Advances and challenges in neoantigen prediction for cancer immunotherapy

Abstract

Neoantigens, derived from tumor-specific mutations, are promising targets of cancer immunotherapy by eliciting tumor-specific T-cell responses while sparing normal cells. Accurate neoantigen prediction relies on immunogenomics and immunopeptidomics. Immunogenomics identifies tumor-specific mutations via next-generation sequencing. Immunopeptidomics detects MHC-presented peptides using mass spectrometry. Integrating these two methods enhances prediction accuracy but faces challenges due to tumor heterogeneity, HLA diversity, and immune evasion. Future advancements will focus on dynamic tumor microenvironment monitoring, multi-omics integration, improved computational models and algorithms to refine neoantigen prediction, and developing optimized personalized vaccines.

Keywords: bioinformatics; cancer immunotherapy; immune evasion; immunopeptidomics; neoantigen prediction; next-generation sequencing.