Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity

Area of Science:

• Immunology
• Oncology
• Bioinformatics

Background:

• Cancer immunogenicity arises from mutations creating neoepitopes.
• Identifying protective neoepitopes is crucial for effective cancer immunotherapy.
• Current methods face challenges in pinpointing the few immunogenic neoepitopes among many potential candidates.

Purpose of the Study:

• To introduce and validate two novel computational tools for accurately identifying protective neoepitopes.
• To expand the understanding of mechanisms underlying neoepitope-mediated antitumor immunity.
• To provide new tools for advancing human cancer immunotherapy strategies.

Main Methods:

• Development of the differential agretopic index based on NetMHC scores.
• Assessment of MHC I-peptide complex conformational stability.
• Validation of identified neoepitopes for their ability to elicit CD8-dependent antitumor T cell responses.

Main Results:

• Two novel tools accurately identify a small subset of protective neoepitopes.
• Identified neoepitopes are characterized by altered MHC binding affinity and increased peptide rigidity.
• Protective neoepitopes can elicit CD8-dependent immunity despite low binding affinity (K(d) < 500 nM).

Conclusions:

• The developed tools significantly improve the accuracy of protective neoepitope identification.
• These findings broaden the scope of potential cancer antigens for therapeutic targeting.
• The study offers novel computational approaches for developing next-generation cancer immunotherapies.