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.
Ze-Xi Chen, Saisai Tian, Jia-Zheng Pei et al.· Cancer Cell· 0 citations
Spatial transcriptomic analyses provide spatially resolved gene expression data that can provide insights into complex biological processes. However, current spatial transcriptomics approaches remain financially prohibitive and restricted in resolution, scalability, and gene coverage, limiting broader adoption for large-scale studies. Here, we developed CarHE (contrastive alignment of gene expression for hematoxylin and eosin images), a multimodal pretraining framework that infers high-dimensional spatial transcriptomic profiles from routine H&E-stained slides. By using contrastive learning to align cell type-specific transcriptomic information with histological features, CarHE achieved high prediction accuracy across evaluated datasets and spatial transcriptomics platforms. CarHE approximated spatially organized pathological microenvironment features consistent with tertiary lymphoid structure (TLS)-associated regions in breast cancer, lung cancer, melanoma, and clear cell renal cell carcinoma. Additionally, CarHE inferred approximated 3D spatial transcriptomic context from 2D images, providing more informative neighborhood context than 2D visualization. In a cohort of 880 lung cancer patients, CarHE-derived features were associated with disease-free survival and outperformed current approaches. Overall, CarHE provides a cost-effective and scalable framework for H&E-based spatial inference, supporting further validation toward translational research applications.
Jiawei Zou, Kai Xiao, Zexi Chen et al.· Cancer Research· 0 citations
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