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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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 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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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.
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’.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–6 and Notes 1–7.
Peptide exports for mono-allelic samples
Peptide exports for multi-allelic samples
Characteristics of HLA alleles and mono-allelic data
Allele similarity and submotifs derived from mono-allelic data
Mono-allelic data reveal length-based preferences
HLA presentation of IFN-γ response genes increases after treatment
Cross-validated model evaluation results on mono-allelic data
Cross-validated model evaluation results on multi-allelic data
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