Prioritizing treatments for individual cancer patients remains challenging, and performing co-clinical studies using patient-derived models in real-time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their pre-existing high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As proof-of-concept, we applied OncoLoop to prostate cancer (PCa) using genetically-engineered mouse models (GEMMs) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human PCa cohorts by Master Regulator (MR) conservation analysis revealed that most advanced PCa patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, Oncoloop-predicted drugs enhanced the efficacy of clinically-relevant drugs, namely the PD1 inhibitor, nivolumab, and the AR-inhibitor, enzalutamide.