Leveraging Artificial Intelligence for Neoantigen Prediction

作者：Jing Zeng, Zhengjun Lin, Xianghong Zhang, Tao Zheng, Haodong Xu, Tang Liu · 发表于：Cancer Research · 年份：2025 · DOI：10.1158/0008-5472.can-24-2553 · 被引用次数：17 · 研究领域：vaccines and immunoinformatics approaches、Immunotherapy and Immune Responses、Monoclonal and Polyclonal Antibodies Research

Neoantigens represent a class of antigens within tumor microenvironments that arise from diverse somatic mutations and aberrations specific to tumorigenesis, holding substantial promise for advancing tumor immunotherapy. However, only a subset of neoantigens effectively elicits antitumor immune responses, and the specific neoantigens recognized by individual T-cell receptors (TCR) remain incompletely characterized. Therefore, substantial research has focused on screening immunogenic neoantigens, mainly through their major histocompatibility complex (MHC) presentation and TCR recognition specificity. Given the resource intensiveness and inefficiency of experimental validation, predictive models based on artificial intelligence (AI) have gradually become mainstream methods to discover immunogenic neoantigens. In this article, we provide a comprehensive summary of current AI methodologies for predicting neoantigens, with a particular focus on their capability to model peptide-MHC (pMHC) and pMHC-TCR binding. Furthermore, a thorough benchmarking analysis was conducted to assess the performance of antigen presentation predictors for scoring the immunogenicity of neoantigens. AI models have potential applications in the treatment of clinical diseases although several limitations must first be overcome to realize their full potential. Anticipated advancements in data accessibility, algorithmic refinement, platform enhancement, and comprehensive validation of immune processes are poi...