A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types

Robbins and colleagues develop and test a machine learning neoantigen ranking model using experimentally validated neoantigens from human tumors, providing a resource of targetable neoantigens for future immunotherapies. Tumor neoepitopes presented by major histocompatibility complex (MHC) class I are recognized by tumor-infiltrating lymphocytes (TIL) and are targeted by adoptive T-cell therapies. Identifying which mutant neoepitopes from tumor cells are capable of recognition by T cells can assist in the development of tumor-specific, cell-based therapies and can shed light on antitumor responses. Here, we generate a ranking algorithm for class I candidate neoepitopes by using next-generation sequencing data and a dataset of 185 neoepitopes that are recognized by HLA class I-restricted TIL from individuals with metastatic cancer. Random forest model analysis showed that the inclusion of multiple factors impacting epitope presentation and recognition increased output sensitivity and specificity compared to the use of predicted HLA binding alone. The ranking score output provides a set of class I candidate neoantigens that may serve as therapeutic targets and provides a tool to facilitate in vitro and in vivo studies aimed at the development of more effective immunotherapies.

Automated summary

Robbins and colleagues developed and tested a machine learning model for ranking tumor neoantigens, aiding in the identification of potential therapeutic targets for future immunotherapies. The model showed improved sensitivity and specificity by incorporating multiple factors impacting epitope presentation and recognition, compared to using predicted HLA binding alone. The ranked output provides a list of potential neoantigens for further in vitro and in vivo studies, facilitating the development of more effective immunotherapies.