Journal
NATURE BIOTECHNOLOGY
Volume 38, Issue 2, Pages 199-+Publisher
NATURE RESEARCH
DOI: 10.1038/s41587-019-0322-9
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Funding
- National Institutes of Health [NCI-1RO1CA155010-02, NHLBI-5R01HL103532-03, NIH/NCI U24 CA224331, NIH/NCI R21 CA216772-01A1, NCI-SPORE-2P50CA101942-11A1, NHGRI T32HG002295, NIH/NCI T32CA207021, NCI 5T32CA009172-41, NIH/NCI U24-CA210986, NIH/NCI U01 CA214125]
- G. Harold and Leila Y. Mathers Foundation
- Koch Institute for Integrative Cancer Research at MIT
- Dana-Farber/Harvard Cancer Center
- John R. Svenson Fellowship
- Parker Institute for Cancer Immunotherapy
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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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