American Association for Cancer Research

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Table S6 from OncoLoop: A Network-Based Precision Cancer Medicine Framework

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posted on 2023-04-04, 00:22 authored by 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. Alvarez, Mark A. Rubin, Michael M. Shen, Andrea Califano, Cory Abate-Shen

Supplementary Table 6. OncoLoop summary table


National Cancer Institute (NCI)

United States Department of Health and Human Services

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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. OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.This article is highlighted in the In This Issue feature, p. 247

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