The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I–Associated Peptides

peer-reviewed · Cancer Immunology Research · 2023

peer-reviewed · Cancer Immunology Research · 2023. Georges Bedran et al. Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions…
Date 2023-06-02
Type peer-reviewed
Venue Cancer Immunology Research
Publisher American Association for Cancer Research (AACR)
Contribution downstream-application
DOI 10.1158/2326-6066.cir-22-0621
Citations (OpenAlex) 19

Abstract

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

Authors

  1. Georges Bedran · University of Gdańsk
  2. Hans-Christof Gasser · University of Edinburgh
  3. Kenneth Weke · University of Gdańsk
  4. Tongjie Wang · University of Edinburgh
  5. Dominika Bedran · University of Gdańsk
  6. Alexander Laird · NHS Lothian, University of Edinburgh, Western General Hospital
  7. Christophe Battail · CEA Grenoble, Commissariat à l’Énergie Atomique et aux Énergies Alternatives, Inserm, Laboratoire Biologie à Grande Échelle, Laboratoire Biosciences et bioingénierie pour la Santé, Université Grenoble Alpes
  8. Fabio Massimo Zanzotto · University of Rome Tor Vergata
  9. Catia Pesquita · Laboratório de Sistemas Informáticos de Grande Escala, University of Lisbon
  10. Håkan Axelson · Lund University
  11. Ajitha Rajan · University of Edinburgh
  12. David J. Harrison · University of St Andrews
  13. Aleksander Palkowski · University of Gdańsk
  14. Maciej Pawlik · AGH University of Krakow, Akademickie Centrum Komputerowe Cyfronet AGH
  15. Maciej Parys · Roslin Institute, University of Edinburgh
  16. J. Robert O’Neill · Cambridge University Hospitals NHS Foundation Trust
  17. Paul M. Brennan · University of Edinburgh
  18. Stefan N. Symeonides · University of Edinburgh
  19. David R. Goodlett · Institute for Systems Biology, University of Gdańsk, University of Victoria
  20. Kevin Litchfield · Cancer Research UK, University College London
  21. Robin Fahraeus · Hématopoïèse normale et pathologique : Emergence, environnement et recherche translationnelle, Inserm, University of Gdańsk
  22. Ted R. Hupp · University of Edinburgh, University of Gdańsk
  23. Sachin Kote · University of Gdańsk
  24. Javier A. Alfaro · University of Edinburgh, University of Gdańsk, University of Victoria

Methods and tools

  • Noncanonical MAP atlas: A proteogenomic pipeline based on deep-learning de novo sequencing plus open search mapped 8,601 noncanonical MHC class I peptides from noncoding regions across 11 cancer types.

Methods it uses

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