A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Abstract

Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.

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Fig. 1: Mass spectrometric characterization of peptides eluted from HLA proteins in mono-allelic cell lines.
Fig. 2: Identification of shared motifs and submotifs amongst HLA-A, -B, -C and -G alleles.
Fig. 3: Mono-allelic data uncover length-specific HLA binding preferences.
Fig. 4: Proteasomal and peptidase shaping of the HLA-associated peptidome.
Fig. 5: Generation and evaluation of allele-and-length-specific and pan-allele-pan-length MS-based models on mono-allelic data.
Fig. 6: Integrative MS-informed models more accurately predict peptides directly observed on primary tumor cells.

Data availability

The original mass spectra for 79 of 95 mono-allelic datasets generated for this study, the protein sequence database and tables of peptide spectrum matches for all 95 alleles have been deposited in the public proteomics repository MassIVE (https://massive.ucsd.edu) and are accessible at ftp://massive.ucsd.edu/MSV000084172/. MS data for the 16 previously published mono-allelic datasets in MassIVE can be downloaded at ftp://massive.ucsd.edu/MSV000080527. Datasets for the patient samples are accessible at ftp://massive.ucsd.edu/MSV000084442/. B721.221 RNA-seq data for HLA-C (C*04:01, C*07:01) are deposited under GEO: GSE131267. Melanoma RNA-seq data are deposited in dbGaP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001451.v1.p1 (ref. 15). Glioblastoma bulk RNA-seq data are available through dbGaP (https://www.ncbi.nlm.nih.gov/gap) with accession number phs001519.v1.p1 (ref. 26). All other data are available from the corresponding authors upon reasonable request.

Code availability

Code used to generate plots characterizing allele-specific preferences (for example, logo plots, entropy plots, peptide projection and clustering, overlap with IEDB data and so on) as well as code to build a sample neural network prediction model is provided as Supplementary Code. The HLAthena predictors are available to use online for research purposes only at http://HLAthena.tools. For commercial usage inquiries please contact the authors or the Broad Institute.

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Acknowledgements

We acknowledge technical assistance from K. Pelton, S. Santagata, O. Spiro, L. Elagina, B. Knisbacher, S. Shukla, J. Brugge and A. Apffel. We further express gratitude for constructive input from M. Rooney, J. Abelin and Z. Hu. We acknowledge support from the National Institutes of Health: grant nos. NCI-1RO1CA155010-02 (to C.J.W.), NHLBI-5R01HL103532-03 (to C.J.W.), NIH/NCI U24 CA224331 (to C.J.W.), NIH/NCI R21 CA216772-01A1 (to D.B.K.), NCI-SPORE-2P50CA101942-11A1 (to D.B.K.), NHGRI T32HG002295 and NIH/NCI T32CA207021 (to S.S.), NCI 5T32CA009172-41 (to D.A.B.), NIH/NCI U24-CA210986 and NIH/NCI U01 CA214125 (to S.A.C.). This work was supported in part by The G. Harold and Leila Y. Mathers Foundation and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center. D.A.B. is supported in part by the John R. Svenson Fellowship. C.J.W. is a scholar of the Leukemia and Lymphoma Society, and is supported in part by the Parker Institute for Cancer Immunotherapy. S.K. is a Cancer Research Institute/Hearst Foundation fellow.

Author information

D.B.K., C.J.W., N.H. and S.A.C. directed the overall study design. S.S. performed computational analyses and developed predictive models. S.K., C.R.H., H.K. and K.R.C. generated the MS data and performed data analysis. D.B.K. and G.L.Z. selected the HLA alleles for analysis. D.B.K., P.M.L. and L.W.L. generated the single-HLA allele cell lines and performed data generation. D.B.K., G.O., K.L.L., D.A.B., P.M.L. and L.W.L. developed the patient-derived tumor cell lines. I.K.Z. and J.M.R. generated and provided cells from an ovarian cancer PDX model. P.B. provided CLL samples for analysis. W.Z. provided expert technical assistance. T.E. generated RNA-seq data for mono-allelic cell lines. T.O. and T.L. generated and quantified ribosome profiling data. J.S. and W.J.L. performed HLA typing and validation of all cell lines. S.J. performed HLA binding validation assays. S.S., S.K., N.H., C.J.W. and D.B.K. wrote the manuscript, with contributions from all co-authors.

Correspondence to Nir Hacohen or Steven A. Carr or Catherine J. Wu or Derin B. Keskin.

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Competing interests

D.B.K. has previously advised Neon Therapeutics, and owns equity in Aduro Biotech, Agenus Inc., Armata Pharmaceuticals, Biomarin Pharmaceutical Inc., Bristol–Myers Squibb Com., Celldex Therapeutics Inc., Editas Medicine Inc., Exelixis Inc., Gilead Sciences Inc., IMV Inc., Lexicon Pharmaceuticals Inc. and Stemline Therapeutics Inc. D.A.B. has received consulting fees from Octane Global, Defined Health, Dedham Group, Adept Field Solutions, Slingshot Insights, Blueprint Partnership, Charles River Associates, Trinity Group and Insight Strategy, and is a member of the RCC translational medicine advisory broad of Bristol–Myers Squibb. K.L.L. owns equity and is a founder of Travera LLC and is an advisor to Bristol–Myers Squibb Com. and Rarecyte. S.A.C. is a member of the scientific advisory boards of Kymera, PTM BioLabs and BioAnalytix and a scientific advisor to Pfizer and Biogen. C.J.W. and N.H. are founders of Neon Therapeutics and members of its scientific advisory board. N.H. is also an advisor for IFM therapeutics. W.J.L. is a member of the scientific advisory board of CareDx. All other authors have no competing interests. Patent applications have been filed on aspects of the described work entitled as follows: ‘HLA single allele lines’, and ‘Methods for identifying neoantigens’.

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Supplementary information

Supplementary Materials

Supplementary Figs. 1–6 and Notes 1–7.

Reporting Summary

Supplementary Data 1

Peptide exports for mono-allelic samples

Supplementary Data 2

Peptide exports for multi-allelic samples

Supplementary Table 1

Characteristics of HLA alleles and mono-allelic data

Supplementary Table 2

Allele similarity and submotifs derived from mono-allelic data

Supplementary Table 3

Mono-allelic data reveal length-based preferences

Supplementary Table 4

HLA presentation of IFN-γ response genes increases after treatment

Supplementary Table 5

Cross-validated model evaluation results on mono-allelic data

Supplementary Table 6

Cross-validated model evaluation results on multi-allelic data

Supplementary Code

Sample scripts for reproducing analysis and models.

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Sarkizova, S., Klaeger, S., Le, P.M. et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat Biotechnol (2019). https://doi.org/10.1038/s41587-019-0322-9

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