AI-driven genotype-phenotype modeling: a framework integrating multi-modal single-cell genomics and reverse vaccinology for de novo design of multi-epitope cancer vaccines.

AI-driven integration of multi-modal single-cell genomics and reverse vaccinology enables de novo design of personalized multi-epitope cancer vaccines, advancing immunotherapy.

• Why it matters: Current bulk sequencing methods obscure tumor heterogeneity, limiting effective vaccine development and personalized treatment options for cancer patients.
• What they did: The framework combines filtering of false-positive targets using single-cell data with AI models—including deep learning and graph neural networks—to identify neoantigens and design multi-epitope vaccines.
• The result: This approach facilitates the creation of highly targeted, adaptable cancer vaccines, overcoming translational challenges and paving the way for more effective, personalized immunotherapies.