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

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Item Type:

Article

Title:

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

Creators Name:

Chong, C. and Müller, M. and Pak, H.S. and Harnett, D. and Huber, F. and Grun, D. and Leleu, M. and Auger, A. and Arnaud, M. and Stevenson, B.J. and Michaux, J. and Bilic, I. and Hirsekorn, A. and Calviello, L. and Simó-Riudalbas, L. and Planet, E. and Lubiński, J. and Bryśkiewicz, M. and Wiznerowicz, M. and Xenarios, I. and Zhang, L. and Trono, D. and Harari, A. and Ohler, U. and Coukos, G. and Bassani-Sternberg, M.

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.

Keywords:

Amino Acid Sequence, High-Throughput Nucleotide Sequencing, Histocompatibility Antigens Class I, Melanoma, Neoplastic Gene Expression Regulation, Peptides, Protein Databases, Proteogenomics, RNA, T-Lymphocytes, Tumor Cell Line

Source:

Nature Communications

ISSN:

2041-1723

Publisher:

Nature Publishing Group

Volume:

Number:

Page Range:

1293

Date:

10 March 2020