OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo, Juan Martín Arriaga, Francisca Nunes De Almeida, Min Zou, Eugene F. Douglass, Florencia Picech, Maho Shibata, Antonio Rodriguez-Calero, Simone De Brot, Antonina Mitrofanova, Chee Wai Chua, Charles Karan, Ronald Realubit, Sergey Pampou, Jaime Y. Kim, Stephanie N. Afari, Timur Mukhammadov, Luca Zanella, Eva Corey, Mariano J. AlvarezMark A. Rubin, Michael M. Shen, Andrea Califano, Cory Abate-Shen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical 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 preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer 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 PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide.

Original languageEnglish
Pages (from-to)386-409
Number of pages24
JournalCancer Discovery
Issue number2
StatePublished - 1 Feb 2023
Externally publishedYes


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