Abstract
Abstract Prostate cancer (PC) remains a prominent cause of cancer-related mortality among men in the United States. A significant factor contributing to the mortality of PC patients is the development of resistance to second-generation antiandrogen therapies, particularly androgen receptor-signaling inhibitors (ARSI), which is pronounced in patients with late-stage PC, metastatic castration-resistance prostate cancer (mCRPC). Therefore, there is an urgent need for novel therapeutics beyond AR signaling inhibition for patients with mCRPC. One of the key drivers of resistance to ARSI and a critical feature of CRPC is cancer metabolism reprogramming to elevated glycolysis. However, the current research on finding drugs to impede high-glycolytic tumor growth is hurdled by a resistance raised from metabolic plasticity. The aim of this research is to quickly repurpose existing drugs for inhibiting high-glycolytic mCRPC growth while proactively circumstancing resistance from metabolic plasticity through computational approaches. To do this, we imputed a large number of drug responses in patients with mCRPC using a computational algorithm (OncoPredict), identified drugs that were projected to show higher sensitivity in high-glycolytic tumors, and performed in vitro validation. Furthermore, to maximize drug efficacy, we identified biomarkers for drug candidates. By implementing this computational pipeline, we identified a number of drugs with various mechanisms of action that show higher efficacy in mCRPC with high glycolysis. Furthermore, we successfully validated the efficacy of these nominated drug candidates including an anti-parasitic agent, an HSP90 inhibitor, and a CDK inhibitor. These drug candidates exhibited superior tumor growth inhibition in in vitro models of high glycolysis-induced advanced PC. In conclusion, this study introduces an innovative in silico approach to unveil the existing drugs’ unknown potential for the treatment of patients with mCRPC. Our findings hold promise for development of effective therapeutics for combating this challenging disease. Citation Format: Mei-Chi Su, Adam M. Lee, R. Stephanie Huang. Identify drugs for metastatic castration-resistance prostate cancer (mCRPC) patients with high glycolytic activity through in silico approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6205.
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