A large peptidome dataset improves HLA class I epitope prediction across most of the human population
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...
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| Published in | Nature biotechnology Vol. 38; no. 2; pp. 199 - 209 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.02.2020
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| 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.
Prediction of HLA class I epitopes is improved in accuracy and breath with peptidomes from 95 mono-allelic cell lines. |
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| AbstractList | 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.Prediction of HLA class I epitopes is improved in accuracy and breath with peptidomes from 95 mono-allelic cell lines. 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. 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. Prediction of HLA class I epitopes is improved in accuracy and breath with peptidomes from 95 mono-allelic cell lines. 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. |
| Audience | Academic |
| Author | Oliveira, Giacomo Rosenbluth, Jennifer M. Hartigan, Christina R. Keskin, Derin B. Bachireddy, Pavan Clauser, Karl R. Lan Zhang, Guang Carr, Steven A. Le, Phuong M. Zhang, Wandi Zervantonakis, Ioannis K. Ligon, Keith L. Hacohen, Nir Klaeger, Susan Keshishian, Hasmik Eisenhaure, Thomas Lane, William J. Sarkizova, Siranush Braun, David A. Wu, Catherine J. Stevens, Jonathan Ouspenskaia, Tamara Li, Letitia W. Justesen, Sune Law, Travis |
| AuthorAffiliation | 8 Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA 12 Center for Cancer Immunology, Massachusetts General Hospital, Boston, Massachusetts, USA 9 Immunitrack, Copenhagen E, Denmark 1 Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA 2 Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA 3 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA 5 Harvard Medical School, Boston, Massachusetts, USA 11 Department of Computer Science, Metropolitan College, Boston University, Boston, Massachusetts, USA 4 Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, Massachusetts, USA 6 Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA 7 Division of Neuropathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA 10 Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA |
| AuthorAffiliation_xml | – name: 4 Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, Massachusetts, USA – name: 3 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA – name: 9 Immunitrack, Copenhagen E, Denmark – name: 5 Harvard Medical School, Boston, Massachusetts, USA – name: 10 Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA – name: 6 Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA – name: 11 Department of Computer Science, Metropolitan College, Boston University, Boston, Massachusetts, USA – name: 8 Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA – name: 12 Center for Cancer Immunology, Massachusetts General Hospital, Boston, Massachusetts, USA – name: 1 Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA – name: 2 Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA – name: 7 Division of Neuropathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA |
| Author_xml | – sequence: 1 givenname: Siranush surname: Sarkizova fullname: Sarkizova, Siranush organization: Department of Biomedical Informatics, Harvard Medical School, Broad Institute of MIT and Harvard – sequence: 2 givenname: Susan orcidid: 0000-0002-0074-5163 surname: Klaeger fullname: Klaeger, Susan organization: Broad Institute of MIT and Harvard – sequence: 3 givenname: Phuong M. surname: Le fullname: Le, Phuong M. organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 4 givenname: Letitia W. surname: Li fullname: Li, Letitia W. organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 5 givenname: Giacomo surname: Oliveira fullname: Oliveira, Giacomo organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 6 givenname: Hasmik surname: Keshishian fullname: Keshishian, Hasmik organization: Broad Institute of MIT and Harvard – sequence: 7 givenname: Christina R. surname: Hartigan fullname: Hartigan, Christina R. organization: Broad Institute of MIT and Harvard – sequence: 8 givenname: Wandi surname: Zhang fullname: Zhang, Wandi organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 9 givenname: David A. surname: Braun fullname: Braun, David A. organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Medicine, Brigham and Women’s Hospital – sequence: 10 givenname: Keith L. surname: Ligon fullname: Ligon, Keith L. organization: Broad Institute of MIT and Harvard, Harvard Medical School, Center for Patient Derived Models, Dana-Farber Cancer Institute, Division of Neuropathology, Brigham and Women’s Hospital – sequence: 11 givenname: Pavan surname: Bachireddy fullname: Bachireddy, Pavan organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Department of Medicine, Brigham and Women’s Hospital – sequence: 12 givenname: Ioannis K. orcidid: 0000-0003-2386-9553 surname: Zervantonakis fullname: Zervantonakis, Ioannis K. organization: Department of Cell Biology, Harvard Medical School – sequence: 13 givenname: Jennifer M. orcidid: 0000-0003-1314-6976 surname: Rosenbluth fullname: Rosenbluth, Jennifer M. organization: Department of Cell Biology, Harvard Medical School – sequence: 14 givenname: Tamara surname: Ouspenskaia fullname: Ouspenskaia, Tamara organization: Broad Institute of MIT and Harvard – sequence: 15 givenname: Travis orcidid: 0000-0002-7399-3299 surname: Law fullname: Law, Travis organization: Broad Institute of MIT and Harvard – sequence: 16 givenname: Sune surname: Justesen fullname: Justesen, Sune organization: Immunitrack – sequence: 17 givenname: Jonathan surname: Stevens fullname: Stevens, Jonathan organization: Department of Pathology, Brigham and Women’s Hospital – sequence: 18 givenname: William J. orcidid: 0000-0002-1097-5229 surname: Lane fullname: Lane, William J. organization: Harvard Medical School, Department of Pathology, Brigham and Women’s Hospital – sequence: 19 givenname: Thomas surname: Eisenhaure fullname: Eisenhaure, Thomas organization: Broad Institute of MIT and Harvard – sequence: 20 givenname: Guang surname: Lan Zhang fullname: Lan Zhang, Guang organization: Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Computer Science, Metropolitan College, Boston University – sequence: 21 givenname: Karl R. surname: Clauser fullname: Clauser, Karl R. organization: Broad Institute of MIT and Harvard – sequence: 22 givenname: Nir orcidid: 0000-0002-2349-2656 surname: Hacohen fullname: Hacohen, Nir email: nhacohen@mgh.harvard.edu organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Center for Cancer Immunology, Massachusetts General Hospital – sequence: 23 givenname: Steven A. orcidid: 0000-0002-7203-4299 surname: Carr fullname: Carr, Steven A. email: scarr@broadinstitute.org organization: Broad Institute of MIT and Harvard – sequence: 24 givenname: Catherine J. orcidid: 0000-0002-3348-5054 surname: Wu fullname: Wu, Catherine J. email: cwu@partners.org organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Medicine, Brigham and Women’s Hospital – sequence: 25 givenname: Derin B. orcidid: 0000-0002-8496-6181 surname: Keskin fullname: Keskin, Derin B. email: derin_keskin@dfci.harvard.edu organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Medicine, Brigham and Women’s Hospital, Department of Computer Science, Metropolitan College, Boston University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31844290$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 COPYRIGHT 2020 Nature Publishing Group 2019© The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 The Author(s), under exclusive licence to Springer Nature America, Inc. 2019. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 D.B.K., C.J.W., N.H. and S.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., D.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 Ribo-seq data. J.S. and W.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. Lead Contact: cwu@partners.org Denotes equal contribution Author Contributions |
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| SubjectTerms | 631/114/2397 631/250/21 631/250/580 631/45/611 692/308/575 Agriculture Algorithms Alleles Amino Acid Motifs Analysis Antigenic determinants Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cancer immunotherapy Cancer vaccines Cell Line Databases, Protein Datasets Epitopes Epitopes - metabolism Genetic Loci Histocompatibility antigen HLA Histocompatibility antigens Histocompatibility Antigens Class I - metabolism HLA histocompatibility antigens Human populations Humans Immunotherapy Life Sciences Ligands Mass spectrometry Mass spectroscopy Peptide Hydrolases - metabolism Peptides Peptides - chemistry Peptides - metabolism Prediction models Predictions Proteasome Endopeptidase Complex - metabolism Proteome - metabolism Transcription Tumor cell lines Vaccines |
| Title | A large peptidome dataset improves HLA class I epitope prediction across most of the human population |
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