UniCure: A multi-modal model for predicting personalized cancer therapy response

Abstract

Predicting drug efficacy across diverse patient contexts remains a major challenge in oncology, as models trained on cancer cell lines often fail to capture patient-specific biology. Emerging biological foundation models and patient-derived technologies offer a promising solution. Here, we present UniCure, a multi-modal model that combines biological and chemical foundation models to predict drug-induced transcriptomic responses across diverse cell and tissue contexts, enabling individualized drug ranking. Trained on 1.9 million transcriptomic perturbation profiles spanning >22,000 compounds, 166 cell types, and 24 tissues, UniCure accurately predicts dose-dependent and combination responses and generalizes across bulk and single-cell data. We further fine-tune UniCure on 345 patient-derived tumor-like cluster (PTC) transcriptomic profiles and validate performance on 396 real-world clinical profiles, demonstrating effective patient-level prediction. The model supports response-based patient stratification and is experimentally validated in cell line and patient-derived models. Overall, UniCure provides a practical framework for translating preclinical data into personalized therapeutic strategies.