TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation

Predicting individual patient responses to anticancer drugs is a central challenge in precision oncology, hindered by the scarcity of clinical pharmacogenomic data and substantial biological dissimilarity between preclinical models and patient tumors. Patient-derived xenograft (PDX) models offer significantly enhanced tumor biological fidelity compared to in vitro cancer cell line models, yet computational methods to translate PDX-based drug response predictions (DRP) into clinical settings remain limited. We developed TRANSPIRE-DRP, a deep learning framework that bridges the translational gap between PDX models and patient tumors through unsupervised domain adaptation. The framework employs a two-stage architecture: first, an autoencoder-based pretraining phase learns domain-invariant genomic representations from large-scale unlabeled data; second, an adversarial adaptation phase aligns these representations while preserving drug response signals from PDX models. We evaluated TRANSPIRE-DRP across three therapeutic agents—Cetuximab, Paclitaxel, and Gemcitabine—in real-life clinical prediction scenarios. TRANSPIRE-DRP consistently outperformed both cell line-based state-of-the-art models and PDX-based baselines, demonstrating superior translational capacity. Notably, the learned representations preserved tumor-specific molecular features and spontaneously recapitulated established drug-cancer type associations without requiring explicit histological annotations. Interpretability analyses revealed biologically coherent pathway enrichments consistent with known drug mechanisms of action, including EGFR-Wnt signaling crosstalk for Cetuximab, mitotic arrest mechanism for Paclitaxel, and NF-κB-mediated immunomodulation for Gemcitabine. TRANSPIRE-DRP establishes a scalable, interpretable, and clinically relevant framework for translating preclinical PDX data into personalized therapeutic predictions, providing a robust computational foundation for advancing precision oncology beyond the inherent limitations of traditional in vitro systems.