Vasciaveo, Alessandro; Arriaga, Juan Martin; Nunes de Almeida, Francisca; Zou, Min; Douglass, Eugene F; Picech, Florencia; Shibata, Maho; Rodriguez-Calero, Antonio; de Brot, Simone; Mitrofanova, Antonina; Chua, Chee Wai; Karan, Charles; Realubit, Ronald; Pampou, Sergey; Kim, Jaime Y; Afari, Stephanie N; Mukhammadov, Timur; Zanella, Luca; Corey, Eva; Alvarez, Mariano J; ... (2023). OncoLoop: A network-based precision cancer medicine framework. Cancer discovery, 13(2), pp. 386-409. American Association for Cancer Research 10.1158/2159-8290.CD-22-0342
Full text not available from this repository. (Request a copy)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.
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