Computational neoantigen prediction for cancer immunotherapy.

Abstract

Cancer represents a significant global health concern, profoundly affecting morbidity and mortality rates worldwide. Due to cancer-associated genetic changes, cancer cells harbor neoantigens (Tumor-Specific Antigens). They are attractive targets for personalized and generalized cancer therapeutics, including cancer vaccines, T cell adoptive therapy, and immunomonitoring. Such antigens can arise at genomic, transcriptomic, and proteomic levels. The host immune system recognizes neoantigens through their presentation on Major Histocompatibility Complexes (MHC), leading to T cell activation and antitumor response, provided sufficient co-stimulatory signals are provided by antigen-presenting cells, including dendritic cells. Computational tools for neoantigen analysis are rapidly advancing, improving prediction accuracy. Bioinformatics tools aid in identifying somatic mutations and selecting neoantigens based on MHC binding and immunogenicity scores. Cost-efficient computational Human Leukocyte Antigen haplotyping uses sequencing data, while proteogenomic strategies, integrating immunopeptidomics, validate neoantigens by detecting peptides naturally presented by tumor cells. Integrating proteome-based validation provides experimental confirmation, strengthening confidence in predictions. Ongoing developments in bioinformatics and multi-omics integration contribute to neoantigen identification, enabling personalized cancer immunotherapies. This review discusses various computational tools/pipelines, their implementation, clinical trials on neoantigenic vaccines, and the limitations/prospects of neoantigen prediction.