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

Yat-Tsai Richie Wan*, Zeynep Koşaloğlu-Yalçın, Bjoern Peters, Morten Nielsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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.
Original languageEnglish
Article numberzcae002
JournalNAR Cancer
Volume6
Issue number1
Number of pages16
ISSN2632-8674
DOIs
Publication statusPublished - 2024

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