Large-scale transcript variants dictate neoepitopes for cancer immunotherapy

作者：Shiliang Ji, Feifan Wang, Yongjie Wu, Haoran Hu, Zhen Xing, Jie Zhu, Shi Xu, Tiyun Han, Guilai Liu, Zengding Wu, Caiyi Fei, Lingming Kong, Jiangning Chen, Zhi Ding, Zhen Huang, Junfeng Zhang · 发表于：Science Advances · 年份：2025 · DOI：10.1126/sciadv.ado5600 · 被引用次数：12 · 研究领域：Immunotherapy and Immune Responses、vaccines and immunoinformatics approaches、Immune Cell Function and Interaction

Precise neoepitope discovery is crucial for effective cancer therapeutic vaccines. Conventional approaches struggle to build a repertoire with sufficient immunogenic epitopes. We developed a workflow leveraging full-length ribosome-nascent chain complex-bound mRNA sequencing (FL-RNC seq) and artificial intelligence-based predictive models to accurately identify the neoepitope landscape, especially large-scale transcript variants (LSTVs) missed by short-read sequencing. In the MC38 mouse model, we identified 22 LSTV-derived neoepitopes encoded by a synthesized mRNA lipid nanoparticle vaccine. As a standalone therapy and combined with anti-PD-1 immunotherapy, the vaccine curbed tumor progression, induced robust T cell-specific immunity, and modulated the tumor microenvironment. This underscores the multifaceted potentials of LSTV-derived vaccines. Our approach expands the neoepitope source repertoire, offering a method for discovering personalized cancer vaccines applicable to a broader tumor range. The results highlight the importance of comprehensive neoepitope identification and the promise of LSTV-based vaccines for cancer immunotherapy.