Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy

Abstract

Neoantigens, which are tumor-specific peptides generated by malignant cells, can be presented to T cells to elicit immune responses. Owing to their tumor-specific properties, neoantigens have emerged as one of the most promising biomarkers and targets for cancer immunotherapy. Previous studies have demonstrated their capacity to mediate tumor-specific immune responses in targeting and eliminating tumor cells while preserving normal cellular function. Driven by advancements in high-throughput sequencing technologies, mass spectrometry, and artificial intelligence, researchers have developed a growing interest in establishing more accurate neoantigen prediction algorithms. Here, we presented a comprehensive review of integrated neoantigen prediction algorithms, encompassing task definition, theoretical developments, benchmark datasets, cutting-edge applications, and future research directions. We systematically evaluated recent advancements in neoantigen source characterization and prediction algorithms, with particular emphasis on innovative methods for HLA-peptide binding and TCR recognition developed. Additionally, we explored the cutting-edge applications of neoantigens in personalized cancer vaccine design and adoptive cell therapies. We delineated potential research directions and the future prospects for neoantigen-based therapies, including integrating multi-omics data to discover universal neoantigens, addressing algorithmic generalization challenges and diversifying neoantigen validation methods.

Topics & Concepts