Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.

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Abstract

Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

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Journal

Journal ID (iso-abbrev): Immunity

Title: Immunity

Publisher: Elsevier BV

ISSN (Electronic): 1097-4180

ISSN (Print): 1074-7613

Publication date (Electronic): Feb 21 2017

Volume: 46

Issue: 2

Affiliations

[1 ] Broad Institute of MIT and Harvard, Cambridge, MA, USA.

[2 ] Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA; Harvard Medical School, Boston, MA, 02115, USA; Department of Computer Science, Metropolitan College, Boston University, Boston, MA, 02215, USA.

[3 ] Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02142, USA.

[4 ] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.

[5 ] La Jolla Institute for Allergy and Immunology, 92037, La Jolla, CA.

[6 ] Tissue Typing Laboratory, Brigham and Women's Hospital, Boston, MA, 02115, USA.

[7 ] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Harvard Medical School, Boston, MA, 02115, USA; Department of Computer Science, Metropolitan College, Boston University, Boston, MA, 02215, USA.

[8 ] Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Center for Cancer Immunology, Massachusetts General Hospital, Boston, MA, 02114, USA. Electronic address: nhacohen@mgh.harvard.edu.

[9 ] Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard/MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, 02139 USA; Neon Therapeutics, Cambridge, MA, 02139, USA. Electronic address: mrooney@neontherapeutics.com.

[10 ] Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: scarr@broad.mit.edu.

[11 ] Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA; Harvard Medical School, Boston, MA, 02115, USA. Electronic address: cwu@partners.org.

Article

Publisher Item ID: S1074-7613(17)30042-0

DOI: 10.1016/j.immuni.2017.02.007

PubMed ID: 28228285

SO-VID: 1200133f-4047-4252-a5ad-06283a4d187b

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