Large-scale transcript variants dictate neoepitopes for cancer immunotherapy.

Abstract

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.