MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

peer-reviewed · Nature Communications · 2024

peer-reviewed · Nature Communications · 2024. Hanqing Liao et al. Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour…
Date 2024-01-22
Type peer-reviewed
Venue Nature Communications
Publisher Springer Science and Business Media LLC
Contribution downstream-application
DOI 10.1038/s41467-023-44460-z
Citations (OpenAlex) 18
Venue 2-year citedness 17.60

Preprint version: MARS: Improved De Novo Peptide Candidate Selection for Non-Canonical Antigen Target Discovery in Cancer (2022-08-04, Research Square)

Abstract

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.

Authors

  1. Hanqing Liao · University of Oxford
  2. Carolina Barra · Technical University of Denmark
  3. Zhicheng Zhou · Centre National de la Recherche Scientifique, Inserm, Institut Cochin, Université Paris Cité
  4. Xu Peng · University of Oxford
  5. Isaac Woodhouse · University of Oxford
  6. Arun Tailor · University of Oxford
  7. Robert Parker · University of Oxford
  8. Alexia Carré · Centre National de la Recherche Scientifique, Inserm, Institut Cochin, Université Paris Cité
  9. Persephone Borrow · University of Oxford
  10. Michael J. Hogan · Children’s Hospital of Philadelphia
  11. Wayne Paes · University of Oxford
  12. Laurence C. Eisenlohr · Children’s Hospital of Philadelphia, University of Pennsylvania
  13. Roberto Mallone · Assistance Publique Hôpitaux de Paris, Centre National de la Recherche Scientifique, Hôpital Cochin, Indiana Biosciences Research Institute, Inserm, Institut Cochin, Université Paris Cité
  14. Morten Nielsen · Technical University of Denmark
  15. Nicola Ternette · Jenner Institute, University of Oxford, Utrecht University

Methods and tools

  • MARS: Non-canonical antigen selection

Data used

  • Cervical Cancer Tissue Immunopeptidomics (as deposited) · PXD046182
  • Contribution of proteasome-catalyzed peptide cis-splicing to viral targeting by CD8+ T cells in HIV-1 infection (as deposited) · PXD015489
  • MS-rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments (as deposited) · PXD011723
  • ProteomeTools (Parts I-III) · PXD004732, PXD010595, PXD021013
  • non-canonical HLA peptides in cancer (as deposited) · PXD013649

Cites (11)

Cited by (5)

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