AI-driven genotype-phenotype modeling: a framework integrating multi-modal single-cell genomics and reverse vaccinology for

Cancer vaccines have emerged as a promising strategy for personalized cancer immunotherapy; however, their development has traditionally relied on bulk sequencing approaches that average molecular information across millions of cells, thereby obscuring the extensive intratumoral heterogeneity that drives disease progression, therapeutic resistance, and immune escape. Recent advances in multi-modal single-cell genomics have transformed the ability to characterize tumors at unprecedented resolution, enabling the identification of distinct cellular populations, clonal evolutionary trajectories, and complex tumor-immune interactions. In parallel, artificial intelligence (AI) has rapidly expanded the capabilities of reverse vaccinology by facilitating large-scale analysis of genomic and immunological datasets for neoantigen discovery and vaccine design. This review aims to present a unique conceptual framework for future personalized cancer immunotherapies, rather than simply integrating the already established approaches. The framework is built on two levels: (1) filtering of false-positive targets using multi-modal single cell data and removing antigen loss clones; and (2) feeding the resulting rigorously filtered data into advanced structural and generative AI models to inform