Tan, Y.; Yang, Z.; Wang, T.; Hu, H.; Fleming, J.; Pan, M.; Eisenlohr, L. C.; Li, B.

Effective anti-tumor T cell response depends on both neoantigen quality (non-selfness) and quantity (abundance). However, existing methods for neoantigen prioritization largely overlook peptide abundance because it is difficult to measure and model. To bridge this gap, we developed epiVIP, a deep learning framework that predicts the abundance of individual HLA-I peptides using widely available (sc)RNA-seq data. Trained on 1.7 million immune peptides paired with gene expression profiles, epiVIP demonstrated strong generalizability across unseen samples. Analyzing 33,711 neoantigens from clinical datasets revealed a compensatory relationship between abundance and non-selfness in determining antigenicity, providing quantitative support for the TCR avidity theory. Importantly, abundance independently predicted tumor reactivity and patient survival across multiple neoantigen vaccine cohorts and immune checkpoint blockade cohorts. Mechanistic interpretation of epiVIP further identified directional regulation of MAGEA3 epitope presentation by PSME4, which was validated experimentally using T cell functional assays. Together, these findings established AI-enabled virtual immunopeptidomics as a powerful strategy to improve cancer immunotherapy.