Abstract
Tumor-specific antigens, also known as neoantigens, have potential utility in anti-cancer immunotherapy, including immune checkpoint blockade (ICB), neoantigen-specific T cell receptor-engineered T (TCR-T), chimeric antigen receptor T (CAR-T), and therapeutic cancer vaccines (TCVs). After recognizing presented neoantigens, the immune system becomes activated and triggers the death of tumor cells. Neoantigens may be derived from multiple origins, including somatic mutations (single nucleotide variants, insertion/deletions, and gene fusions), circular RNAs, alternative splicing, RNA editing, and polymorphic microbiome. An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources, which may require inputs from different multi-omics data. In addition, calculating the peptide–major histocompatibility complex (MHC) affinity can aid in selecting putative neoantigens, as high binding affinities facilitate antigen presentation. Based on these approaches and previous experiments, many resources were developed to reveal the landscape of tumor neoantigens across multiple cancer types. Herein, we summarized these tools, algorithms, and resources to provide an overview of computational analysis for neoantigen discovery and prioritization, as well as the future development of potential clinical utilities in this field.
Accepted manuscripts
