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Nat Commun. 2020 Mar 10;11(1):1293. doi: 10.1038/s41467-020-14968-9.

Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes.

Author information

1
Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland.
2
Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland.
3
Vital IT, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland.
4
Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany.
5
School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland.
6
Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland.
7
Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, ul. Rybacka 1, 70-204, Szczecin, Poland.
8
International Institute for Molecular Oncology, Jakuba Krauthofera 23, 60-203, Poznań, Poland.
9
Poznan University of Medical Sciences, Fredry 10, 61-701, Poznań, Poland.
10
Genome Center Health 2030, Chemin de Mines 9, 1202, Genève, Switzerland.
11
Department of Training and Research, CHUV/UNIL Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland.
12
Center for Research on Reproduction and Women's Health, University of Pennsylvania, 421 Curie Boulevard, Philadelphia, PA, 19104, USA.
13
Department of Obstetrics and Gynecology, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
14
Departments of Biology and Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany.
15
Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland. michal.bassani@chuv.ch.
16
Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland. michal.bassani@chuv.ch.

Abstract

Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides derived from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single-cell transcriptomics, ribosome profiling, and two MS/MS search tools in combination. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides, including an immunogenic peptide derived from an open reading frame downstream of the melanoma stem cell marker gene ABCB5. These findings hold great promise for the discovery of previously unknown tumor antigens for cancer immunotherapy.

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