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

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    Date Issued
    2020-02
    Publisher Version
    10.1038/s41587-019-0322-9
    Author(s)
    Sarkizova, Siranush
    Klaeger, Susan
    Le, Phuong M.
    Li, Letitia W.
    Oliveira, Giacomo
    Keshishian, Hasmik
    Hartigan, Christina R.
    Zhang, Wandi
    Braun, David A.
    Ligon, Keith L.
    Bachireddy, Pavan
    Zervantonakis, Ioannis K.
    Rosenbluth, Jennifer M.
    Ouspenskaia, Tamara
    Law, Travis
    Justesen, Sune
    Stevens, Jonathan
    Lane, William J.
    Eisenhaure, Thomas
    Lan Zhang, Guang
    Clauser, Karl R.
    Hacohen, Nir
    Carr, Steven A.
    Wu, Catherine J.
    Keskin, Derin B.
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    Permanent Link
    https://hdl.handle.net/2144/41361
    Version
    Accepted manuscript
    Citation (published version)
    Siranush Sarkizova, Susan Klaeger, Phuong M Le, Letitia W Li, Giacomo Oliveira, Hasmik Keshishian, Christina R Hartigan, Wandi Zhang, David A Braun, Keith L Ligon, Pavan Bachireddy, Ioannis K Zervantonakis, Jennifer M Rosenbluth, Tamara Ouspenskaia, Travis Law, Sune Justesen, Jonathan Stevens, William J Lane, Thomas Eisenhaure, Guang Lan Zhang, Karl R Clauser, Nir Hacohen, Steven A Carr, Catherine J Wu, Derin B Keskin. 2020. "A large peptidome dataset improves HLA class I epitope prediction across most of the human population.." Nat Biotechnol, Volume 38, Issue 2, pp. 199 - 209. https://doi.org/10.1038/s41587-019-0322-9
    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.
    Description
    Published in final edited form as: Nat Biotechnol. 2020 February ; 38(2): 199–209. doi:10.1038/s41587-019-0322-9.
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