人工智能用于转化型个体化新抗原癌症疫苗开发
Artificial intelligence for translational personalized neoantigen cancer vaccine development
个体化新抗原癌症疫苗通过靶向患者特有的突变来源肿瘤抗原实现精准免疫治疗，早期临床研究已证明其可行性、安全性和免疫原性，与免疫检查点阻断联合时效果尤其令人鼓舞。但转化仍受一系列瓶颈限制：假阳性新抗原选择、抗原加工与HLA呈递建模不完善、T细胞受体识别预测有限，以及制剂、递送和生产的挑战。文章综述了AI在四个阶段的潜在贡献：多组学整合发现新抗原、考虑加工过程的HLA呈递预测、结构感知与TCR信息的免疫原性建模、以及針對脂质纳米颗粒递送系统的数据驱动制剂优化，并指出AI是缩小搜索空间、改进优先级排序的工具，不能替代实验验证。未来还需解决数据多样性、模型可解释性、前瞻性基准测试、生产可追溯性和个体化mRNA免疫疗法的监管框架等问题。
为什么推荐给您：系统梳理AI在个体化新抗原疫苗全流程的应用前景与瓶颈，属值得关注的前沿综述。
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摘要Abstract
Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection, imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.