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

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, Roberto Mallone, Morten Nielsen and Nicola Ternette ()
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Hanqing Liao: 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
Arun Tailor: University of Oxford
Robert Parker: 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
Laurence C. Eisenlohr: Children’s Hospital of Philadelphia
Roberto Mallone: Université Paris Cité, Institut Cochin, CNRS, INSERM
Morten Nielsen: Technical University Denmark
Nicola Ternette: University of Oxford

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-023-44460-z

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