A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes

• Published in: NAR cancer 2024-03, Vol.6 (1), p.zcae002
• Main Authors: Wan, Yat-Tsai Richie, Koşaloğlu-Yalçın, Zeynep, Peters, Bjoern, Nielsen, Morten
• Format: Article
• Language: English
• Subjects: Cancer Computational Biology
• ISSN: 2632-8674; 2632-8674
• DOI: 10.1093/narcan/zcae002

Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.