Wang Zhenchang, Gu Yu, Sun Xiao, Huang Hao
Institute of Microphysiological Systems, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Biomark Res. 2025 Jul 9;13(1):96. doi: 10.1186/s40364-025-00808-9.
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.
新抗原是由恶性细胞产生的肿瘤特异性肽，可呈递给T细胞以引发免疫反应。由于其肿瘤特异性特性，新抗原已成为癌症免疫治疗中最有前景的生物标志物和靶点之一。先前的研究表明，它们有能力在靶向和消除肿瘤细胞的同时介导肿瘤特异性免疫反应，同时保留正常细胞功能。在高通量测序技术、质谱和人工智能进步的推动下，研究人员对建立更准确的新抗原预测算法越来越感兴趣。在此，我们对综合新抗原预测算法进行了全面综述，涵盖任务定义、理论发展、基准数据集、前沿应用和未来研究方向。我们系统评估了新抗原来源表征和预测算法的最新进展，特别强调了所开发的HLA-肽结合和TCR识别的创新方法。此外，我们探讨了新抗原在个性化癌症疫苗设计和过继性细胞疗法中的前沿应用。我们阐述了基于新抗原的疗法的潜在研究方向和未来前景，包括整合多组学数据以发现通用新抗原、应对算法泛化挑战以及使新抗原验证方法多样化。
