A large peptidome dataset improves HLA class I epitope prediction across most of the human population.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
received:
28
05
2019
accepted:
24
10
2019
pubmed:
18
12
2019
medline:
10
4
2020
entrez:
18
12
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31844290
doi: 10.1038/s41587-019-0322-9
pii: 10.1038/s41587-019-0322-9
pmc: PMC7008090
mid: NIHMS1541512
doi:
Substances chimiques
Epitopes
0
Histocompatibility Antigens Class I
0
Ligands
0
Peptides
0
Proteome
0
Peptide Hydrolases
EC 3.4.-
Proteasome Endopeptidase Complex
EC 3.4.25.1
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
199-209Subventions
Organisme : NCI NIH HHS
ID : P01 CA229092
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA101942
Pays : United States
Organisme : NHGRI NIH HHS
ID : T32 HG002295
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA009172
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA224331
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA216772
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA155010
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA214125
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA207021
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL103532
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA210986
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA206978
Pays : United States
Organisme : NCI NIH HHS
ID : K08 CA248458
Pays : United States
Références
Lefranc, M.-P. et al. IMGT®, the international ImMunoGeneTics information system® 25 years on. Nucleic Acids Res. 43, D413–D422 (2015).
pubmed: 25378316
Robinson, J. et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 43, D423–D431 (2015).
pubmed: 25414341
Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).
pubmed: 28978689
pmcid: 5679736
Abelin, J. G. et al. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 (2017).
pubmed: 28228285
pmcid: 5405381
O’Donnell, T. J. et al. MHCflurry: open-source class I MHC binding affinity prediction. Cell Syst. 7, 129–132.e4 (2018).
pubmed: 29960884
Gfeller, D. et al. The length distribution and multiple specificity of naturally presented HLA-I ligands. J. Immunol. 201, 3705–3716 (2018).
pubmed: 30429286
Bulik-Sullivan, B. et al. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat. Biotechnol. 37, 55–63 (2018).
Nielsen, M. & Andreatta, M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 8, 33 (2016).
pubmed: 27029192
pmcid: 4812631
Rajasagi, M. et al. Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia. Blood 124, 453–462 (2014).
pubmed: 24891321
pmcid: 4102716
de Kruijf, E. M. et al. HLA-E and HLA-G expression in classical HLA class I-negative tumors is of prognostic value for clinical outcome of early breast cancer patients. J. Immunol. 185, 7452–7459 (2010).
pubmed: 21057081
Zhang, R.-L. et al. Predictive value of different proportion of lesion HLA-G expression in colorectal cancer. Oncotarget 8, 107441–107451 (2017).
pubmed: 29296176
pmcid: 5746078
Dawson, D. V., Ozgur, M., Sari, K., Ghanayem, M. & Kostyu, D. D. Ramifications of HLA class I polymorphism and population genetics for vaccine development. Genet. Epidemiol. 20, 87–106 (2001).
pubmed: 11119299
Gragert, L., Madbouly, A., Freeman, J. & Maiers, M. Six-locus high resolution HLA haplotype frequencies derived from mixed-resolution DNA typing for the entire US donor registry. Hum. Immunol. 74, 1313–1320 (2013).
pubmed: 23806270
Solberg, O. D. et al. Balancing selection and heterogeneity across the classical human leukocyte antigen loci: a meta-analytic review of 497 population studies. Hum. Immunol. 69, 443–464 (2008).
pubmed: 18638659
pmcid: 2632948
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
pubmed: 28678778
pmcid: 5577644
Pearson, H. et al. MHC class I-associated peptides derive from selective regions of the human genome. J. Clin. Invest. 126, 4690–4701 (2016).
pubmed: 27841757
pmcid: 5127664
Vita, R. et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 43, D405–D412 (2015).
pubmed: 25300482
Sette, A. & Sidney, J. HLA supertypes and supermotifs: a functional perspective on HLA polymorphism. Curr. Opin. Immunol. 10, 478–482 (1998).
pubmed: 9722926
Robinson, J., Malik, A., Parham, P., Bodmer, J. G. & Marsh, S. G. E. IMGT/HLA database—a sequence database for the human major histocompatibility complex. Tissue Antigens 55, 280–287 (2000).
pubmed: 10777106
Parham, P. & Moffett, A. Variable NK cell receptors and their MHC class I ligands in immunity, reproduction and human evolution. Nat. Rev. Immunol. 13, 133–144 (2013).
pubmed: 23334245
pmcid: 3956658
Nielsen, M. et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2, e796 (2007).
pubmed: 17726526
pmcid: 1949492
Rist, M. J. et al. HLA peptide length preferences control CD8
pubmed: 23749632
Maenaka, K. et al. Nonstandard peptide binding revealed by crystal structures of HLA-B*5101 complexed with HIV immunodominant epitopes. J. Immunol. 165, 3260–3267 (2000).
pubmed: 10975842
Kaur, G. et al. Structural and regulatory diversity shape HLA-C protein expression levels. Nat. Commun. 8, 15924 (2017).
pubmed: 28649982
pmcid: 5490200
Celik, A. A., Simper, G. S., Hiemisch, W., Blasczyk, R. & Bade-Döding, C. HLA-G peptide preferences change in transformed cells: impact on the binding motif. Immunogenetics 70, 485–494 (2018).
pubmed: 29602958
pmcid: 6061458
Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).
pubmed: 30568305
Javitt, A. et al. Pro-inflammatory cytokines alter the immunopeptidome landscape by modulation of HLA-B expression. Front. Immunol. 10, 141 (2019).
pubmed: 30833945
pmcid: 6387973
Di Marco, M. et al. Unveiling the peptide motifs of HLA-C and HLA-G from naturally presented peptides and generation of binding prediction matrices. J. Immunol. 199, 2639–2651 (2017).
pubmed: 28904123
Liepe, J. et al. A large fraction of HLA class I ligands are proteasome-generated spliced peptides. Science 354, 354–358 (2016).
pubmed: 27846572
Faridi, P. et al. A subset of HLA-I peptides are not genomically templated: evidence for cis- and trans-spliced peptide ligands. Sci. Immunol. 3, eaar3947 (2018).
pubmed: 30315122
Mylonas, R. et al. Estimating the contribution of proteasomal spliced peptides to the HLA-I ligandome. Mol. Cell. Proteom. 17, 2347–2357 (2018).
Rolfs, Z., Solntsev, S. K., Shortreed, M. R., Frey, B. L. & Smith, L. M. Global identification of post-translationally spliced peptides with neo-fusion. J. Proteome Res. 18, 349–358 (2018).
pubmed: 30346791
pmcid: 6465104
Rolfs, Z., Müller, M., Shortreed, M. R., Smith, L. M. & Bassani-Sternberg, M. Comment on ‘A subset of HLA-I peptides are not genomically templated: evidence for cis- and trans-spliced peptide ligands’. Sci. Immunol. 4, eaaw1622 (2019).
pubmed: 31420320
Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7, 13404 (2016).
pubmed: 27869121
pmcid: 5121339
Schuster, H. et al. The immunopeptidomic landscape of ovarian carcinomas. Proc. Natl Acad. Sci. USA 114, E9942–E9951 (2017).
pubmed: 29093164
Girdlestone, J. Regulation of HLA class I loci by interferons. Immunobiology 193, 229–237 (1995).
pubmed: 8530148
Chong, C. et al. High-throughput and sensitive immunopeptidomics platform reveals profound interferonγ-mediated remodeling of the human leukocyte antigen (HLA) ligandome. Mol. Cell. Proteom. 17, 533–548 (2018).
Kidera, A., Konishi, Y., Oka, M., Ooi, T. & Scheraga, H. A. Statistical analysis of the physical properties of the 20 naturally occurring amino acids. J. Protein Chem. 4, 23–55 (1985).
Bremel, R. D. & Homan, E. J. An integrated approach to epitope analysis I: dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches. Immunome Res. 6, 7 (2010).
pubmed: 21044289
pmcid: 2990731
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv:1802.03426 [stat.ML] (2018).
Harndahl, M. et al. Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays. J. Biomol. Screen. 14, 173–180 (2009).
pubmed: 19196700
Bassani-Sternberg, M., Pletscher-Frankild, S., Jensen, L. J. & Mann, M. Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation. Mol. Cell. Proteom. 14, 658–673 (2015).
Hunt, D. F. et al. Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science 255, 1261–1263 (1992).
pubmed: 1546328
Rammensee, H. G., Friede, T. & Stevanoviíc, S. MHC ligands and peptide motifs: first listing. Immunogenetics 41, 178–228 (1995).
pubmed: 7890324
Rammensee, H., Bachmann, J., Emmerich, N. P., Bachor, O. A. & Stevanović, S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219 (1999).
pubmed: 10602881
Kim, Y., Sidney, J., Pinilla, C., Sette, A. & Peters, B. Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior. BMC Bioinformatics 10, 394 (2009).
pubmed: 19948066
pmcid: 2790471