Little research has been done to address the huge opportunities that may exist to reuse existing known (i.e., FDA approved, generic, and experimental) drugs for new applications in cancer treatment. Furthermore, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged in part due to the lack of systematic methods to define drug off-target effects (OTEs) that might affect important cancer cell signaling pathways. In this talk, we address this critical gap by developing a systemic strategy to characterize OTE of drugs in order to repurpose drugs for cancer therapy, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSBs) and integrated it with Bayesian Factor Regression Model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof of concept studies were performed in?breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to >90% of FDA-approved drugs and >75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce Rb-dependent repression of important E2F-dependent cell cycle genes. In addition, we provide a clinical application of coupling the computational analysis using CSB-BFRM with the in vitro and in vivo biological validations to repurpose two known drugs for breast cancer brain metastasis, and these two drugs are now in phase 2 trials at The Methodist Hospital and Baylor College of Medicine. All together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.
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