MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.
Hanqing LiaoCarolina BarraZhicheng ZhouXu PengIsaac B WoodhouseArun TailorRobert ParkerAlexia CarréPersephone BorrowMichael J HoganWayne PaesLaurence C EisenlohrRoberto MalloneMorten NielsenNicola TernettePublished in: Nature communications (2024)
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.
Keyphrases
- papillary thyroid
- endothelial cells
- squamous cell
- machine learning
- single cell
- small molecule
- induced apoptosis
- magnetic resonance imaging
- randomized controlled trial
- gene expression
- optical coherence tomography
- magnetic resonance
- cell proliferation
- signaling pathway
- cell death
- oxidative stress
- case report
- artificial intelligence