide identified using the methods described herein
specific to subject specific TCRs.
[0585] In some embodiments, an immunogenic antigen composition or vaccine
is selected based on TCRs identified in a subject. In one embodiment,
identifying a T cell repertoire and testing it in functional assays is
used to determine an immunogenic composition or vaccine to be
administered to a subject with a condition or disease. In some
embodiments, the immunogenic composition is an antigen vaccine. In some
embodiments, the antigen vaccine comprises subject specific antigen
peptides. In some embodiments, antigen peptides to be included in an
antigen vaccine are selected based on a quantification of subject
specific TCRs that bind to the antigens. In some embodiments, antigen
peptides are selected based on a binding affinity of the peptide to a
TCR. In some embodiments, the selecting is based on a combination of both
the quantity and the binding affinity. For example, a TCR that binds
strongly to an antigen in a functional assay but is not highly
represented in a TCR repertoire can be a good candidate for an antigen
vaccine because T cells expressing the TCR would be advantageously
amplified.
[0586] In some embodiments, antigens are selected for administering to a
subject based on binding to TCRs. In some embodiments, T cells, such as T
cells from a subject with a disease or condition, can be expanded.
Expanded T cells that express TCRs specific to an immunogenic antigen
peptide identified using the method described herein can be administered
back to a subject. In some embodiments, suitable cells, e.g., PBMCs, are
transduced or transfected with polynucleotides for expression of TCRs
specific to an immunogenic antigen peptide identified using the method
described herein and administered to a subject. T cells expressing TCRs
specific to an immunogenic antigen peptide identified using the method
described herein can be expanded and administered back to a subject. In
some embodiments, T cells that express TCRs specific to an immunogenic
antigen peptide identified using the method described herein that result
in cytolytic activity when incubated with autologous diseased tissue can
be expanded and administered to a subject. In some embodiments, T cells
used in functional assays result in binding to an immunogenic antigen
peptide identified using the method described herein can be expanded and
administered to a subject. In some embodiments, TCRs that have been
determined to bind to subject specific immunogenic antigen peptides
identified using the method described herein can be expressed in T cells
and administered to a subject.
[0587] The methods described herein can involve adoptive transfer of
immune system cells, such as T cells, specific for selected antigens,
such as tumor or pathogen associated antigens. Various strategies can be
employed to genetically modify T cells by altering the specificity of the
T cell receptor (TCR), for example by introducing new TCR .alpha.- and
.beta.-chains with specificity to an immunogenic antigen peptide
identified using the method described herein (see, e.g., U.S. Pat. No.
8,697,854; PCT Patent Publications: WO2003020763, WO2004033685,
WO2004044004, WO2005114215, WO2006000830, WO2008038002, WO2008039818,
WO2004074322, WO2005113595, WO2006125962, WO2013166321, WO2013039889,
WO2014018863, WO2014083173; U.S. Pat. No. 8,088,379).
[0588] Chimeric antigen receptors (CARs) can be used to generate
immunoresponsive cells, such as T cells, specific for selected targets,
such a immunogenic antigen peptides identified using the method described
herein, with a wide variety of receptor chimera constructs (see, e.g.,
U.S. Pat. Nos. 5,843,728; 5,851,828; 5,912, 170; 6,004,811; 6,284,240;
6,392,013; 6,410,014; 6,753,162; 8,211,422; and, PCT Publication
WO9215322). Alternative CAR constructs can be characterized as belonging
to successive generations. First-generation CARs typically consist of a
single-chain variable fragment of an antibody specific for an antigen,
for example comprising a VL linked to a VH of a specific antibody, linked
by a flexible linker, for example by a CD8a hinge domain and a CD8a
transmembrane domain, to the transmembrane and intracellular signaling
domains of either CD3.zeta. or FcRy or scFv-FcRy (see, e.g., U.S. Pat.
Nos. 7,741,465; 5,912,172; 5,906,936). Second-generation CARs incorporate
the intracellular domains of one or more costimulatory molecules, such as
CD28, OX40 (CD134), or 4-1BB (CD137) within the endodomain, e.g.,
scFv-CD28/OX40/4-1BB-CD3 (see, e.g., U.S. Pat. Nos. 8,911,993; 8,916,381;
8,975,071; 9,101,584; 9,102,760; 9,102,761). Third-generation CARs
include a combination of costimulatory endodomains, such a CD3C-chain,
CD97, GDI 1a-CD18, CD2, ICOS, CD27, CD154, CDS, OX40, 4-1BB, or CD28
signaling domains, e.g., scFv-CD28-4-1BB-CD3C or scFv-CD28-OX40-CD3Q
(see, e.g., U.S. Pat. Nos. 8,906,682; 8,399,645; 5,686,281; PCT
Publication No. WO2014134165; PCT Publication No. WO2012079000). In some
embodiments, costimulation can be coordinated by expressing CARs in
antigen-specific T cells, chosen so as to be activated and expanded
following, for example, interaction with antigen on professional
antigen-presenting cells, with costimulation. Additional engineered
receptors can be provided on the immunoresponsive cells, e.g., to improve
targeting of a T cell attack and/or minimize side effects.
[0589] Alternative techniques can be used to transform target
immunoresponsive cells, such as protoplast fusion, lipofection,
transfection or electroporation. A wide variety of vectors can be used,
such as retroviral vectors, lentiviral vectors, adenoviral vectors,
adeno-associated viral vectors, plasmids or transposons, such as a
Sleeping Beauty transposon (see U.S. Pat. Nos. 6,489,458; 7,148,203;
7,160,682; 7,985,739; 8,227,432), can be used to introduce CARs, for
example using 2nd generation antigen-specific CARs signaling through CD3
and either CD28 or CD137. Viral vectors can, for example, include vectors
based on HIV, SV40, EBV, HSV or BPV.
[0590] Cells that are targeted for transformation can, for example,
include T cells, Natural Killer (NK) cells, cytotoxic T lymphocytes
(CTL), regulatory T cells, human embryonic stem cells, tumor-infiltrating
lymphocytes (TIL) or a pluripotent stem cell from which lymphoid cells
can be differentiated. T cells expressing a desired CAR can, for example,
be selected through co-culture with .gamma.-irradiated activating and
propagating cells (APC), which co-express the cancer antigen and
co-stimulatory molecules. The engineered CAR T cells can be expanded, for
example, by co-culture on APC in presence of soluble factors, such as
IL-2 and IL-21. This expansion can, for example, be carried out so as to
provide memory CAR T cells (which, for example, can be assayed by
non-enzymatic digital array and/or multi-panel flow cytometry). In this
way, CAR T cells that have specific cytotoxic activity against
antigen-bearing tumors can be provided (optionally in conjunction with
production of desired chemokines such as interferon-.gamma.). CAR T cells
of this kind can, for example, be used in animal models, for example to
threaten tumor xenografts.
[0591] Approaches such as the foregoing can be adapted to provide methods
of treating and/or increasing survival of a subject having a disease,
such as a neoplasia or pathogenic infection, for example by administering
an effective amount of an immunoresponsive cell comprising an antigen
recognizing receptor that binds a selected antigen, wherein the binding
activates the immunoresponsive cell, thereby treating or preventing the
disease (such as a neoplasia, a pathogen infection, an autoimmune
disorder, or an allogeneic transplant reaction). Dosing in CAR T cell
therapies can, for example, involve administration of from 106 to 109
cells/kg, with or without a course of lymphodepletion, for example with
cyclophosphamide.
[0592] To guard against possible adverse reactions, engineered
immunoresponsive cells can be equipped with a transgenic safety switch in
the form of a transgene that renders the cells vulnerable to exposure to
a specific signal. For example, the herpes simplex viral thymidine kinase
(TK) gene can be used in this way, for example by introduction into
allogeneic T lymphocytes used as donor lymphocyte infusions following
stem cell transplantation. In such cells, administration of a nucleoside
prodrug such as ganciclovir or acyclovir causes cell death. Alternative
safety switch constructs include inducible caspase 9, for example
triggered by administration of a small-molecule dimerizer that brings
together two nonfunctional icasp9 molecules to form the active enzyme. A
wide variety of alternative approaches to implementing cellular
proliferation controls have been described (see, e.g., U.S. Patent
Publication No. 20130071414; PCT Patent Publication WO2011146862; PCT
Patent Publication WO201401 1987; PCT Patent Publication WO2013040371).
In a further refinement of adoptive therapies, genome editing can be used
to tailor immunoresponsive cells to alternative implementations, for
example providing edited CAR T cells.
[0593] Cell therapy methods can also involve the ex vivo activation and
expansion of T cells. In some embodiments, T cells can be activated
before administering them to a subject in need thereof. Examples of these
type of treatments include the use tumor infiltrating lymphocyte (TIL)
cells (see U.S. Pat. No. 5,126,132), cytotoxic T cells (see U.S. Pat.
Nos. 6,255,073; and 5,846,827), expanded tumor draining lymph node cells
(see U.S. Pat. No. 6,251,385), and various other lymphocyte preparations
(see U.S. Pat. Nos. 6,194,207; 5,443,983; 6,040,177; and 5,766,920).
[0594] An ex vivo activated T cell population can be in a state that
maximally orchestrates an immune response to cancer, infectious diseases,
or other disease states, e.g., an autoimmune disease state. For
activation, at least two signals can be delivered to the T cells. The
first signal is normally delivered through the T cell receptor (TCR) on
the T cell surface. The TCR first signal is normally triggered upon
interaction of the TCR with peptide antigens expressed in conjunction
with an MHC complex on the surface of an antigen-presenting cell (APC).
The second signal is normally delivered through co-stimulatory receptors
on the surface of T cells. Co-stimulatory receptors are generally
triggered by corresponding ligands or cytokines expressed on the surface
of APCs.
[0595] It is contemplated that the T cells specific to immunogenic antigen
peptides identified using the method described herein can be obtained and
used in methods of treating or preventing disease. In this regard, the
disclosure provides a method of treating or preventing a disease or
condition in a subject, comprising administering to the subject a cell
population comprising cells specific to immunogenic antigen peptides
identified using the method described herein in an amount effective to
treat or prevent the disease in the subject. In some embodiments, a
method of treating or preventing a disease in a subject, comprises
administering a cell population enriched for disease-reactive T cells to
a subject in an amount effective to treat or prevent cancer in the
mammal. The cells can be cells that are allogeneic or autologous to the
subject.
[0596] The disclosure further provides a method of inducing a disease
specific immune response in a subject, vaccinating against a disease,
treating and/or alleviating a symptom of a disease in a subject by
administering the subject an antigenic peptide or vaccine.
[0597] The peptide or composition of the disclosure can be administered in
an amount sufficient to induce a CTL response. An antigenic peptide or
vaccine composition can be administered alone or in combination with
other therapeutic agents. Exemplary therapeutic agents include, but are
not limited to, a chemotherapeutic or biotherapeutic agent, radiation, or
immunotherapy. Any suitable therapeutic treatment for a particular
disease can be administered. Examples of chemotherapeutic and
biotherapeutic agents include, but are not limited to, aldesleukin,
altretamine, amifostine, asparaginase, bleomycin, capecitabine,
carboplatin, carmustine, cladribine, cisapride, cisplatin,
cyclophosphamide, cytarabine, dacarbazine (DTIC), dactinomycin,
docetaxel, doxorubicin, dronabinol, epoetin alpha, etoposide, filgrastim,
fludarabine, fluorouracil, gemcitabine, granisetron, hydroxyurea,
idarubicin, ifosfamide, interferon alpha, irinotecan, lansoprazole,
levamisole, leucovorin, megestrol, mesna, methotrexate, metoclopramide,
mitomycin, mitotane, mitoxantrone, omeprazole, ondansetron, paclitaxel
(Taxol.RTM.), pilocarpine, prochloroperazine, rituximab, tamoxifen,
taxol, topotecan hydrochloride, trastuzumab, vinblastine, vincristine and
vinorelbine tartrate. In addition, the subject can be further
administered an anti-immunosuppressive or immunostimulatory agent. For
example, the subject can be further administered an anti-CTLA antibody or
anti-PD-1 or anti-PD-L1.
[0598] The amount of each peptide to be included in a vaccine composition
and the dosing regimen can be determined by one skilled in the art. For
example, a peptide or its variant can be prepared for intravenous (i.v.)
injection, sub-cutaneous (s.c.) injection, intradermal (i.d.) injection,
intraperitoneal (i.p.) injection, intramuscular (i.m.) injection.
Exemplary methods of peptide injection include s.c, i.d., i.p., i.m., and
i.v. Exemplary methods of DNA injection include i.d., i.m., s.c, i.p. and
i.v. Other methods of administration of the vaccine composition are known
to those skilled in the art.
[0599] A pharmaceutical composition can be compiled such that the
selection, number and/or amount of peptides present in the composition
is/are disease and/or patient-specific. For example, the exact selection
of peptides can be guided by expression patterns of the parent proteins
in a given tissue to avoid side effects. The selection can be dependent
on the specific type of disease, the status of the disease, earlier
treatment regimens, the immune status of the patient, and the
HLA-haplotype of the patient. Furthermore, the vaccine according to the
present disclosure can contain individualized components, according to
personal needs of the particular patient. Examples include varying the
amounts of peptides according to the expression of the related antigen in
the particular patient, unwanted side-effects due to personal allergies
or other treatments, and adjustments for secondary treatments following a
first round or scheme of treatment.
Computer Control Systems
[0600] The present disclosure provides computer control systems that are
programmed to implement methods of the disclosure. FIG. 10 shows a
computer system (1001) that is programmed or otherwise configured to
train a machine-learning HLA-peptide presentation prediction model. The
computer system (1001) can regulate various aspects of the present
disclosure, such as, for example, inputting amino acid position
information, transferring imputed information into datasets, and
generating a trained algorithm with the datasets. The computer system
(1001) can be an user electronic device or a remote computer system. The
electronic device can be a mobile electronic device.
[0601] The computer system (1001) includes a central processing unit (CPU,
also "processor" and "computer processor" herein) (1005), which can be a
single core or multi core processor, either through sequential processing
or parallel processing. The computer system (1001) also includes a memory
unit or device (1010) (e.g., random-access memory, read-only memory,
flash memory), a storage unit (1015) (e.g., hard disk), a communication
interface (1020) (e.g., network adapter) for communicating with one or
more other systems, and peripheral devices (1025), either external or
internal or both, such as a printer, monitor, USB drive and/or CD-ROM
drive. The memory (1010), storage unit (1015), interface (1020) and
peripheral devices (1025) are in communication with the CPU (1005)
through a communication bus (solid lines), such as a motherboard. The
storage unit (1015) can be a data storage unit (or data repository) for
storing data. The computer system (1001) can be operatively coupled to a
computer network ("network") (1030) with the aid of the communication
interface (1020). The network (1030) can be the Internet, an internet
and/or extranet, or an intranet and/or extranet that is in communication
with the Internet. The network (1030) in some cases is a
telecommunication and/or data network. The network (1030) can include one
or more computer servers, which can enable a peer-to-peer network that
supports distributed computing. The network (1030), in some cases with
the aid of the computer system (1001), can implement a client-server
structure, which may enable devices coupled to the computer system (1001)
to behave as a client or a server.
[0602] The CPU (1005) can execute a sequence of machine-readable
instructions, which can be embodied in a program or software. The
instructions may be stored in memory (1010). The instructions can be
directed to the CPU (1005), which can subsequently program or otherwise
configure the CPU (1005) to implement methods of the present disclosure.
Examples of operations performed by the CPU (1005) can include fetch,
decode, execute, and writeback.
[0603] The CPU (1005) can be part of a circuit, such as an integrated
circuit. One or more other components of the system (1001) can be
included in the circuit. In some cases, the circuit is an application
specific integrated circuit (ASIC).
[0604] The storage unit (1015) can store files, such as drivers, libraries
and saved programs. The storage unit (1015) can store user data, e.g.,
user preferences and user programs. The computer system (1001) in some
cases can include one or more additional data storage units that are
external to the computer system (1001), such as located on a remote
server that is in communication with the computer system (1001) through
an intranet or the Internet.
[0605] The computer system (1001) can communicate with one or more remote
computer systems through the network (1030). For instance, the computer
system (1001) can communicate with a remote computer system or user.
Examples of remote computer systems include personal computers (e.g.,
portable PC), slate or tablet PC's (e.g., Apple.RTM. iPad, Samsung.RTM.
Galaxy Tab), telephones, Smart phones (e.g., Apple.RTM. iPhone,
Android-enabled device, Blackberry.RTM.), or personal digital assistants.
The user can access the computer system (1001) via the network (1030).
[0606] Methods as described herein can be implemented by way of machine
(e.g., computer processor) executable code stored on an electronic
storage location of the computer system (1001), such as, for example, in
memory (1010) or a data storage unit (1015). The machine executable or
machine readable code can be provided in the form of software. During
use, the code can be executed by the processor (1005). In some cases, the
code can be retrieved from the storage unit (1015) and stored in memory
(1010) for ready access by the processor (1005). In some situations, the
storage unit (1015) can be precluded, and machine-executable instructions
are stored in memory (1010).
[0607] The code can be pre-compiled and configured for use with a machine
having a processor adapted to execute the code, or it can be compiled
during runtime. The code can be supplied in a programming language that
can be selected to enable the code to execute in a pre-compiled or
as-compiled fashion.
[0608] Aspects of the systems and methods provided herein, such as the
computer system (1001), can be embodied in programming Various aspects of
the technology may be thought of as "products" or "articles of
manufacture" typically in the form of machine (or processor) executable
code and/or associated data that is carried on or embodied in a type of
machine readable medium. Machine-executable code can be stored on a
storage unit, such as a hard disk, or in memory (e.g., read-only memory,
random-access memory, flash memory). "Storage" type media can include any
or all of the tangible memory of the computers, processors or the like,
or associated modules thereof, such as various semiconductor memories,
tape drives, disk drives and the like, which may provide non-transitory
storage at any time for the software programming. All or portions of the
software may at times be communicated through the Internet or various
other telecommunication networks. Such communications, for example, may
enable loading of the software from one computer or processor into
another, for example, from a management server or host computer into the
computer platform of an application server. Thus, another type of media
that may bear the software elements includes optical, electrical and
electromagnetic waves, such as used across physical interfaces between
local devices, through wired and optical landline networks and over
various air-links. The physical elements that carry such waves, such as
wired or wireless links, optical links or the like, also may be
considered as media bearing the software. As used herein, unless
restricted to non-transitory, tangible "storage" media, terms such as
computer or machine "readable medium" refer to any medium that
participates in providing instructions to a processor for execution.
[0609] Hence, a machine readable medium, such as computer-executable code,
may take many forms, including but not limited to, a tangible storage
medium, a carrier wave medium or physical transmission medium.
Non-volatile storage media include, for example, optical or magnetic
disks, such as any of the storage devices in any computer(s) or the like,
such as may be used to implement the databases, etc. shown in the
drawings. Volatile storage media include dynamic memory, such as main
memory of such a computer platform. Tangible transmission media include
coaxial cables; copper wire and fiber optics, including the wires that
comprise a bus within a computer system. Carrier-wave transmission media
may take the form of electric or electromagnetic signals, or acoustic or
light waves such as those generated during radio frequency (RF) and
infrared (IR) data communications. Common forms of computer-readable
media therefore include for example: a floppy disk, a flexible disk, hard
disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM,
any other optical medium, punch cards paper tape, any other physical
storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a
FLASH-EPROM, any other memory chip or cartridge, a carrier wave
transporting data or instructions, cables or links transporting such a
carrier wave, or any other medium from which a computer may read
programming code and/or data. Many of these forms of computer readable
media may be involved in carrying one or more sequences of one or more
instructions to a processor for execution.
[0610] The computer system (1001) can include or be in communication with
an electronic display (1035) that comprises a user interface (UI) (1040)
for providing, for example, probability that one or more proteins encoded
by a class II MHC allele of a cancer cell of the subject will present a
given sequence of a peptide sequence identified. Examples of UI's
include, without limitation, a graphical user interface (GUI) and
web-based user interface.
[0611] Methods and systems of the present disclosure can be implemented by
way of one or more algorithms. An algorithm can be implemented by way of
software upon execution by the central processing unit (1005). The
algorithm can, for example, input amino acid position information,
transfer imputed information into datasets, and generate a trained
algorithm with the datasets.
EXAMPLES
[0612] The examples provided below are for illustrative purposes only and
do not limit the scope of the claims provided herein.
Example 1. HLA Class II Binding Predictor Performance
[0613] In this example, a validation dataset comprising observed mass spec
peptides and decoy peptides which are generated at a ratio of 1:19
(hits:decoys) by randomly shuffling the hit peptides were used to analyze
the performance of the binding predictor neonmhc2 (NEON) and NetMHCIIpan
(FIG. 4). For the NEON binding predictor, a separate model was built for
each MHC II allele shown. The height of the bars showed the positive
predictive value (PPV). The alleles are sorted by the model's performance
when predicting for that allele. The NEON binding predictor showed higher
PPV across all the alleles when compared with NetMHCIIpan.
[0614] In this example, the effect of SPI thresholds on binding predictor
validation was also tested (FIG. 5). The performance of the HLA class II
binding predictor was shown when trained/validated on sets of peptides
with different scored peak intensity (SPI) cutoffs. The different SPI
cutoffs conditions were used: trained and evaluated on datasets using
observed MS hit peptides of larger than or equal to 70 SPI, trained on
peptides with larger than or equal to 50 SPI and validated on peptides
with larger than or equal to 70 SPI, and trained and validated on
peptides with larger than or equal to 50 SPI.
[0615] In this example, data for 35 HLA-DR alleles, which had >95%
population coverage for HLA-DR (USA allele frequencies), were collected
to show the number of observed peptides by allele profiling by LC-MS/MS
with larger than or equal to 70 scored peak intensity (SPI) cutoffs (FIG.
6).
[0616] In one exemplary set up, a model PPV analysis was applied to test
partition data for each class II allele that were generated thus far for
Neonmhc2 program. The test partition data was composed of positive
example (e.g. a hit sample peptide) that are MS-observed class II binders
and negative examples (e.g. a decoy sample peptide) that are scrambled
versions of the positive examples. The hit:decoy ratio was kept 1:19, for
example, for each positive sample, 19 negative samples were included
(i.e., 5% positive sample) and test partition was performed for
validation. PPV scores were generated by selecting the best-scoring 5% of
the peptides, in the test partition and interrogating what fraction of
those are positive. Results are indicated in FIG. 7A.
[0617] It was observed that for the HLA-DR alleles collected, when the
training set size increased, the value of PPV increased (FIG. 7B).
[0618] In this example, the processing-related variables improved
prediction further (FIG. 8). On the training data partition, a logistic
regression was fit to predict HLA class II presentation using binding
strength (NetMHCIIpan or Neon's predictor) and processing features
(RNA-Seq expression and a derived gene-level bias term). On a separate
evaluation partition, exonic positions overlapping MS-observed MHC II
peptides ("hits") was scored alongside random exonic positions not
observed in MS (1:499 ratio). In general, Neon with processing-related
variables showed higher PPV than NetMHCIIpan, Neon's predictor, and
NetMHCIIpan with processing-related variables.
Example 2. A Neural Network Architecture
[0619] In this example, a neural network was used to obtain the training
algorithm (FIG. 9). Input peptides were represented as 20mers, with
shorter peptides being filled in with "missing" characters. Each peptide
had a 31-dimensional embedding, so the input into the neural network was
a 20.times.31 matrix. Before being processed by the neural network,
feature normalization on the 20.times.31 matrix was performed based on
feature value means and standard deviations in the training set. The
first convolutional layer had a kernel of 9 amino acids and 50 filters
(also called channels) with a ReLU activation function. This was followed
by batch normalization and then spatial dropout with a dropout rate of
20%. This was followed by another convolutional layer with a kernel of 3
and with 20 filters and a ReLU activation function and then again
followed by batch normalization and spatial dropout with a dropout rate
of 20%. Global max pooling was then applied, taking the
maximally-activated neuron in each of the 20 filters and then these 20
values were passed into a fully connected (dense) layer with a single
neuron using a Sigmoid activation function. This output was treated as
the binding/non-binding prediction. L2 regularization was applied to the
weights of the first convolutional layer, second convolutional layer, and
dense layer with weights of 0.05, 0.1, and 0.01, respectively.
Example 3. A Scalable Protocol for Mono-Allelic MHC Class II Ligand
Profiling
[0620] Currently knowledge of MHC Class II binding motifs can be based on
two in vitro binding assays, one that calculates an EC50 using cellular
MHC and another that calculates an IC50 using purified MHC. The leading
HLA class II prediction algorithm NetMHCIIpan is trained exclusively on
these data.
[0621] Limited number of human HLA class II alleles are currently
supported by more than 200 examples of confirmed binding peptides
(affinity <100 nM) (FIG. 12E), which are nearly all 15mers. These
experiments cover only the most common Caucasian HLA-DR alleles with
limited coverage of alleles specific to non-Caucasian populations (e.g.,
HLA-DRB1*15:02) and almost no coverage for common HLA-DP and HLA-DQ
alleles. Current HLA class II prediction performance, even on the common
Caucasian alleles, significantly lags the accuracy of MHC class I; ROC
curves are only modestly better than random.
[0622] With these limitations in mind, a novel biotechnology was developed
herein that was termed Mono-Allelic Capture by Tagged Allele capture
(MAPTAC.TM.) that enables efficient isolation of HLA class II binding
peptides binding an MHC protein encoded by a single allele for MS-based
identification (FIGS. 11A and 11B); this approach works for HLA class I
as well. As applied to HLA class II, the alpha and beta chains of a
chosen allele are encoded on a genetic construct, with a biotin-acceptor
peptide (BAP) sequence placed at the C-terminus of the beta chain. These
cells are then lysed and incubated with BirA enzyme to biotinylate the
C-terminus of the beta chain of the capture allele. NeutrAvidin pulldown
purifies a population of MHC-bound peptides, which are further isolated
by size exclusion and sequenced with best-in-class LC-MS/MS protocols.
[0623] In some embodiments, the LC-MS/MS analysis is evaluated using high
field asymmetric waveform ion mobility spectrometry (FAIMS). In some
embodiments, peptides are subjected to both acidic reverse-phase (aRP)
and basic reverse-phase (bRP) offline fractionation prior to analysis by
nLC-MS/MS.
[0624] A two-day transfection was sufficient to achieve robust expression
of the construct (FIG. 12B) with appropriate cell surface localization in
three distinct cell lines (expi293, A375, and B721) for four different
alleles (FIG. 12C).
[0625] Because HLA-DRA is functionally invariant, this approach achieves
single-allele resolution even if the capture beta chain pairs with
endogenous alpha chain. This means that the approach can be used to
profile HLA-DR alleles regardless of pre-existing HLA genotype and
expression level in the given cell line.
[0626] For HLA-DP and HLA-DQ, the alpha and beta chains are both variable
and both contribute to peptide binding, so single-allele resolution is
expected only if the native alpha chain is not expressed or if the native
allele is homozygous and matches the capture allele. Alternatively, one
can use a beta chain-only capture to establish a background of peptides
corresponding to the native alpha chain.
[0627] Profiled alleles included five HLA-DR alleles (DRB1*03:01,
DRB1*09:01, DRB1*11:01, DRB3*01:01, and DRB3*02:02) as well as one HLA-DP
allele (DPB1*01:01/DPA1*01:03), one HLA-DQ allele
(DQB1*06:02/DQA1*01:02), and two Class I alleles (Table 1). In all cases,
2-3 replicates were sufficient to observe at least 1500 unique peptides
(FIG. 11B). Among the alleles profiled, only a small percentage of hits
corresponded to known contaminants or perfect tryptics; on the other
hand, mock transfections returned relatively few peptides, which were
mostly identifiable as known contaminants or perfect tryptics (FIG. 11B).
[0628] Table 1 shows a summary of the samples used in the exemplary
experiments.
TABLE-US-00001
TABLE 1
Donor DR1 DR3/4/5
D001000763 DRB1*03:01, DRB1*11:01 DRB3*01:01, DRB3*02:02
HD84 DRB1*01:01 Not present
RG1248 DRB1*03:01 DRB3*01:01
RG1095 DRB1*03:01, DRB1*11:01 DRB3*01:01, DRB3*02:02
RG1104 DRB1*01:01, DRB1*11:01 DRE3*02:02
2010113472 DRB1*01:01, DRB1*11:01 DRE3*02:02
2010113438 DRB1*03:01, DRB1*11:01 DRB3*01:01, DRB3*02:02
[0629] Since the ends of MHC II binding peptides do not need to fit within
the MHC binding groove, multiple distinct peptide species can bind
equally well if they share the same core binding sequence. When the
peptides were pooled with overlapping sequence into "nested sets",
500-700 unique nested sets per HLA class II allele were observed; these
were typically derived from 500-600 unique genes. Length distributions
for HLA class I and HLA class II binding peptides match those observed in
previous MS studies that used antibody-based pulldowns (FIG. 11C).
[0630] Among the putative MHC-binding peptides, most amino acids were
represented at levels consistent with their source proteome frequencies.
Exceptions included cysteine, methionine, and tryptophan, which were
depleted, consistent with previous MS-based studies of MHC II peptides.
Depletions of cysteine, methionine, and tryptophan were not observed in
allele-matched high-affinity peptides (<50 nM) from IEDB; however, the
IEDB peptides did show enrichments in leucine and methionine and
depletions of proline, aspartic acid, and glutamic acid with respect to
the proteome.
Example 4. MAPTAC.TM. Protocol Uncovers Known and Novel MHC II Binding
Motifs
[0631] Since the MHC-binding subsequence of Class II peptides are not at a
fixed position with respect to the N- or C-terminus, accurate Class II
motif discovery must dynamically consider different binding register
possibilities for each binder peptide. The Gibb's Cluster tool addresses
this challenge through an expectation maximization (EM) algorithm. The
use of a novel motif discovery approach using convolutional neural
networks (CNNs) was explored. CNNs have been successful in the field of
computer vision, which similarly seeks to achieve translationally
invariant pattern recognition. CNNs were trained to distinguish MHC
binding peptides from scrambled versions of themselves and then aligned
the positive examples according to the subsequences that had achieved
maximum node activation in the penultimate network layer. As applied to
the mono-allelic MS data, this approach yielded motifs consistent with
Gibbs clustering and showed anchors at relative positions 1, 4, 6, and 9
(FIG. 13). These motifs were highly consistent with CNN-derived motifs
observed for high affinity binders from IEDB (affinity <50 nM; FIG.
13). For DRB1*11:01, was further validated that the motif was stable
across cell lines and consistent with a DRB1*11:01 homozygous cell line
previously profiled with a pan-DR antibody. Similarly, motifs derived for
MHC Class I alleles were consistent with those from affinity-based
methods and previous MS-based studies (FIG. 14A).
[0632] Although all the MHC class II alleles showed discernable motifs,
the entropy at anchor positions was notably higher than that observed for
MHC class I alleles. Accordingly, preferred amino acids at each anchor
position for each MHC class II allele were defined and it was observed
that only 10-20% of peptides exhibit ideal residues in all four anchor
positions and as many as 60% exhibit two or fewer expected anchors (FIG.
14B and FIG. 30C). 13-17mers were scored for binding potential using
NetMHCIIpan, and while the MS-observed peptides were enriched for
predicted binding potential in all cases, there was significant overlap
with scores of length-matched random peptides (FIG. 14C, and FIG. 36A).
Example 5. Algorithms Trained on Mono-Allelic MHC II MS Data Predict
Immunogenicity
[0633] Next, whether data from the mono-allelic MS platform could generate
improved MHC class II binding predictors were considered. Building on the
CNN approach, a multi-layer network with filter sizes, skip connections,
and a total receptive field were created (FIG. 31A). To train and assess
this deep learning model, termed neonmhc2, the proteome were partitioned
into three partitions representing 75%, 12.5%, and 12.5% of genes. The
first partition was used to train CNNs via stochastic gradient descent,
and the second was used for architecture and hyper-parameter
optimization. The third partition was used to evaluate performance only
once at the end of analysis. To ensure the integrity of the evaluation,
care was taken to place all genes in paralogous gene groups in the same
partition.
[0634] Since MS exhibits some degree of residue bias, particularly against
cysteine (FIG. 12D), this problem was mitigated by using negative
training examples (termed decoys) generated by randomly permuting the
sequences of positive examples. Since this approach carries the risk of
learning sequence properties of natural proteins, which could
artificially inflate prediction performance, model evaluation employed a
distinct decoy generation strategy wherein decoys were sampled randomly
from non-observed subsequences of peptide source genes. Calculating
positive predictive value (PPV) at a 1:19 hit-to-decoy ratio showed that
neonmhc2 has improved PPV relative to NetMHCIIpan in predicting MS
peptides in the evaluation partition (FIG. 4 and FIG. 31B). Experiments
artificially down-sampling the size of neonmhc2's training dataset
suggest that its performance is data-limited and would improve with
deeper coverage data (FIG. 16).
[0635] The ability of neonmhc2 was explored to predict binding affinity,
the data type on which NetMHCIIpan is trained. To deprive NetMHCpan the
benefit of training and evaluating on the same peptide measurements, the
evaluation was run using a slightly older version of NetMHCIIpan scoring
peptides deposited to IEDB. Using a Kendall Tau statistic to assess
prediction accuracy, NetMHCIIpan score similarly or slightly better than
the MS-based predictor in all cases (FIG. 15B). Interestingly,
performance depended on the type of affinity assay performed. While the
neonmhc2 modestly lagged NetMHCIIpan when predicting affinity
measurements from Sette and colleagues, it more substantially lagged
NetMHCIIpan when predicting measurements from Buus and colleagues.
Considering these results collectively, there appeared to be intrinsic
differences between these platforms, but it was not immediately clear
which approach was more correct.
[0636] To achieve improved clarity, the ability to predict natural CD4 T
cell responses was assessed. Data from IEDB was generally unsuitable for
this purpose since the allele restriction of responses is almost always
either undefined or imputed. Therefore, a large dataset of
tetramer-guided epitope mapping (TGEM) data was assembled. These studies
all used comprehensive overlapping peptide screening rather than
prediction prioritization, removing observation bias in favor of
NetMHCIIpan. Meanwhile, the allele restriction is unambiguous. For all
alleles for which there was sufficient data for assessment, the neonmhc2
substantially out-performed NetMHCpan, which performed only slightly
better than random. Thus, MAPTAC.TM. platform may be the best-in-class
for training models that identify immunogenic MHC class II epitopes.
Example 6. Algorithms Trained on Multi-Allelic MS Data are Inferior
[0637] Given that there are numerous multiallelic class II databases in
the public domain based on standard pan-DR and pan-II antibody
purification, whether a suitable predictor could have been trained using
multi-allelic data only was tested. Several groups have shown success in
deconvolving MHC class I allele motifs from multi-allelic Class I data,
though these efforts have not yet translated into a publicly available
predictor. Deconvolution of Class II motifs is additionally complicated
by the need to simultaneously resolve both the binding register and
cluster membership of each peptide. While the Gibbs Cluster tool has been
used to explore the possibility of Class II deconvolution, the fidelity
of this approach has not been extensively validated.
[0638] To assess the accuracy of Class II deconvolution, publicly
available pan-DR datasets with known genotype were selected. For each
dataset, twenty peptides of our mono-allelic data were spiked in for each
allele in the donor's genotype (1-2 DR1 alleles plus 0-2 DR3/4/5 alleles,
depending on haplotype and zygosity). Gibbs Clustering tool was run on
each dataset and whether the spike-in peptides were appropriately
co-clustered were observed according to their known allele of origin. In
early versions of this analysis, either the cluster number to the allele
number was fixed or the Gibbs cluster was allowed to automatically
determine the most optimal number of clusters; however, neither approach
appeared to deconvolve the peptides accurately). To give the algorithm an
assist, the most optimal cluster count was selected by calculating the
adjusted mutual information between the true source alleles of the
spike-in peptides and their assigned clusters. Nonetheless, in all but
several cases, peptides were distributed across diverse clusters without
respect to their source allele (FIG. 17A). These results suggest that
current deconvolution protocols may not be reliably accurate for MHC
Class II.
[0639] One caveat to this analysis is that some peptides may be capable of
binding more than one allele. In line with that, the next question is
whether binding motifs derived from multi-allelic data may nonetheless
reasonably match those observed from mono-allelic data. To assess this,
clusters with the best correspondence to the capture peptides of each
single allele were selected and motifs based on these populations were
built. (see, for example, FIG. 17B). While many motifs clearly
demonstrated some of the known anchors, other positions were discordant
with the mono-allelic motif or discordant between source data sets.
Additionally, there were clear cases in which spurious anchors had
emerged. Finally, we assessed whether the deconvolved data could be used
to train CNNs that could predict peptides in the evaluation partition of
our mono-allelic dataset. Models trained on deconvoluted multi-allelic
data fell short of MAPTAC.TM.-trained models in all cases (FIG. 17C).
Example 7. Source Protein Features Influence Presentation Likelihood
[0640] For MHC Class I, the proteasome plays an important role in
determining the repertoire of presented epitopes; therefore, how
protein-to-peptide processing shapes the Class II repertoire that was
characterized.
[0641] First, the exact positions of the N- and C-termini of MHC Class II
peptides observed in several tissue-based peptide profiling data sets
were focused on. Comparing position-based amino acid frequencies with
respect to decoy peptides, significant enrichments and depletions was
observed. This pattern is consistent with recent observations.
Interestingly, the overall pattern does not match the known cleavage
preference of Cathepsin S ([RPI][FMLW][KQTR][ALS]), the best
characterized Class II processing enzyme.
[0642] To determine the predictive potential of this motif, NN-based
predictors for the N- and C-termini were built and a logistic regression
that used the two cleavage variables along with predicted binding
potential (per MS-trained CNN) was fit to distinguish true MS peptides
from length-matched decoy peptides sampled from the same source genes.
[0643] This predictor provided a modest improvement in peptide prediction
over a model that considered binding potential alone; however, since the
immunogenicity of MHC class II binding epitopes (interchangeably termed,
Class II epitopes) may not depend on the exact position of peptide
cleavage, the question is whether the model would still add value if the
exact site of cleavage was unknown. Therefore, the prediction scheme was
run a second time, withholding the exact cleavage positions of hits and
decoys, instead scoring composite cleavability scores across protein
positions in the vicinity (+/-15 AA) of the imputed binding core.
Interestingly, there was no improvement in performance over the
binding-only predictor. These results are consistent with previous work,
which showed that the addition of Class II cleavage prediction could
improve prediction of MS-observed ligands, but not T cell recognition,
which is presumably agnostic to the exact peptide termini.
[0644] A model was suggested in which a significant fraction of MHC II
peptides are "chewed back" from their N- and C-termini after MHC binding.
Under this model, the penultimate proline signature arises because
proline blocks the procession of exopeptidases. In this scenario, the
motif derived from direct analysis MHC ligand termini is potentially
misleading because it reflects downstream editing rather than the initial
step of peptide fragment generation. Therefore, other sequence features
were determined in the vicinity of Class II peptides that might be able
to explain their generation. First, the canonical Cathepsin S signature
was searched for, but there was no enrichment in Cathepsin S sites near
MS-observed Class II peptides vs. length-matched decoy peptides sampled
from the peptide source genes. Because this processing signature may
reflect a complex ensemble of enzymes, a de novo CNN was trained based on
the upstream and downstream protein context (+-25 AAs) around observed
peptides and decoys.
[0645] A third model in which peptide availability is determined by the
folded or semi-unfolded state of the protein rather than its primary
sequence was considered. Homology-based ACCPRO was used to predict
secondary structure and regions of solvent accessibility, and an ensemble
of predictors was used to identify intrinsically disordered domains.
[0646] If processing-preferred regions are inherently difficult to
predict, it might be possible to simply build a catalog of all protein
regions covered by at least one peptide in a large collection of
previously published multi-allelic Class II MS data and use overlap as a
prediction feature. Admittedly, the overlap feature is contaminated with
binding information since the alleles represented in the previously
published data may have the same or similar binding motifs. Nonetheless,
even this feature only modestly improved the prediction of presented
peptides suggesting that MHC Class II peptides may not be subject to
strong processing hotspots.
[0647] The next question was which genes contribute the most to the Class
II binding peptides repertoire. Gene-level features, such as expression
level, are already known to provide a large boost when predicting MHC
Class I ligands. Leveraging previously published MS datasets profiling
the Class II binding repertoires of human tissues, it was observed that
MS-observed peptides are more highly expressed than random decoy peptides
(sampled from the proteome) by an order of magnitude (FIG. 18A).
Nonetheless, it was noticed that about 5% of Class II peptides map to a
gene that is ostensibly not expressed according to representative RNA-Seq
data. Based on this pattern, the degree to which each gene was over- or
under-represented in the Class II peptide repertoire was sought to be
quantified by proposing a baseline expectation that the number of
observations for each gene should be proportional to the product of its
length and expression level (FIG. 18B). Among the over-represented genes,
there was a clear enrichment for proteins expressed in human tissue
serum, which produced many Class II-binding peptides but were ostensibly
not expressed in the native tissue. This is consistent with the known
role of MHC Class II in presenting antigens sampled from the
extracellular environment.
[0648] Since autophagy is another well-established Class II processing
pathway, the ratio of observed to expected peptides for each gene
(excluding any gene with fewer than five observed peptides and fewer than
five expected peptides) was determined and determined if there was
enrichment with respect to the physical partners of known autophagy genes
or genes stabilized by Atg5 knockout in mice (FIG. 18C). Neither gene set
appeared to be enriched in the Class II data; in fact, physical partners
of autophagy genes seemed to be modestly under-represented.
[0649] Looking across all cellular localizations (FIG. 18D and FIG. 18E),
few compartments were definitively over- or under-represented. The two
most enriched compartments were the cell membrane and the lysosome, each
generating approximately twice the expected number of Class II peptides.
It is not clear whether the enrichment of membrane proteins relates to
membrane recycling into autophagosomes or Golgi routing of membrane
proteins directly into the autophagy pathway. The apparent contradiction
between the enrichment of lysosomal proteins and the previously observed
depletion of autophagy genes indicated that these trends are highly
sensitive to the specific subset of autophagy-related genes being
considered. FIG. 18F shows relative concordance of peptide observations
with respect to two different gene expression profiles, bulk tumor and
professional antigen presenting cells.
Example 8. Accurate MHC II Prediction Requires Understanding the Endocytic
Pathway
[0650] In addition to understanding the source pathway of Class II genes,
it may be critical to understand which cell types are responsible for
most Class II presentation. In the case of cancer, non-professional APCs,
including fibroblasts and the tumor itself, are thought to present Class
II within inflamed tumor microenvironments (TMEs). To gain further
insight, HLA-DRB1 expression was analyzed in three recently published
single-cell RNA-Seq datasets that profiled lung cancer, head and neck
cancer, and melanoma. Averaging across cells to the patient-cell type
level, it was clear that canonical APCs (macrophages, dendritic cells,
and B cells) present much greater levels of Class II than the tumor and
other stromal cell types, and this trend is consistent across multiple
patients and tumor types.
[0651] To probe whether immunotherapy disrupted this trend, additional
single-cell RNA-Seq from checkpoint blockade-responsive tumor types were
analyzed, and HLA-DRB1 expression was assessed before and after
treatment. A melanoma cohort, which included one confirmed responder,
showed uniformly low HLA-DRB1 expression by tumor cells in both the
pre-therapy and post-therapy biopsies (FIG. 19C). A basal cell carcinoma
cohort which showed a 55% clinical response rate to anti-PD-1 therapy,
likewise exhibited low tumor cell-derived HLA-DRB1 expression regardless
of time point (FIG. 19C).
[0652] These results suggested that most intra-tumoral HLA class II
presentation is driven primarily by professional APCs and "hot" TME
conditions do not guarantee divergence from the general pattern.
[0653] Because tumor cells can outnumber APCs in the tumor
microenvironment, their lower levels of MHC class II expression may
nonetheless be immunologically relevant. To assess how much of overall
Class II expression comes from tumor cells vs. stroma, TCGA patients with
mutations in Class II-specific genes (focusing on CITTA, CD74, and CTSS)
were identified and the fraction of RNA-Seq reads exhibited the somatic
(tumor-specific) variant was determined. This information was used to
impute what fraction of HLA-DRB1 expression derived from tumor vs. stroma
(FIG. 19B). Based on mutations identified in 153 patients representing 17
distinct tumor types, a dominant pattern was observed in which most Class
II expression appears to arise from non-tumor cells. Focusing on just the
patients with highest levels of T cell infiltration (top 10%, as
identified using a previously published 18-gene signature (Ayers et al.,
2017), low tumor HLA-DR expression still appears to be the norm, with
only 3 of 16 patients expressing >1000 TPM (tumor progression and
metastasis).
[0654] To probe whether immunotherapy disrupted this trend, additional
single-cell RNA-Seq from checkpoint blockade-responsive tumor types were
analyzed, and HLA-DRB1 expression was assessed before and after
treatment. A melanoma cohort, which included one confirmed responder,
showed uniformly low HLA-DRB1 expression by tumor cells in both the
pre-therapy and post-therapy biopsies (FIG. 19C). A basal cell carcinoma
cohort which showed a 55% clinical response rate to anti-PD-1 therapy,
likewise exhibited low tumor cell-derived HLA-DRB1 expression regardless
of time point (FIG. 19C).
[0655] These results suggested that most intra-tumoral HLA class II
presentation is driven primarily by professional APCs and "hot" TME
conditions do not guarantee divergence from the general pattern.
Example 9. New Prediction Concepts Enable More Accurate Identification of
Immunogenic Neoantigens
[0656] In order to explore the utility of neonmhc2 and associated
processing rules, the performance in several prediction scenarios was
considered. First, the ability to predict MS-identified peptides was
assessed on PMBC from seven healthy donors profiled with a pan-DR
antibody. This analysis can control for any systematic biases inherent to
the MAPTAC.TM. system or our production cell lines. Using a 1:499 ratio
of hits to decoys and sampling decoys at random from the protein-coding
exome, the positive predictive value of neonmhc2 and NetMHCIIpan base
models as well as models that incorporated additional processing features
were assessed (expression, gene-level bias per FIG. 18B, and overlap with
a previous MHC II peptide). These models confirmed substantial
improvements in both binding and processing prediction (FIG. 20).
[0657] FIG. 21A shows a comparison of the NetMHCIIpan and neonmhc2 with
further processing parameter or features as indicated. Prediction
performance for eight MS samples profiled by HLA-DR antibody (the same
samples analyzed in Example 6, FIG. 17A). Predictors minimally employ
HLA-binding prediction (either NetMHCIIpan or neonmhc2) and optionally
employ additional processing related variables: gene expression, gene
bias (e.g., per FIG. 18B, FIG. 18C, FIG. 19B), and overlap with a
previously observed HLA-DQ peptide. In this example, decoys were sampled
from the proteome at random (including genes that never produced an
MS-observed peptide) to achieve a 1:499 ratio of hits to decoys, which
nearly saturates available decoy sequences. Positive predictive value was
calculated in a manner analogous to FIG. 4, e.g., the top 0.2% of
peptides were called as positives and PPV is the fraction of positives
that are true MS-observed peptides. For each candidate peptide in each
sample, the binding score was calculated as the maximum across the HLA-DR
alleles present in the sample genotype. Although there is a fair
correlation in the trends of peptides found by the two methods, the model
described herein shows a more robust outcome. FIG. 21B (see also, FIG.
33B) represents prediction performance for tumor-derived peptides
presented by dendritic cells (Lysate) using the same hit:decoy ratio and
performance metrics as in FIG. 21A. Performance is shown for NetMHCIIpan
and models described herein with and without use of processing features.
FIG. 21C shows the expression level and gene bias score for each
heavy-labeled peptide. FIG. 21D is a diagram representing overlap of
heavy-labeled peptide source genes according to the lysate and
UV-treatment experiments.
Example 10. Expression of Class-II HLA Peptides in Cell Lines and
Isolating MHC-II-Bound Peptides
Construct Design, Cell Culture and HLA-Peptide Immunoprecipitation
[0658] In this exemplary study, mono-allelic cell lines were generated by
transfecting a single affinity-tagged HLA construct into cell lines
(A375, HEK293T, Expi293, HeLa) and affinity-tagged HLA-peptide complexes
were immunoprecipitated. In FIGS. 12A and 12E, MHC Class II allele
frequencies are allele frequencies obtained from allelefrequencies.net/
unless otherwise noted. Allele frequencies for the U.S. population were
imputed by assuming an admixture of 62.3% European, 13.3% African, 6.8%
Asian, and 17.6% Hispanic.
[0659] With regards to FIG. 12A and FIG. 12E, the mhc_ligand_full.csv
dataset was downloaded from IEDB data on Sep. 21, 2018. Valid affinity
measurements were required to have a "Method/Technique" equal to
"cellular MHC/competitive/fluorescence", "cellular
MHC/competitive/radioactivity", "cellular MHC/direct/fluorescence",
"purified MHC/competitive/fluorescence", "purified
MHC/competitive/radioactivity", or "purified MHC/direct/fluorescence" and
an "Assay Group" equal to "dissociation constant KD", "dissociation
constant KD (.about.EC50)", "dissociation constant KD (IC50)", "half
maximal effective concentration (EC50)", or "half maximal inhibitory
concentration (IC50)". A measurement was attributed to the Soren Buus
group (University of Copenhagen, Denmark) if the string "Buus" appeared
in the "Authors" field. Otherwise, if the authors field included the
strings "Sette" or "Sidney", a measurement was attributed to the
Alessandro Sette group (La Jolla Institute for Immunology, U.S.A). All
other measurements were labeled as "Other". For the purposes of
enumerating strong binders, only peptides with a measured affinity
stronger than 50 nM were counted (FIG. 12A). FIG. 12E includes additional
data from toolsiedb.org/main/datasets/, and strong binders with affinity
<100 nM are enumerated.
DNA Construct Design
[0660] The gene sequences for HLA class I and HLA class II alleles were
identified by the IPD-IMGT/HLA webpage (ebi.ac.uk/ipd/imgt/hla) and used
to design recombinant expression constructs. For HLA class I, the
.alpha.-chain was fused with a C-terminal GSGGSGGSAGG linker (SEQ ID NO:
10), followed by the biotin-acceptor-peptide (BAP) tag sequence
GLNDIFEAQKIEWHE (SEQ ID NO: 11), a stop codon, and a variable DNA
barcode, and cloned into the pSF Lenti vector (Oxford Genetics, Oxford,
UK) via the NcoI and XbaI restriction sites. The HLA class II constructs
were similarly cloned into pSF Lenti via the NcoI and XbaI restriction
sites and consisted of the .beta.-chain sequence fused on the C-terminus
to the linker-BAP sequence from the class I construct
(SGGSGGSAGGGLNDIFEAQKIEWHE (SEQ ID NO: 12)), followed by another short
GSG linker an a F2A ribosomal skipping sequence (VKQTLNFDLLKLAGDVESNPGP
(SEQ ID NO: 13)), the sequence of the .alpha.-chain, an HA tag
(GSYPYDVPDYA (SEQ ID NO: 14)), a stop codon, and a variable DNA barcode.
The identity of all DNA sequences was verified by Sanger sequencing.
Cell Culture and Transient Transfections
[0661] Expi293 cells (Thermo Scientific) were grown in Expi293 medium
(Thermo Scientific) with 8% CO.sub.2 at 37.degree. C. with shaking at 125
rpm. Expi293 cells were maintained at cell densities between
0.5.times.10.sup.6/mL and 6.times.10.sup.6/mL with regular biweekly
passaging. 30 mL of the Expi293 cell suspension was used for transient
transfections at a cell density of approximately 3.times.10.sup.6/mL and
>90% viability. Briefly, 30 ug DNA (1 .mu.g/mL DNA per mL cell
suspension) was diluted into 1.5 mL Opti-MEM medium (Thermo Scientific)
in one tube while 80 .mu.L ExpiFectamine.TM. 293 transfection reagent
(Thermo Scientific) was diluted into a second tube containing 1.5 mL
Opti-MEM. These two tubes were incubated at room temperature for five
minutes, combined, mixed gently, and incubated at room temperature for 30
minutes. The DNA and ExpiFectamine mixture was added to Expi293 cells and
incubated at 37.degree. C., 8% CO.sub.2, 80% relative humidity. After 48
h, transfected cells were harvested in four technical replicates at
50.times.10.sup.6 cells per tube, centrifuged, washed once with 1.times.
Gibco DPBS (Thermo Scientific), and flash frozen in liquid nitrogen for
mass spectrometric analysis. An aliquot of 1.times.10.sup.6 cells was
collected from each transfection batch and analyzed via anti-BAP
(Rockland Immunochemicals Inc., Limerick, Pa.) or anti-HA (Bio-Rad,
Hercules, Calif.) western blot to verify affinity-tagged HLA protein
expression.
[0662] A375 cells (ATCC) were grown in DMEM with 10% FBS and maintained at
cultures at no greater than 80% confluence with regular passaging. For
mass spectrometry experiments A375 cells were cultured in a 500 cm.sup.2
plate at a seeding density of 18.5.times.10.sup.6 cells/mL in 100 mL, as
calculated from a 70% confluent cell number. After 24 hours, cells were
transfected with TransIT-X2 (Mirus Bio, Madison, Wis.) by following the
TransIT system protocol adjusted for the total culture volume. After 48
h, cell medium was aspirated, and cells were washed with 1.times. Gibco
DPBS (Thermo Scientific). For harvest, A375 cells were incubated for 10
minutes at 37.degree. C. with 30 mL non-enzymatic cell dissociation
solution (Sigma-Aldrich), centrifuged, washed with 1.times.DPBS, and
aliquoted at 50.times.10.sup.6 cells per sample. 293T and HeLa cells were
purchased from ATCC and were cultured at 37.degree. C. at 5% CO2 in DMEM,
10% FBS, 2 mM L-glutamine or DMEM+10% FBS, respectively. Both cell lines
were transfected with the HLA constructs using the TransIT LT1 reagent
(Mirus Bio, Madison, Wis.) following the manufactures instructions and
processed 48 h after transfection as described for the A375 cells. From
all samples, an aliquot of 1.times.10.sup.6 cells was collected from each
transfection and analyzed via anti-BAP (Rockland Immunochemicals Inc.,
Limerick, Pa.) or anti-HA (Bio-Rad, Hercules, Calif.) western blot to
verify affinity-tagged HLA protein expression.
BirA Protein Expression and Purification
[0663] The pET19 vector encoding E. coli BirA fused to a C-terminal
hexa-histidine tag (SEQ ID NO: 15) was used. Chemical competent E. coli
BL21 (DE3) cells (New England Biolabs) were transformed with the BirA
expression plasmid, grown at 37.degree. C. in LB broth plus 100 .mu.g/ml
ampicillin to an OD.sub.600 of 0.6-0.8 and cooled to 30.degree. C. before
expression was induced by adding 0.4 mM
isopropyl-.beta.-D-thiogalactopyranoside. E. coli cell growth continued
at 30.degree. C. for 4 h. E. coli cells were harvested by centrifugation
at 8000.times.g for 30 minutes at 4.degree. C. and stored at -80.degree.
C. until use. Frozen cell pellets expressing recombinant BirA were
resuspended in IMAC buffer (50 mM NaH.sub.2PO.sub.4 pH 8.0, 300 mM NaCl)
with 5 mM Imidazole, incubated with 1 mg/ml lysozyme for 20 minutes on
ice and the lysed by sonication. Cellular debris and insoluble materials
were removed by centrifugation at 16,000.times.g for 30 minutes at
4.degree. C. The cleared supernatant was subsequently loaded on a HisTrap
HP 5 mL column using the AKTA pure chromatography system (GE Healthcare),
washed with IMAC buffer plus 25 mM and 50 mM imidazole before elution
with 500 mM imidazole. Fractions containing BirA were pooled and dialyzed
against 20 mM Tris-HCl pH 8.0 with 25 mM NaCl and were loaded on a HiTrap
Q HP 5 mL column (GE Healthcare) and eluted by applying a linear gradient
from 25 to 600 mM NaCl. Fractions containing highly pure BirA were
pooled, buffer exchanged in storage buffer (20 mM Tris-HCl pH 8.0 100 mM
NaCl, 5% glycerol) and concentrated to around 5-10 mg/mL, aliquoted, and
flash frozen in liquid nitrogen for storage at -80.degree. C. BirA
protein concentration was determined by UV spectroscopy at OD.sub.280 nm
using a calculated extinction coefficient of .epsilon.=47,440 M.sup.-1
cm.sup.-1.
Western Blotting Protocol
[0664] Samples were added to XT Sample Buffer and XT Reducing Agent
(Bio-Rad, Hercules, Calif.), heated at 95.degree. C. for five minutes,
then a volume corresponding to .about.400,000 cells was loaded into 10%
Criterion XT Bis-Tris gels (Bio-Rad) and electrophoresed at 200 V for 35
minutes using a PowerPac Basic Power Supply (Bio-Rad, Hercules, Calif.)
with XT MES Running Buffer (Bio-Rad, Hercules, Calif.). The gels were
rinsed briefly with water, then proteins were transferred to PVDF
membranes within Invitrogen iBlot Transfer Stacks (Thermo Fisher
Scientific) using setting P3 on an Invitrogen iBlot2 Gel Transfer Device
(Thermo Scientific). The Precision Plus Protein All Blue Standard
(Bio-Rad, Hercules, Calif.) was used to monitor molecular weights. Next,
membranes were washed 3.times.five minutes with Pierce TBS Tween 20
(TBST) buffer (25 mM Tris, 0.15 mM NaCl, 0.05% (v/v) Tween 20, pH 7.5),
blocked for 1 h at room temperature in TBST-M (TBST containing 5% (w/v)
nonfat instant dry milk), then incubated overnight at 4.degree. C. in
TBST-B (TBST containing 5% (w/v) Bovine Serum Albumin (Sigma Aldrich)]
and a 1:5,000 dilution of both rabbit anti-beta tubulin antibody (catalog
# ab6046, Abcam) and rabbit anti-biotin ligase epitope tag antibody
(catalog #100-401-B21, Rockland Immunochemicals). Next, the membranes
were washed 3.times.five minutes with TBST, incubated for 1 h at room
temperature in TBST-M containing a 1:10,000 dilution of goat anti-rabbit
IgG (H+L-horseradish peroxidase-conjugated antibody (catalog #170-6515,
Bio-Rad, Hercules, Calif.), then washed at room temperature 3.times.five
minutes with TBST. Finally, membranes were bathed with Pierce ECL Western
Blotting Substrate (Thermo Fisher Scientific, Rockford, Ill.), developed
using a ChemiDoc XRS+ Imager (Bio-Rad), and visualized using Image Lab
software (Bio-Rad).
Affinity-Tagged HLA-Peptide Complex Isolation
[0665] Affinity-tagged HLA-peptide complex isolations were performed from
cells expressing BAP-tagged HLA alleles and negative control cell lines
that expressed only endogenous HLA-peptide complexes without BAP tags.
The NeutrAvidin beaded agarose resin was washed three times with 1 mL
cold PBS before use in HLA-peptide affinity purification. Frozen pellets
containing 50.times.10.sup.6 cells expressing BAP-tagged HLA peptides
were thawed on ice for 20 minutes and gently lysed by hand pipetting in
1.2 mL cold lysis buffer [20 mM Tris-Cl pH 8, 100 mM NaCl, 6 mM
MgCl.sub.2, 1.5% (v/v) Triton X-100, 60 mM octyl glucoside, 0.2 mM of
2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM PMSF, 1.times. complete EDTA-free
protease inhibitor cocktail (Roche, Basel, Switzerland)]. Lysates were
incubated end/over/end at 4.degree. C. for 15 minutes with .gtoreq.250
units Benzonase nuclease (Sigma-Aldrich) to degrade DNA/RNA and
centrifuged at 15,000.times.g at 4.degree. C. for 20 minutes to remove
cellular debris and insoluble materials. Cleared supernatants were
transferred to new tubes and BAP-tagged HLA peptides were biotinylated by
incubating end/over/end at room temperature for 10 minutes in a 1.5 mL
tube with 0.56 .mu.M biotin, 1 mM ATP, and 3 .mu.M BirA. The supernatants
were incubated end/over/end at 4.degree. C. for 30 minutes with a volume
corresponding to 200 .mu.L of Pierce high-capacity NeutrAvidin beaded
agarose resin (Thermo Scientific) slurry to affinity-enrich
biotinylated-HLA-peptide complexes. Finally, the HLA-bound resin was
washed four times with 1 mL of cold wash buffer (20 mM Tris-Cl pH 8, 100
mM NaCl, 60 mM octyl glucoside, 0.2 mM of 2-Iodoacetamide, 1 mM EDTA pH
8), then washed four times with 1 mL of cold 10 mM Tris-Cl pH 8. Between
washes, the HLA-bound resin was gently mixed by hand then pelleted by
centrifugation at 1,500.times.g at 4.degree. C. for one minute. The
washed HLA-bound resin was stored at -80.degree. C. or immediately
subjected to HLA-peptide elution and desalting.
Antibody-Based HLA-Peptide Complex Isolation
[0666] HLA class II DR-peptide complexes were isolated from healthy donor
peripheral blood mononuclear cells (PBMC5). A volume corresponding to 75
.mu.L of GammaBind Plus Sepharose resin was washed three times with 1 mL
cold PBS, incubated end/over/end with 10 .mu.g of the antibody at
4.degree. C. overnight, then washed with three times with 1 mL cold PBS
before use in HLA-peptide immunoprecipitation. Frozen PBMC pellets
containing 50.times.10.sup.6 cells were thawed on ice for 20 minutes and
gently lysed by pipetting in 1.2 mL cold lysis buffer [20 mM Tris-Cl pH
8, 100 mM NaCl, 6 mM MgCl2, 1.5% (v/v) Triton X-100, 60 mM octyl
glucoside, 0.2 mM of 2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM PMSF, 1.times.
complete EDTA-free protease inhibitor cocktail (Roche, Basel,
Switzerland)]. Lysates were incubated end/over/end at 4.degree. C. for 15
minutes with >250 units Benzonase nuclease (Sigma-Aldrich) to degrade
DNA/RNA and centrifuged at 15,000.times.g at 4.degree. C. for 20 minutes
to remove cellular debris and insoluble materials. The supernatants were
then incubated end/over/end at 4.degree. C. for 3 hours with an anti-HLA
DR antibody (TAL 1B5, product # sc-53319; Santa Cruz Biotechnology,
Dallas, Tex.) bound to GammaBind Plus Sepharose resin (GE Life Sciences)
to immunoprecipitate HLA DR-peptide complexes. Finally, the HLA-bound
resin was washed four times with 1 mL of cold wash buffer (20 mM Tris-Cl
pH 8, 100 mM NaCl, 60 mM octyl glucoside, 0.2 mM of 2-Iodoacetamide, 1 mM
EDTA pH 8), then washed four times with 1 mL of cold 10 mM Tris-Cl pH 8.
Between washes, the HLA-bound resin was gently mixed then pelleted by
centrifugation at 1,500.times.g at 4.degree. C. for 1 minute. The washed
HLA-bound resin was stored at -80.degree. C. or immediately subjected to
HLA-peptide elution and desalting.
HLA-Peptide Elution and Desalting
[0667] HLA-peptides were eluted from affinity-tagged and endogenous HLA
complexes and simultaneously desalted using a Sep-Pak (Waters, Milford,
Mass.) solid-phase extraction system. In brief, Sep-Pak Vac 1 cc (50 mg)
37-55 .mu.m particle size tC18 cartridges were attached to a 24-position
extraction manifold (Restek), activated two times with 200 .mu.L MeOH
followed by 100 .mu.L of 50% (v/v) ACN/1% (v/v) FA, then washed four
times with 500 .mu.L 1% (v/v) FA. To dissociate HLA-peptides from
affinity-tagged HLA peptides and facilitate peptide binding to the tC18
solid-phase, 400 .mu.L of 3% (v/v) ACN/5% (v/v) FA was added to the tubes
containing HLA-bound beaded agarose resin. The slurry was mixed by
pipetting, then transferred to the Sep-Pak cartridges. The tubes and
pipette tips were rinsed with 1% (v/v) FA (2.times.200 .mu.L) and the
rinsate was transferred to the cartridges. 100 fmol of Pierce Peptide
Retention Time Calibration (PRTC) mixture (Thermo Scientific) was added
to the cartridges as a loading control. The beaded agarose resin was
incubated two times for five minutes with 200 .mu.L of 10% (v/v) AcOH to
further dissociate HLA-peptides from the affinity-tagged HLA peptides,
then washed four times with 500 .mu.L 1% (v/v) FA. HLA-peptides were
eluted off the tC18 into new 1.5 mL micro tubes (Sarstedt) by step
fractionating with 250 .mu.L of 15% (v/v) ACN/1% (v/v) FA followed by
2.times.250 .mu.L of 30% (v/v) ACN/1% (v/v) FA. The solutions used for
activation, sample loading, washing, and elution flowed via gravity, but
vacuum (.ltoreq.-2.5 PSI) was used to remove the remaining eluate from
the cartridges. Eluates containing HLA-peptides were frozen, dried via
vacuum centrifugation, and stored at -80.degree. C. before being
subjected to a second desalting workflow.
[0668] Secondary desalting of the HLA-peptide samples was performed with
in-house built StageTips packed using two 16-gauge punches of Empore C18
solid phase extraction disks (3M, St. Paul, Minn.) as previously
described. StageTips were activated two times with 100 .mu.L of MeOH
followed by 50 .mu.L of 50% (v/v) ACN/0.1% (v/v) FA, then washed three
times with 100 .mu.L of 1% (v/v) FA. The dried HLA-peptides were
solubilized by adding 200 .mu.L of 3% (v/v) ACN/5% (v/v) then and loaded
onto StageTips. The tubes and pipette tips were rinsed with 1% (v/v) FA
(2.times.100 .mu.L) and the rinse volume was transferred to the
StageTips, then the StageTips were washed five times with 100 .mu.L 1%
(v/v) FA. Peptides were eluted using a step gradient of 20 .mu.L 15%
(v/v) ACN/0.1% (v/v) FA followed by two 20 .mu.L cuts of 30% (v/v)
ACN/0.1% (v/v) FA. Sample loading, washes, and elution were performed on
a tabletop centrifuge with a maximum speed of 1,500-3,000.times.g.
Eluates were frozen, dried via vacuum centrifugation, and stored at
-80.degree. C.
HLA-Peptide Sequencing by Tandem Mass Spectrometry
[0669] All nanoLC-ESI-MS/MS analyses employed the same LC separation
conditions described below. Samples were chromatographically separated
using a Proxeon Easy NanoLC 1200 (Thermo Scientific, San Jose, Calif.)
fitted with a PicoFrit (New Objective, Inc., Woburn, Mass.) 75 .mu.m
inner diameter capillary with a 10-tam emitter was packed at 1000 psi of
pressure with He to .about.30-40 cm with 1.9 .mu.m particle size/200
.ANG. pore size of C18 Reprosil beads and heated at 60.degree. C. during
separation. The column was equilibrated with 10.times. bed volume of
buffer A (0.1% (v/v) FA and 3% (v/v) ACN), samples were loaded in 4 .mu.L
3% (v/v) ACN/5% (v/v) FA, and peptides were eluted with a linear gradient
from 7-30% of Buffer B (0.1% (v/v) FA and 80% (v/v) ACN) over 82 minutes,
30-90% Buffer B over six minutes, then held at 90% Buffer B for 15
minutes to wash the column A subset of samples was eluted with a linear
gradient from 6-40% of Buffer B over 84 minutes 40-60% Buffer B over nine
minutes, then held at 90% Buffer B for five minutes and 50% Buffer B for
nine minutes to wash the column Linear gradients for sample elution were
run at a rate of 200 nL/min and yielded .about.13 sec median peak widths.
[0670] During data-dependent acquisition, eluted peptides were introduced
into an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific)
equipped with a Nanospray Flex Ion source (Thermo Scientific) at 2.2-2.5
kV. A full-scan MS was acquired at a resolution of 60,000 from 300 to
1,700 m/z (AGC target 4e5, 50 ms max IT). Each full scan was followed by
a 2 sec cycle time, or top 10, of data-dependent MS2 scans at resolution
15,000, using an isolation width of 1.0 m/z, a collision energy of 34
(HLA class I data) and 38 (HLA class II data), an ACG Target of 5e4, and
a max fill time of 250 ms max ion time. An isolation width of 1.0 m/z was
used because HLA class II peptides tend to be longer (median 16 amino
acids with a subset of peptides >40 amino acids), so the monoisotopic
peak is not always the tallest peak in the isotope cluster and the mass
spectrometer acquisition software places the tallest isotopic peak in the
center of the isolation window in the absence of a specified offset. The
1.0 m/z isolation window will therefore allow for the co-isolation of the
monoisotopic peak even when it is not the tallest peak in the isotopic
cluster as the charge states of class II peptides are often +2 or higher.
Dynamic exclusion was enabled with a repeat count of 1 and an exclusion
duration of 5 sec to enable .about.3 PSMs per precursor selected.
Isotopes were excluded while dependent scans on a single charge state per
precursor was disabled because HLA-peptide identification relies on PSM
quality, so multiple PSMs of different charge states further increases
our confidence of peptide identifications. Charge state screening for HLA
class II data collection was enabled along with monoisotopic precursor
selection (MIPS) using Peptide Mode to prevent triggering of MS/MS on
precursor ions with charge state 1 (only for alleles with basic anchor
residues), >7, or unassigned. For HLA class I data collection,
precursor ions with charge state 1 (mass range 800-1700 m/z) and 2-4 were
selected, while charge states >4 and unassigned were excluded.
[0671] Detection of peptides using High field asymmetric waveform ion
mobility spectrometry (FAIMS) was assessed using the following protocol.
Endogenously processed and presented HLA class I and HLA class II
peptides from A375 cells were subjected to both acidic reverse phase
(aRP) and basic reverse phase (bRP) offline fractionation prior to
analysis by nLC-MS/MS using orbitrap fusion lumos tribid mass
spectrometer equipped without or with FAIMS interface. FIG. 42A
demonstrates the workflow. FIG. 42B, shows benchmarking of FAIMS with low
amounts of tryptic samples Jurkat cell, HeLa cells. Both HLA class I
binding and HLA class II binding peptides were analyzed using FAIMS (FIG.
43A and FIG. 43B; and FIG. 44A and FIG. 44B respectively). In each case,
a slight improvement in peptide detection, especially with bRP
fractionated peptides. FIG. 45 and FIG. 46 show the intersection size.
Interpretation of LC-MS/MS Data
[0672] This section is related to, for example, FIG. 29. Mass spectra were
interpreted using the Spectrum Mill software package v6.0 pre-Release
(Agilent Technologies, Santa Clara, Calif.). MS/MS spectra were excluded
from searching if they did not have a precursor MH+ in the range of
600-2000 (Class I)/600-4000 (Class II), had a precursor charge >5
(Class I)/>7 (Class II), or had a minimum of <5 detected peaks.
Merging of similar spectra with the same precursor m/z acquired in the
same chromatographic peak was disabled. MS/MS spectra were searched
against a database that contained all UCSC Genome Browser genes with hg19
annotation of the genome and its protein coding transcripts (63,691
entries; 10,917,867 unique 9mer peptides) combined with 264 common
contaminants. Prior to the database search, all MS/MS had to pass the
spectral quality filter with a sequence tag length >2, e.g., minimum
of 3 masses separated by the in-chain mass of an amino acid. A minimum
backbone cleavage score (BCS) of 5 was set, and ESI QExactive HLAv2
scoring scheme was used. All spectra from native HLA-peptide samples, not
reduced and alkylated, were searched using a no-enzyme specificity, fixed
modification of cysteine as cysteinylation, with the following variable
modifications: oxidized methionine (m), pyroglutamic acid (N-term q),
carbamidomethylation (c). Reduced and alkylated HLA-peptide samples were
searched using a no-enzyme specificity, fixed modification of cysteine as
carbamidomethylation, with the following variable modifications: oxidized
methionine (m), pyroglutamic acid (N-term q), cysteinylation (c). A
precursor mass tolerance of .+-.10 ppm, product mass tolerance of .+-.10
ppm, and a minimum matched peak intensity of 30% was used for both native
and reduced and alkylated HLA-peptide datasets. Peptide spectrum matches
(PSMs) for individual spectra were automatically designated as
confidently assigned using the Spectrum Mill autovalidation module to
apply target-decoy based FDR estimation at the PSM rank to set scoring
threshold criteria. An auto thresholds strategy using a minimum sequence
length of 7, automatic variable range precursor mass filtering, and score
and delta Rank1-Rank2 score thresholds optimized across all LC-MS/MS runs
for an HLA allele yielding a PSM FDR estimate of <1.0% for each
precursor charge state.
[0673] Identified peptides that passed the PSM FDR estimate of <1.0%
were further filtered for contaminants by removing all peptides assigned
to the 264 common contaminants proteins in the reference database and by
removing peptides identified in the negative control MAPTAC.TM. affinity
pulldowns. Additionally, all peptide identifications that mapped to an in
silico tryptic digest of the reference database were removed, as these
peptides cannot be ruled out as tryptic contaminants from sample
carry-over on the uPLC column.
Monoallelic Assignment of HLA-DR, -DQ, DP Heterodimers Using MAPTAC.TM.
Protocol
[0674] Mono-allelic HLA assignment to LC-MS/MS identified peptides
followed two approaches. Because allelic variation in HLA-DRA1 is limited
and not considered to influence peptide binding, all data from DR
experiments (profiling DRB1, 3, 4 and 5) were considered as mono-allelic
meaning peptides were most likely bound to HLA class II heterodimers
comprising capture beta chains paired with the capture alpha chains.
However, the possibility remains that some peptides may have bound to HLA
II heterodimers comprising knock-in the beta chains paired with a
distinct endogenously expressed alpha chains.
[0675] Conversely, for HLA-DP and HLA-DQ loci, the alpha chains exhibit
important allelic variants such that the presence of both knock-in and
endogenous alpha chain alleles creates the potential for multiple
heterodimers. For example, knock-in alpha and beta chains coding for
distinct HLA-DP and HLA-DQ heterodimers can each pair with endogenously
expressed alpha and beta chains making up to four unique heterodimers for
each HLA-DP and HLA-DQ MAPTAC.TM. construct. Therefore, binding
specificities among the purified MAPTAC.TM. peptide population are not
mono-allelic. To mitigate this endogenous pairing problem, a construct
that lacked the alpha chain was used that (sans-alpha knock-ins) enabled
us to identify the population of peptides that likely bind to HLA
heterodimers comprising endogenously alpha chains and MAPTAC.TM. beta
chains. These peptides were computationally subtracted from the
corresponding alpha+beta chain MAPTAC.TM. experiments to approximate a
population of peptides specific to the mono-allelic MAPTAC.TM. alpha+beta
combination.
[0676] Each peptide was assigned to one or more protein-coding transcripts
within the UCSC hg19 gene annotation. Since many peptide identifications
overlap others and thus constitute mostly redundant information, the
peptides were grouped into "nested sets", each meant to correspond to
.about.1 unique binding event, as shown in FIG. 11C. For instance, the
peptides GKAPILIATDVASRGLDV (SEQ ID NO: 16), GKAPILIATDVASRGLD (SEQ ID
NO: 17), and KAPILIATDVASRGLDV (SEQ ID NO: 18) all contain the conserved
sequence KAPILIATDVASRGLD (SEQ ID NO: 19), and probably all bind MHC in
the same register. In order to nest peptides of a given data set, a graph
was built in which each node corresponded to a unique peptide, and an
edge was created between any pair of peptides sharing at least one 9mer
and mappable to at least one common transcript. The clusters command in
the R package igraph was used to identify clusters of connected nodes,
and each cluster was defined as a nested set. This procedure guarantees
that any two peptides that meet the edge criteria (.gtoreq.1 common 9mer
and .gtoreq.1 common transcript) are placed within the same nested set.
Analysis of Previously Published MS Data
[0677] The following section relates to at least FIGS. 12A-12F, FIG. 35,
FIG. 36A-36B, FIG. 38D, FIGS. 39A-39C, FIGS. 40A-40B. Published LC-MS/MS
datasets that provided .raw files were reprocessed using the Spectrum
Mill software package v6.0 pre-Release (Agilent Technologies, Santa
Clara, Calif.). Datasets that were collected on Thermo Orbitrap
instruments (e.g. Velos, QExactive, Fusion, Lumos) that utilized HCD
fragmentation and MS and MS/MS data collection in the orbitrap (high
resolution) were analyzed using the parameters described in the above
section "Interpretation of LC-MS/MS Data". For MS and MS/MS high
resolution datasets that utilized CID fragmentation, the same parameters
as above were used with an ESI Orbitrap scoring scheme. For datasets with
MS data collection in the orbitrap and MS/MS data collection in the ion
trap, the following same parameters above were also used with the
following deviations. For HCD data, the ESI QExactive HLAv2 scoring
scheme was used, while the ESI Orbitrap scoring scheme was used for CID
data. A precursor mass tolerance of .+-.10 ppm, product mass tolerance of
.+-.0.5 Da was used. For both high- and low-resolution MS/MS datasets,
peptide spectrum matches (PSMs) for individual spectra were automatically
designated as confidently assigned using the Spectrum Mill autovalidation
module to apply target-decoy based FDR estimation at the PSM rank to set
scoring threshold criteria. An auto thresholds strategy using a minimum
sequence length of 7, automatic variable range precursor mass filtering,
and score and delta Rank1-Rank2 score thresholds optimized across all
LC-MS/MS runs for an HLA allele yielding a PSM FDR estimate of <1.0%
for each precursor charge state.
[0678] Amino acid frequencies in the human proteome were calculated based
on sequences for all protein-coding genes in the UCSC hg19 annotation
(selecting one transcript at random for genes represented by multiple
transcript isoforms), as shown in FIG. 11D. IEDB frequencies were
determined by identifying the unique set of peptides with at least one
affinity observation .ltoreq.50 nM (excluding some peptides with
hexavalent polyhistidine at their C-terminus). MAPTAC.TM. frequencies
were first considered in the context of the standard forward-phase
protocol across five DRB1 alleles alleles (DRB1*01:01, DRB1*03:01,
DRB1*09:01, and DRB1*11:01), using only one peptide (the longest) per
nested set. In addition, MAPTAC.TM. frequencies were separately
calculated for the basic reverse-phase protocol across several alleles.
MS data from external datasets were analyzed without respect to potential
allele of origin and likewise using the longest peptide per nested set.
Building Class I (HLA Class I Binding Peptide) Sequence Logos
[0679] For each Class I allele (as depicted in FIG. 14A), a length-9
sequence logo was created by profiling amino acid frequencies in the
first five positions (mapping to logo positions 1-5) and last four
positions (mapping to logo positions 6-9) of corresponding peptides. In
this manner, peptides contributed to the sequence logo regardless of
their length. As in the Class II logos, letter heights are proportional
to the frequency of each amino acid in each position, and color coding is
used for low-entropy positions.
Predicted Affinities of MS-Observed Peptides
[0680] This section is at least related to FIG. 14C. For each class II
allele, all unique peptides length 14 through 17 were identified and
scored for binding potential using NetMHCIIpan. For comparison, random
length-matched peptides were sampled from the human proteome. Density
distributions (as depicted in FIG. 14C) were determined based on
log-transformed values. Some alleles were excluded from the analysis
since NetMHCIIpan does not support their prediction.
Measured Affinities for MS-Observed Peptides
[0681] Peptides were selected for affinity measurement if they had poor
predicted NetMHCIIpan binding affinity (>100 nM for DRB1*01:01 or
>500 nM for DRB1*09:01 and DRB1*11:01) or if they exhibited .ltoreq.2
of the heuristically defined anchors to be testing in a previously
published biochemical MHC-peptide affinity assay.
Establishment of Training, Tuning, and Testing Proteome Partitions
[0682] This section is related to at least FIG. 15A (see also, FIG. 31A).
A graph was created in which each node represents a protein-coding
transcript and edges are present between all pairs of transcripts sharing
at least 5 unique 9mers of amino sequence content (UCSC hg19 gene
annotation). The clusters command in the R package igraph
(cran.r-project.org/web/packages/igraph/citation.html) was used to
identify clusters of connected nodes, and each cluster was defined as a
"transcript group". In this manner, if two transcripts shared an edge
(.gtoreq.5 shared 9mers), they were guaranteed to be placed in the same
transcript group. Transcript groups were randomly sampled, placing 75% in
the train partition, 12.5% in the tune partition, and 12.5% in the test
partition. In all analyses, MS-observed peptides (and non-observed decoy
peptides) were placed in partitions (train, tune, or test) according to
the partition of their source transcripts. In very rare instances in
which the source transcripts mapped to multiple different partitions, the
assignment preference order was train (most preferred), tune, and then
test (least preferred). The same partitioning of the proteome was used in
all partition-based analyses. The graph-based approach of partitioning
the proteome was used to minimize the likelihood that similar peptide
sequences would appear during training and evaluation, which could
artificially inflate prediction performance.
Architecture and Training of a CNN-Based Class II Binding Predictor
[0683] In relation to at least FIG. 15A, while amino acids may be
represented by a "one-hot" encoding, others have opted to encode amino
acids using the PMBEC matrix and the BLOSUM matrix, in which similar
amino acids have similar feature profiles. For the purposes of our
peptide featurization, a unique matrix based amino acid proximities was
generated in solved protein structures. The concept of this approach is
that the typical neighbors of an amino acid should reflect its chemical
properties. For each amino acid in each of 100,000 DSSP protein
structures (cdn.rcsb.org/etl/kabschSander/ss.txt.gz), the residue that
was closest in 3D space but at least 10 amino acids away in primary
sequence was determined. Using this data, the number of times the nearest
neighbor of alanine was alanine was determined, the number of times the
nearest neighbor of alanine was a cysteine, etc., to create a 20.times.20
matrix of proximity counts. Each element of the matrix was divided by the
product of its corresponding column and row sums, and the entire matrix
was log-transformed. Finally, the mean value of the entire matrix was
subtracted from each element. Three additional physical
features--hydrophobicity, charge, and size--were added as additional
columns such that each amino acid was represented by 23 input features.
Benchmarking Prediction Performance on MAPTAC.TM.-Observed Peptides,
Related to FIGS. 21A-B
[0684] For the purpose of assessing prediction performance for a given
allele, it was necessary to define a set of peptides that could have been
observed (because they are present in the proteome) but were not observed
in the MS data. These negative examples were termed "natural decoys" (in
contrast to the "scrambled decoys" described above). As guiding
principles, it was decided:
1. The length distribution of natural decoys should match the length
distribution of MS-observed hits. 2. Natural decoys should not contain
sequence redundant with other natural decoys. 3. Natural decoys should
not overlap hits. 4. Natural decoys should come from genes that produced
at least one hit.
[0685] The following pseudocode represents the process an evaluation
satisfying these principles was created:
TABLE-US-00002
Initialize two empty lists of hits, H.sub.minimal and H.sub.exhaustive
For each nested set S of MS-observed peptides:
If none of the peptides in S can be mapped to a transcript in the train
or tune partition:
Add the shortest peptide in S to H.sub.minimal
Add all peptides in S to H.sub.exhaustive
Initialize an empty list of decoy peptides, D
For each protein-coding transcript (longest first, shortest last) in the
test partition:
If no peptides in H.sub.exhaustive map to the transcript:
Skip to the next transcript
Cover the transcript's protein sequence with a set of overlapping
peptides P, where the peptide lengths
are randomly sampled from the length distribution of H.sub.minimal. The
overlap is 8 amino acids. (The last peptide
in P will typically dangle over the end of the protein.)
While the last peptide in P still dangles:
Subtract 1 amino acid from the length of the longest peptide in P
For each peptide in P:
If it does not share a 9mer with a peptide in H.sub.exhaustive and it
does contain a 9mer not observed in any
peptide in D:
Add the peptide to D
Otherwise:
Reject the peptide
H.sub.minimal and D constitute the evaluation data set
[0686] To evaluate performance on this set, all n hit peptides were
evaluated by the predictor (neonmhc2 or NetMHCIIpan) and scored along
with a set of 19n decoys (randomly sampled without replacement from the
complete set of decoys). The top 5% of peptides in the combined set were
labeled as positive calls, and the positive predictive value (PPV) was
calculated as the fraction of positive calls that were hits. Note that
since the number of positives is constrained to be equal to the number of
hits, recall is equal to PPV in this evaluation scenario. The application
of a consistent 1:19 ratio across alleles helps stabilize the performance
values, which are otherwise highly influenced by the number of hits
observed for each allele. This was deemed appropriate since it was
assumed the number of hits relates more to experimental conditions and
replicate count than intrinsic properties of the allele. The 1:19 ratio
is not far from what was to be used if down-sampling was not implemented.
Benchmarking Prediction Performance on IEDB Affinity Measurements
[0687] As related to FIG. 15B, since NetMHCIIpan is trained on IEDB
affinity data, its out-of-sample performance was evaluated using a
slightly outdated version and IEDB measurements. The correspondence of
predicted and measured affinities was determined by Kendall's tau
coefficient. The same statistic was used to assess the performance of
neonmhc2 on the same set of measurements. Evaluations were conducted
separately by allele and by publishing group (Sette or Buus).
Benchmarking Prediction Performance of Natural CD4+ T Cell Responses
[0688] Since the vast majority of CD4+ T cell responses documented in IEDB
have an unknown or computationally imputed Class II allele restriction,
the subset of records was focused on that were confirmed experimentally
by Class II tetramer. Nearly all such records were deposited by the
William Kwok Laboratory (Benaroya Research Institute, Seattle, Wash.),
which uses the blood of immune-reactive individuals to perform
tetramer-guided epitope mapping (TGEM) of diverse pathogens and
allergens. Since negative peptides were posted for some studies but not
others, the source publications were reviewed to reconstruct the complete
set of positive and negative peptide reactivities. All 20-mer peptides
were scored by neonmhc2 and by NetMHCIIpan. To calculate positive
predictive value (PPV) across alleles in a comparable way across alleles,
the negative examples for each allele were randomly down-sampled until
there was 1:19 ratio of positives to negatives. PPV was calculated as the
fraction of experimentally confirmed positives among the top-scored 5% of
peptides. Performance was also evaluated by receiver-operator curves.
Assessing the Performance of MHC II Peptide Deconvolution
[0689] To assess the ability the GibbsCluster (v2.0) tool to cluster
multi-allelic MHC Class II peptide data by allele of origin, peptides
from a diverse set of published DR-specific experiments on subjects of
known DR genotype (Table 2) were first curated. In some cases, the
original publication provided HLA-DRB1 typing but omitted typing for
HLA-DRB3/4/5. To address these cases, it was assumed the DR1:DR3/4/5
linkages provided by IMGT, and if that was insufficient to resolve
four-digit typing, the linkages observed in the population
"USASanFranciscoCaucasian" (allelefrequencies.net, population ID 3098:
Table 2 were used.
TABLE-US-00003
TABLE 2
DRB1 DRB3/4/5 Haplotype Freq. (%)
DRB1*03:01 DRB3*01:01 23.2
DRB1*04:01 DRB4*01:01 9
DRB1*04:02 DRB4*01:01 2.2
DRB1*04:04 DRB4*01:01 3.2
DRB1*07:01 DRB4*01:01 13.2
DRB1*09:01 DRB4*01:01 2.8
DRB1*11:01 DRB3*02:02 7.2
DRB1*11:04 DRB3*02:02 2.8
DRB1*13:01 DRB3*02:02 2.6
DRB1*13:02 DRB3*03:01 2.6
DRB1*14:01 DRB3*02:02 2.2
DRB1*15:01 DRB5*01:01 28.6
Note that DRB4*01:01 has been shown to be identical to DRB4*01:03
(ebi.ac.uk/cgi-bin/ipd/imgt/hla/get_allele.cgi?DRB4*0101)
[0690] For each DRB1/3/4/5 allele present in each (imputed) genotype,
twenty peptides from our mono-allelic MAPTAC.TM. data were spiked in.
These augmented datasets were then submitted to GibbsCluster-v2.0.
Characterizing Observed Cleavage Sites of MHC II Peptides
[0691] Disclosed herein is a large dataset of naturally processed and
presented peptides MHC II peptides by merging peptide identifications
across several studies that used immunopurification to profile human
tissues (Table 2). Since many peptides share the same N-terminus (e.g.
GKAPILIATDVASRGLDV (SEQ ID NO: 16) and GKAPILIATDVASRGLD (SEQ ID NO: 17))
or the same C-terminus (e.g. GKAPILIATDVASRGLD (SEQ ID NO: 17) and
KAPILIATDVASRGLD (SEQ ID NO: 19)), two sets of non-redundant cut sites
were curated, one for N-termini and one for C-termini. Then, an
equivalent number of unique non-observed N-terminal and C-terminal cut
sites were sampled at random from the set of genes that had produced at
least one MHC II peptide. These four data sets were referred to as
N-terminal hits, C-terminal hits, N-terminal decoys, and C-terminal
decoys. In addition, a naming system was used to refer to positions
upstream of peptides, within peptides, and downstream of peptides which
is shown in FIG. 41.
[0692] The frequency of each amino acid was determined for positions U10
through N3 for N-terminal hits, and these frequencies were compared to
those observed for N-terminal decoys. To determine whether hits and
decoys showed a significant difference in the rate of a given amino acid
at a given position, a 2.times.2 table (e.g. count of hits for which U1
is lysine, count of decoys for which U1 is lysine; count of hits for
which U1 is not lysine, and count of decoys for which U1 is not lysine)
was created and scored by a Chi-square test. An analogous approach was
use for analyzing amino acid frequencies in positions C3 through D10 of
C-terminal hits and decoys.
[0693] A second analysis considered statistical linkages between residues
immediately preceding and following cleavage events. First, the count of
U1:N1 pairs (A:A, A:C, A:D, Y:V, Y:W, Y:Y) was compared for N-terminal
hits vs. N-terminal decoys, and significance of enrichment/depletion for
each pair was determined by a Chi-square test of a 2.times.2 contingency
table (e.g. count of hits with P:K, count of decoys with P:K; count of
hits without P:K, count of decoys without P:K). An analogous approach was
used for analyzing C1:D1 pair frequencies of C-terminal hits and decoys.
Benchmarking the Performance of Various Class II Cleavage Predictors
[0694] Peripheral blood from healthy donors was profiled for DR-binding
peptides. These samples were used to benchmark the ability of
cleavage-related variables/predictors to enhance the identification of
presented Class II epitopes.
[0695] To build integrated predictors that predict peptide presentation
using both binding potential and cleavage potential, a dataset was first
constructed using the same approach described for FIG. 4 but using the
"tune" partition rather than the "test" partition. In short, this meant
using a 1:20 ratio of hits to decoys, where decoys are length-matched to
hits and are randomly sampled from the set of genes that generated at
least one hit. Binding potential was calculated using neonmhc2, and
because these samples were multi-allelic, the binding score for each
peptide candidate peptide was taken to be the average of the 1-4 DR
alleles indicated by each donor's genotype. This fitting process
determined the relative weights that would be placed on the binding and
cleavage variables in forward prediction.
[0696] To determine the performance of forward prediction, evaluation hits
and decoys (1:19 ratio) were obtained from the "test" partition using the
same protocol just described. PPV was calculated in the same manner as
for FIG. 4. Several different cleavage predictors were assessed as shown
in Table 3.
TABLE-US-00004
TABLE 3
PSM with known cleavage site
PSM with unknown cleavage site
To score a candidate peptide, the 9mer core was first imputed. Next, the
maximum PSM score was calculated across three regions: the 10 amino
acids upstream of the core (regardless of the location of the true N-
terminal cleavage site), the core sequence, and the 10 amino acids
after core. A logistic regression was trained on the tune partition
(see above) using the neonmhc2 binding score as well as the three
region-specific PSM scores.
Novel neural network, unknown cleavage site
Cathepsin motifs, unknown cleavage site
Structural features
Within the SCRATCH suite (Cheng J, Randall A Z, Sweredoski M J &
Baldi P (2005) SCRATCH: a protein structure and structural feature
prediction server. Nucleic Acids Res. 33: W72-6 Available at:
dx.doi.org/10.1093/nar/gki396), the tool ACCpro20 was used to predict
relative solvent accessibility (RSA), and the tool SSpro8 was used to
predict features of secondary structure (H: alpha-helix G: 310-helix
I: pi-helix E: extended strand B: beta-bridge T: turn S: bend C: other).
Per-residue scores of sequence disorder (d2p2.pro/download) were
likewise determined over the entirety of the proteome, scoring on a 0-5
scale according to the number of prediction engines labeling the position
as disordered (servers used: anchor, espritz-d, espritz-n, espritz-x,
iupred-l, and iupred-s). Finally, topological domains (transmembrane,
signal, extracellular, lumenal, and disulfide) were determined at the
residue level using Uniprot (uniprot_sprot.dat). For each candidate
peptide, average RSA and average disorder score were calculated over
a 21mer window centered on the predicted 9mer core. Over the same 21-
residue window, percent composition was calculated for secondary
structure features and topological features. Four logistic regression
models were trained on the "tune" partition using the following
input features:
1. Neonmhc2 binding score and solvent Accessibility (RSA)
2. Neonmhc2 binding score and disorder score
3. Neonmhc2 binding score and eight SSpro8-derived features
representing secondary structure
4. Neonmhc2 binding score and five Uniprot features representing
topology
Overlap with a previously observed peptide
MS-based peptide identifications were pooled across many multi-
allelic Class II experiments from the public domain. A new
feature was created representing whether a new candidate peptide
overlapped with one of these previously observed peptides.
Specifically, the feature was set to 1 if it shared at least one
9mer with any peptide in the large set of previously observed
MHC II ligands; otherwise the feature was set to 0. A logistic
regression was trained on the "tune" partition using the
neonmhc2 binding score and the overlap feature.
Relationship Between MHC Class II Presentation and Expression
[0697] Peptides were pooled across previously published MS experiments
that profiled the HLA-DR ligandomes of human ovarian tissues. For each
sample with available RNA-Seq data, the raw fastqs were downloaded from
SRA and aligned to the UCSC hg19 transcriptome using bowtie2. Transcript
level gene quantification was performed using transcripts per million
(TPM) as calculated by RSEM. The expression estimates were further
processed by summing to the gene level, dropping non-coding genes, and
renormalizing such that the total TPM summed to 1000000 (renormalizing
across protein-coding genes accounts for library-to-library variation in
ncRNA abundance).
[0698] For each gene in each tissue sample, its expression level in the
sample and whether it produced at least one peptide in the sample was
considered. Across all MS experiments, these observations were binned
according to expression level and peptide generating status (see FIG.
18A).
Identification of Over- and Under-Represented Genes
[0699] To identify genes over- and under-represented in MHC II ligandomes,
data was compiled from five previous studies that profiled ovarian
tissue, colorectal tissue, and cutaneous melanomas, lung cancers and head
and neck cancers. For each gene, our baseline assumption was that it
should yield peptides in proportion to its length multiplied by its
expression level. To determine the length of each gene, the unique 9mers
across all transcript isoforms were enumerated. Gene-level expression was
obtained by summing across transcript isoforms. The observed number of
peptides mapping to each gene was determined at the nested set level
(e.g. peptides GKAPILIATDVASRGLDV (SEQ ID NO: 16), GKAPILIATDVASRGLD (SEQ
ID NO: 17), and KAPILIATDVASRGLDV (SEQ ID NO: 18) counted as a single
observation).
[0700] Many samples from the ovarian study had corresponding RNA-Seq data,
but some did not. In those cases, expression was estimated averaging
across the samples with available RNA-Seq data. For the colorectal and
melanoma studies, there was no corresponding RNA-Seq for any samples, so
averages were calculated across surrogate samples using data from GTEx
and TCGA. In all cases, raw fastqs were obtained and aligned and
quantified them according to the same protocols as described above for
the ovarian study's RNA-Seq.
[0701] Two matrices were created representing expected and observed
counts, referred to as E and O, respectively, wherein rows correspond to
genes and columns correspond to samples. The matrix E was first populated
by multiplying each gene's length by its expression in each sample; then
the columns of E were rescaled to make the column sums of E match the
column sums of O. Finally, analysis was made at the gene level by
comparing the row sums of E to the row sums of O. Genes were highlighted
according to their presence and concentration in human plasma.
Analysis of Genes Related to Autophagy
[0702] Two autophagy-related gene sets were defined. The first set
comprised proteins experimentally identified as physical interaction
partners of known autophagy-related genes. For each canonical
autophagy-related gene (genenames.org/cgi-bin/genefamilies/set/1022) used
as bait in an IP-MS experiment deposited in the Autophagy Interaction
Network data base (accessed from
besra.hms.harvard.edu/ipmsmsdbs/cgi-bin/downloads.cgi), the top 100
protein identifications according to the "WD" confidence score
(besra.hms.harvard.edu/ipmsmsdbs/cgi-bin/tutorial.cgi) were identified.
Pooling across 22 experiments, a set of 1004 unique genes were obtained
confidently associated with at least one canonical autophagy-related
gene. (FIG. 18C)
[0703] A second set of autophagy-related genes were identified using a
study that measured pan-proteome protein abundance in baby mouse kidney
epithelial (iBMK) cells pre- and post-ATG5 knockout using SILAC
(sciencedirect.com/science/article/pii/51097276514006121). Genes with a
t-statistic >5 were classified as being stabilized by ATG5 knockout
(pre-starvation conditions; variable "Intercept_t" in supplemental data
file mmc2.xls). To map each mouse Uniprot ID to an hg19 UCSC ID, the
human UCSC protein sequence was determined with which the mouse Uniprot
sequence shared the most 9mers. (FIG. 18C)
[0704] Based on FIG. 18B, the ratio R of observed to expected peptides per
gene was calculated, adding a pseudocount of one to both the numerator
and the denominator, e.g. R=(O+1)/(E+1). Log(R) was taken to represent
the relative enrichment (Log(R)>0) or depletion (Log(R)<0) of each
gene felt that there was insufficient information to quantify relative
enrichment/depletion for these genes. Among the genes with valid Log(R)
calculations, Log(R) distributions were plotted for those in the IP-MS
dataset, those in the SILAC dataset, and those in neither dataset (FIG.
18C).
Analysis of Source Gene Localization, Related to FIG. 18D
[0705] Using the same log(R) score as described above, distributions
according to the localization of each source gene was plotted (FIG. 18D).
Source gene localization was determined using Uniprot
(uniprot_sprot.dat).
Analysis of Class II Expression Data in Single-Cell RNA-Seq Data, Related
to FIG. 19A
[0706] Single-cell RNA-Seq data were obtained from three previously
published data sets that profiled human tumor samples.
[0707] The first study included data from cutaneous melanomas. The file
"GSE72056_melanoma_single_cell_revised_v2.txt" was downloaded from Gene
Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession: GSE72056). Cells
with tumor status flag "2" were treated as tumor cells, and cells labeled
with tumor status flag "1" and immune cell type flag equal to "1" through
"6" were treated as T cells, B cells, Macrophages, Endothelium,
Fibroblasts, and NKs, respectively. All other cells were dropped. Data
were natively presented in units of log 2(TPM/10+1) and were thus
mathematically converted to a TPM scale. Once on the TPM scale, the data
for each cell was renormalized to sum to 1,000,000 over the set of
protein-coding UCSC gene symbols (protein-coding genes not appearing in
the expression matrix were implicitly treated as having zero expression).
Finally, single-cell observations corresponding to the same cell type and
same source biopsy where averaged to produce expression estimates at the
patient-cell type level.
[0708] The second study included data from head and neck tumors. The file
"GSE103322_HNSCC_all_data.txt" was downloaded from the Gene Expression
Omnibus (ncbi.nlm.nih.gov/geo/; accession: GSE103322). The data in this
table are in units of log 2(TPM/10+1); therefore, the values were
mathematically converted to TPM units. As with the melanoma study, the
data for each cell was renormalized to sum to 1,000,000 over the set of
protein-coding UCSC gene symbols, and single-cell observations
corresponding to the same cell type and same source biopsy where
averaged. Data corresponding to the lymph node biopsies were excluded.
[0709] The third study included data from untreated non-small cell lung.
The files "RawDataLung.table.rds" and "metadata.xlsx" were downloaded
from ArrayExpress (ebi.ac.uk/arrayexpress/; accessions: E-MTAB-6149 and
E-MTAB-6653). The data (already in TPM) units, were re-scaled to sum to
1,000,000 over the set of protein-coding genes as previously described.
Finally, single-cell observations corresponding to the same cell type and
same source biopsy where averaged to produce expression estimates at the
patient-cell type level. Similar studies in colorectal and ovarian
cancers were performed. Results are indicated in FIG. 19A.
[0710] For simplicity, cell types were merged to a coarser granularity
than natively reported in Table 4.
TABLE-US-00005
TABLE 4
Coarse
designation Constituent cell types
Alveolar "Alveolar", excluding "cuboidal
alveolar type 2 (AT2) cells"
FO B cells "follicular B cells"
Plasma cells "plasma B cells"
CLEC9A+ DCs "cross-presenting dendritic cells"
monoDCs "monocyte-derived dendritic cells"
pDCs "plasmacytoid dendritic cells"
Langerhans "Langerhans cells"
Macrophages "macrophages"
Granulocytes "granulocytes"
Endothelium "normal endothelial cell", "tumor
endothelial cell", and "lower quality
endothelial cell", excluding "lymphatic EC"
Epithelium "epithelial cell" and "lower quality
epithelial cell"
Fibroblasts "COL12A1-expressing fibroblasts",
"COL4A2-expressin fibroblasts", "GABARAP-
expressing fibroblasts", "lower quality
fibroblasts", "normal lungfibroblasts",
"PLA2G2A-expressing fibroblasts", and
"TFPI2-expressing fibroblasts"
T cells "regulatory T cells", "CD4+ T cells"
and "CD8+ T cells"
NKs "natural killer cells"
Tumor "cancer cells"
Excluded "erythroblasts" and "MALT B cells"
from analysis
[0711] Expression levels of HLA-DRB1 in the five studies are plotted in
FIG. 19A.
Characterization of Tumor-Derived Vs. Stroma-Derived Class II Expression
[0712] To determine the relative amount of MHC class II binding peptide
expression attributable to tumor vs. stroma, mutations were identified in
Class II pathways genes in TCGA patients (called based on DNA), and for
each patient bearing a Class II mutation, the relative expression of the
mutated and non-mutated copies were quantified of the gene the
corresponding RNA-Seq. Further, it was assumed:
1. Mutated reads arise from the tumor 2. Non-mutated reads arise for the
stroma or the wildtype allele in the tumor 3. The tumor retains a
wildtype copy with expression approximately equal to the mutated copy
[0713] Based on this, it was determined that for an observed mutant allele
fraction off the fraction of Class II expression attributable to tumor
was approximately 2f and not greater than 100%. Three genes--CIITA, CD74,
and CTSS--were selected as core Class II pathway genes and assessed for
mutations (not excluding synonymous and UTR mutations) in TCGA (data
downloaded from TumorPortal (tumorportal.org/): BRCA, CRC, HNSC, DLBCL,
MM, LUAD; TCGA bulk download (tcga-data.nci.nih.gov): CESC, LIHC, PAAD,
PRAD, KIRP, TGCT, UCS; Synapse (synapse.org/#!Synapse:syn1729383): GBM,
KIRC, LAML, UCEC, LUSC, OV, SKCM; or the original TCGA publication
(cancergenome.nih.gov/publications): BLCA, KICH, STAD, and THCA). These
genes were selected based on their known roles in Class II expression and
their tight correlation with HLA-DRB1 across a cohort of 8500 GTEx
samples. Other genes with equivalent correlation with HLA-DRB1 (HLA-DRA1,
HLA-DPA1, HLA-DQA1, HLA-DQB1, and HLA-DPB1) were excluded because their
polymorphic nature makes them prone to false positive mutation calls.
Naturally, only a small fraction of patients had a mutation in CIITA,
CD74, or CTSS, and for some tumor types, there were no patients available
to analyze.
[0714] Sequences of original whole exome sequencing (WES) in Binary
Sequence Alignment/Map (BAM) format were visually assessed (IGV tool) to
confirm that the mutation was present in the tumor sample and not present
in the normal sample. Mutant vs. wildtype read counts were obtained from
corresponding RNA-Seq using pysam. Overall HLA-DRB1 expression was
determined based on expression data downloaded from the Genomic Data
Commons (gdc.cancer.gov), which was renormalized to sum to 1,000,000 over
the set of protein-coding genes. The fraction of HLA-DRB1 expression
attributable to the tumor (FIG. 19B) was estimated as min(1,2f), where f
is the fraction of RNA-Seq reads in CIITA, CD74, or CTSS exhibiting a
mutation.
Assessing Prediction Overall Performance on Natural Donor Tissues
[0715] Peripheral blood from seven healthy donors was profiled with a
DR-specific antibody as described in the section "Antibody-based
HLA-peptide complex isolation" above. Based on these results, two
datasets were defined: one for fitting multivariate logistic regressions
and another for evaluating the prediction performance of the regressions.
[0716] The first dataset was built by using the hit and decoy selection
algorithm previously described in relation to FIG. 4. In short, this
means representing each nested set with one hit peptide (the shortest
peptide in the nested set) and tiling length-matched decoys over genes
such that they overlap minimally with hits and minimally with each other.
However, two important details differ from algorithm outlined for FIG. 4.
First, the hits and decoys are selected from genes in the "tune"
partition (rather than the "test" partition), and second, decoys were
allowed to map to genes that showed zero hits. Logistic regression models
with MHC binding scores (from NetMHCIIpan or neonmhc2) as well as other
input features (expression, gene bias, etc.) were trained on this
dataset.
[0717] The second data set (used for evaluation), was built in an
identical manner, except it used the hits and decoys drawn from the
"test" partition. In addition to binding scores, the following variables
were used in a subset of the regressions, as shown in Table 5.
TABLE-US-00006
TABLE 5
NetMHCIIpan Derived from NetMHCIIpan. For each candidate
Affinity peptide, the strongest score was taken across
all DR alleles in the donor's genotype.
MS-based Derived from neonmhc2. For each candidate
binding score peptide, the strongest score was taken across
all DR alleles in the donor's genotype.
Expression Gene expression estimates were obtained by
analyzing data from bowtie2, RSEM, and
renormalization over protein-coding genes
only, values averaged over N samples
Hotspot Indicator variable (0/1) for whether the
candidate peptide shares at least one 9mer
with any of the previously published multi-
allelic datasets
Gene bias (1 + observed)/(1 + expected) per the
analysis
Cross Indicator variable (0/1) for whether the
presentation candidate peptide comes from a gene observed
to be cross-presented on MHC class II by DCs.
[0718] For the purpose of performance evaluation, all n hit peptides were
evaluated by the given logistic regression and scored along with a set of
499n decoys (randomly sampled without replacement from the complete set
of decoys). The top 0.2% of peptides in the combined set were labeled as
positive calls, and the positive predictive value (PPV) was calculated as
the fraction of positive calls that were hits. Note that since the number
of positives is constrained to be equal to the number of hits, recall is
exactly equal to PPV in this evaluation scenario. The application of a
consistent 1:499 ratio across alleles helps stabilize the performance
values, which are otherwise highly influenced by the number of hits
observed for each donor. This was deemed appropriate since it was assumed
the number of hits relates more to experimental conditions than intrinsic
properties of the donor's cells. The 1:499 ratio is not far from what
would be used if down-sampling was not implemented.
[0719] While preferred embodiments of the present invention have been
shown and described herein, it will be obvious to those skilled in the
art that such embodiments are provided by way of example only. It is not
intended that the invention be limited by the specific examples provided
within the specification. While the invention has been described with
reference to the aforementioned specification, the descriptions and
illustrations of the embodiments herein are not meant to be construed in
a limiting sense. Numerous variations, changes, and substitutions will
now occur to those skilled in the art without departing from the
invention. Furthermore, it shall be understood that all aspects of the
invention are not limited to the specific depictions, configurations or
relative proportions set forth herein which depend upon a variety of
conditions and variables. It should be understood that various
alternatives to the embodiments of the invention described herein may be
employed in practicing the invention. It is therefore contemplated that
the invention shall also cover any such alternatives, modifications,
variations or equivalents. It is intended that the following claims
define the scope of the invention and that methods and structures within
the scope of these claims and their equivalents be covered thereby.
Example 11: High-Throughput Identification and Validation of HLA Class II
Allele Binding Epitopes
[0720] In this example, a representative reliable, high-throughput method
using time resolved fluorescence energy transfer (TR-FRET) for
identification and validation of novel MHC-II allele-binding peptides is
described. The assay has several parts, (1) transfecting cells with a
vector construct suitable for expressing and secreting MHC-II .alpha. and
.beta. chains having a fluorescence tag for the FRET assay, (2) purifying
the secreted MHC-II construct protein products, (3) performing a peptide
exchange assay (FIG. 22A). FIG. 22B and FIG. 23 further exemplify the
design and the procedure. The assay as described herein promotes fast and
efficient detection and validation protocol, as it may not require stable
cell lines, and encompasses simple isolation strategies. In addition, the
tetramer or multimer can be used to detect antigen-specific CD4 cells,
for example, after neonmhc2 predicted epitopes are administered in vivo,
and the immune response generated thereafter is used to verify CD4+ T
cell response.
CLIP-TR-FRET Assay for Identifying High Affinity MHC Class-II Binding
Peptides
[0721] Presented herein are exemplary vectors for expression of HLA class
II .alpha. and .beta. chains driven by a CMV promoter in a single
construct, the protein product of which yields a properly folded .alpha.
and .beta. chain pairs. In a properly folded .alpha. and .beta. chain
form, the .alpha.1 subunit and the .beta.1 subunit are in dimer form, the
.alpha.1 subunit and the .beta.1 subunits forming the open accepting end,
capable of accepting a peptide, resembling physiological configurations.
For the purpose of this assay, these vector expressed HLA protein
products with the properly folded .alpha. and .beta. chain form are
called HLA monomers. The expression construct comprises a linker, one or
more peptide cleavage sites, secretion signal, dimerization factors, for
example c-Fos and Jun, linked with a biotinylation motif (BAP) and a
10.times.-His-Tag (SEQ ID NO: 20). A placeholder peptide is used to
stabilize the monomers and help in secretion. A placeholder peptide can
be a CMV peptide. A placeholder peptide can be a CLIP peptide. A
placeholder peptide can be a peptide identified via MS based ligandome
for the alleles. A placeholder peptide can be bound covalently to the HLA
peptides at the open .alpha.1-.beta.1 peptide accepting end.
[0722] An exemplary construct used herein encodes a CLIP placeholder
peptide with a thrombin cleavage moiety placed between the CLIP and the
.beta. chain, as shown in FIG. 23 (upper panel). Upon transfection and
culture of the transfected cells, such that they reach optimal growth,
the cell culture supernatant (medium) comprising the secreted proteins
(monomers) were collected and passed through nickel (Ni.sup.2+) columns
for purification (FIG. 24). Expression levels and purification was
examined by Coomassie staining. The 28-mer CLIP peptide remains
associated with the .beta. chain, which is cleaved by treatment with
thrombin (FIG. 24) and thereafter may be dislodged by competing with test
(e.g., candidate) HLA-Class-II binding peptide. A test peptide that
successfully dislodges the CLIP peptide is accountably a cognate peptide
for binding to the MHC-II heterodimer of the construct, based on ability
to displace the CLIP peptide as measured by its IC50. A de novo test
peptide could be used for a competitive displacement reaction as
described above can then be identified by mass spectrometry (MS).
[0723] A large collection of HLA-DR heterodimer constructs were made with
CLIP placeholder peptides which were successfully secreted and peptide
exchange assays were performed.
[0724] It was observed that the peptide placeholder CLIP, derived from
CD74, has significant effect on the secretion of HLA class II monomers.
The edited canonical CLIP peptide having the CD74 sequence
PVSKMRMATPLLMQA (SEQ ID NO: 1) (designated as CLIP0 in FIG. 25A-25C) was
generally used as placeholder sequence. However, it was seen that some
HLA-DR peptides, for example, DRB1*12:01 and DRB1*13:02 had low yield
with the canonical peptide (Table 6). It was observed that certain HLA
DRB allelic dimers have a binding sequence longer sequence covering the
whole or parts of the amino acids in the sequence:
LPKPPKPVSKMRMATPLLMQALPM (SEQ ID NO: 21) (CLIP1) (FIG. 25A). Indeed,
using CLIP1 sequence instead of CLIP0 sequence in case of DRB1*12:01 and
DRB1*13:02 improved the secretion yield of the HLA dimers (FIG. 25B-25C).
TABLE-US-00007
TABLE 6
SEQ ID Yield
DRB1* Peptide placeholder NO: (mg/L)
01:01 PVSKMRMATPLLMQA (CLIP) 1 20
LPLKMLNIPSINVH (CMV) 22 100
PKYVKQNTLKLAT (HA) 23 85
12:01 PVSKMRMATPLLMQA (CLIP) 1 <3
13:02 PVSKMRMATPLLMQA (CLIP) 1 <3
De Novo Screen of Peptides by Successful Peptide Exchange Assay Using
STII-TR-FRET
[0725] Peptides can be screened de novo using the assay involving
expressing HLA-monomer proteins described above in cell lines, such as
Expi293 cells, collected and purified from the supernatant, and subjected
to peptide exchange assay. HLA class II binding peptides predicted by the
prediction algorithms were tested using peptide exchange assay. Peptides
exchange assay can be performed using a method involving fluorescence
polarization. For example, any fluorophore can be used to label either
the placeholder peptide, or to label the test peptide, or to label both
using two different fluorophores. Change is florescence either by loss of
the bound placeholder peptide that was previously labeled with a
fluorophore, or by fluorescence emission of a released fluorophore that
was otherwise quenched by biochemical reactions in its HLA bound form,
can be recorded for quantitative assessment of the displacement reaction.
Alternatively, replacement of a non-fluorescent placeholder peptide with
a labeled fluorescent peptide could be recorded to quantitatively
determine the displacement reaction. In an exemplary assay, FITC-labeled
placeholder CLIP peptide was used to displace an existing covalently
bound peptide such as a CMV peptide. The FITC-labeled peptide when bound
with HLA induces high polarization. When the FITC-placeholder peptide is
titrated with a test peptide, the test peptide displaces the FITC-CLIP,
which leads to lowering of fluorescence.
[0726] A peptide exchange assay can also be performed using time resolved
FRET (TR-FRET) technology instead of fluorescence polarization as
described herein. In an exemplary TR-FRET assay described herein, cells
were transfected with an HLA monomer construct having a placeholder
peptide that comprises a Streptag II (STII) moiety. The STII moiety was
detected by an Alexa-647-tagged antibody for STII. At the same time, the
His-tag attached to the Jun terminal of the monomer construct described
earlier in this example, which is present close to the .alpha.2-.beta.2
end of the HLA peptides, was detected by an Europium III (Eu) compound
coupled anti-His antibody (FIG. 26A). The Eu complex acts as an energy
donor, whereas the Alexa647 acts as the acceptor in the FRET reaction
when the placeholder peptide remains bound to the HLA monomer. When a
test peptide displaced the STII-CLIP placeholder, Alexa-647-.alpha.STII
peptide is freed but it can no longer be detected by fluorescence. The
TR-FRET assay was found to be more reliable than the fluorescence
polarization. Additionally, the assay had much reduced background signal.
(Fluorescence readout data are shown in FIGS. 26B-26E). The assay
provides high throughput identification platform for HLA-peptide pairs.
As shown in Table 7 below, the test peptide (or candidate peptides) P-156
to P-191 exhibited a wide range of displacement capabilities, and binding
affinity as determined by the calculated IC50 with each run. Lower IC50
demonstrates higher displacement capability, and higher binding affinity.
TABLE-US-00008
TABLE 7
IC50 (nm) IC50 (nm) Avg IC50 Std. Dev
Peptide Run 1 Run 2 (nM) (nM) Comments
P- 156 36294 38246 37270 976
P- 157 2550 2243 2397 154
P- 158 5786 5815 5800 15
P-159 58 94 76 18
P-160 1668 2020 1844 176
P-161 13541 14401 13971 430
P-162 3298 3636 3467 169
P-163 >40,000 >40,000 >40,000 N/A <50%
displacement
at 40 .mu.M
P-164 4553 4353 4453 100
P-165 357 422 389 32
P-166 3448 3104 3276 172
P-167 6612 5906 6259 353
P-189 4597 -- -- --
P-190 1137 -- -- --
P-191 5167 -- -- --
P-192 >40,000 No
displacement
up to 40 .mu.M
HA 23 23 23 0
ASP51 >40,000 >40,000 N/A N/A No
displacement
up to 40 .mu.M.
Peptide Exchange Validation Using Differential Scanning Fluorometry (DSF)
[0727] In this method a high throughput assay for screening peptides that
can bind to a particular HLA allele and also, the intensity of the
peptide binding to the HLA dimer is determined (FIG. 26F). In this assay
a fluorescent probe is used, which binds to the hydrophobic residues of a
protein and therefore can bind to the MHC alleles, only when the alleles
dissociate from each other by application of heat. When an MHC class II
dimer binds a cognate peptide, the dimers are held together in its
dimeric form. When heat is applied to an MHC dimer-peptide in bound form,
the weak binding peptides dissociate faster from the MHC class II protein
dimer, allowing the fluorophore to bind to the dissociated MHC alpha and
beta chains and producing high fluorescence. Fluorescence is recorded as
a function of temperature. Representative melting curves are shown in
FIG. 26F. Melting curves can be compared to determine the strong binders
(fluorescence detected at higher temperature) from weak binders
(fluorescence detected at lower temperature).
[0728] Use of Soluble HLA-DM (HLA-sDM) as a Catalyst for MHC Class II
Peptide Exchange:
[0729] HLA-DM is a natural chaperone and peptide exchange catalyst for
HLA-DR, -DP, and -DQ molecules. It is an integral membrane protein and
occurs as a heterodimer of alpha and beta polypeptide chains (DMA and
DMB). Peptide exchange as described in this section is performed using a
soluble form of HLA-DM (e.g., HLA-sDM protein) as chaperon for the
HLA-DR, -DP, and -DQ exchanges. HLA-sDM protein is produced via a
transient transfection in Expi-CHO cells as shown in FIG. 26G. Briefly, a
recombinant HLA-sDM construct is designed, as shown graphically in FIG.
26G upper half. The recombinant HLA-sDM construct comprises a CMV
constitutive promoter, upstream of a leader sequence and operably linked
with the promoter. The leader sequence helps in the secretion of the
product (secretion signal). At the 3'-end of the leader sequence a coding
sequence for HLA-DM beta chain ectodomain (and lacks a transmembrane
domain) is introduced. A sequence encoding a biotinylation motif (BAP) is
ligated 3' of the beta chain-encoding sequence. A sequence encoding the
HLA-DM alpha chain ectodomain (and lacks a transmembrane domain) is
placed with a secretion sequence (leader) at its 5' end, separated from
the BAP sequence by an intervening ribosomal skipping sequence. The
HLA-DM alpha chain sequence is ligated at the 3' end with a
10.times.HIS-tag (SEQ ID NO: 20). Once formed the heterodimeric HLA-sDM
is secreted outside the cell. When this construct is expressed in
Expi-CHO cells the HLA-sDM protein is secreted into the medium culture
medium.
[0730] Expi-CHO cells were transfected with a plasmid vector expressing
the HLA-sDM construct, and cultured over a period of about 14 days. The
protein was secreted into the culture medium over the period of culture.
The HLA-sDM protein was purified from the culture in a process very
similar to purifying MHC-II proteins. MHC-II peptide exchange can be
performed efficiently with acid and HLA-sDM, or without acid, and with
octyl glucoside. Size exclusion chromatography was performed to assess
peptide exchange, results were as shown in FIG. 2611. All peptide
exchange assays were performed using of HLA-sDM or octyl-glucoside as the
catalyst.
HLA-Class II Tetramer (or Multimer) Repertoire
[0731] A large repertoire of HLA class II tetramers were generated for the
purpose of testing epitope: HLA binding and dissociation kinetics in a
biochemical assay. These class II tetramers thus generated are used for
assaying peptide binding and presentation. For example, the tetramers
were used in peptide exchange assay. As shown in FIG. 27A, 12 tetramers
were generated and stored at a concentration of greater than 15 mg/ml;
six tetramers and four at <5 mg/ml. HLA tetramers are used for flow
cytometry to identify neo-antigen reactive CD4+ T cells. Influenza virus
epitope (HA) and HIV epitopes were tested for T cell recognition when
presented by HLA tetramers (FIG. 27E).
[0732] FIGS. 27B-27D depict various subsets of HLA class II tetramers that
were generated and purified. As shown in FIG. 27B, a large repertoire of
DRB1 heterodimer tetramers were constructed and purified at greater than
15 mg/L concentration. FIGS. 27C and 27D summarize the coverage of human
MHC class II allele constructs produced and validated for fluorescent
based peptide binding assays. Table 8A, Table 8B and Table 8C provide
lists of the allelic tetramers manufactured, with corresponding secretion
yield concentrations of the purified product.
TABLE-US-00009
TABLE 8A
HLA heterodimer Secretion Yield
DRB1*01:01 >15 mg/L
DRB1*04:01 >15 mg/L
DRB1*04:02 >15 mg/L
DRB1*04:04 >15 mg/L
DRB1*04:05 >15 mg/L
DRB1*08:01 >15 mg/L
DRB1*09:01 >15 mg/L
DRB1*11:01 >15 mg/L
DRB1*13:03 >15 mg/L
DRB1*14:01 >15 mg/L
DRB1*15:03 >15 mg/L
DRB1*01:02 >15 mg/L
DRB1*11:04 >15 mg/L
DRB1*15:02 >15 mg/L
DRB4*01:01 >15 mg/L
DRB1*07:01 5-15 mg/L
DRB1*13:01 5-15 mg/L
DRB1*13:02 5-15 mg/L
DRB1*15:01 5-15 mg/L
DRB1*15:02 5-15 mg/L
DRB3*01:01 5-15 mg/L
DRB1*08:03 5-15 mg/L
DRB1*11:02 5-15 mg/L
DRB1*16:02 5-15 mg/L
DRB3*02:01 5-15 mg/L
DRB3*02:02 5-15 mg/L
DRB3*03:01 5-15 mg/L
murine I-Ab 5-15 mg/L
DRB1*03:01 <5 mg/L
DRB1*12:01 <5 mg/L
DRB5*01:01 <5 mg/L
DPA*01:03/DPB*04:01 <5 mg/L
TABLE-US-00010
TABLE 8B
HLA heterodimer Secretion Yield
DPB1*05:01 >15 mg/L
DPB1*13:01 >15 mg/L
DPB1*03:01 5-15 mg/L
DPB1*04:02 5-15 mg/L
DPB1*06:01 5-15 mg/L
DPB1*11:01 5-15 mg/L
DPB1*01:01 <5 mg/L
DPB1*02:01 <5 mg/L
DPB1*02:02 <5 mg/L
DPB1*04:01 <5 mg/L
DPB1*17:01 <5 mg/L
TABLE-US-00011
TABLE 8C
HLA heterodimer Secretion Yield
A1*02:02 + B1*06:02 >15 mg/L
A1*02:01 + B1*02:02 >15 mg/L
A1*01:03 + B1*06:03 >15 mg/L
A1*02:01 + B1*03:03 >15 mg/L
A1*01:02 + B1*06:04 5-15 mg/L
A1*05:01 + B1*02:01 <5 mg/L
A1*05:05 + B1*03:01 <5 mg/L
A1*01:01 + B1*05:01 <5 mg/L
A1*03:01 + B1*03:02 <5 mg/L
A1*03:03 + B1*03:01 <5 mg/L
[0733] The MHC-II tetramer product pipeline further includes DRB3, 4, and
5 alleles, and DP and DQ alleles.
Peptide Exchange Validation Using Fluorescence Polarization (FP)
[0734] Fluorescence polarization microscopy was used in an assay to
distinguish peptide bound to MHC class II proteins versus free peptides.
A fluorescence-tagged placeholder peptide when bound to an MHC class II
dimer, results in high polarized light by fluorescence polarization (FP)
microscopy, compared to its released form, when a non-fluorophore tagged
competing epitope peptide remains bound to the MHC class II dimer by
displacing the placeholder peptide. FIG. 28A exhibits the principle via a
graphical representation. In brief, the assay is performed in the
following generalized method, and variations are either indicated in the
respective descriptions or are easily understood by one of skill in the
art.
[0735] Reagents as described in Table 9 (below) are assembled in a
reaction tube (e.g., 1.5 ml Eppendorf tube), mixed well and incubated at
37.degree. C. for 2 hours. 25 ml of 10.times.PBS is added to the mixture
at the end of incubation time to neutralize the peptide exchange
reaction.
TABLE-US-00012
TABLE 9
Stock Final
Ingredients Concentrations Concentration
Thrombin digested MHC Variable 5 .mu.M
class II allele
Exchange Peptide/ 10 mM 50 .mu.M
peptide of interest
Sodium Acetate, pH 5.2 1M 100 mM
Sodium Chloride 5M 50 mM
Soluble DM variable 5 .mu.M
MiliQ water Up to 100 .mu.l
[0736] The exchanged peptide is detected, for example, by staining; or
stored at -80.degree. C. by snap freezing in liquid nitrogen for
evaluation later.
[0737] FIGS. 28B and 28C provide an overview of the assay development for
HLA DRB1*01:01, using FP, and the various conditions used. In some
examples, the effect of pH on the assay was determined. In short, both
the full length and the soluble alleles are expressed in cells. The
membrane bound full length allele form is harvested by permeabilizing the
membrane, while the secreted form is harvested from the cell supernatant.
The harvested HLA class II proteins are purified by passing through
nickel (Ni.sup.2+) columns. In some examples, effect of detergent (1%
Octyl glucoside vs 1.6% NP40 was evaluated in membrane permeabilization
for harvesting full length MHCII alleles. In some examples, effect of
temperature, or the probe used, or the purification methods or the target
format were individually evaluated (FIG. 28B).
[0738] Effect of purification method using either conformation specific
antibody L243 or His-tag purification were evaluated. The results are
shown in FIG. 28D. Each data point is depicted in the table on the left
and is represented as a single dot in the graph on the right. The dots in
the graph align roughly along a 45-degree angle to either axes, and with
a r value of 0.9621, which indicate that the IC50 values from both the
purification methods are in agreement with each other. It also shows that
the rank order of peptide potencies does not change between the
purification methods.
[0739] Effect of the choice of the HLA class II proteins in soluble form
(sDR1) versus the full-length form (fDR1) was evaluated and FIG. 28E
shows that choice of target format does not affect the peptide potency.
Shown on the left are average IC50 values from experiments using sDR1
form or fDR1. These data are plotted to obtain the graph on the right
hand side. The data points, each depicted by a single dot, align roughly
along a 45 degree angle to either axes, and with a r value of 0.9365,
which indicate that the IC50 values from both the forms used correlate
well with each other. It also shows that the rank order of peptide
potencies does not change between the purification methods.
[0740] FIG. 28F shows a graphical view of an exemplary evaluation method
of neonmhc2 and NetMHCIIpan predicted peptides in binding assay and
identification of discordant peptides. Fluorescence polarization assay
was used to evaluate the Neonmhc2 and NetMHCIIpan predicted peptides in
actual peptide binding assays. For the assay, 60 nM of thrombin digested
soluble HLA-DRB1*15:01 was incubated with an FITC-tagged super binder
probe peptide (PVVHFFK(FITC)NIVTPRTPPY (SEQ ID NO: 24)) (10 nM per assay)
and the assay peptide for 5 hours at 37.degree. C. in an assay buffer (pH
5.2). Fluorescence polarization was examined from which, % probe
displacement was calculated. As shown in FIG. 28G, inhibition of the
super binder fluorescent peptide was proportional to the concentration of
the predicted peptide indicating good specificity of the assay. Striking
differences are seen between the performances of the Neonmhc2 and the
NetMHCIIpan predicted peptides. With Neonmhc2 predicted peptides more
peptides were positively bound, and with higher degree of inhibition;
whereas the NetMHCIIpan predicted peptides were overall poor performers
in comparison to the Neonmhc2 peptides. FIG. 2811 summarizes the
evaluation of Neonmhc2 predicted peptides in binding assay. As depicted
by the pie charts, of the double negative peptides (peptides that were
not predicted by any of the NetMHCIIpan or Neonmhc2) only 5% turned out
to be binders and 95% non-binders. Of the NetMHCII predicted peptides,
40% were binders by fluorescence polarization detection of probe
displacement assay, and of the Neonmhc2 predicted peptides to be binders,
100% were found to be true binders by the probe displacement assay.
[0741] FITC-labelled probes were prepared by reviewing previously
published peptides shown by Sette et al., to bind specific alleles. These
peptide sequences were then analyzed using predicted class II binding
core to identify the minimal 9-mer core of the peptide and the anchor
residues. This information was then considered when selecting a residue
position for lysine substitution and FITC labelling. For example, in the
table below (Table 10) the sequences as described in Sette et al. (Sidney
J, Southwood S, Moore C, et al. Measurement of MHC/peptide interactions
by gel filtration or monoclonal antibody capture. Curr Protoc Immunol.
2013; Chapter 18:Unit-18.3. doi:10.1002/0471142735.im1803s100)
(hereinafter "Sette's Sequences")) are listed. The predicted class II
binding core for each peptide were underlined in the context of a
specific allele. The bold font denotes anchor positions that were
identified as a result of epitope improvement. In some cases, the same
peptide sequence can be used for different alleles.
TABLE-US-00013
TABLE 10
Shorthand SEQ ID Probe SEQ ID
ID Allele Sette's Sequences NO: Sequences Selected NO:
SB-DR7/11 DRB1*07:01 YATFFIKANSKFIGITE 25 YATFFI ANSKFIGITE 25
SB-DR7/11 DRB1*11:01 YATFFIKANSKFIGITE 25 YATFFI ANSKFIGITE 25
SB-DR9 DRB1*09:01 TLSVTFIGAAPLILSY 26 TLSVTFIGAAP ILSY 29
SB-DR4/15 DRB1*15:01 PVVHFFKNIVTPRTPPY 27 PVVHFF NIVTPRTPPY 27
SB-DR4/15 DRB1*04:01 PVVHFFKNIVTPRTPPY 27 PVVHFF NIVTPRTPPY 27
SB-DR3 DRB1*03:01 YARIRRDGCLLRLVD 28 YARI RDGCLLRLVD 30
[0742] Based on positioning as described above, an internal lysine for
FITC conjugation was chosen by focusing on positions within the binding
core (underlined) that are not in blue-highlighted in red were these
positions as appropriate positions for FITC conjugation. For sequences
that did not have an internal lysine for FITC conjugation, a manual
approach was undertaken where a comparison to an allele's binding motif
to the peptide sequence was performed, and a position for internal lysine
substitution was selected for the DRB1*09:01 and DRB1*03:01 peptides (see
above table). More specifically, a leucine residue for DRB1*09:01, and an
arginine residue for DRB1*03:01 were substituted with lysines to allow
for FITC conjugation. This substitution strategy was based on the
MAPTAC-derived motifs, where manual identification of positions with no
strong amino acid preference (also in the middle of the neonmhc2
predicted 9-mer core) because the conjugated fluorophore may be more
likely to emit polarized light when bound (i.e., more restricted motion
of the fluorophore).
Example 12: HLA Class II Binding and Processing Rules for Identifying
Therapeutically Targetable Cancer Antigens
[0743] Increasing evidence indicates CD4+ T cells can recognize
cancer-specific antigens and control tumor growth. However, it remains
difficult to predict the antigens that will be presented by human
leukocyte antigen class II molecules (HLA class II)--hindering efforts to
optimally target them therapeutically. Obstacles include inaccurate
peptide-binding prediction and unsolved complexities of the HLA class II
pathway. In this Example, an improved technology for discovering HLA
class II binding motifs is described. Further, described herein is a
comprehensive analysis of tumor-ligandomes conducted to learn processing
rules relevant in the tumor microenvironment (TME).
[0744] 40 HLA class II alleles were profiled and it was shown that binding
motifs are highly sensitive to HLA-DM, a peptide loading chaperone. The
intratumoral HLA class II presentation was revealed to be dominated by
professional antigen presenting cells (APCs), rather than cancer cells.
Integrating these observations, algorithms were developed as described
herein, that accurately predict APC ligandomes, including peptides from
phagocytosed cancer cells. These tools and biological insights can
enhance HLA class II directed cancer therapies.
[0745] A promising new class of therapies seeks to treat cancer by
inducing T cell responses against cancer antigens and somatically mutated
sequences called neoantigens. At present, these efforts have focused
primarily on eliciting CD8+ T cell responses toward HLA class I (HLA
class I) presented ligands. However, several recent studies have shown
that CD4+ T cells can also recognize HLA class II presented ligands and
contribute to tumor control. Cancer vaccines and other immunotherapies
would ideally take advantage of directing CD4+ T cell responses, but
current efforts have forgone HLA class II antigen prediction entirely
because the accuracy of current prediction tools is inadequate.
[0746] A key factor preventing the accurate identification of HLA class II
cancer antigens is the availability of comprehensive, high-quality data
required to learn the rules of peptide binding. Data are needed for the
three highly polymorphic canonical HLA class II loci, HLA-DR, -DP, and
-DQ, wherein each allelic variant exhibits distinct peptide binding
preferences. A widely used method to define peptide-binding motifs is a
biochemical assay that measures the affinity of a single peptide in the
absence of physiological chaperones, such as HLA-DM. Measured affinity
data coverage is limited to common Caucasian HLA-DR alleles, and even for
these alleles, prediction accuracy significantly lags that of HLA class
I. In principle, mass spectrometry (MS)-based ligandomics should enable
improved prediction by offering scalability and endogenous
peptide-loading conditions. Nonetheless, natural ligandomes are
multi-allelic, concealing the peptide-to-allele mapping information
required to obtain accurate training data. There has been progress
solving this problem for HLA class I, which uses both deconvolution and
mono-allelic HLA class II cell lines mono-allelic HLA class II ligandome
datasets have been generated using low-throughput transgenic mouse models
HLA class II deficient cell lines, or cell lines that have homozygous
HLA-DR allele.
[0747] Another challenge is the ambiguity around which tumor antigens are
most likely to enter the HLA class II presentation pathway. Recent
MS-based studies have surveyed the HLA class II ligandomes of tumor
samples but have not addressed if professional APCs or the cancer cells
are presenting the therapeutically relevant HLA class II antigens.
Furthermore, it is not currently known whether HLA class II processing of
tumor antigens is primarily dependent on phagocytosis or autophagy.
Depending on which pathway dominates in the relevant cell type, there
could be drastic differences in terms of which proteins are preferred as
sources for HLA class II peptide ligands. Compounding the problem, there
is no systematic approach for determining which regions within proteins
are most likely to produce HLA class II ligands, even though prevailing
theories hold that protein sequence features should influence HLA class
II processing potential.
[0748] To investigate the processing and presentation rules of
therapeutically targetable HLA class II antigens, a two-pronged approach
of i) improving peptide-binding prediction and ii) determining how HLA
class II ligands are processed and presented in the TME was followed. In
order to learn allele-specific peptide binding rules, a scalable
mono-allelic HLA ligandome profiling workflow called MAPTAC.TM.
(Mono-Allelic Purification with Tagged Allele Constructs) was developed,
that utilizes MS to sequence endogenously presented HLA class II ligands.
MAPTAC.TM. allowed to clearly resolve peptide binding motifs for 40 HLA
class II alleles and train binding prediction algorithms that could
accurately identify immunogenic viral epitopes and neoantigens. To
improve HLA class II processing prediction, tumor samples were analyzed,
establishing professional APCs as the primary source of intratumoral HLA
class II expression and defining the set of genes and gene regions
preferentially processed by these cells. It was then demonstrated that
algorithms that integrate binding and processing features can predict
natural APC ligandomes and, more importantly, the subset of HLA class II
ligands derived from endocytosed cancer cells. These advances in
understanding the processing and presentation rules of therapeutically
relevant HLA class II antigens will enable therapies that aim to harness
CD4+ T cell responses.
Experimental Procedures
MAPTAC.TM. Construct Design and Cell Culture
[0749] For HLA class I, the .alpha.-chain was fused with a C-terminal GSG
linker, followed by the biotin-acceptor-peptide (BAP) sequence, a stop
codon, and a variable DNA barcode, and cloned into the pSF Lenti vector
(Oxford Genetics). The HLA class II constructs were similarly cloned into
pSF Lenti and consisted of the .beta.-chain sequence with the same
linker-BAP sequence fused on the C-terminus, followed by another short
GSG linker, an F2A ribosomal skipping sequence, the sequence of the
.alpha.-chain with a C-terminal HA tag, a stop codon, and a variable DNA
barcode. MAPTAC.TM. constructs were transfected or transduced into
Expi293, HEK293T, A375, HeLa, KG-1, K562 and B721.221 cells.
HLA-Peptide Isolation Protocols
[0750] Flash frozen cell pellets containing 50.times.10.sup.6 cells
expressing BAP-tagged HLA were thawed on ice for 20 minutes and gently
lysed by hand pipetting in 1.2 mL cold lysis buffer. After clearing DNA,
RNA, and cellular debris, supernatants were transferred to new 1.5 mL
tubes and BAP-tagged HLA were biotinylated by incubation at room
temperature for 10 minutes with 0.56 .mu.M biotin, 1 mM ATP, and 3 .mu.M
BirA. The biotinylated lysates were incubated with 200 .mu.L of
NeutrAvidin resin at 4.degree. C. for 30 minutes to affinity-enrich
biotinylated HLA-peptide complexes. After washes, the HLA-bound resin was
pelleted by centrifugation at 1,500.times.g at 4.degree. C. for one
minute and stored at -80.degree. C. or immediately subjected to
HLA-peptide elution and desalting using Sep-Pak solid-phase extraction.
For profiling the endogenous HLA class II ligandomes of healthy donor
materials, HLA-peptide complexes were isolated using in-house generated
anti-HLA-DR antibody L243 or with the commercially available TAL 1B5
antibody.
HLA-Peptide Sequencing by Tandem Mass Spectrometry
[0751] All nanoLC-ESI-MS/MS analyses employed the same LC separation
conditions, instrument parameters, and data analytics. Briefly, samples
were chromatographically separated using a Proxeon Easy NanoLC 1200
fitted with a PicoFrit column packed in-house with C18 Reprosil beads and
heated at 60.degree. C. During data-dependent acquisition, eluted
peptides were introduced into an Orbitrap Fusion Lumos mass spectrometer
equipped with a Nanospray Flex Ion source. Mass spectra were interpreted
using the Spectrum Mill software package v6.0 pre-Release. Identified
peptides that passed the PSM FDR estimate of <1% were filtered for
contaminants by removing all peptides assigned to the 264 common
contaminants proteins in the reference database and by removing peptides
identified negative control MAPTAC.TM. affinity pulldowns. Additionally,
all peptide that mapped to an in silico tryptic digest of the reference
database were removed to account for tryptic sample carry-over. Raw mass
spectrometry datasets will be deposited in MassIVE upon acceptance
(massive.ucsd.edu).
Machine Learning Approaches for Binding Motifs and Binding Prediction
[0752] For each allele, an ensemble of convolution neural networks was
trained to distinguish MAPTAC.TM. peptides from scrambled decoys. Each
network comprised two ReLU-activated convolutional layers, each with 50
6-wide filters. The maximum and average activation per filter per layer
were routed into a final dense layer with sigmoid activation.
Regularization was achieved through L2-norm, 20% spatial dropout after
each convolutional layer, and early stopping, and tuned per allele
according to a hold-out partition of non-redundant peptides
(.about.12.5%). In performance benchmarking, NetMHCIIpan-v3.1 predictions
were calculated as the maximum-scoring 15mer within each query peptide,
an approach which performed uniformly better than the native
NetMHCIIpan-v3.1 predictions.
CD4+ T Cell Induction Assay
[0753] PBMCs were co-cultured with peptide pulsed mDCs at a 1:10 ratio for
a total of 3 stimulations. Induced T cells were then labelled with a
unique two-color barcode as described previously and cultured overnight
at a 1:10 ratio with peptide pulsed and matured autologous mDCs. Cells
were subsequently assessed for production of IFN-.gamma. in response to
peptide by flow cytometry. Induction samples that positively responded to
peptide were samples that induced IFN-.gamma. production at 3% higher
than the no peptide control.
APC Endocytosis of SILAC-Labeled Tumor Cells
[0754] K562 cells (ATCC, Manassas, Va.) were grown for 5 doublings in RPMI
media for SILAC (ThermoFisher) containing the heavy isotopically amino
acids, L-Lysine 2HCl .sup.13C.sub.6 .sup.15N.sub.2 (Life Technologies)
and L-leucine .sup.13C.sub.6 (Life Technologies). Monocytic derived
dendritic cells (mDCs) were co-cultured at a 1:3 ratio either overnight
with UV-treated K562 cells or for 5 h with lysate generated following
HOC1 treatment. Cells were harvested, pelleted, and flash frozen in
liquid nitrogen for proteomic analysis.
Results
MAPTAC.TM.: A Scalable Platform for Mono-Allelic HLA Class II Ligand
Profiling
[0755] Current knowledge of HLA class II binding motifs is based primarily
on data generated using two biochemical binding assays. In one such
former approach, an assay peptide and a radio-labeled competitor peptide
are co-incubated with cellularly-derived HLA extracts to determine an
IC50. In another approach, a conformationally specific antibody measures
the proportion of HLA bound to the assay peptide in order to determine an
EC50. Data from these assays are compiled in the Immune Epitope Database
(IEDB) and used to train HLA class II prediction algorithms such as
NetMHCIIpan. The five most common Caucasian HLA-DRB1 alleles are
well-supported in IEDB (3326-8967 peptides each), though only about 29%
of these are strong binders (affinity<100 nM), and 85% of IEDB
peptides overall are exact 15mers (FIG. 12B, FIG. 12E). HLA-DP and HLA-DQ
alleles and non-Caucasian HLA-DR alleles (e.g. HLA-DRB1*15:02) are
supported by considerably less data.
[0756] To create a high-quality dataset with the allelic breadth to
support a diverse patient population, the MAPTAC.TM. was developed, a
technology that enables efficient isolation of HLA class II peptides
binding a single allele for MS-based identification (FIG. 11A). The alpha
and beta chains of a chosen HLA class II heterodimer are encoded by a
genetic construct with a biotin-acceptor peptide (BAP) sequence placed at
the C-terminus of the beta chain Since HLA-DRA is functionally invariant,
MAPTAC.TM. yields mono-allelic HLA-DR results regardless of potential
pairing between exogenous beta chain and endogenous alpha chain. For
HLA-DP and HLA-DQ, which exhibit a limited set of functional alpha chain
variants, the cell line is chosen to have matching or non-expressed alpha
chain alleles. Importantly, this approach also works for HLA class I,
with the BAP tag appended to the HLA class I heavy chain.
[0757] A 48-hour transfection achieved robust expression of the MAPTAC.TM.
construct (FIG. 12C) with normal levels of cell surface presentation
(FIGS. 34A and 34B). This was demonstrated in 7 distinct cell lines
(expi293, A375, KG-1, K562, HeLa, HEK293, and B72.221) and for 40 HLA
class II alleles, providing data for all three canonical HLA class II
loci: HLA-DR, -DP, and -DQ. The average number of unique peptide
identifications per replicate (.about.50 million cells) that passed
quality control filters ranged from 236 to 2580 across alleles (FIG. 29),
with a median of 1319 peptides. Several process variations were employed
to increase data depth, including HLA-DM over-expression, and peptide
reduction and alkylation. Only a small percentage of MS hits corresponded
to known contaminants, tryptic peptides, and mock transfections (empty
plasmid) (FIG. 11B and FIG. 29). Length distributions for MAPTAC.TM. HLA
class I and HLA class II peptides match those observed in previous MS
studies utilizing antibody-based pulldowns (FIG. 11C).
[0758] Among the MAPTAC.TM. HLA class II peptides, most amino acids were
represented at levels consistent with source proteome frequencies (FIG.
12D and FIG. 12F). Exceptions included C, M, and W, which were depleted
by 85%, 34%, and 42%, respectively, consistent with previous MS-based
studies of HLA class II peptides. Reduction and alkylation of HLA class
II peptides nearly tripled the frequency of C, though it was still
under-represented with respect to the proteome (FIG. 12F). Depletions of
C, M, and W were not observed in allele-matched high-affinity peptides
(<100 nM) from IEDB. Conversely, IEDB binders exhibited depletions of
D (-39%) and E (-37%) as well as an enrichment in M (+65%) when compared
to IEDB non-binders (>5000 nM). Thus, MAPTAC.TM. exhibits defined
biases that are in line with those observed with other technologies.
MAPTAC.TM. Resolves HLA Class II Peptide Binding Motifs
[0759] MAPTAC.TM. was used to resolve allele-specific HLA class II binding
motifs. 40 HLA class II alleles were profiled, 15 of which were
previously uncharacterized (<30 peptides with <100 nM affinity in
IEDB) including alleles common in non-Caucasian populations (DRB1*12:02,
DRB1*15:03, and DRB1*04:07). Since HLA class II peptides can be longer
than the number of residues in the binding groove, it is not immediately
evident which portion of each peptide is HLA-interacting (the "core") vs.
overhanging; however, resolving the binding core is critical to
characterizing binding motifs. To identify the binding core, peptides to
a consensus binding core were aligned using the tool GibbsCluster-2.0,
which uses an expectation maximization algorithm to iteratively nominate
a binding register for each peptide and re-learn the binding motif across
peptides. With few exceptions, binding core motifs for common HLA-DR
alleles showed strong agreement with IEDB-based motifs (FIG. 35).
MAPTAC.TM.-observed peptides did not always show strong NetMHCIIpan
scores for common alleles (FIG. 36A); yet, observed binders that were
poorly predicted by NetMHCIIpan were shown to have very strong measured
affinities (FIG. 36B) indicating that these observations are unlikely to
be false positives. Notably, MAPTAC.TM. motifs were always stable across
multiple cell lines (FIG. 36C).
[0760] Typically, MAPTAC.TM. and IEDB agreed on the highest frequency
amino acids at anchor positions (.about.4 most highly conserved
positions), but MAPTAC.TM. motifs generally showed lower entropy
(manifested by taller letter heights in sequence logos). Interestingly,
when cells were co-transfected with MAPTAC.TM. constructs and HLA-DM, the
entropy at anchor positions decreased even further for most alleles (FIG.
30A and FIG. 37A). This was consistently observed across 12 HLA-DR
alleles, showing HLA-DM's pervasive effect as a repertoire "editor" and
suggesting that models based on affinity assays that lack HLA-DM and
other loading chaperones may learn binding rules that don't apply in
vivo. The effect of HLA-DM was also evident with respect to the presence
of CLIP peptides. Without HLA-DM co-transfection, CD74-derived peptides
were observed in 10 HLA-DR alleles and matched known CLIP variants (FIG.
37C); meanwhile, CLIP peptides were not observed in any of our HLA-DM
co-transfection experiments.
[0761] The effect of HLA-DM was not evident for the HLA-DP alleles
analyzed (FIG. 30A and FIG. 37A), which may relate to the presence of an
unusual positively charged P1 anchor not previously reported. HLA-DM is
thought to act primarily on N-terminal side of bound peptides, as such,
the unusual P1 anchor was not a marker for HLA-DM insensitivity as HLA-DP
motifs with hydrophobic P1 anchors were also unaltered by the presence of
HLA-DM (FIG. 37A). On the other hand, HLA-DQB1*06:04/A1*01:02 was
profoundly affected by HLA-DM (FIG. 30A). Without HLA-DM co-transfection,
this allele's binding motif was not discernable, indicating that
chaperone-free loading onto some HLA-DQ alleles yields a large proportion
of non-physiological binders.
[0762] Given the availability of published multi-allelic HLA class II
datasets, whether our allele-specific peptides could have been
effectively identified was investigated, using in silico deconvolution
methods. Several groups have shown success in deconvolving HLA class I
allele motifs from multi-allelic HLA class I data; however, deconvolution
of HLA class II motifs is complicated by the need to simultaneously
resolve both the binding core and allele assignment of each peptide. To
assess the accuracy of HLA class II deconvolution, the HLA-DR ligandomes
were analyzed from eight samples profiled by pan-DR antibody (PBMCs and
published cell lines. For each dataset, twenty peptides were spiked in of
mono-allelic data matching each allele in the sample's genotype (1-2 DR1
alleles plus 0-2 DR3/4/5 alleles, depending on haplotype and zygosity.
GibbsCluster tool (which can also be used for deconvolution; was used to
partition peptides into groups and observed whether the spike-in peptides
were appropriately co-clustered according to their known origin allele.
In all cases, peptides were distributed across diverse clusters, showing
only modest association with the correct source alleles (FIG. 30B) and
indicating that HLA class II training data based on deconvolution is
likely to bear significant errors.
[0763] To understand the poor performance of the deconvolution, the
mono-allelic MAPTAC.TM. data was reviewed to determine the frequency of
"obvious" anchors that could serve as guideposts for GibbsCluster.
Accordingly, obvious amino acids (those with frequency >10%) at each
anchor position (the four positions with lowest entropy) for each HLA
class II allele were defined. Only 10-20% of peptides exhibit ideal
residues in all four anchor positions and as many as 50% exhibit two or
fewer obvious anchors (FIG. 30C). Given the low frequency of peptides
that exhibit most of the expected anchors, it is not surprising that a
large fraction of peptides would be hard to classify on a purely
computational basis. Thus, MAPTAC.TM. addresses a major source of
ambiguity that is non-trivial to resolve with in silico methods.
[0764] The motifs for HLA class I alleles could also be defined using
MAPTAC.TM.. This included alleles whose binding profiles were previously
undefined (e.g. B*52:01, common in Japan). For previously characterized
alleles, it was seen that there was good correspondence in the motifs
derived from affinity-based methods and previous mono-allelic MS studies.
Nonetheless, it was noted that some discrepancies exist with respect to
multi-allelic MS-based studies that employed deconvolution methods to
define motifs (FIG. 37B).
Algorithms Trained on MAPTAC.TM. Data Predict Immunogenicity
[0765] Whether MAPTAC.TM. data could generate HLA class II binding
predictors with improved accuracy was considered. Since the HLA-binding
subsequence of HLA class II peptides are not at a fixed position with
respect to the N- or C-terminus, the learning algorithm must dynamically
consider different binding core possibilities for each peptide. To
address this constraint, convolutional neural networks (CNNs) were
employed, which have been successful in the field of computer vision
because of their proficiency in translationally invariant pattern
recognition. For each allele, an ensemble of CNNs were trained (FIG.
31A), calling the overall predictor "neonmhc2."
[0766] To account for the fact that MS exhibits some degree of amino acid
residue bias, particularly against C, negative training examples (termed
decoys) were generated by randomly permuting the sequences of observed
binders (termed hits). As this approach carries the risk of learning
sequence properties of natural proteins, decoys were sampled randomly
from non-observed subsequences of peptide source genes of HLA class II
ligands. To calculate positive predictive value (PPV) for each allele, n
MS-observed peptides were scored in conjunction with 19n length-matched
decoys sampled from the same set of source genes, and each predictor's n
top-ranked peptides (i.e. the top 5%) were called as positives. PPV in
this case is identical to recall because the number of false positives
and the number of false negatives is equal. Calculating positive
predictive value (PPV) at a 1:19 hit-to-decoy ratio showed that neonmhc2
improved PPV relative to NetMHCIIpan in predicting MAPTAC.TM.-observed
peptides (FIG. 31B; Table 11).
TABLE-US-00014
TABLE 11
PPV for PPV for
MHC Class II allele NetMHCIIpan neonmhc2
DRB1*16:01 0.13 0.66
DRB1*15:01 0.17 0.61
DRB4*01:01 0.23 0.62
DPB1*02:01/DPA1*01:03 0.12 0.52
DRB1*11:04 0.22 0.60
DRB1*14:01 0.14 0.59
DRB1*13:03 0.08 0.58
DPB1*06:01/DPA1*01:03 0.01 0.48
DRB3*03:01 0.21 0.57
DRB1*03:01 0.35 0.55
DRB1*01:01 0.34 0.56
DRB5*01:01 0.35 0.54
DRB1*01:02 0.27 0.54
DRB3*01:01 0.43 0.54
DPB1*01:01/DPA1*01:03 0.08 0.53
DRB1*07:01 0.29 0.54
DRB1*04:04 0.22 0.55
DRB1*11:01 0.17 0.52
DRB1*15:03 0.02 0.50
DPB1*04:01/DPA1*01:03 0.27 0.52
DPB1*04:02/DPA1*01:03 0.25 0.53
DRB1*15:02 0.17 0.50
DRB1*10:01 0.15 0.48
DRB1*08:02 0.21 0.50
DRB1*13:01 0.16 0.49
DRB1*04:05 0.13 0.50
DRB1*09:01 0.19 0.47
DQB1*06:02/DQA1*01:02 0.14 0.49
DRB1*11:02 0.07 0.46
DRB1*12:01 0.1 0.46
DRB1*04:01 0.27 0.45
DRB1*04:02 0.1 0.46
DRB1*04:03 0.18 0.45
DRB3*02:02 0.22 0.45
DRB1*08:04 0.1 0.44
DRB1*12:02 0.12 0.44
DPB1*03:01/DPA1*01:03 0.03 0.48
DRB1*04:07 0.21 0.40
DRB1*08:01 0.11 0.37
DQB1*06:04/DQA1*01:02 0.06 0.37
DRB1*03:02 0.31 0.29
DRB1*08:03 0.1 0.24
DRB1*13:02 0.06 0.22
[0767] Saturation experiments, in which the training dataset size is
down-sampled to varying degrees, suggests that neonmhc2's performance is
data-limited and would likely improve with more data (FIG. 38Ai).
[0768] Analysis of the observation of low fidelity of HLA class II
deconvolution in FIG. 30B suggest that comparable prediction performance
could not have been achieved without mono-allelic data. To test this, a
recently published computational workflow that uses deconvolution to
train allele-specific binding predictors on multi-allele MS data (Barra
et al., 2018) was followed. Inspecting GibbsCluster logos for eleven
multi-allelic samples (the same samples in FIG. 30B), it was observed
that many clusters (13/32) did not bear any resemblance to an allele
known to be in the sample (FIG. 38Aii). Using pre-existing knowledge of
what the motifs should look like, only the legitimate clusters (marked in
FIG. 38Aii) were selected and predictors with the same CNN architecture
was built. These models were then evaluated alongside neonmhc2 on bona
fide mono-allelic data (a hold-out partition of MAPTAC.TM. data not used
for training) The models trained on deconvolved multi-allelic data
usually exceeded NetMHCIIpan but were inferior to MAPTAC.TM.-trained
neonmhc2 (FIG. 31E). The superiority of the monoallelic data was
maintained even when the MAPTAC.TM. dataset was down sampled such that
the size of the respective training data sets was identical.
[0769] In order to ensure that the apparent prediction improvements would
hold when evaluated on non-MS data, a large dataset of allele-specific
CD4+ memory T cell responses were curated which were detected by
tetramer-guided epitope mapping (TGEM). Notably, these tetramer data rely
on chaperone-free peptide exchange, so they may be subject to the same
biases as conventional affinity assays (Archila and Kwok, 2017).
Nonetheless, neonmhc2 out-performed NetMHCIIpan for all alleles with
sufficient data for assessment (at least 20 positive examples) (FIG.
31C). The performance (measured by PPV) of NetMHCIIpan was variable,
dropping as low as 5% for DRB1*15:01 (in contrast, neonmhc2's performance
never fell below 30% PPV), and approached that of neonmhc2 on only two
alleles, including the well-studied HLA-DRB1*01:01. On the other hand,
neonmhc2 showed convincing improvement on all other evaluated alleles,
including the two most common Caucasian HLA-DR alleles (DRB1*07:01 and
DRB1*15:01). These results indicate that prediction improvements of
neonmhc2 over NetMHCIIpan can be validated in a non-MS-based benchmark
and likely extend across most alleles.
[0770] To assess the therapeutic relevance of neonmhc2, it was determined
whether neonmhc2 could identify neoantigens capable of eliciting CD4+ T
cell responses in an ex vivo induction assay (see Methods). Focusing on
DRB1*11:01, which is a common allele with many affinity assay-confirmed
binders in IEDB (only surpassed by DRB1*01:01 and DRB1*07:01; FIG. 12E),
a set of The Cancer Genome Atlas (TCGA)-observed neoantigen sequences was
scored and a subset was selected that were preferred by neonmhc2 (top 1%
of predictions) but were not selected by NetMHCIIpan (bottom 90% of
predictions). This set was further refined by removing peptides that may
bind other HLA-DR alleles present in the induction materials. Most
neonmhc2-selected peptides (8/12) yielded CD4+ T cell responses as
measured by IFN.gamma. expression in response to recall with the peptide
(FIG. 31D, FIG. 38B and FIG. 38C). These results demonstrate that
MAPTAC.TM.-trained predictor can identify immunogenic HLA class II
neoantigen sequences not identified by NetMHCIIpan.
Professional APCs are the Dominant HLA Class II Presenters in the Tumor
Microenvironment
[0771] Having developed a technology that enabled both characterization
and prediction of HLA class II allele-specific peptide binding
preferences, it was sought to complement the binding prediction
improvements with further insights into antigen processing, which are
critical for prioritizing the protein sequences most likely to produce
HLA class II cancer antigens. To address these questions in the context
of the TME, non-MAPTAC.TM. datasets were analyzed including single cell
RNA-Seq and published MS-based studies that surveyed HLA class II
ligandomes in tumors. Which cell types in the microenvironment are most
likely to present therapeutically targetable cancer antigens was
considered. Currently, there is no consensus as to whether cancer
antigens are presented by professional APCs that have endocytosed tumor
proteins or by the tumor cells themselves. To that end, HLA-DRB1
expression was analyzed in five published single-cell RNA-Seq datasets
that profiled lung cancer, head and neck cancer, colorectal cancer,
ovarian cancer, and melanoma, and found that canonical APCs (macrophages,
dendritic cells, and B cells) express much greater levels of HLA class II
than the tumor cells and other stromal cell types in the TME. This
observation is consistent across multiple patients and tumor types (FIG.
19A). Because tumor cells can outnumber APCs in the TME, their lower
levels of HLA class II expression may nonetheless be immunologically
relevant. To assess how much of the overall HLA class II expression comes
from tumor cells vs. stroma, TCGA patients with mutations in HLA class
II-specific genes (focusing on CITTA, CD74, and CTSS) were identified and
determined what fraction of RNA-Seq reads exhibited the somatic variant
in order to impute what fraction of HLA-DRB1 expression derived from
tumor vs. stroma (FIG. 19B, see Methods). Based on mutations identified
in 153 patients representing 17 distinct tumor types, most HLA class II
expression appeared to arise from non-tumor cells. In fact, 45% percent
of patients showed zero tumor-derived HLA class II expression. Focusing
on the patients with highest levels of T cell infiltration (top 10%, as
identified using a previously published 18-gene signature (Ayers et al.,
2017), low tumor HLA-DR expression still appeared to be the norm, with
only 3 of 16 patients expressing >1000 TPM. To probe whether
immunotherapy disrupted this trend, we analyzed additional single-cell
RNA-Seq from checkpoint blockade-responsive tumor types and assessed
HLA-DRB1 expression before and after treatment. A melanoma cohort, which
included one confirmed responder, showed uniformly low HLA-DRB1
expression by tumor cells in both the pre-therapy and post-therapy
biopsies (FIG. 19C). A basal cell carcinoma cohort which showed a 55%
clinical response rate to anti-PD1 therapy, likewise exhibited low tumor
cell-derived HLA-DRB1 expression regardless of time point (FIG. 19C).
These results suggest that most intra-tumoral HLA class II presentation
is driven primarily by professional APCs and "hot" TME conditions do not
guarantee divergence from this general pattern.
Specific Genes have Privileged Access to the HLA Class II Presentation
Pathway
[0772] In order to determine source genes of epitopes that are
preferentially presented by tumor-resident APCs and whether they arise
from autophagy or endocytosis three published HLA class II ligandome
studies were analyzed, that were performed using tumor tissues.
[0773] First, the degree that each gene was represented in tumor HLA class
II ligandomes was quantified assuming that the number of observations for
each gene should be proportional to the product of its length and
expression level (FIG. 18B). A clear enrichment for proteins expressed in
human plasma was observed, especially albumin, fibrinogen, complement
factors, apolipoprotein, and transferrin, despite not being expressed in
the native tissue. Concerned that these identifications represent
non-specific binding in HLA ligandomes, neonmhc2 binding scores were
assessed for plasma-derived peptides in four PBMC HLA-DR ligandomes (FIG.
39A); the peptides displayed strong binding scores, suggesting that they
were HLA binding. Plasma-derived proteins were not significantly enriched
in tumor HLA class I ligandome data (FIG. 39B). The enrichment of plasma
genes in HLA class II ligandomes is consistent APCs "sipping"
extracellular proteins from tissue serum via micropinocytosis. Additional
enrichments for genes involved in leukocyte cellular adhesion was also
observed, such as ITGAM (11.times.-enriched), LCP1 (8.times.), ITGAV
(6.times.), and ICAM1 (6.times.) suggesting that APCs are actively
recycling their own membranes. MUC16, which was recently reported as
enriched in ovarian cancer HLA class I ligandomes, was not
over-represented.
[0774] Cellular localization was also considered to further interrogate
gene bias in the HLA class II antigen presentation pathway. When genes
were grouped by localization, secreted and membrane genes were
represented twice as often as expected based on gene expression,
underscoring an important role for macropinocytosis in shaping HLA class
II ligandomes. Nonetheless, more than half of HLA class II peptides arise
from compartments inconsistent with macropinocytosis, such as the nucleus
and cytoplasm. It was reasoned that if many of these genes are presented
via autophagy, then there should be a corresponding deficit of genes
known to be cleared by the proteasome. Indeed, proteins known to contain
ubiquitin sites generated peptides less often that would have been
expected based on their length and expression (FIG. 32C). Depletion was
also observed for proteins whose levels are known to increase upon
proteasome inhibition. These are patterns that would be expected for
autophagy but not necessarily for phagocytosis, suggesting that APC
peptide ligandomes partially represent their own intracellular proteomes.
[0775] To address the origin of HLA class II antigens presented by APCs in
the TME, it was considered whether it might be possible to directly
deconvolve the origin of source genes by determining whether nuclear and
cytosolic peptide identifications were more consistent with an
APC-specific or a bulk tumor gene expression profile (FIG. 39C). Though
there was significant uncertainty in the estimates (assessed by
regression-based model and bootstrap resampling; Supplemental Methods),
HLA class II ligandomes were best explained by a mixture of both tumor
and APC gene expression profiles. Taken together with the observed
depletion of proteasome-cleared proteins, this result suggests that
intratumoral APCs present a mixture of exogenous and endogenous proteins.
Some Gene Regions are Preferentially Processed but Lack Evident Cleavage
Motifs
[0776] There are multiple theories about which sequences are preferred for
antigen processing (FIG. 32D). According to one model, enzymes cleave
source proteins before they bind HLA class II, as is the case with HLA
class I (Sercarz and Maverakis, 2003). A second model poses that the
peptide binding occurs first and bound peptides are subsequently trimmed
by exopeptidase enzymes until they are sterically hindered by HLA class
II. In a third model, peptide cleavage events occur both before and after
HLA-binding. Because there are competing models for how HLA class II
peptides are generated, three different frameworks for prediction were
generated (FIG. 32D). The first assumes that endopeptidases dominate
("cleave first"); a second model assumes that HLA class II engages
full-length proteins that are subsequently trimmed inward by
exopeptidases ("bind first"); and a third model poses that enzymatic
digestion occurs both before and after HLA binding ("hybrid"). Each model
required a different algorithmic approach. Specifically, an algorithm
motivated by the cleave-first perspective should focus on the amino acids
motifs at the edges of MS-observed ligands); however, an algorithm
motivated by bind-first perspective would do better to ignore these
motifs and focus on local protein structural properties that dictate HLA
binding accessibility. A hybrid model-inspired algorithm should look
upstream and downstream of observed HLA class II peptides for candidate
precursor cut sites.
[0777] Of the three approaches considered, only the cleave-first algorithm
yielded a measurable improvement over baseline models (FIG. 32E and FIG.
40B). However, it appears that this approach learns hallmarks of
exopeptidase trimming present in the positive example peptides (e.g., a
penultimate proline signature (Barra et al., 2018)) as it failed to add
value if the exact cut sites of query peptides were masked (STAR
Methods).
[0778] Pivoting to a purely empirical approach, protein regions observed
in published HLA-DQ ligandomes (Bergseng et al., 2015) were catalogued
and used overlap to predict HLA-DR ligands. The overlap variable yielded
a modest improvement in prediction performance (3.1% increase in PPV on
average over neonmhc2 alone) (FIG. 32E). Assuming that HLA-DQ and HLA-DR
alleles share the same HLA-II processing environment but do not share
binding motifs, this result indicates certain gene regions are indeed
favored for processing but are not tied to cleavage motifs or
conformational properties in obvious ways.
[0779] Groups have reported positive results using the observed termini of
MS-observed peptides to train processing algorithms, an approach that
assumes the "cleave-first" model. However, in reviewing amino acid
enrichments adjacent to peptide termini in multiple distinct cell lines
and tissue types (FIG. 40A), patterns were observed that seemed more
consistent with post-binding trimming. These included the lack of
correspondence with the known motif of the HLA class II processing
enzyme, cathepsin S, and the enrichment of poorly-cleavable P at
penultimate peptide positions, a motif that could arise if P blocks the
procession of trimming enzymes. To test whether the "cleave-first"
assumption is correct, neural network models were trained on peptide
termini and evaluated them in two different ways: i) scoring cleavability
on the exact N- and C-termini of each peptide or ii) scoring the best
site in a range .+-.15AA of each peptide's predicted binding core
(Supplemental Methods). It was hypothesized that both approaches should
add predictive value if the cleave-first model is correct, but only the
first approach did (FIG. 32E and FIG. 40B). Thus, the neural network can
discern HLA class II from decoys based on telltale features in ligands
(e.g. penultimate P) but it is irrelevant when the cut sites are not
known a priori--as is always the case when predicting immunogenic
peptides from a primary protein sequence. This subtle distinction has the
potential to cause confusion in the field.
[0780] With the "bind first" theory, MS-observed and decoy peptides were
scored for solvent accessibility, as well as for intrinsically disordered
domains. Solvent accessible or disordered domains could be enriched in
HLA class II ligands if protein structure dictates availability for HLA
binding. However, these features also proved non-predictive (FIG. 32E). A
hybrid model was then considered in which enzymes partially digest the
protein before peptide binding, after which additional trimming occurs.
In this model, precursor cleavage sites exist further upstream and
downstream of the observed termini of MS ligands. Accordingly, a CNN was
trained based on the extended protein context (.+-.30 AAs) to detect
distal signals corresponding to precursor cuts. This model did not show
predictive value either (FIG. 32E). Finally, as processing-preferred
regions proved difficult to predict based on primary sequence, protein
regions observed in published HLA-DQ ligandomes were catalogued and used
overlap to predict HLA-DR ligands. The overlap variable yielded a modest
improvement in prediction performance (3.1% increase in PPV on average
over neonmhc2 alone) (FIG. 32E). Assuming that HLA-DQ and HLA-DR alleles
share the same HLA class II processing environment but do not share
peptide binding motifs, this result indicates certain gene regions are
indeed favored for processing but do not show obvious cleavage motifs or
special conformational properties.
Integrating Presentation Rules Greatly Enhances HLA-DR Ligandome
Prediction
[0781] To quantify how binding rules synergize with processing-related
features, a multi-variate models was created for predicting HLA-DR
ligandomes of HLA class II-presenting cell lines, dendritic cells, and
healthy donor peripheral blood mononuclear cells (PBMC5). Although the
presented peptides are not mutated, the prediction scenario mimics that
of neoantigen prediction, in which randomly sampled genomic loci must be
evaluated in terms of their ability to produce HLA class II peptides.
Using a 1:499 ratio of hits to decoys and sampling decoys at random from
the protein-coding exome, the performance of neonmhc2- and
NetMHCIIpan-based models was assessed as well as models that incorporated
additional processing features including RNA-Seq-derived expression,
gene-level bias (per FIG. 32A, see related FIG. 39B), and overlap with a
previously observed HLA-DQ peptides. To make the model consistent with
how mutated tumor epitope targets are prioritized for the treatment of
cancer, the gene-level bias feature was modified to neutralize preference
for plasma genes that are not relevant sources of neoantigens.
[0782] These integrative algorithms confirmed substantial improvements in
both binding and processing prediction (FIG. 21A). Specifically, the full
model showed a 7.4.times. to 61.times. fold-change improvement over a
model using NetMHCIIpan binding predictions alone, depending on the
dataset being evaluated. Expression and gene bias both provided
substantial independent contributions to prediction accuracy. The DQ
overlap feature made smaller contribution but consistently provided a
positive improvement. Importantly, affinity-based models were only half
as accurate as MAPTAC.TM.-based models even when provided the full
benefit of processing-related prediction variables.
Benchmarking Prediction Accuracy Using Tumor-Derived HLA Class II Peptides
Presented by Professional APCs
[0783] Having assessed our accuracy in predicting HLA class II ligandomes,
attention was shifted to testing whether tumor-derived ligands
endocytosed by professional APCs could be predicted. Our observation that
most HLA class II expression in the TME is from professional APC's
indicates that this processing route is likely the most relevant pathway
for tumor antigen presentation. Unfortunately, conventional MS-based
ligandomes of tumor tissues do not identify which peptides originate from
endocytosed tumor proteins. Therefore, an experiment was devised in which
were profiled the HLA-DR ligandomes of dendritic cells (DCs) that had
been "fed" SILAC-labeled tumor cells (FIG. 33A).
[0784] To label tumor-derived proteins, an HLA class II-deficient cancer
cell line (K562) was grown in media containing isotopically-labeled L and
K achieving greater than 95% labeling efficiency. DCs were fed either
lysed tumor cells (to mimic macropinocytosis of tumor debris) or
UV-treated whole tumor cells (to mimic phagocytosis of whole cells).
HLA-DR binding peptides were profiled using MS to identify peptides
bearing heavy- or light-labeled amino acids. The experiment yielded 29
heavy-labeled peptides and the whole-cell experiment yielded 56
heavy-labeled peptides for the lysate and UV experiments, respectively
(Table 10 (Data S1B)). Peptides bearing more than one L or K showed
complete labeling in all but two cases indicating that the heavy-labeled
peptides originated from tumor cells and not from newly translated DC
proteins, which would show discordant labeling. Both untreated DCs and
DCs that were harvested after incubating 10 minutes with lysate yielded
no heavy-labeled peptides.
[0785] Using the integrated prediction algorithm disclosed here, the
ability to predict tumor-derived peptides was assessed. Consistent with
our previous result in predicting natural HLA class II ligandomes,
neonmhc2-based models achieved much greater prediction accuracy than
NetMHCIIpan-based models (FIG. 33D).
[0786] Unlike gene expression, the gene bias and DQ-overlap features did
not improve prediction of the endocytosed antigens suggesting that the
patterns that were learned from bulk tissue ligandomes were not as
relevant for this class of epitopes. Analyzing the source genes of
heavy-labeled peptides, the RNA-binding proteins (RBPs) DNA-binding
proteins (DBPs) heat shock proteins (HSPs) and mitochondrial proteins
(FIG. 21D) were noticed as opposed to the predominance of secreted and
membrane proteins seen in the ligandome experiments (FIG. 32A). It was
not clear whether this represented distinct processing preferences.
Indeed, the source proteins were typically highly expressed in K562
(median expression 430 TPM compared to 130 TPM for unlabeled peptides),
suggesting the detection limit might drive the observed gene preferences.
[0787] To gain clarity, logistic regression models were built to test
whether gene localization and functional categories could improve peptide
prediction beyond models that already account for gene expression. RBPs,
DBPs and HSPs were no longer significant when the binding and expression
were accounted for, but mitochondrial proteins remained significant
(p=2.6e-4: FIG. 33E). Notably, the pattern of enrichment was completely
distinct from what was observed in the light labeled peptides.
[0788] To determine whether mitochondrial enrichment could improve
prediction, data were collected from new donor with the aim for deeper
coverage by increasing the cellular input, focusing on the UV-treatment
protocol only, and adding a 24-hour incubation timepoint in addition to
the overnight timepoint. This experiment yielded 77 and 59 heavy labeled
peptides for the overnight and 24-hour timepoints, respectively, and
jointly identified 78 unique source genes. Using a logistic regression
model that accounts for mitochondrial preference (trained on the original
SILAC data), we were able to improve PPV by a net increase of 8-12% over
models that include binding and expression only (FIG. 33G). These
improvements were significant (p=1.1e-9 and p=1.5e=-8, for 16 h and 24 h,
respectively). These preferences could not have been learned from bulk
ligandomes and can be used to enable more accurate epitope prediction.
[0789] The presence of HLA class II presentation in the TME has been
associated with positive outcomes in patients treated with cancer
immunotherapies. Unfortunately, the inaccuracy of HLA class II ligand
prediction and the ambiguities around how tumor antigens are presented in
the TME have slowed the development of therapies that target HLA class II
antigens. Therefore, a mono-allelic-profiling technology called
MAPTAC.TM. was developed as described herein, and comprehensively
analyzed tumor ligandomes to define HLA class II ligand processing rules.
MAPTAC.TM. enabled rapid profiling of 40 HLA class II alleles, including
35 HLA-DRB1 alleles that cover 95% of U.S. patients. Furthermore,
neonmhc2, our binding prediction algorithm trained on MAPTAC.TM. data,
outperformed NetMHCIIpan in predicting memory CD4+ T cell responses, even
for the alleles with the most pre-existing affinity measurements
available for NetMHCIIpan training. It was observed that neonmhc2 was
superior in performance to NetMHCIIpan in identifying memory CD4.sup.+ T
cell responses in the TGEM validation dataset. Furthermore, the
algorithms disclosed herein also excelled at predicting ex vivo induced
CD4.sup.+ T cell responses against neoantigens, successfully identifying
immunogenic neoepitopes which would not have been prioritized by
NetMHCIIpan. Meanwhile, analysis of single-cell RNA-Seq tumor data
revealed that the most relevant tumor antigens are likely dominantly
expressed by infiltrating APCs phagocytosing tumor cells. Thus, which
genes and gene regions are preferentially presented in the TME was
investigated and multivariate models were created that accurately
predicted HLA-DR ligandomes and tumor-derived ligands presented by
phagocytic APCs. These models greatly exceed the positive predictive
value of NetMHCIIpan.
[0790] An advantage of directly profiling endogenously processed and
presented HLA class II ligands using MAPTAC.TM. in contrast to
conventional peptide binding assays is that peptide loading chaperones
such as HLA-DM are present. HLA-DM is known to play a role in editing the
HLA class II peptide repertoire of APCs, which motivated us to study the
effects of its differential expression on HLA class II ligandomes. When
HLA-DM was over-expressed in HLA-DR MAPTAC.TM. experiments, the binding
motifs were more clearly resolved than in the experiments without HLA-DM
over-expression. Surprisingly, HLA-DM had a profound effect on
HLA-DQB1*06:04/A1*01:02, demonstrating that learning accurate peptide
binding rules for some HLA-DQ alleles may require the presence of this
peptide loading chaperone. Conversely, two HLA-DP alleles showed no
effect (Yin et al., 2015), suggesting a relationship between HLA-DM
sensitivity and P1 anchor preferences that were unusual for these two
HLA-DP alleles. Beyond HLA-DM, the MAPTAC.TM. platform provides a way to
rapidly learn how other key chaperones and proteins involved in the HLA
class II pathway, such as CD74 or HLA-DO, may impact the peptide binding
repertoires of HLA class II alleles.
[0791] With respect to tumor biology, our most consequential observation
was that APCs are responsible for dominant HLA class II expression in the
TME for the tumor types evaluated. This suggests that the presentation of
therapeutically relevant tumor antigens likely depends on the
phagocytosis of apoptotic tumor cells or macropinocytosis of secreted
tumor proteins. Although there are reports of direct CD4 T cell killing,
the data provided suggests that CD4 T cells more typically play a
supportive role in the TME, primarily recognizing tumor antigens
presented on infiltrating leukocytes. Thus, the anti-tumor effects of CD4
T cells are probably mostly mediated by the secretion of chemokines and
cytokines that regulate the trafficking and activation of other immune
cells, including those with direct cytolytic function. While this is more
mechanistically complicated, one benefit is that the tumor has less
control over whether HLA class II antigens get presented, suggesting that
immune escape via loss-of-function mutations, a common mechanism by which
tumors avoid HLA class I presentation, may not be as frequent with HLA
class II. Future studies that carefully define which APC populations are
responsible for presenting endocytosed tumor antigens and whether there
are ways to enhance recruitment of these phagocytic cells to the TME will
be beneficial for the field. Additionally, it would be useful to
understand how different modes of tumor cell death, such as hypoxia,
chemotherapy, and radiation, result in various levels of tumor antigen
capture by these APCs, which may lead to optimal therapeutic combinations
with HLA class II targeting therapies.
[0792] Finally, a comprehensive analysis of HLA class II ligandomes led to
the observation that certain genes appear to be presented more often than
their transcript expression levels would predict. Learning gene level
biases from tumor cells facilitated improved prediction of APC HLA class
II ligandomes; however, it is possible that some of these signals are
less relevant for neoantigen prediction. For example, enrichments were
detected that appear to relate to autophagy and membrane recycling in
APCs rather than the uptake of exogenous antigens. Interestingly, when
"tumor cells were "fed" to dendritic cells in vitro, the source gene
identifications instead showed enrichment for RNA-binding proteins. It is
tempting to speculate that RNA-binding proteins are preferentially
presented since such a mechanism would promote the presentation of
pathogen epitopes and potentially explain reactivities against
RNA-binding proteins observed in systemic lupus erythematosus and other
autoimmune conditions. In any case, it is important to note that the
utility of our SILAC-based HLA ligandomics workflow is not limited to
tumor antigens, as it can also be applied to study antigens involved in
infectious disease and autoimmunity.
[0793] In summary, the rules of HLA class II processing and presentation
are significantly more complex than for HLA class I. For this reason, the
antigens that drive CD4+ T cell responses often remain undefined. Our
advances in defining HLA class II binding and processing rules will
enable the identification of targetable cancer antigens and other
disease-related epitopes that can be translated to more effective
therapies.
Example 13: Supplemental Information
[0794] Summary of Experiments and Data Sources with Associated Meta Data
[0795] Exhaustive list of data sets, including MAPTAC.TM. data,
non-MAPTAC.TM. manuscript data, and previously published data. Relevant
associated features, such as sample genotype, are provided where
appropriate. B) Unique peptide identifications merged across experimental
MAPTAC.TM. replicates, PBMC donors, cell lines, and SILAC-feeding
experiments. Contaminants and perfect tryptic peptides are removed. See
for example, at least FIGS. 12E-12F, 34A-34B, among others.
Spike-in Peptides for Deconvolution Analysis
[0796] An exemplary list of 20 example peptides used per allele in the
spike-in analysis. Peptides were selected by requiring a minimum SPI of
70, length between 12 and 20 amino acids, and by not allowing a 9mer
overlap with any binders observed for other MAPTAC.TM.-profiled DR
alleles. Additionally, no two spike-in peptides for a given allele share
a 9mer. See for example, at least FIGS. 35, 36A-36C, among others.
Collated TGEM Data Set for Selected Alleles Supplemental Experimental
Procedures
[0797] HLA class II tetramer results for DRB1*01:01, DRB1*03:01,
DRB1*04:01, DRB1*07:01, DRB1*11:01, and DRB1*15:01 for diverse pathogen
and allergen peptides and their corresponding NetMHCIIpan and neonmhc2
predictions. Data were curated from papers published by Kwok and
colleagues. See FIG. 38A-38C, among others.
Supplemental Methods
HLA Class II Allele Frequencies and Affinity Data Statistics, Related to
FIGS. 12A and 12E
[0798] Allele frequencies were obtained from resource,
bioinformatics.bethematchclinical.org/hla-resources/haplotype-frequencies-
/high-resolution-hla-alleles-and-haplotypes-in-the-us-population. The
mhc_ligand_full.csv dataset was downloaded from IEDB data
(iedb.org/database_export_v3.php) on Sep. 21, 2018. Valid affinity
measurements were required to have a "Method/Technique" equal to
"cellular MHC/competitive/fluorescence", "cellular
MHC/competitive/radioactivity", "cellular MHC/direct/fluorescence",
"purified MHC/competitive/fluorescence", "purified
MHC/competitive/radioactivity", or "purified MHC/direct/fluorescence" and
an "Assay Group" equal to "dissociation constant KD", "dissociation
constant KD (.about.EC50)", "dissociation constant KD (.about.IC50)",
"half maximal effective concentration (EC50)", or "half maximal
inhibitory concentration (IC50)". A measurement was attributed to the
Soren Buus group (University of Copenhagen, Denmark) if the string "Buus"
appeared in the "Authors" field. Otherwise, if the authors field included
the strings "Sette" or "Sidney", a measurement was attributed to the
Alessandro Sette group (La Jolla Institute for Immunology, U.S.A). All
other measurements were labeled as "Other". For the purposes of
enumerating strong binders, only peptides with a measured affinity
stronger than 100 nM were counted
MAPTAC.TM. Protocol Overview, Related to FIG. 2: DNA Construct Design
[0799] The gene sequences for HLA class I and HLA class II alleles were
identified by the IPD-IMGT/HLA webpage (ebi.ac.uk/ipd/imgt/hla) and used
to design recombinant expression constructs. For HLA class I, the
.alpha.-chain was fused with a C-terminal GSGGSGGSAGG linker (SEQ ID NO:
10), followed by the biotin-acceptor-peptide (BAP) tag sequence
GLNDIFEAQKIEWHE (SEQ ID NO: 11), a stop codon, and a variable DNA
barcode, and cloned into the pSF Lenti vector (Oxford Genetics, Oxford,
UK) via the NcoI and XbaI restriction sites. The HLA class II constructs
(DR, DP and DQ) were similarly cloned into pSF Lenti via the NcoI and
XbaI restriction sites and consisted of the .beta.-chain sequence fused
on the C-terminus to the linker-BAP sequence from the HLA class I
construct (SGGSGGSAGGGLNDIFEAQKIEWHE (SEQ ID NO: 12)), followed by
another short GSG linker an a F2A ribosomal skipping sequence
(VKQTLNFDLLKLAGDVESNPGP (SEQ ID NO: 13)), the sequence of the
.alpha.-chain, an HA tag (GSYPYDVPDYA (SEQ ID NO: 14)), a stop codon, and
a variable DNA barcode. For all DR alleles the beta-chain was paired with
DRA*01:01. The HLA-DM construct was cloned similarly to the HLA class II
constructs except that it lacked the BAP-sequence and the HA-tag. HLA-DM
was added to a subset of the HLA class II experiments. The identity of
all DNA sequences was verified by Sanger sequencing.
Cell Culture and Transient Transfections
[0800] Expi293 cells (Thermo Scientific) were grown in Expi293 medium
(Thermo Scientific) with 8% CO.sub.2 at 37.degree. C. on an orbital
shaker at 125 rpm. Expi293 cells were maintained at cell densities
between 0.5.times.10.sup.6/mL and 6.times.10.sup.6/mL with regular
biweekly passaging. 30 mL of the Expi293 cell suspension was used for
transient transfections at a cell density of approximately
3.times.10.sup.6/mL and >90% viability. Briefly, 30 ug DNA (1 .mu.g
DNA per mL cell suspension) was diluted into 1.5 mL Opti-MEM medium
(Thermo Scientific) in one tube while 80 .mu.L ExpiFectamine.TM. 293
transfection reagent (Thermo Scientific) was diluted into a second tube
containing 1.5 mL Opti-MEM. These two tubes were incubated at room
temperature for five minutes, combined, mixed gently, and incubated at
room temperature for 30 minutes. The DNA and ExpiFectamine mixture were
added to Expi293 cells and incubated at 37.degree. C., 8% CO.sub.2, 80%
relative humidity. After 48 h, transfected cells were harvested in four
technical replicates at 50.times.10.sup.6 cells per tube, centrifuged,
washed once with 1.times. Gibco DPBS (Thermo Scientific), and flash
frozen in liquid nitrogen for mass spectrometric analysis. An aliquot of
1.times.10.sup.6 cells was collected from each transfection batch and
analyzed via anti-BAP (Rockland Immunochemicals Inc., Limerick, Pa.) or
anti-HA (Bio-Rad, Hercules, Calif.) using western blot analysis to verify
affinity-tagged HLA protein expression. Expi293's endogenous HLA class II
genotype was determined to be DRB1*15:01, DRB1*01:01, DPB1*04:02,
DPA1*01:03, DQB1*06:02, DQA1*01:02 (Laboratory Corporation of America,
Burlington, N.C.). In some experiments, the HLA class II alleles were
co-transfected with HLA-DM, in which case the DNA concentration used for
both plasmids was dropped to 0.5 .mu.g DNA per mL cell suspension.
[0801] A375 cells (ATCC) were grown in DMEM with 10% FBS and maintained at
cultures at no greater than 80% confluence with regular passaging. For
mass spectrometry experiments A375 cells were cultured in a 500 cm.sup.2
plate at a seeding density of 18.5.times.10.sup.6 cells/mL in 100 mL, as
calculated from a 70% confluent cell number. After 24 hours, cells were
transfected with TransIT-X2 (Mirus Bio) by following the TransIT system
protocol adjusted for the total culture volume. After 48 h, cell medium
was aspirated, and cells were washed with 1.times. Gibco DPBS (Thermo
Scientific). For harvest, A375 cells were incubated for 10 minutes at
37.degree. C. with 30 mL non-enzymatic cell dissociation solution
(Sigma-Aldrich), centrifuged, washed with 1.times.DPBS, and aliquoted at
50.times.10.sup.6 cells per sample. 293T and HeLa cells were purchased
from ATCC and were cultured at 37.degree. C. at 5% CO2 in DMEM, 10% FBS,
2 mM L-glutamine or DMEM+10% FBS, respectively. Both cell lines were
transfected with the HLA constructs using the TransIT LT1 reagent (Minis
Bio) following the manufactures instructions and processed 48 h after
transfection as described for the A375 cells. From all samples, an
aliquot of 1.times.10.sup.6 cells was collected from each transfection
and analyzed via anti-BAP (Rockland Immunochemicals Inc., Limerick, Pa.)
or anti-HA (Bio-Rad, Hercules, Calif.) western blot to verify
affinity-tagged HLA protein expression. B721.221 cells were obtained from
Fred Hutchison Cancer Center (Seattle, Wash.) and were cultured in
RPMI-1640 plus glutamax (Thermo Fisher Scientific) with 10% heat
inactivated fetal bovine serum plus 1% penicillin/streptomycin (both
Thermo Fisher Scientific). Cells were cultured twice weekly and discarded
after 25 passages. K562 cells and KG-1 cells (ATCC, Manassas, Va.) were
grown in IMDM (Thermo Fisher Scientific) media plus 10% heat inactivated
FBS, 1% penicillin/streptomycin, 1% sodium pyruvate, and 1% MEM-NEAA.
Cells were cultured twice weekly and discarded after 25 passages.
[0802] Lentivirus for transduction of B721.221, KG-1, and K562 cells were
produced in HEK293T cells grown to 80% confluency. Six micrograms of the
genome vector psFLenti encoding HLA class I or HLA class II (described in
previous sections) was mixed with 5.3 ug of the lentivirus packaging
vector psPAX2 and 1.81 ug of the envelope vector pMD.2. DNA was mixed
with Opti-MEM (Thermo Fisher Scientific) and the transfection reagent,
Fugene HD (Promega, Madison, Wis.), and the mixture was incubated at room
temperature for 15 minutes. The mixture was then added dropwise onto the
dish of HEK293T cells and incubated for 72 hours. Supernatant was then
harvested, and lentiviral titers were tested using Lenti-X GoStix (Takara
Bio Inc., Japan). For transduction, cells were seeded in 12-well flat
bottom plates (Corning Inc., Corning, N.Y.) and mixed with lentiviral
supernatant with 6 ug/ml polybrene (Sigma-Aldrich). Cells mixed with
lentivirus were spun at 32.degree. C. at 800.times.g for 90 minutes.
Cells were resuspended in warm media and incubated in a 37.degree. C.
incubator at 5% CO.sub.2 for 72 hours. Cells were then selected using 1
ug/ml puromycin for 2 weeks. After selection, at least 50 million cells
were harvested, centrifuged, washed once with 1.times.Gibco DPBS (Thermo
Scientific), and flash frozen in liquid nitrogen for mass spectrometric
analysis.
BirA Protein Expression and Purification
[0803] The pET19 vector encoding E. coli BirA fused to a C-terminal
hexa-histidine tag (SEQ ID NO: 15) was used. Chemical competent E. coli
BL21 (DE3) cells (New England Biolabs) were transformed with a BirA
expression plasmid (pET19 vector encoding E. coli BirA fused to a
C-terminal hexa-histidine (SEQ ID NO: 15)), grown at 37.degree. C. in LB
broth plus 100 .mu.g/ml ampicillin to an OD.sub.600 of 0.6-0.8 and cooled
to 30.degree. C. before expression was induced by adding 0.4 mM
isopropyl-.beta.-D-thiogalactopyranoside. E. coli cell growth continued
at 30.degree. C. for 4 h. E. coli cells were harvested by centrifugation
at 8000.times.g for 30 minutes at 4.degree. C. and stored at -80.degree.
C. until use. Frozen cell pellets expressing recombinant BirA were
resuspended in IMAC buffer (50 mM NaH.sub.2PO.sub.4 pH 8.0, 300 mM NaCl)
with 5 mM Imidazole, incubated with 1 mg/ml lysozyme for 20 minutes on
ice and the lysed by sonication. Cellular debris and insoluble materials
were removed by centrifugation at 16,000.times.g for 30 minutes at
4.degree. C. The cleared supernatant was subsequently loaded on a HisTrap
HP 5 mL column using the AKTA pure chromatography system (GE Healthcare),
washed with IMAC buffer plus 25 mM and 50 mM imidazole before elution
with 500 mM imidazole. Fractions containing BirA were pooled and dialyzed
against 20 mM Tris-HCl pH 8.0 with 25 mM NaCl and were loaded on a HiTrap
Q HP 5 mL column (GE Healthcare, Chicago, Ill.) and eluted by applying a
linear gradient from 25 to 600 mM NaCl. Fractions containing highly pure
BirA were pooled, buffer exchanged in storage buffer (20 mM Tris-HCl pH
8.0 100 mM NaCl, 5% glycerol) and concentrated to around 5-10 mg/mL,
aliquoted, and flash frozen in liquid nitrogen for storage at -80.degree.
C. BirA protein concentration was determined by UV spectroscopy at
OD.sub.280 nm using a calculated extinction coefficient of c=47,440
M.sup.-1 cm.sup.-1.
Western Blotting Protocol
[0804] Samples were added to XT Sample Buffer and XT Reducing Agent
(Bio-Rad, Hercules, Calif.), heated at 95.degree. C. for five minutes,
then a volume corresponding to 100,000 cells was loaded into 10%
Criterion XT Bis-Tris gels (Bio-Rad, Hercules, Calif.) and
electrophoresed at 200 V for 35 minutes using a PowerPac Basic Power
Supply (Bio-Rad, Hercules, Calif.) with XT MES Running Buffer (Bio-Rad,
Hercules, Calif.). The gels were rinsed briefly with water, then proteins
were transferred to PVDF membranes within Invitrogen iBlot Transfer
Stacks (Thermo Fisher Scientific) using setting P3 on an Invitrogen
iBlot2 Gel Transfer Device (Thermo Scientific). The Precision Plus
Protein All Blue Standard (Bio-Rad, Hercules, Calif.) was used to monitor
molecular weights. Next, membranes were washed 3.times.five minutes with
Pierce TBS Tween 20 buffer [(TBST) 25 mM Tris, 0.15 mM NaCl, 0.05% (v/v)
Tween 20, pH 7.5, Thermo Fisher Scientific)], blocked for 1 h at room
temperature in TBST-M [TBST containing 5% (w/v) nonfat instant dry milk],
then incubated overnight at 4.degree. C. in TBST-B [TBST containing 5%
(w/v) Bovine Serum Albumin (Sigma Aldrich)] and a 1:5,000 dilution of
both rabbit anti-beta tubulin antibody (catalog # ab6046, Abcam,
Cambridge, Mass.) and rabbit anti-biotin ligase epitope tag antibody
(catalog #100-401-B21, Rockland Immunochemicals, Limerick, Pa.). Next,
the membranes were washed 3.times.five minutes with TBST, incubated for 1
h at room temperature in TBST-M containing a 1:10,000 dilution of goat
anti-rabbit IgG (H+L-horseradish peroxidase-conjugated antibody (catalog
#170-6515, Bio-Rad), then washed at room temperature 3.times.five minutes
with TBST. Finally, membranes were bathed with Pierce ECL Western
Blotting Substrate (Thermo Fisher Scientific), developed using a ChemiDoc
XRS+ Imager (Bio-Rad), and visualized using Image Lab software (Bio-Rad).
Affinity-Tagged HLA-Peptide Complex Isolation
[0805] Affinity-tagged HLA-peptide complex isolations were performed from
cells expressing BAP-tagged HLA alleles and negative control cell lines
that expressed only endogenous HLA-peptide complexes without BAP tags.
The NeutrAvidin beaded agarose resin was washed three times with 1 mL
cold PBS before use in HLA-peptide affinity purification. Frozen pellets
containing 50.times.10.sup.6 cells expressing BAP-tagged HLA molecules
were thawed on ice for 20 minutes and gently lysed by hand pipetting in
1.2 mL cold lysis buffer [20 mM Tris-Cl pH 8, 100 mM NaCl, 6 mM
MgCl.sub.2, 1.5% (v/v) Triton X-100, 60 mM octyl glucoside, 0.2 mM of
2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM PMSF, 1.times. complete EDTA-free
protease inhibitor cocktail (Roche). Lysates were incubated end/over/end
at 4.degree. C. for 15 minutes with .gtoreq.250 units benzonase nuclease
(Sigma-Aldrich) to degrade DNA/RNA and centrifuged at 15,000.times.g at
4.degree. C. for 20 minutes to remove cellular debris and insoluble
materials. Cleared supernatants were transferred to new tubes and
BAP-tagged HLA molecules were biotinylated by incubating end/over/end at
room temperature for 10 minutes in a 1.5 mL tube with 0.56 .mu.M biotin,
1 mM ATP, and 3 .mu.M BirA. The supernatants were incubated end/over/end
at 4.degree. C. for 30 minutes with a volume corresponding to 200 .mu.L
of Pierce high-capacity NeutrAvidin beaded agarose resin (Thermo
Scientific) slurry to affinity-enrich biotinylated-HLA-peptide complexes.
Finally, the HLA-bound resin was washed four times with 1 mL of cold wash
buffer (20 mM Tris-Cl pH 8, 100 mM NaCl, 60 mM octyl glucoside, 0.2 mM of
2-Iodoacetamide, 1 mM EDTA pH 8), then washed four times with 1 mL of
cold 10 mM Tris-Cl pH 8. Between washes, the HLA-bound resin was gently
mixed by hand then pelleted by centrifugation at 1,500.times.g at
4.degree. C. for one minute. The washed HLA-bound resin was stored at
-80.degree. C. or immediately subjected to HLA-peptide elution and
desalting.
Antibody-Based HLA-Peptide Complex Isolation
[0806] HLA DR-peptide complexes were isolated from healthy donor
peripheral blood mononuclear cells (PBMC5). A volume corresponding to 75
.mu.L of GammaBind Plus Sepharose resin was washed three times with 1 mL
cold PBS, incubated end/over/end with 10 .mu.g of the antibody at
4.degree. C. overnight, then washed with three times with 1 mL cold PBS
before use in HLA-peptide immunoprecipation. Frozen PBMC pellets
containing 50.times.10.sup.6 cells were thawed on ice for 20 minutes and
gently lysed by pipetting in 1.2 mL cold lysis buffer [20 mM Tris-Cl pH
8, 100 mM NaCl, 6 mM MgCl2, 1.5% (v/v) Triton X-100, 60 mM octyl
glucoside, 0.2 mM of 2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM PMSF, 1.times.
complete EDTA-free protease inhibitor cocktail (Roche). Lysates were
incubated end/over/end at 4.degree. C. for 15 minutes with >250 units
benzonase nuclease (Sigma-Aldrich) to degrade DNA/RNA and centrifuged at
15,000.times.g at 4.degree. C. for 20 minutes to remove cellular debris
and insoluble materials. The supernatants were then incubated
end/over/end at 4.degree. C. for 3 hours with an anti-HLA DR antibody
(TAL 1B5, product # sc-53319; Santa Cruz Biotechnology, Dallas, Tex.)
bound to GammaBind Plus Sepharose resin (GE Life Sciences) to
immunoprecipitate HLA DR-peptide complexes. Finally, the HLA-bound resin
was washed four times with 1 mL of cold wash buffer (20 mM Tris-Cl pH 8,
100 mM NaCl, 60 mM octyl glucoside, 0.2 mM of 2-Iodoacetamide, 1 mM EDTA
pH 8), then washed four times with 1 mL of cold 10 mM Tris-Cl pH 8.
Between washes, the HLA-bound resin was gently mixed then pelleted by
centrifugation at 1,500.times.g at 4.degree. C. for 1 minute. The washed
HLA-bound resin was stored at -80.degree. C. or immediately subjected to
HLA-peptide elution and desalting.
HLA-Peptide Elution and Desalting
[0807] HLA-peptides were eluted from affinity-tagged and endogenous HLA
complexes and simultaneously desalted using a Sep-Pak (Waters)
solid-phase extraction system. In brief, Sep-Pak Vac 1 cc (50 mg) 37-55
.mu.m particle size tC18 cartridges were attached to a 24-position
extraction manifold (Restek), activated two times with 200 .mu.L MeOH
followed by 100 .mu.L of 50% (v/v) ACN/1% (v/v) FA, then washed four
times with 500 .mu.L 1% (v/v) FA. To dissociate HLA-peptides from
affinity-tagged HLA molecules and facilitate peptide binding to the tC18
solid-phase, 400 .mu.L of 3% (v/v) ACN/5% (v/v) FA was added to the tubes
containing HLA-bound beaded agarose resin. The slurry was mixed by
pipetting, then transferred to the Sep-Pak cartridges. The tubes and
pipette tips were rinsed with 1% (v/v) FA (2.times.200 .mu.L) and the
rinsate was transferred to the cartridges. 100 fmol of Pierce Peptide
Retention Time Calibration (PRTC) mixture (Thermo Scientific) was added
to the cartridges as a loading control. The beaded agarose resin was
incubated two times for five minutes with 200 .mu.L of 10% (v/v) AcOH to
further dissociate HLA-peptides from the affinity-tagged HLA molecules,
then washed four times with 500 .mu.L 1% (v/v) FA. HLA-peptides were
eluted off the tC18 into new 1.5 mL micro tubes (Sarstedt) by step
fractionating with 250 .mu.L of 15% (v/v) ACN/1% (v/v) FA followed by
2.times.250 .mu.L of 30% (v/v) ACN/1% (v/v) FA. The solutions used for
activation, sample loading, washing, and elution flowed via gravity, but
vacuum (.ltoreq.-2.5 PSI) was used to remove the remaining eluate from
the cartridges. Eluates containing HLA-peptides were frozen, dried via
vacuum centrifugation, and stored at -80.degree. C. before being
subjected to a second desalting workflow. Secondary desalting of the
HLA-peptide samples was performed with in-house built StageTips packed
using two 16-gauge punches of Empore C18 solid phase extraction disks
(3M, St. Paul, Minn.) as previously described. StageTips were activated
two times with 100 .mu.L of MeOH followed by 50 .mu.L of 50% (v/v)
ACN/0.1% (v/v) FA, then washed three times with 100 .mu.L of 1% (v/v) FA.
The dried HLA-peptides were solubilized by adding 200 .mu.L of 3% (v/v)
ACN/5% (v/v) then and loaded onto StageTips. The tubes and pipette tips
were rinsed with 1% (v/v) FA (2.times.100 .mu.L) and the rinse volume was
transferred to the StageTips, then the StageTips were washed five times
with 100 .mu.L 1% (v/v) FA. Peptides were eluted using a step gradient of
20 .mu.L 15% (v/v) ACN/1% (v/v) FA followed by two 20 .mu.L cuts of 30%
(v/v) ACN/1% (v/v) FA. Sample loading, washes, and elution were performed
on a tabletop centrifuge with a maximum speed of 1,500-3,000.times.g.
Eluates were frozen, dried via vacuum centrifugation, and stored at
-80.degree. C.
HLA-Peptide Sequencing by Tandem Mass Spectrometry
[0808] All nanoLC-ESI-MS/MS analyses employed the same LC separation
conditions described below. Samples were chromatographically separated
using a Proxeon Easy NanoLC 1200 (Thermo Scientific, San Jose, Calif.)
fitted with a PicoFrit (New Objective, Inc., Woburn, Mass.) 75 .mu.m
inner diameter capillary with a 10-.mu.m emitter was packed at 1000 psi
of pressure with He to .about.30-40 cm with 1.9 .mu.m particle size/200
.ANG. pore size of C18 Reprosil beads (Dr. Maisch GmbH, Ammerbuch,
Germany) and heated at 60.degree. C. during separation. The column was
equilibrated with 10.times. bed volume of buffer A [0.1% (v/v) FA and 3%
(v/v) ACN], samples were loaded in 4 .mu.L 3% (v/v) ACN/5% (v/v) FA, and
peptides were eluted with a linear gradient from 7-30% of Buffer B [0.1%
(v/v) FA and 80% (v/v) ACN] over 82 minutes, 30-90% Buffer B over six
minutes, then held at 90% Buffer B for 15 minutes to wash the column. A
subset of samples was eluted with a linear gradient from 6-40% of Buffer
B over 84 minutes 40-60% Buffer B over nine minutes, then held at 90%
Buffer B for five minutes and 50% Buffer B for nine minutes to wash the
column Linear gradients for sample elution were run at a rate of 250
nL/min and yielded .about.13 sec median peak widths.
[0809] During data-dependent acquisition, eluted peptides were introduced
into an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific, San
Jose, Calif.) equipped with a Nanospray Flex Ion source (Thermo
Scientific, San Jose, Calif.) at 2.2-2.5 kV. A full-scan MS was acquired
at a resolution of 60,000 from 300 to 1,700 m/z (AGC target 4e5, 50 ms
max IT). Each full scan was followed by a 2 sec cycle time, or top 10, of
data-dependent MS2 scans at resolution 15,000, using an isolation width
of 1.0 m/z, a collision energy of 34 (HLA class I data) and 38 (HLA class
II data), an ACG Target of 5e4, and a max fill time of 250 ms max ion
time. An isolation width of 1.0 m/z was used because HLA class II
peptides tend to be longer (median 16 amino acids with a subset of
peptides >40 amino acids), so the monoisotopic peak is not always the
tallest peak in the isotope cluster and the mass spectrometer acquisition
software places the tallest isotopic peak in the center of the isolation
window in the absence of a specified offset. The 1.0 m/z isolation window
will therefore allow for the co-isolation of the monoisotopic peak even
when it is not the tallest peak in the isotopic cluster as the charge
states of HLA class II peptides are often +2 or higher. Dynamic exclusion
was enabled with a repeat count of 1 and an exclusion duration of 5 sec
to enable .about.3 PSMs per precursor selected. Isotopes were excluded
while dependent scans on a single charge state per precursor was disabled
because HLA-peptide identification relies on PSM quality, so multiple
PSMs of different charge states further increases our confidence of
peptide identifications. Charge state screening for HLA class II data
collection was enabled along with monoisotopic precursor selection (MIPS)
using Peptide Mode to prevent triggering of MS/MS on precursor ions with
charge state 1 (only for alleles with basic anchor residues), >7, or
unassigned. For HLA class I data collection, precursor ions with charge
state 1 (mass range 800-1700 m/z) and 2-4 were selected, while charge
states >4 and unassigned were excluded.
Interpretation of LC-MS/MS Data, Related to FIG. 29
[0810] Mass spectra were interpreted using the Spectrum Mill software
package v6.0 pre-Release (Agilent Technologies, Santa Clara, Calif.).
MS/MS spectra were excluded from searching if they did not have a
precursor MH+ in the range of 600-2000 (HLA class I)/600-4000 (HLA class
II), had a precursor charge >5 (HLA class I)/>7 (HLA class II), or
had a minimum of <5 detected peaks. Merging of similar spectra with
the same precursor m/z acquired in the same chromatographic peak was
disabled. MS/MS spectra were searched against a database that contained
all UCSC Genome Browser genes with hg19 annotation of the genome and its
protein coding transcripts (63,691 entries; 10,917,867 unique 9mer
peptides) combined with 264 common contaminants. Prior to the database
search, all MS/MS had to pass the spectral quality filter with a sequence
tag length >2, e.g., minimum of 3 masses separated by the in-chain
mass of an amino acid. A minimum backbone cleavage score (BCS) of 5 was
set, and ESI QExactive HLAv2 scoring scheme was used. All spectra from
native HLA-peptide samples, not reduced and alkylated, were searched
using a no-enzyme specificity, fixed modification of cysteine as
cysteinylation, with the following variable modifications: oxidized
methionine (m), pyroglutamic acid (N-term q), carbamidomethylation (c).
Reduced and alkylated HLA-peptide samples were searched using a no-enzyme
specificity, fixed modification of cysteine as carbamidomethylation, with
the following variable modifications: oxidized methionine (m),
pyroglutamic acid (N-term q), cysteinylation (c). A precursor mass
tolerance of .+-.10 ppm, product mass tolerance of .+-.10 ppm, and a
minimum scored peak intensity of 30% was used for both native and reduced
and alkylated HLA-peptide datasets. Peptide spectrum matches (PSMs) for
individual spectra were automatically designated as confidently assigned
using the Spectrum Mill autovalidation module to apply target-decoy based
FDR estimation at the PSM rank to set scoring threshold criteria. An auto
thresholds strategy using a minimum sequence length of 7, automatic
variable range precursor mass filtering, and score and delta Rank1-Rank2
score thresholds optimized across all LC-MS/MS runs for an HLA allele
yielding a PSM FDR estimate of <1% for each precursor charge state.
[0811] Identified peptides that passed the PSM FDR estimate of <1.0%
were further filtered for contaminants by removing all peptides assigned
to the 264 common contaminants proteins in the reference database and by
removing peptides identified in the negative control MAPTAC.TM. affinity
pulldowns. Additionally, all peptide identifications that mapped to an in
silico tryptic digest of the reference database were removed, as these
peptides cannot be ruled out as tryptic contaminants from sample
carry-over on the uPLC column.
[0812] To remove potential false positive PSM identifications from the
SILAC DC-feeding experiment, it was applied additional quality filters to
PSMs identified using the methods described above. All peptides with
FDR<1% were filtered for high quality PSMs using the following
thresholds: i) scored peak intensity >60% ii) backbone cleavage score
8 and iii) ppm mass tolerance of .+-.1 ppm from the median ppm observed
across all PSM identifications in the same LC-MS/MS replicate.
Monoallelic Assignment of HLA-DR, -DQ, -DP Heterodimers Using MAPTAC.TM.
Protocol
[0813] Since only the beta chain of HLA class II is tagged in the
MAPTAC.TM. protocol, the pull-down step isolates peptide-MHC complexes
regardless of whether they contain knock-in or endogenous alpha chain. In
the case of HLA-DR, the allelic variation in the alpha chain is not
considered to influence peptide binding; therefore, the relative degree
of pairing with endogenous alpha pairing is irrelevant to data
interpretation--the data is effectively mono-allelic. However, for HLA-DP
and HLA-DQ loci, the alpha chains exhibit important allelic variants such
that the presence of both knock-in and endogenous alpha chain alleles
creates the potential for 1-3 distinct specificities (depending on
whether the cell line has one or two alpha chain alleles and whether
either matches the knock-in allele). In principle, this problem can be
mitigated by running the protocol with and without a knock-in alpha chain
and identifying the set of peptides specific to the with-alpha
experiment. The approach of using a cell line was taken herein that
expresses a single alpha allele that matches the knock-in alpha allele.
Analysis of Previously Published MS Data, Related to FIGS. 12A-12F, FIGS.
30A-30C, FIGS. 31A-31D, FIG. 21A, FIG. 39A-39B, and FIG. 40A-B
[0814] Published LC-MS/MS datasets that provided .raw files were
reprocessed using the Spectrum Mill software package v6.0 pre-Release
(Agilent Technologies, Santa Clara, Calif.). Datasets that were collected
on Thermo Orbitrap instruments (e.g., Velos, QExactive, Fusion, Lumos)
that utilized HCD fragmentation and MS and MS/MS data collection in the
orbitrap (high resolution) were analyzed using the parameters described
in the above section "Interpretation of LC-MS/MS Data". For MS and MS/MS
high resolution datasets that utilized CID fragmentation, the same
parameters as above were used with an ESI Orbitrap scoring scheme. For
datasets with MS data collection in the orbitrap and MS/MS data
collection in the ion trap, the following same parameters above were also
used with the following deviations. For HCD data, the ESI QExactive HLAv2
scoring scheme was used, while the ESI Orbitrap scoring scheme was used
for CID data. A precursor mass tolerance of .+-.10 ppm, product mass
tolerance of .+-.0.5 Da was used. For both high- and low-resolution MS/MS
datasets, peptide spectrum matches (PSMs) for individual spectra were
automatically designated as confidently assigned using the Spectrum Mill
auto validation module to apply target-decoy based FDR estimation at the
PSM rank to set scoring threshold criteria. An auto thresholds strategy
using a minimum sequence length of 7, automatic variable range precursor
mass filtering, and score and delta Rank1-Rank2 score thresholds
optimized across all LC-MS/MS runs for an HLA allele yielding a PSM FDR
estimate of <1.0% for each precursor charge state. Analysis of peptide
identifications from some previously published data revealed a high rate
of 9mers (>10%). Since these could potentially represent contaminating
HLA class I ligands, short peptides were dropped (length <12) from all
external data sets.
Mapping Peptides to Genes and "Nested Sets", Related to FIGS. 30A-30C,
FIGS. 31A-31D, and FIGS. 32A-32E
[0815] Each peptide was assigned to one or more protein-coding transcripts
within the UCSC hg19 gene annotation (genome.ucsc.edu/cgi-bin/hgTables).
Since many peptide identifications overlap others and thus constitute
mostly redundant information, peptides were grouped into "nested sets",
each meant to correspond to .about.1 unique binding event. For instance,
the peptides GKAPILIATDVASRGLDV (SEQ ID NO: 16), GKAPILIATDVASRGLD (SEQ
ID NO: 17), and KAPILIATDVASRGLDV (SEQ ID NO: 18) all contain the
conserved sequence KAPILIATDVASRGLD (SEQ ID NO: 19), and probably all
bind MHC in the same register. In order to nest peptides of a given data
set, a graph was built in which each node corresponded to a unique
peptide, and an edge was created between any pair of peptides sharing at
least one 9mer and mappable to at least one common transcript. The
clusters command in the R package igraph (Team, 2014)
(cran.r-project.org/web/packages/igraph/citation.html) was used to
identify clusters of connected nodes, and each cluster was defined as a
nested set. This procedure guarantees that any two peptides that meet the
edge criteria (.gtoreq.1 common 9mer and .gtoreq.1 common transcript) are
placed within the same nested set. The nests were used for sequence logo
generation (logos were generated using the shortest peptide in each
nested set; FIG. 30A-30C, machine learning (importance weights across
peptides in a nested set sum to one; FIG. 31A-31D), and the gene bias
analysis (each nested set was counted as one observation rather than each
individual peptide; FIG. 32A-32E).
Analysis of Amino Acid Frequencies, Related to FIG. 12F
[0816] Amino acid frequencies in the human proteome were calculated based
on sequences for all protein-coding genes in the UCSC hg19 annotation
(selecting one transcript at random for genes represented by multiple
transcript isoforms). IEDB frequencies were determined by identifying the
unique set of peptides with at least one affinity observation .ltoreq.100
nM (excluding peptides with hexavalent polyhistidine at their
C-terminus). MAPTAC.TM. frequencies were first considered in the context
of the standard forward-phase protocol across five DRB1 alleles
(DRB1*01:01, DRB1*03:01, DRB1*09:01, and DRB1*11:01), using only one
peptide (the longest) per nested set. In addition, MAPTAC.TM. frequencies
were separately calculated for the subset of samples processed by the
reduction and alkylation protocol. MS data from external datasets were
analyzed without respect to potential allele of origin and likewise using
the longest peptide per nested set.
Building HLA Class I Sequence Logos, Related to FIG. 37B
[0817] For each HLA class I allele, a length-9 sequence logo was created
by profiling amino acid frequencies in the first five positions (mapping
to logo positions 1-5) and last four positions (mapping to logo positions
6-9) of corresponding peptides. In this manner, peptides contributed to
the sequence logo regardless of their length. As in the HLA class II
logos, letter heights are proportional to the frequency of each amino
acid in each position, and color coding is used for amino acids with
frequency .gtoreq.10%.
Assessing the Performance of HLA Class II Peptide Deconvolution, Related
to FIG. 30B
[0818] To assess the ability the GibbsCluster (v2.0) tool to cluster
multi-allelic HLA class II peptide data by allele of origin, its
performance on eight samples were analyzed, including 4 PBMC samples, 1
melanoma cell line (A375), and 3 previously published lymphoblastoid cell
lines. For each DRB1/3/4/5 allele present in each sample genotype, twenty
peptides were spiked in from our mono-allelic MAPTAC.TM. data. The spiked
peptides were restricted to 12-20mers with SPI.gtoreq.70 that did not
share a 9mer with any peptides in MAPTAC.TM. data for other HLA-DR
alleles or with any spiked peptides for the allele of interest. These
augmented datasets were then submitted to GibbsCluster-v2.0 using default
HLA class II settings except that was enforced a hydrophobic preference
at position 1, as others have previously for deconvolution. For each
sample, the number of clusters in the solution was manually specified and
set equal to the number of HLA-DR alleles present in the genotype.
Calculating the Fraction of Peptides with Preferred Anchor Residues,
Related to FIG. 30C
[0819] Anchor positions were defined as the four positions with the lowest
entropy, and within those positions, "preferred" amino acids included all
those with frequency .gtoreq.10%. When calculating the fraction of
peptides with preferred amino acids at n positions, only one peptide was
used per nested set (the shortest).
Predicted Affinities of MS-Observed Peptides, Related to FIG. 36A
[0820] For each HLA class II allele, all unique peptides length 14 through
17 were identified and scored for binding potential using
NetMHCIIpan-v3.1. For comparison, 50,000 random length-matched peptides
were sampled from the human proteome. Density distributions were
determined based on log-transformed values.
Measured Affinities for MS-Observed Peptides, Related to FIG. 36B
[0821] Peptides were selected for affinity measurement if they had poor
predicted NetMHCIIpan-v3.1 binding affinity (>100 nM for DRB1*01:01 or
>500 nM for DRB1*11:01) or if they exhibited .ltoreq.2 of the
heuristically defined anchors.
Establishment of Cross-Validation Partitions, Related to FIG. 31A
[0822] A graph was created in which each node represents a protein-coding
transcript and edges are present between all pairs of transcripts sharing
at least 5 unique 9mers of amino sequence content (UCSC hg19 gene
annotation). The clusters command in the R package igraph (Team, 2014)
(cran.r-project.org/web/packagesigraphicitation.html) was used to
identify clusters of connected nodes, and each cluster was defined as a
"transcript group". In this manner, if two transcripts shared an edge
(.gtoreq.5 shared 9mers), they were guaranteed to be placed in the same
transcript group. Transcript groups were randomly sampled, dividing the
proteome into eight roughly equally sized partitions. MS-observed
peptides (and non-observed decoy peptides) were placed in partitions
according to the partition of their source transcripts, and these
partitions were used for cross-validation and hyper-parameter tuning. The
graph-based approach of partitioning the proteome was used to minimize
the likelihood that similar peptide sequences would appear during
training and evaluation, which could artificially inflate prediction
performance.
Architecture and Training of a CNN-Based HLA Class II Binding Predictor,
Neonmhc2, Related to FIG. 31A
[0823] Negative examples (decoys) were generated for training by randomly
shuffling the sequences of hit peptides. It was chosen this method of
decoy generation, rather than selecting unobserved regions from the
proteome, in order to eliminate MS biases that could result in a general
amino acid preference. In this way, our binding predictor is unaware of
the relative depletion of cysteine, for example (FIG. 12F). Similarly,
this prevents our model from learning MS biases related to global
properties of the peptides, such as the overall hydrophobicity. This
method is related to results depicted in FIG. 31A.
[0824] Models were trained for two application scenarios: validating on
internal MAPTAC.TM. data (FIG. 31B) and on external data (FIG. 31C, FIG.
21A, and FIG. 21B). When training models for the former, it was adopted a
simple training procedure where network weight optimization was learned
using six partitions of the data (train partitions), hyper-parameter
optimization and early stopping was performed using the seventh partition
(tune partition), and the final validation was performed on the eighth
partition (evaluation partition) after the model design was finalized. In
the case of external validation, cross-validation was employed, building
an ensemble of models for each partition of data whereby it was held out
that partition for hyper-parameter tuning and early stopping and used the
remaining seven partitions for network weight optimization. Additionally,
when scoring non-MS data (FIG. 31C and FIG. 31D), each 12-20mer substring
of the target peptide was scored and the highest score was kept.
[0825] When training our models, each hit and decoy was down-weighted in
the loss function by the size of its source nested set such that each
nested set as a whole carried equal weight. When evaluating the model for
hyper-parameter tuning, the shortest peptide from each nested set was
used in the relevant partition as the positive examples and scrambled
versions of those hits as the decoys. Additionally, an overall weighting
factor was applied such that the summed weight of the hits equaled the
summed weight of the decoys when training. For the final evaluation of
the model, as shown in FIG. 31B, the shortest peptide was again selected
from each nested set in the evaluation partition (partition 8) but
sampled decoys randomly from non-observed subsequences of peptide source
genes ("natural decoys", described in a subsequent section). In this way,
any biases learned by the model in order to simply discriminate natural
sequences from scrambled ones did not inflate our performance on the
evaluation partition.
[0826] Models were trained using an Adam optimizer with an initial
learning rate of 0.003, beta_1 value of 0.9, beta_2 value of 0.999 and no
decay (default Keras parameters, except for the learning rate) and used a
binary cross-entropy loss function. The initial model weights were set
using He initialization. After every 5 epochs of training, the positive
predictive value (PPV, described in subsequent section) on the tune
partition was measured and the maximum value was tracked. After each
epoch, if the training loss did not decrease, the learning rate was
multiplied by 1/3. Similarly, each time the PPV was measured on the tune
partition, if it did not increase compared to the running maximum the
learning rate by 1/3 was multiplied. An early stopping scheme was
implemented where, if the training loss failed to decrease for three
consecutive epochs or the tune PPV failed to increase above the running
maximum for 3 consecutive checks, then training was stopped. When
training the model, a fixed hit-to-decoy ratio of 1:39 was used in the
training set, and 1:19 in the tune partition.
[0827] Featurization: While amino acids may be represented by a "one-hot"
encoding, others have opted to encode amino acids using the PMBEC matrix
and the BLOSUM matrix (Henikoff and Henikoff, 1992), in which similar
amino acids have similar feature profiles. For the purposes of our
peptide featurization, a novel matrix based on amino acid proximities was
generated in solved protein structures. The concept of this approach is
that the typical neighbors of an amino acid should reflect its chemical
properties. For each amino acid in each of .about.100,000 DSSP protein
structures (cdn.rcsb.org/etl/kabschSander/ss.txt.gz), the residue that
was closest in 3D space but at least 10 amino acids away in primary
sequence was determined. Using this data, the number of times the nearest
neighbor of alanine was alanine was determined, the number of times the
nearest neighbor of alanine was a cysteine, etc., to create a 20.times.20
matrix of proximity counts. Each element of the matrix was divided by the
product of its corresponding column and row sums, and the entire matrix
was log-transformed. Finally, the mean value of the entire matrix was
subtracted from each element.
[0828] Each amino acid was also encode with 11 binary features describing
properties of the amino acid, such as whether it is: acidic (N, Q),
aliphatic (I, L, V), aromatic (H, F, W, Y), basic (H, K, R), charged (D,
E, H, K, R), hydrophobic (A, C, F, H, I, K, L, M, T, V, W, Y), hydroxylic
(S, T), polar (C, S, N, Q, T, D, E, H, K, R, Y, W), small (V, P, A, G, C,
S, T, N, D), very small (A, G, C, S), or contains sulfur (M, C). Two
features were used to describe the position of each amino acid, one
monotonically increasing across the peptide and one indicating an
absolute distance from the center of the peptide, both in units of
position (not physical distance). Lastly, a single binary feature was
included to indicate whether an amino acid was "missing" from that
position, which would happen beyond the edges of shorter peptides. The
result is that each amino acid is encoded by 20 amino acid proximity
features, 11 amino acid property features, 2 position features, and 1
missing character feature for a total of 34 features. All peptides were
encoded as 20mers where the central 20 amino acids were used for longer
peptides and the missing character value was added symmetrically to the
edges of peptides shorter than 20 amino acids.
[0829] When examples are input into the neural network, both for training
and evaluating, each of the 34 features are normalized by subtracting
their mean and dividing by their standard deviation. The mean and
standard deviation are calculated based solely on the training set and
without regard to position within the peptide.
[0830] For each allele, an ensemble of convolutional neural networks was
trained in order to predict binding. A sketch of the model architecture
is shown in FIG. 31A, depicting two convolutional layers with a kernel
size of 6 and 50 filters each. After each layer, global max and mean
pooling was applied and the resulting values were input into a final
output neuron with sigmoid activation. It is implied but not shown that
ReLU activation, batch normalization (Ioffe and Szegedy, 2015), and 20%
spatial dropout were applied immediately after each convolutional layer.
[0831] When training an ensemble of models for each allele, the
architecture was fixed but the amount of L2 regularization was varied. A
base L2 regularization weight of 0.05 was used for the first
convolutional layer and 0.1 for the second convolutional layer. To vary
the amount of L2 regularization, these values were multiplied by 0.1,
0.5, and 1. For each iteration in the ensemble, one model per
regularization level was trained and kept the best based on performance
on the tune partition. Benchmarking prediction performance on
MAPTAC.TM.-observed peptides, related to FIG. 7A
[0832] In some exemplary assessments of prediction performance value for a
given peptide or protein encoded by an HLA allele, a method comprising
"scrambled decoys" can be used. The scrambled decoys are peptides having
the same peptide length and amino acids as a peptide that is known to
bind to given HLA peptide or protein based on, for example, mass
spectroscopy data, but the sequence of the amino acids are scrambled. For
every single peptide that was identified by mass spectrometry, 19 such
scrambled peptide decoys were employed (hit:decoy is 1:19) as shown in
FIG. 7A. The presentation prediction model was tested and PPV was
determined by analyzing the best-scoring 5% of peptides in the test
partition and interrogating what fraction of these were positive. The PPV
thus generated is shown in FIG. 7A and Table 12 below.
TABLE-US-00015
TABLE 12
MHC Class II allele PPV for neonmhc2
DPB1-0101_DPA1-0103 0.48
DPB1-0101_DPA1-0201 0.41
DPB1-0101_DPA1-0202 0.56
DPB1-0201_DPA1-0103 0.48
DPB1-0202_DPA1-0103 0.27
DPB1-0301_DPA1-0103 0.36
DPB1-0401_DPA1-0103 0.52
DPB1-0402_DPA1-0103 0.54
DPB1-0501_DPA1-0201 0.59
DPB1-0501_DPA1-0202 0.46
DPB1-0601_DPA1-0103 0.43
DPB1-0901_DPA1-0201 0.53
DPB1-1001_DPA1-0201 0.49
DPB1-1101_DPA1-0201 0.31
DPB1-1301_DPA1-0201 0.58
DPB1-1401_DPA1-0201 0.49
DPB1-1701_DPA1-0201 0.39
DQB1-0201_DQA1-0201 0.49
DQB1-0201_DQA1-0501 0.08
DQB1-0202_DQA1-0201 0.3
DQB1-0301_DQA1-0501 0.27
DQB1-0301_DQA1-0505 0.13
DQB1-0301_DQA1-0601 0.28
DQB1-0302_DQA1-0301 0.1
DQB1-0303_DQA1-0201 0.46
DQB1-0303_DQA1-0301 0.39
DQB1-0401_DQA1-0301 0.47
DQB1-0402_DQA1-0401 0.53
DQB1-0501_DQA1-0101 0.62
DQB1-0502_DQA1-0101 0.26
DQB1-0502_DQA1-0102 0.19
DQB1-0601_DQA1-0102 0.17
DQB1-0601_DQA1-0103 0.35
DQB1-0602_DQA1-0102 0.42
DQB1-0603_DQA1-0103 0.56
DQB1-0604_DQA1-0102 0.28
DRB1_0101 0.48
DRB1_0102 0.49
DRB1_0301 0.47
DRB1_0302 0.29
DRB1_0401 0.38
DRB1_0402 0.49
DRB1_0403 0.43
DRB1_0404 0.42
DRB1_0405 0.41
DRB1_0407 0.36
DRB1_0410 0.61
DRB1_0701 0.49
DRB1_0801 0.36
DRB1_0802 0.46
DRB1_0803 0.30
DRB1_0804 0.36
DRB1_0901 0.35
DRB1_1001 0.54
DRB1_1101 0.43
DRB1_1102 0.42
DRB1_1104 0.51
DRB1_1201 0.47
DRB1_1202 0.55
DRB1_1301 0.42
DRB1_1302 0.23
DRB1_1303 0.56
DRB1_1401 0.56
DRB1_1501 0.53
DRB1_1502 0.43
DRB1_1503 0.41
DRB1_1601 0.61
DRB3_0101 0.57
DRB3_0201 0.6
DRB3_0202 0.48
DRB3_0301 0.53
DRB4_0103 0.67
DRB5_0101 0.54
DRB5_0102 0.65
DRB5_0202 0.63
Benchmarking Prediction Performance on MAPTAC.TM.-Observed Peptides,
Related to FIG. 31B
[0833] For the purpose of assessing prediction performance for a given
allele, it was necessary to define a set of peptides that could have been
observed (because they are present in the proteome) but were not observed
in the MS data. These negative examples were trained "natural decoys" (in
contrast to the "scrambled decoys" described above). As guiding
principles, it was decided: the length distribution of natural decoys
should match the length distribution of MS-observed hits, natural decoys
should not contain sequence redundant with other natural decoys, natural
decoys should not overlap hits, and/or natural decoys should come from
genes that produced at least one hit.
[0834] The following pseudocode represents the process implemented to
create an evaluation satisfying these principles:
[0835] Initialize two empty lists of hits, H.sub.minimal and
H.sub.exhaustive
[0836] For each nested set S of MS-observed peptides:
[0837] If none of the peptides in S can be mapped to a transcript in the
train or tune partition:
[0838] Add the shortest peptide in S to H.sub.minimal
[0839] Add all peptides in S to H.sub.exhaustive
[0840] Initialize an empty list of decoy peptides, D
[0841] For each protein-coding transcript (longest first, shortest last)
in the test partition:
[0842] If no peptides in H.sub.exhaustive map to the transcript:
[0843] Skip to the next transcript
[0844] Cover the transcript's protein sequence with a set of overlapping
peptides P, where the peptide lengths are randomly sampled from the
length distribution of H.sub.minimal. The overlap is 8 amino acids. (The
last peptide in P will typically dangle over the end of the protein.)
[0845] While the last peptide in P still dangles:
[0846] Subtract 1 amino acid from the length of the longest peptide in P
[0847] For each peptide in P:
[0848] If it does not share a 9mer with a peptide in H.sub.exhaustive nor
with any 9mer observed in any peptide in D:
[0849] Add the peptide to D
[0850] Otherwise:
[0851] Reject the peptide
[0852] H.sub.minimal and D constitute the evaluation data set
[0853] To evaluate performance on this set, all n hit peptides were
evaluated by the predictor (neonmhc2 or NetMHCIIpan) and scored along
with a set of 19n decoys (randomly sampled without replacement from the
complete set of decoys). The top 5% of peptides in the combined set were
labeled as positive calls, and the positive predictive value (PPV) was
calculated as the fraction of positive calls that were hits. Note that
since the number of positives is constrained to be equal to the number of
hits, recall is exactly equal to PPV in this evaluation scenario. The
application of a consistent 1:19 ratio across alleles helps stabilize the
performance values, which are otherwise influenced by the number of hits
observed for each allele. This was deemed appropriate since it was
assumed the number of hits relates more to experimental conditions and
replicate count than intrinsic properties of the allele.
Calculation of NetMHCIIpan Affinities for Non-15Mers, Related to FIG.
31A-D, FIGS. 40A-B, and 33A-D
[0854] In early analyses, NetMHCIIpan-v3.1 affinity and percent rank
predictions for non-15mers performed poorly on benchmarks. However, the
following approach markedly improved performance: If a peptide was longer
than 15 amino acids all constituent were scored 15mers and selected the
strongest prediction as the overall peptide score; if a peptide was
shorter than 15 amino acids, G's were padded on the N-terminus to force
the peptide to length 15 and scored the resulting extended peptide.
Performance as a Function of Training Set Size, Related to FIG. 38A
[0855] To understand how our model's performance is limited by the size of
our datasets, a saturation analysis was performed. This involved
retraining ensembles of models while varying the fraction of the training
data used in order to understand how this affects performance on a
hold-out partition. FIG. 38A shows the evaluation partition (partition 8)
PPV as a function of the number of hit peptides used in the training set.
Each datapoint shows the mean PPV across a collection of 10 models, with
the error bar indicating the standard deviation.
Benchmarking Prediction Performance of Natural CD4+ T Cell Responses,
Related to FIG. 31C
[0856] Since the vast majority of CD4+ T cell responses documented in IEDB
(tcell_full_v3.zip at iedb.org/database_export_v3.php) have an unknown or
computationally imputed HLA class II allele restriction, the subset of
records that were confirmed experimentally by HLA class II tetramer were
focused on. Nearly all such records were deposited by the William Kwok
Laboratory (Benaroya Research Institute, Seattle, Wash.), which uses the
blood of immune-reactive individuals to perform tetramer-guided epitope
mapping (TGEM) of diverse pathogens and allergens. Since negative
peptides were posted for some studies but not others, the source
publications were reviewed to reconstruct the complete set of positive
and negative peptide reactivities. In some cases, the source publication
explicitly listed the negative peptides. In other cases, the negatives
were imputed by following the tiling procedure specified in the
publication's methods and confirming that the peptide boundaries were
consistent with the known positive examples. In this assay depicted in
FIG. 31C, viral epitopes were mapped from viral genes of influenza and
rhinovirus, and peptide sequences comprising the epitopes were used to
predict HLA class II protein binders to each epitope. In this case, a PPV
for CD4+ memory T cell response to the peptides was predicted for
respective HLA-DRB1 protein. The PPV was determined by asking, of the
positive binders, what fraction of the top-ranking epitopes were true
hits. Given that the positive pairing of an HLA class II protein molecule
with a peptide, and where the HLA molecule is present in the subject
infected by the respective virus, a CD4 response will be generated in the
subject. A comparison of the predictive efficiency (PPV, in other words
predicting the number of true hits) between Neonmhc2 and a publicly
available predictor (NetMHCIIpan) is shown in FIG. 31C per each DRB1
protein tested in this exemplary study. Neonmhc2 outperformed NetMHCIIpan
for each of the six alleles tested.
[0857] All 20mer peptides were scored by neonmhc2 and by NetMHCIIpan-v3.1.
PPV was calculated as the fraction of experimentally confirmed positives
among the n top-scored peptides, where there were n experimentally
confirmed peptides total (FIG. 31C).
T Cell Induction Protocol and Immunogenicity Readouts, Related to FIG. 31D
[0858] To generate monocyte derived dendritic cells (mDCs), CD14+
monocytes were isolated from HLA-DRB1*11:01+ healthy donor peripheral
blood monocytes (PBMCs) by magnetic separation using human CD14
microbeads as per manufacturer's protocol (Miltenyi Biotec). Isolated
CD14+ cells were differentiated for 5 days in Cellgenix GMP DC media
supplemented with 800 U/ml rh GM-CSF and 400 U/ml rh IL-4 (Cellgenix). On
day 5, mDCs were harvested and pulsed with 0.4 .mu.M peptide for 1 hour
at 37 degrees Celsius followed by maturation using 10 ng/ml TNF-.alpha.,
10 ng/ml IL-1.beta., 10 ng/ml IL-6 (Cellgenix), and 0.5 ug/ml PGE1
(Cayman Pharma). After forty-eight hours, mDCs were co-cultured with
autologous PBMCs, at a 1:10 ratio in media containing AIMV/RPMI
(ThermoFisher), 10% human serum (Sigma-Aldrich), 1% Pen/Strep
(ThermoFisher) and supplemented with 5 ng/ml of IL7 and IL15 (Cellgenix).
On day 12, T cells were harvested and restimulated on 0.4 .mu.M peptide
pulsed matured DCs for 7 days for two additional stimulations, for a
total of 3 stimulations.
[0859] Induced T cells were labelled with a unique two-color barcode
labelling system as described previously and cultured overnight at a 1:10
ratio with peptide pulsed and matured autologous mDCs derived from CD14+
monocytes as described above. The next morning, cells were assessed for
production of IFN-.gamma. in response to peptide by flow cytometry. Cells
were treated with Golgi Plug/Golgi Stop (BD Biosciences) for four hours
at 37.degree. C. Cells were then stained with surface marker antibodies
against CD19, CD16, CD14, CD3, CD4, CD8 (BD Biosciences, San Jose,
Calif.), as well as Live/Dead Fixable Dead Cell stain (ThermoFisher); see
Table 13 below. Samples were then permeabilized and fixed with BD
Cytofix/Cytoperm kit (BD Biosciences) per manufacturer's protocol and
stained with intracellular antibodies against IFN-.gamma. (BD
Biosciences). Samples were run on a BD Fortessa X-20 flow cytometer and
analyzed using FlowJo software (Treestar). Induction samples that
positively responded to peptide were samples that induced IFN-gamma
production at 3% higher than the no peptide control.
TABLE-US-00016
TABLE 13
Marker Fluorophore Vendor Cat# Clone
Live dead stain IR dye ThermoFisher L34976
CD19 BUV395 BD 563551 SJ25C1
CD16 BUV395 BD 563784 3G8
CD14 BUV395 BD 563562 M.phi.P9
CD3 BUV805 BD 565511 SK7
CD4 AF700 BD 557922 RPA-T4
CD8 PerCP-Cy5.5 BD 565310 SK1
IFN-.gamma. APC BD 554702 B27
Analysis of HLA Class II Expression Data in Single-Cell RNA-Seq, Related
to FIG. 19A
[0860] Single-cell RNA-Seq data were obtained from three previously
published data sets that profiled human tumor samples. The first study
included data from cutaneous melanomas. The file
"GSE72056_melanoma_single_cell_revised_v2.txt" was downloaded from Gene
Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession: GSE72056). Cells
with tumor status flag "2" were treated as tumor cells, and cells labeled
with tumor status flag "1" and immune cell type flag equal to "1" through
"6" were treated as T cells, B cells, Macrophages, Endothelium,
Fibroblasts, and NKs, respectively. All other cells were dropped. Data
were natively presented in units of log 2 (TPM/10+1) and were thus
mathematically converted to a TPM scale. Once on the TPM scale, the data
for each cell was renormalized to sum to 1,000,000 over the set of
protein-coding UCSC gene symbols (protein-coding genes not appearing in
the expression matrix were implicitly treated as having zero expression).
Finally, single-cell observations corresponding to the same cell type and
same source biopsy where averaged to produce expression estimates at the
patient-cell type level.
[0861] The second study included data from head and neck tumors. The file
"GSE103322_HNSCC_all_data.txt" was downloaded from the Gene Expression
Omnibus (ncbi.nlm.nih.gov/geo/; accession: GSE103322). Per personal
correspondence with Itay Tirosh (Aug. 22, 2018), the data in this table
were also in units of log 2 (TPM/10+1); therefore, the values were
mathematically converted to TPM units. As with the melanoma study, the
data for each cell was renormalized to sum to 1,000,000 over the set of
protein-coding UCSC gene symbols, and single-cell observations
corresponding to the same cell type and same source biopsy where
averaged. Data corresponding the lymph node biopsies were excluded.
[0862] The third study included data from untreated non-small cell lung.
The files "RawDataLung.table.rds" and "metadata.xlsx" were downloaded
from ArrayExpress (ebi.ac.uk/arrayexpress/; accessions: E-MTAB-6149 and
E-MTAB-6653). The data (already in TPM) units, were re-scaled to sum to
1,000,000 over the set of protein-coding genes as previously described.
Finally, single-cell observations corresponding to the same cell type and
same source biopsy where averaged to produce expression estimates at the
patient-cell type level. For simplicity, cell types were merged to a
coarser granularity than natively reported in Table 14 below.
TABLE-US-00017
TABLE 14
Coarse
designation Constituent cell types
Alveolar "Alveolar", excluding "cuboidal alveolar
type 2 (AT2) cells"
FO B cells "follicular B cells"
Plasma cells "plasma B cells"
CLEC9A+ DCs "cross-presenting dendritic cells"
monoDCs "monocyte-derived dendritic cells"
pDCs "plasmacytoid dendritic cells"
Langerhans "Langerhans cells"
Macrophages "macrophages"
Granulocytes "granulocytes"
Endothelium "normal endothelial cell", "tumor endothelial
cell", and "lower quality endothelial cell",
excluding "lymphatic EC"
Epithelium "epithelial cell" and "lower quality epithelial
cell"
Fibroblasts "COL12A1-expressing fibroblasts", "COL4A2-
expressing fibroblasts", "GABARAP-expressing
fibroblasts", "lower quality fibroblasts",
"normal lung fibroblasts", "PLA2G2A-expressing
fibroblasts", and "TFPI2-expressing fibroblasts"
T cells "regulatory T cells", "CD4+ T cells" and
"CD8+ T cells"
NKs "natural killer cells"
Tumor "cancer cells"
Excluded "erythroblasts" and "MALT B cells"
from analysis
[0863] A fourth study included data from colorectal tumors. The file
"GSE81861_CRC_tumor_all_cells_FPKM.csv" was downloaded from the Gene
Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession: GSE81861). The data
(already in TPM) units, were re-scaled to sum to 1,000,000 over the set
of protein-coding genes as previously described. Finally, single-cell
observations corresponding to the same cell type and same source biopsy
where averaged to produce expression estimates at the patient-cell type
level. For this study, cells labeled as "epithelium" are presumed to
represent a mixture of tumor cells and normal epithelium.
[0864] A fifth study included data from serous ovarian cancer tumors.
Single-cell RNA sequencing data of 6 ovarian epithelial cancer of two
low-grade serous ovarian cancer patients (LG1,LG2) and 4 high-grade
serous ovarian cancer patients (HG1,HG2F,HG3,HG4) were obtained from
elsewhere. Quality filtering, clustering and analysis followed the steps
outlined by Shih et al., 2018. Briefly, the Seurat analysis tool was used
to cluster cells passing quality filtering (minimum of 200 expressed
genes, where each gene must be detected in at least 3 different cells; in
total, 2258 cells). The effects of cell-cycle and the unique transcript
count were regressed out. Cells were clustered following principal
component analysis, and clusters were assigned to cell types based on
their expression of the gene signatures from the original publication.
The TPM for the HLA-DRB1 gene was calculated from the normalized unique
transcript count of protein coding genes for each cell type for each
patient.
[0865] Expression levels of HLA-DRB1 in the four studies are plotted in
FIG. 19A.
Characterization of Tumor-Derived Vs. Stroma-Derived HLA Class II
Expression, Related to FIG. 19B
[0866] To determine the relative amount of HLA class II expression
attributable to tumor vs. stroma, mutations called from DNA sequencing in
HLA class II pathways genes in TCGA patients were identified, and for
each patient bearing an HLA class II mutation, the relative expression of
the mutated and non-mutated copies of the gene were quantified in the
corresponding RNA-Seq. Further, it was assumed mutated reads arise from
the tumor, non-mutated reads arise for the stroma or the wildtype allele
in the tumor, and the tumor retains a wildtype copy with expression
approximately equal to the mutated copy.
[0867] Based on this, it was determined that for an observed mutant allele
fraction off the fraction of HLA class II expression attributable to
tumor was approximately 2f and not greater than 100%. Three genes--CIITA,
CD74, and CTSS--were selected as core HLA class II pathway genes and
assessed for mutations (not excluding synonymous and UTR mutations) in
TCGA (data downloaded from TumorPortal (tumorportal.org/): BRCA, CRC,
HNSC, DLBCL, MM, LUAD; TCGA bulk download (tcga-data.nci.nih.gov): CESC,
LIHC, PAAD, PRAD, KIRP, TGCT, UCS; Synapse
(synapse.org/#!Synapse:syn1729383): GBM, KIRC, LAML, UCEC, LUSC, OV,
SKCM; or the original TCGA publication
(cancergenome.nih.gov/publications): BLCA, KICH, STAD, and THCA). These
genes were selected based on their known roles in HLA class II expression
and their tight correlation with HLA-DRB1 across a cohort of 8500 GTEx
samples. Other genes with equivalent correlation with HLA-DRB1 (HLA-DRA1,
HLA-DPA1, HLA-DQA1, HLA-DQB1, and HLA-DPB1) were excluded because their
polymorphic nature makes them prone to false positive mutation calls.
Naturally, only a small fraction of patients had a mutation in CIITA,
CD74, or CTSS, and for some tumor types, there were no patients available
to analyze.
[0868] Original whole exome sequencing (WES) BAMS were visually assessed
(IGV) to confirm that the mutation was present in the tumor sample and
not present in the normal sample. Mutant vs. wildtype read counts were
obtained from corresponding RNA-Seq using pysam. Overall HLA-DRB1
expression was determined based on expression data downloaded from the
Genomic Data Commons (gdc.cancer.gov/), which was renormalized to sum to
1,000,000 over the set of protein-coding genes. The fraction of HLA-DRB1
expression attributable to the tumor (FIG. 19B) was estimated as
min(1,2f), where f is the fraction of RNA-Seq reads in CIITA, CD74, or
CTSS exhibiting a mutation.
Identification of Over- and Under-Represented Genes, Related to FIG. 32A
and FIG. 39B
[0869] Samples were analyzed from previously published MS experiments that
profiled the MHC-II ligandomes of ovarian cancer, colorectal cancer, and
melanoma. Many samples from the ovarian cancer dataset had available
RNA-Seq; data for these samples was downloaded from SRA (NCBI BioProject
PRJNA398141) and aligned to the UCSC hg19 transcriptome using STAR
aligner. For ovarian samples that did not have available RNA-Seq,
expression was estimated averaging across the samples with available
RNA-Seq. For the colorectal and melanoma studies, there was no
corresponding RNA-Seq for any samples, so averages were calculated across
surrogate samples using data from TCGA (The Cancer Genome Atlas Network).
Transcript level gene quantification was performed using transcripts per
million (TPM) as calculated by RSEM version-1.2.31. The expression
estimates were further processed by summing to the gene level, dropping
non-coding genes, and renormalizing such that the total TPM summed to
1000000 (renormalizing across protein-coding genes accounts for
library-to-library variation in ncRNA abundance).
[0870] To identify genes over- and under-represented in HLA class II
ligandomes, it was analyzed the same three datasets used in the
expression analysis. For each gene, our baseline assumption was that it
should yield peptides in proportion to its length multiplied by its
expression level. To determine the length of each gene, the unique 9mers
across all transcript isoforms were enumerated. Gene-level expression was
obtained by summing across transcript isoforms. The observed number of
peptides mapping to each gene was determined at the nested set level
(e.g. peptides GKAPILIATDVASRGLDV (SEQ ID NO: 16), GKAPILIATDVASRGLD (SEQ
ID NO: 17), and KAPILIATDVASRGLDV (SEQ ID NO: 18) counted as a single
observation).
[0871] Two matrices were created representing expected and observed
counts, referred to as E and O, respectively, wherein rows correspond to
genes and columns correspond to samples. The values in 0 were determined
by counting peptides per sample at the nested set level. The matrix E was
first populated by multiplying each gene's length by its expression in
each sample; then the columns of E were rescaled to make the column sums
of E match the column sums of O. Finally, analysis was made at the gene
level by comparing the row sums of E to the row sums of O (FIG. 32A).
Genes were highlighted according to their presence and concentration in
human plasma. An identical approach was used for identifying over- and
under-represented gene in HLA class I data, using melanoma, colorectal
cancer, and ovarian cancer data from the same set of studies. For the HLA
class I analysis, no nesting was applied, but only unique peptides were
counted.
Assessment of Binding Scores in Over-Represented Genes, Related to FIG.
39A
[0872] It was observed that many of the over-represented genes were plasma
genes. A comprehensive list of serum genes was obtained and the neonmhc2
binding scores were compared for HLA DR-bound peptides derived from
plasma genes with HLA-DR-bound peptides derived from non-serum genes, as
well as with length-matched, non-binding (e.g. not observed in MS)
peptides sampled from genes that were represented in the immunopeptidome.
For genotyped, multi-allelic datasets that had HLA class II peptides
profiled with a pan-DR antibody (the same samples analyzed in FIG. 30B),
peptides with neonmhc2 for each DR allele that the sample expressed were
scored. The best score output by neonmhc2 over all expressed alleles was
taken as the representative score for each peptide. The data was pooled
across all usable datasets, and the distributions of the scores for each
category of peptide were visualized with a boxplot.
Analysis of Genes Related to Protein Turnover, Related to FIG. 32C
[0873] Two gene sets were identified meant to represent proteins whose
turnover is regulated by the proteasome. The first gene set comprised
genes with at least one observed ubiquitination site in the cell lines
KG1, Jurkat, or MM1S. The second set comprised genes whose levels
increased upon application of the proteasome inhibitor Bortezomib (BTZ)
of a published paper, applying a p-value filter of 0.01 and selecting the
300 genes with the largest upward fold change.
Comparing Explanatory Power of Bulk Tumor Vs. Antigen Presenting Cell
Gene Expression, Related to FIG. 39C
[0874] Four gene expression profiles were created. The first was meant to
represent APCs and estimated by averaging cell type-specific profiles
from the above-described single-cell RNA-Seq experiments. The average
included "macrophages" (from the head and neck study, the lung study, and
the melanoma study), "CLEC9A DCs" (from the lung study), and "monoDCs"
(from the lung study). The three other expression profiles correspond to
bulk tumor profiles from ovarian cancer, colorectal cancer, and melanoma
(Data FIG. 19A). The ovarian profile was an average of the samples
published by Schuster et al, and the other profiles are derived from the
five TCGA samples with the highest tumor cellularity, per tumor type, as
previously inferred using the "Absolute" algorithm. For each tumor type,
the number of peptides (at the nested-set level) per gene were counted
and modeled each gene's peptide count as a function of gene length,
APC-specific gene expression, and tumor-specific gene expression using
linear regression. The output variable and all input variables were
transformed via log(x+1). Using the model's parameter estimates, the
contribution of tumor was calculated as
.beta..sub.tumor/(.beta..sub.tumor+.beta..sub.APC), and the contribution
of APCs was calculated as
.beta..sub.APC/(.beta..sub.tumor+.beta..sub.APC). For each sample,
bootstrap re-sampling (M=100) at the gene level was used to calculate
confidence intervals for the explanatory proportions.
Characterizing Observed Cleavage Sites of HLA Class II Peptides, Related
to FIG. 40A
[0875] Naturally processed and presented HLA class II peptides were
analyzed from six datasets: PBMC draws, the DC-like MUTZ3 cell line,
colorectal cancer tissue, melanoma, ovarian cancer, and the expi293 cell
line. Since many peptides share the same N-terminus (e.g.
GKAPILIATDVASRGLDV (SEQ ID NO: 16) and GKAPILIATDVASRGLD (SEQ ID NO: 17))
or the same C-terminus (e.g. GKAPILIATDVASRGLD (SEQ ID NO: 17) and
KAPILIATDVASRGLD (SEQ ID NO: 19)), two sets of non-redundant cut sites
were curated, one for N-termini and one for C-termini. The naming system
shown in FIG. 41 was used to refer to positions upstream of peptides,
within peptides, and downstream of peptides. Upstream and downstream
frequencies ( . . . U1 and D1 . . . ), were compared against proteome
amino acid frequencies and scored significant deviations via Chi-square
test. Peptide positions (N1 . . . C1), were compared against frequencies
as observed in MS peptides.
Benchmarking the Performance of Various HLA Class II Cleavage Predictors,
Related to FIG. 40B
[0876] Four PBMC samples and published datasets were used to benchmark the
ability of cleavage-related variables/predictors to enhance the
identification of presented HLA class II epitopes.
[0877] To build integrated predictors that predict peptide presentation
using both binding potential and cleavage potential, constructed datasets
were first using the same approach described for FIG. 31B. This meant
using a 1:19 ratio of hits to decoys, where decoys are length-matched to
hits and are randomly sampled from the set of genes that generated at
least one hit. Different datasets were built in this manner for three
different purposes:
1. For the solvent accessibility- and disorder-based cleavage predictors,
logistic models were fit using HLA class II ligandome data from human
tumor tissues. It was presumed that for a peptide to have been observed
in a ligandome experiment, it must have been successfully processed. (For
the neural network and CNN-based cleavage predictors, training data was
generated using the same datasets in a distinct fashion, as explained in
the table below.) 2. To evaluate if a given cleavage predictor boosted
performance over binding alone, models were fit using mono-allelic
MAPTAC.TM. data generated with B721 and KG1 cells, the most functionally
APC-like cell lines were interrogated. Binding potential was calculated
using neonmhc2, and a logistic regression determined the relative weights
that would be placed on the binding and cleavage variables in forward
prediction. 3. To evaluate the performance of forward prediction,
datasets were constructed for the PBMC samples and published datasets in
the same manner as before. However, because these samples were
multi-allelic, the binding score for each peptide candidate peptide was
taken to be the maximum scoring of the 1-4 DR alleles indicated by each
donor's genotype. PPV was calculated as described for FIG. 31B.
[0878] Several different cleavage predictors were assessed
Cleave First Model, Cut Site Known (Neural Network)
[0879] To learn a cleavage signal from the MS-observed cut sites, all
unique 6mer amino acid sequences from U3 to N3, and C3 to D3 (using the
nomenclature introduced in the section, "Characterizing observed cleavage
sites of HLA class II peptides, related to FIG. 40A") in the tumor
tissue-derived HLA class II ligandomes from were used as positive
examples for training two distinct neural networks, modeling N-terminal
cuts and C-terminal cuts, respectively. As before, an equivalent number
of unique non-observed N-terminal and C-terminal cut sites (negative
examples) were synthetically generated by drawing from the amino acid
frequency of the proteome for the context, and from the MS-observed
ligandome for the peptide. The amino acid sequences were encoded with a
subset of the same features used in neonmhc2, specifically, the amino
acid proximities based on protein structures, and amino acid properties
(e.g. acidic, aliphatic, etc.). For each of the N-terminal and C-terminal
observed cut site models, (lr=0.0005, Adam optimizer, binary
cross-entropy as loss function) a fully connected neural network was then
trained with two hidden layers (20 neurons in one layer, followed by 10
neurons in the next) with ReLu activations, followed by a final sigmoid
layer. For regularization, a dropout rate of 20% was used L2 norm of
0.001 (for C-terminal model only), and max norm constraint of 4.
[0880] To score a candidate peptide, the N-terminal model was applied to
the 6mer sequence U3 to N3 with respect to the peptide, and the
C-terminal model was applied to C3 to D3. Both N-terminal and C-terminal
models were also applied to 6mer sequences tiling across the candidate
peptide to evaluate the cleavage propensity of the sequence within the
peptide itself. A logistic regression was trained on the MAPTAC.TM. data
using the neonmhc2 binding score as well as four neural network outputs,
corresponding to the N-terminus, C-terminus, and maximum scoring cut
sites for the N-terminal and C-terminal models within the peptide.
Cleave First Model, Cut Site Unknown (+/-15AAs) (Neural Network)
[0881] To determine if the cleavage models learned from observed cut sites
would be predictive when the precise termini of peptides was not known,
the same neural networks learned above was applied to extended context,
15 amino acids beyond the peptide termini. To score a candidate peptide
in this case, the maximum score was calculated across three regions: the
15 amino acids upstream of the peptide (regardless of the location of the
true N-terminal cleavage site), which was scored with the N-terminal
model, the peptide sequence, which was scored with both the N-terminal
and C-terminal models, and the 15 amino acids downstream of the peptide,
which was scored with the C-terminal model. A logistic regression was
trained on the MAPTAC.TM. data using the neonmhc2 binding score as well
as the four region-specific (since the peptide itself contributes two
sets of values, from the N-terminal and C-terminal models) scores.
Bind First Model, Solvent Accessibility
[0882] Within the SCRATCH suite, the tool ACCpro20 was used to predict
relative solvent accessibility. The likelihood of a peptide being
processed given the peptide's mean solvent accessibility score was then
fit with a logistic regression using the tumor tissue data. Finally, a
logistic regression was trained on the mono-allelic data using the
neonmhc2 binding score and the output from the tumor tissue-trained
predictor.
Bind First Model, Disorder
[0883] Per-residue scores of sequence disorder were determined over the
entirety of the proteome, scoring on a 0-5 scale according to the number
of prediction engines labeling the position as disordered (servers used:
anchor, espritz-d, espritz-n, espritz-x, iupred-l, and iupred-s). The
average disorder score was calculated over each candidate peptide, with
the six disorder predictor outputs summed. As with solvent accessibility,
first a logistic model was fit using this overall disorder score with the
tumor tissue data. This was followed by training a logistic regression on
the mono-allelic data using the neonmhc2 binding score and the output
from the tumor tissue-trained predictor.
Hybrid Model, Precursor Cut Scan (+/-30AAs) (CNN)
[0884] Training data for hits was generated as described for the `Cleave
first, cut site known` cleavage predictor, with the exception that
instead of using the unique 6mer sequences from U3 to N3, and C3 to D3,
the 30 amino acids flanking the peptides (U30 to U1, and D1 to D30) were
taken from as model input. Furthermore, whether a 30mer sequence came
from the N-terminal or C-terminal flank was not distinguished, and
instead the data was pooled to train a single model to learn a precursor
cut signal that was assumed may occur on either side of an observed
peptide. In this setting, instead of using synthetic decoys, flanking
sequences from un-observed peptides drawn from the same source genes was
used as negative examples. Sequences were encoded as before, using the
amino acid proximities based on protein structures, and amino acid
properties (e.g. acidic, aliphatic, etc.). The architecture of the CNN
consisted of two convolutional layers, the first layer with a kernel size
of 2 with 48 filters, followed by a layer with a kernel size of 3 and 40
filters. These layers had ReLu activations. The convolutional layers were
followed with a global max pooling layer, after which was a final dense
layer with a sigmoid activation. The CNN was trained with a learning rate
of 0.001, with Adam optimization and binary cross-entropy as the loss
function.
[0885] To score a candidate peptide, the CNN was applied to the 30 amino
acids upstream and 30 amino acids downstream of the peptide, producing an
N-terminal flank score and a C-terminal flank score. A logistic
regression was trained on the MAPTAC.TM. data using the neonmhc2 binding
score and the two CNN scores.
DQ Overlap
[0886] MS-based peptide identifications were pooled across HLA-DQ
ligandomes from Bergseng et al., 2015. A new feature was created
representing whether a new candidate peptide overlapped with one of these
previously observed peptides. Specifically, the feature was set to 1 if
it shared at least one 9mer with any peptide in the set of previously
observed HLA-DQ ligands; otherwise the feature was set to 0. A logistic
regression was trained on the mono-allelic data using the neonmhc2
binding score and the overlap feature.
[0887] The integrated binding and cleavage models were also all fit and
evaluated using NetMHCIIpan as the binding predictor instead in FIG. 40B.
Assessing Prediction Overall Performance on Natural Donor Tissues, Related
to FIG. 21A-21B
[0888] Peripheral blood from seven healthy donors was profiled with a
DR-specific antibody as described in the section "Antibody-based
HLA-peptide complex isolation" above. Training and evaluation datasets
were constructed using the hit and decoy selection algorithm previously
described in relation to FIG. 31B. In short, this means representing each
nested set with one hit peptide (the shortest peptide in the nested set)
and tiling length-matched decoys over genes such that they overlap
minimally with hits and minimally with each other. In this setting, the
decoy selection was not constrained to MS-observed genes, and decoys were
instead randomly sampled without replacement from the entire proteome. A
1:499 hit to decoy rate was utilized, reflecting a rough estimate of the
frequency of HLA-DR-presented peptides in the proteome. Logistic
regression models with MHC binding scores (from NetMHCIIpan or neonmhc2)
as well as other input features (expression, gene bias, and DQ overlap)
were trained on MAPTAC.TM. data from KG1 and B721 cell lines.
[0889] The following variables in Table 15 were used in a subset of the
regressions.
TABLE-US-00018
TABLE 15
NetMHCIIpan Derived from NetMHCIIpan-v3.1. For each candidate
percent rank peptide, the strongest score was taken across all
DR alleles in the donor's genotype.
neonmhc2 Derived from neonmhc2. For each candidate peptide,
percent rank the strongest score was taken across all DR alleles
in the donor's genotype.
Expression Gene expression estimates were obtained by analyzing
data from (bowtie2, RSEM, and renormalization over
protein-coding genes only), values averaged over N
samples. Expression was either thresholded (0/1,
indicating if expression was non-zero) or treated
as a continuous variable.
Gene bias (1 + observed)/(1 + expected) per the analysis
in FIG. 32A
DQ overlap Indicator variable (0/1) for whether the candidate
peptide shares at least one 9mer with any of the
HLA-DQ datasets. HLA-DQ-binding peptides have
distinct binding motifs from HLA-DR-binding peptides,
so binding propensity should not be learned with
this feature.
[0890] The performance of these models on HLA-DR ligandomes from natural
donor tissue (PBMC samples, etc.) were then evaluated. Decoys are sampled
from the proteome at random (including genes that never produced an
MS-observed peptide) to achieve a 1:499 ratio of hits to decoys, which
nearly saturates available decoy sequences. A 1:499 hit to decoys rate
was used for evaluation (as well as training) The top 0.2% scored
peptides in the evaluated dataset were labeled s positive calls, and the
PPV was calculated as the fraction of positive calls that were hits (see,
e.g., FIG. 21A and Table 15). Note that since the number of positives is
constrained to be equal to the number of hits, recall is exactly equal to
PPV in this evaluation scenario. The application of a consistent 1:499
ratio across alleles helps stabilize the performance values, which are
otherwise highly influenced by the number of hits observed for each
donor. This was deemed appropriate since the number of hits was assumed
to relate more to experimental conditions than intrinsic properties of
the donor's cells.
TABLE-US-00019
TABLE 16
PPV for PPV for
MS Sample NetMHCIIpan neonmhc2
Lung 0.05 >0.3
Spleen 0.025 ~0.3
Heyder LCLs 0.01 ~0.2
A375 0.01 ~0.3
mDCs 0.05 >0.44
PBMC sample #1 0.01 ~0.3
PBMC sample #2 0.01 >0.3
PBMC sample #3 0.025 >0.3
PBMC sample #4 0.0025 ~0.275
Ritz DOHH2 0.01 0.3
Ritz Maver1 0.015 ~0.175
SILAC-Based Identification of DC-Presented Tumor Peptides, Related to FIG.
33A
[0891] To generate monocyte derived dendritic cells (mDCs), CD14+
monocytes were isolated from healthy donor peripheral blood monocytes
(PBMCs) by magnetic separation using human CD14 microbeads as per
manufacturer's protocol (Miltenyi Biotec). Isolated cells were
differentiated for 6 days in CellGenix GMP DC media supplemented with 800
U/ml rh GM-CSF and 400 U/ml rh IL-4 (CellGenix, Germany). K562 cells
(ATCC, Manassas, Va.) were isotopically labeled using Stable Isotope
Labeling with Amino acids in Cell culture (SILAC). Cells were grown for 5
doublings in the presence of RPMI 1640 media for SILAC (ThermoFisher)
containing the heavy isotopically amino acids, L-Lysine 2HCl 13C6 15N2
(Life Technologies, Carlsbad, Calif.) and L-leucine 13C6 (Life
Technologies, Carlsbad, Calif.) with 15% heat inactivated, dialyzed fetal
bovine serum (ThermoFisher). SILAC labeled K562 cells were lysed using 60
.mu.M hypochlorous acid (HOC1) as described previously or treated with UV
for 3 hours at room temperature to induce apoptosis and rested overnight.
Seventy-five million mDCs were co-cultured with UV treated SILAC labelled
K562 cells at a 1:3 ratio for 14 hours at 37.degree. C. or cultured with
a 1:3 ratio of K562 lysed with HOC1 for 10 minutes or 5 hours at
37.degree. C. After co-culture, cells were harvested, pelleted and flash
frozen in liquid nitrogen for proteomic analysis.
Prediction and Expression Analysis of DC-Presented Tumor Peptides, Related
to FIG. 33B and FIG. 21C
[0892] To calculate PPV for the prediction of heavy-labeled
(tumor-derived) peptides, the same model was used and evaluation approach
as used in FIG. 33B. Expression for the K562 cell lines was determined
based on data from ENCODE (encodeproject.org/experiments/ENCSR545DKY/;
libraries ENCLB075GEK and ENCLB365AUY; (ENCODE Project Consortium,
2012)). Expression for dendritic cells was determined based on GSE116412
(averaging of accessions GSM3231102, GSM3231111, GSM3231121, GSM3231133,
GSM3231145.
Example 14. Benchmarking FAIMS with Tryptic Peptides
[0893] In this example, a standard HLA-peptidomic workflow for using high
field asymmetric waveform ion mobility spectrometry (FAIMS) is described.
Endogenously processed and presented HLA class I and HLA class II
peptides from A375 cells were characterized. The peptides were subjected
to both acidic reverse-phase (aRP) and basic reverse-phase (bRP) offline
fractionation prior to analysis by nLC-MS/MS using a Thermo Scientific
Orbitrap Fusion Lumos Tribrid mass spectrometer equipped without (-) and
with (+) the FAIMS Pro interface. The workflow is indicated in a diagram
depicted in FIG. 42A. FIG. 42B shows results indicating the FAIMS
improves peptide detection in tryptic samples as low as 10 ng. FAIMS
increases HLA-1 and HLA class II peptide detections throughout the LC
gradient despite the lower MS1 intensity (FIG. 43A and FIG. 44A, data for
HLA-1 and HLA class II peptides, respectively). Strikingly, an increase
in unique peptide detection is observed in the acidic and basic reverse
phase samples, with FAIMS evaluation (FIG. 43B, and FIG. 44B, data for
HLA-1 and HLA class II peptides, respectively). This study indicated that
Detections of HLA class I and HLA class II peptides increase throughout
the LC gradient with FAIMS despite lower MS1 intensity. Combining offline
fractionation and FAIMS increases the analysis depth of HLA peptide
repertoires, as shown in FIG. 45 and FIG. 46, (HLA-1 and HLA class II
peptides, respectively).
Example 15--Differential Scanning Fluorometry (DSF) Peptide Exchange Assay
[0894] A peptide exchange assay was performed as follows: The following
reagents (Table 17) were combined and mixed at 37.degree. C. for 18
hours.
TABLE-US-00020
TABLE 17
Stock Final
Ingredients Concentrations Concentration
Thrombin digested DRB1*15:01 85.6 mM 5 mM
(CLIP0), ''DR15''
Exchange Peptide/peptide 10 mM 100 mM
of interest.sup.A
Sodium Acetate 1 M 100 mM
Sodium Chloride 5 M 50 mM
Octyl glucoside 10% 1%
MiliQ water -- Up to 100 ml
.sup.ADMSO or peptides with the following sequences were used:
PPIDGYPNHPCFEPE (SEQ ID NO: 31) (M230), PQILPYPAPEEAQEN (SEQ ID NO: 32)
(M231), PQLRQWWAQGADPLA (SEQ ID NO: 33) (M247), LLRPGQIVAFDSTAQ (SEQ ID
NO: 34) (M248) or ASLRSWPSTWAPWAS (SEQ ID NO: 35) (M371.
[0895] The buffer was then exchanged using a PD minitrap G-25 desalting
column Sypro orange dye (Fisher S6651) was diluted to 1000.times. in 100%
DMSO. 50 .mu.L working stock of Sypro orange dye at 100.times. was
prepared in desalting buffer. 2 .mu.L of 100.times. sypro orange dye and
18 .mu.L of desalted peptide exchanged sample was transferred to wells of
a 384 white PCR microplate and mixed. The plate was then covered with a
transparent plate sealer and the plate was subjected to the following
program in a Roche lightcycler 480: (1) heat to 25.degree. C., hold for
10 seconds; (2) increase the temperature to 99.degree. C., read plate 20
times/1.degree. C. (3) bring the temperature down to 25.degree. C. and
hold for 10 seconds. Melting temperatures were then calculated. Exemplary
results are shown in Table 18 below.
TABLE-US-00021
TABLE 18
DT.sub.m (sample
Sample T.sub.m - control T.sub.m) Average T.sub.m Slope Initial Low Peak
DR15 + M230 21.4 79.7 0.5 2.8 2.0 4.8
DR15 + M231 15.6 73.9 0.3 2.7 1.9 5.8
DR15 + M247 20.1 78.4 0.4 3.0 2.3 5.0
DR15 + M248 5.6 63.9 0.4 1.8 1.4 5.1
DR15 + M371 19.9 78.2 0.4 2.5 1.9 4.9
DR15 + DMSO (Control) NA 58.3 0.8 2.1 1.7 9.2
Sequence CWU
1
1
96115PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 1Pro Val Ser Lys Met Arg Met Ala Thr Pro Leu Leu Met Gln Ala1
5 10 15215PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 2Pro
Glu Ala Ser Leu Tyr Gly Ala Leu Ser Lys Gly Ser Gly Gly1 5
10 15315PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 3Pro
Ala Thr Tyr Ile Leu Ile Leu Lys Glu Phe Cys Leu Val Gly1 5
10 15415PRTArtificial
SequenceDescription of Artificial Sequence Synthetic
peptideMISC_FEATURE(1)..(15)This sequence may encompass 4-15 residues
4His His His His His His His His His His His His His His His1
5 10 1558PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 5His
His His His His His His His1 5616PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 6Glu
Gly Arg Gly Ser Leu Thr Cys Gly Asp Val Glu Asn Pro Gly Pro1
5 10 15717PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 7Ala
Thr Asn Phe Ser Leu Lys Gln Ala Gly Asp Val Glu Asn Pro Gly1
5 10 15Pro819PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 8Gln
Cys Thr Asn Tyr Ala Leu Lys Leu Ala Gly Asp Val Glu Ser Asn1
5 10 15Pro Gly Pro921PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 9Val
Lys Gln Thr Leu Asn Phe Asp Leu Lys Leu Ala Gly Asp Val Glu1
5 10 15Ser Asn Pro Gly Pro
201011PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 10Gly Ser Gly Gly Ser Gly Gly Ser Ala Gly Gly1
5 101115PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 11Gly Leu Asn Asp Ile Phe Glu Ala Gln Lys
Ile Glu Trp His Glu1 5 10
151225PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 12Ser Gly Gly Ser Gly Gly Ser Ala Gly Gly Gly Leu Asn Asp
Ile Phe1 5 10 15Glu Ala
Gln Lys Ile Glu Trp His Glu 20
251322PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 13Val Lys Gln Thr Leu Asn Phe Asp Leu Leu Lys Leu Ala Gly Asp
Val1 5 10 15Glu Ser Asn
Pro Gly Pro 201411PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 14Gly Ser Tyr Pro Tyr Asp Val
Pro Asp Tyr Ala1 5 10156PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 15His
His His His His His1 51618PRTArtificial SequenceDescription
of Artificial Sequence Synthetic peptide 16Gly Lys Ala Pro Ile Leu
Ile Ala Thr Asp Val Ala Ser Arg Gly Leu1 5
10 15Asp Val1717PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 17Gly Lys Ala Pro Ile Leu Ile
Ala Thr Asp Val Ala Ser Arg Gly Leu1 5 10
15Asp1817PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 18Lys Ala Pro Ile Leu Ile Ala Thr Asp Val
Ala Ser Arg Gly Leu Asp1 5 10
15Val1916PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 19Lys Ala Pro Ile Leu Ile Ala Thr Asp Val Ala Ser
Arg Gly Leu Asp1 5 10
152010PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 20His His His His His His His His His His1 5
102124PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 21Leu Pro Lys Pro Pro Lys Pro Val Ser Lys
Met Arg Met Ala Thr Pro1 5 10
15Leu Leu Met Gln Ala Leu Pro Met 202214PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 22Leu
Pro Leu Lys Met Leu Asn Ile Pro Ser Ile Asn Val His1 5
102313PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 23Pro Lys Tyr Val Lys Gln Asn Thr Leu Lys
Leu Ala Thr1 5 102417PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 24Pro
Val Val His Phe Phe Lys Asn Ile Val Thr Pro Arg Thr Pro Pro1
5 10 15Tyr2517PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 25Tyr
Ala Thr Phe Phe Ile Lys Ala Asn Ser Lys Phe Ile Gly Ile Thr1
5 10 15Glu2616PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 26Thr
Leu Ser Val Thr Phe Ile Gly Ala Ala Pro Leu Ile Leu Ser Tyr1
5 10 152717PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 27Pro
Val Val His Phe Phe Lys Asn Ile Val Thr Pro Arg Thr Pro Pro1
5 10 15Tyr2815PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 28Tyr
Ala Arg Ile Arg Arg Asp Gly Cys Leu Leu Arg Leu Val Asp1 5
10 152916PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 29Thr
Leu Ser Val Thr Phe Ile Gly Ala Ala Pro Lys Ile Leu Ser Tyr1
5 10 153015PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 30Tyr
Ala Arg Ile Lys Arg Asp Gly Cys Leu Leu Arg Leu Val Asp1 5
10 153115PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 31Pro
Pro Ile Asp Gly Tyr Pro Asn His Pro Cys Phe Glu Pro Glu1 5
10 153215PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 32Pro
Gln Ile Leu Pro Tyr Pro Ala Pro Glu Glu Ala Gln Glu Asn1 5
10 153315PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 33Pro
Gln Leu Arg Gln Trp Trp Ala Gln Gly Ala Asp Pro Leu Ala1 5
10 153415PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 34Leu
Leu Arg Pro Gly Gln Ile Val Ala Phe Asp Ser Thr Ala Gln1 5
10 153515PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 35Ala
Ser Leu Arg Ser Trp Pro Ser Thr Trp Ala Pro Trp Ala Ser1 5
10 15369PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 36Gly
Lys Ser Val Val Cys Glu Ala Leu1 53715PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 37Leu
Pro Asn Gly Gly Phe Ala Ser Ile Leu Leu Tyr Lys Ile Glu1 5
10 153816PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 38Phe
Cys Lys Gly Ser Phe Ala Ser Ile Leu Lys Leu Leu Gly Glu Phe1
5 10 153942PRTArtificial
SequenceDescription of Artificial Sequence Synthetic
polypeptideMOD_RES(6)..(19)Any amino acid 39Gly Asp Thr Gly Leu Xaa Xaa
Xaa Xaa Xaa Xaa Xaa Xaa Xaa Xaa Xaa1 5 10
15Xaa Xaa Xaa Gly Gly Gly Gly Ser Leu Val Pro Arg Gly
Ser Gly Gly 20 25 30Gly Gly
Ser Gly Asp Thr Arg Pro Arg Phe 35
404013PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 40Asp Arg Tyr Glu Met Glu Asp Gly Lys Val Ile Glu Arg1
5 104116PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 41Gly Gly His Met Thr Thr Leu
Ser Gly Glu Glu Ile Ser Tyr Thr Gly1 5 10
154214PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 42Lys Thr Phe Asp Gln Leu Thr Pro Glu Glu
Ser Lys Glu Arg1 5 104314PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 43Leu
Pro Arg Tyr Glu Ala Leu Arg Gly Glu Gln Pro Pro Asp1 5
104416PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 44Asp Lys Lys Asn Ile Ile Leu Glu Glu Gly
Lys Glu Ile Leu Val Gly1 5 10
154514PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 45Lys Glu Ala Ala Tyr His Pro Glu Val Ala Pro Asp
Val Arg1 5 104615PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 46Asp
Ala Glu Phe Arg His Asp Ser Gly Tyr Glu Val His His Gln1 5
10 154716PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 47Gly
His Pro Asp Leu Gln Gly Gln Pro Ala Glu Glu Ile Phe Glu Ser1
5 10 154816PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 48Leu
Gly Lys Asn Phe Asp Phe Gln Lys Ser Asp Arg Ile Asn Ser Glu1
5 10 154914PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 49Met
Pro Ser Phe Val Pro Ser Asp Gly Arg Gln Ala Ala Asp1 5
105015PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 50Gly Leu Arg Tyr Lys Lys Leu His Asp Pro
Lys Gly Trp Ile Thr1 5 10
155115PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 51Thr Ile Glu Lys Phe Glu Lys Glu Ala Ala Glu Met Gly Lys
Gly1 5 10
155213PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 52Gly Val Gln Arg Gly Leu Val Gly Glu Ile Ile Lys Arg1
5 105311PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 53Thr Trp Phe Asn Gln Pro Ala
Arg Lys Ile Arg1 5 105414PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 54Glu
Phe Lys Lys Tyr Glu Met Met Lys Glu His Glu Arg Arg1 5
105515PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 55Ser Pro His Ala Phe Lys Thr Glu Ser Gly
Glu Glu Thr Asp Leu1 5 10
155613PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 56Asn Asn Leu Cys Pro Ser Gly Ser Asn Ile Ile Ser Asn1
5 105714PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 57Glu Arg Pro Tyr Trp Asp Met
Ser Asn Gln Asp Val Ile Asn1 5
105814PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 58Val Gly Gly Thr Met Val Arg Ser Gly Gln Asp Pro Tyr Ala1
5 105913PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 59Lys Glu Ala Leu Glu Pro Ser
Gly Glu Asn Val Ile Gln1 5
106015PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 60Arg Gln Arg Arg Leu Leu Gly Ser Val Gln Gln Asp Leu Glu
Arg1 5 10
156114PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 61Ile Asp Arg Ala Leu Asn Glu Ala Cys Glu Ser Val Ile Gln1
5 106216PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 62Asn Glu Asn Asn Leu Glu Ser
Ala Lys Gly Leu Leu Asp Asp Leu Arg1 5 10
156316PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 63Asp Ser Lys Ser Leu Arg Thr Ala Leu Gln
Lys Glu Ile Thr Thr Arg1 5 10
156413PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 64Thr Gly Gly Asp Ile Asn Ala Ala Ile Glu Arg Leu
Leu1 5 106516PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 65Ser
Leu Asp Asn Leu Lys Ala Ser Val Ser Gln Val Glu Ala Asp Leu1
5 10 156614PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 66Asp
Glu Arg Arg Phe Lys Ala Ala Asp Leu Asn Gly Asp Leu1 5
106715PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 67Lys Pro Ala Pro Ala Leu Arg Ser Ala Arg
Ser Ala Pro Glu Asn1 5 10
156814PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 68Arg Thr Ser Tyr Val Thr Ser Val Glu Glu Asn Thr Val Asp1
5 106915PRTArtificial SequenceDescription
of Artificial Sequence Synthetic peptide 69Arg Ala Val Glu Phe Gln
Glu Ala Gln Ala Tyr Ala Asp Asp Asn1 5 10
157014PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 70Arg Asp Leu Ala Gln Tyr Asp Ala Ala His
His Glu Glu Phe1 5 107113PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 71Gly
Gln Gln Trp Thr Tyr Glu Gln Arg Lys Ile Val Glu1 5
107214PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 72Glu Gly Glu Tyr Gln Gly Ile Pro Arg Ala Glu Ser
Gly Gly1 5 107316PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 73Ser
Pro Glu Glu Phe Asp Glu Val Ser Arg Ile Val Gly Ser Val Glu1
5 10 157415PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 74Ile
Ala Gly Glu Trp Gln Val Leu His Arg Glu Gly Ala Ile Thr1 5
10 157515PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 75Glu
Pro Ala Glu Phe Ile Ile Asp Thr Arg Asp Ala Gly Tyr Gly1 5
10 157615PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 76Asp
Leu Glu Glu Leu Glu Val Leu Glu Arg Lys Pro Ala Ala Gly1 5
10 157716PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 77Gly
Met Asn Ile Val Glu Ala Met Glu Arg Phe Gly Ser Arg Asn Gly1
5 10 157815PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 78Gly
Phe Asn Trp Asn Trp Ile Asn Lys Gln Gln Gly Lys Arg Gly1 5
10 157913PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 79Gly
Pro Phe Ser Phe Ser Val Ile Asp Lys Pro Pro Gly1 5
108014PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 80Leu Asn Glu Leu Lys Pro Ile Ser Lys Gly Gly His
Ser Ser1 5 108115PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 81Asp
Val Asn Glu Tyr Ala Pro Val Phe Lys Glu Lys Ser Tyr Lys1 5
10 158215PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 82Leu
Ala Pro Thr Trp Glu Glu Leu Ser Lys Lys Glu Phe Pro Gly1 5
10 158315PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 83Thr
Arg Asn Glu Val Ile Pro Met Ser His Pro Gly Ala Val Asp1 5
10 158414PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 84Glu
Asn Pro Tyr Phe Ala Pro Asn Pro Lys Ile Ile Arg Gln1 5
108515PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 85Tyr Glu Asp Lys Phe Arg Asn Asn Leu Lys
Gly Lys Arg Leu Asp1 5 10
158615PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 86Lys Asn Gly Ala Tyr Lys Val Glu Thr Lys Lys Tyr Asp Phe
Tyr1 5 10
158715PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 87Gly Glu Gly Leu Phe Gln Pro Ala His Arg Tyr Pro Asp Ala
Gly1 5 10
158815PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 88Arg Asn Gln Trp Lys Cys Leu Gly Lys Pro Val Gly Ala Glu
Met1 5 10
158915PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 89Val Pro Ala Trp Thr Arg Ala Trp Arg Asn Ser Ser Pro Lys
Gly1 5 10
159015PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 90Leu His Pro Glu Leu Leu Pro Leu Trp Arg Leu Leu Pro Asp
Gly1 5 10
159115PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 91Asp Gly Gly Ser Tyr Phe Ser Leu Trp Lys Ile Trp Thr Gln
Val1 5 10
159215PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 92His Glu Gly Gly Phe Pro Pro Leu Leu Arg Arg Ala Ala Glu
Asp1 5 10
159315PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 93Leu Ser Pro Val Trp Cys Leu Gln Trp Lys Leu Ser Gly Thr
Asp1 5 10
159415PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 94Lys Asn Thr Ile Val Tyr Thr Thr Lys Gln Val Gln Ser Cys
Gln1 5 10
159515PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 95Asn Asn Glu Gln Phe Gln Trp Lys Ile Arg His Val Gly Pro
Glu1 5 10
159615PRTArtificial SequenceDescription of Artificial Sequence Synthetic
peptide 96Tyr Lys Gly Gly Tyr Glu Leu Val Lys Lys Ser Gln Thr Glu
Leu1 5 10 15
* * * * *
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