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

Author

Listed:
  • Hanqing Liao

    (University of Oxford
    University of Oxford)

  • Carolina Barra

    (Technical University Denmark)

  • Zhicheng Zhou

    (Université Paris Cité, Institut Cochin, CNRS, INSERM)

  • Xu Peng

    (University of Oxford)

  • Isaac Woodhouse

    (University of Oxford
    University of Oxford)

  • Arun Tailor

    (University of Oxford
    University of Oxford)

  • Robert Parker

    (University of Oxford
    University of Oxford)

  • Alexia Carré

    (Université Paris Cité, Institut Cochin, CNRS, INSERM)

  • Persephone Borrow

    (University of Oxford)

  • Michael J. Hogan

    (Children’s Hospital of Philadelphia)

  • Wayne Paes

    (University of Oxford
    University of Oxford)

  • Laurence C. Eisenlohr

    (Children’s Hospital of Philadelphia
    University of Pennsylvania)

  • Roberto Mallone

    (Université Paris Cité, Institut Cochin, CNRS, INSERM
    Assistance Publique Hôpitaux de Paris, Service de Diabétologie et Immunologie Clinique, Cochin Hospital)

  • Morten Nielsen

    (Technical University Denmark)

  • Nicola Ternette

    (University of Oxford
    University of Oxford
    University of Utrecht, Department of Pharmaceutical Sciences)

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.

Suggested Citation

  • Hanqing Liao & Carolina Barra & Zhicheng Zhou & Xu Peng & Isaac Woodhouse & Arun Tailor & Robert Parker & Alexia Carré & Persephone Borrow & Michael J. Hogan & Wayne Paes & Laurence C. Eisenlohr & Rob, 2024. "MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44460-z
    DOI: 10.1038/s41467-023-44460-z
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    1. Asolina Braun & Louise C. Rowntree & Ziyi Huang & Kirti Pandey & Nikolas Thuesen & Chen Li & Jan Petersen & Dene R. Littler & Shabana Raji & Thi H. O. Nguyen & Emma Jappe Lange & Gry Persson & Michael, 2024. "Mapping the immunopeptidome of seven SARS-CoV-2 antigens across common HLA haplotypes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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