Large-scale transcript variants dictate neoepitopes for cancer immunotherapy.

Area of Science:

• Immunology
• Genomics
• Bioinformatics

Background:

• Effective cancer vaccines require precise neoepitope discovery, but current methods have limitations in identifying immunogenic epitopes.
• Conventional approaches often fail to capture the full spectrum of potential neoepitopes, particularly those arising from complex transcriptomic events.

Purpose of the Study:

• To develop and validate a novel workflow for comprehensive neoepitope identification, focusing on large-scale transcript variants (LSTVs).
• To assess the therapeutic potential of LSTV-derived neoepitopes in a preclinical cancer model.

Main Methods:

• Utilized full-length ribosome-nascent chain complex-bound mRNA sequencing (FL-RNC seq) for neoepitope discovery.
• Integrated artificial intelligence (AI)-based predictive models to enhance neoepitope identification accuracy.
• Developed and tested a synthesized mRNA lipid nanoparticle vaccine encoding identified LSTV-derived neoepitopes in the MC38 mouse model.

Main Results:

• Identified 22 LSTV-derived neoepitopes in the MC38 mouse model.
• Demonstrated that the LSTV-based mRNA vaccine curbed tumor progression as a standalone therapy and in combination with anti-PD-1 immunotherapy.
• Observed robust T cell-specific immunity and modulation of the tumor microenvironment following vaccination.

Conclusions:

• The developed workflow expands the neoepitope source repertoire for personalized cancer vaccines.
• LSTV-derived vaccines show significant potential for broader application in cancer immunotherapy.
• Comprehensive neoepitope identification is critical for advancing cancer vaccine development.