Identifying Efficacious Therapies for High Glycolysis Metastatic Castration-Resistant Prostate Cancer

Abstract

Metastatic castration-resistant prostate cancer (mCRPC) remains a deadly disease due to a lack of efficacious treatments. The reprogramming of cancer metabolism toward elevated glycolysis is a hallmark of mCRPC. However, the current drug discovery on finding drugs efficiently inhibiting high glycolysis tumor growth is challenging due to metabolic plasticity. Our goal is to identify therapeutics specifically associated with high glycolysis while reducing the risk of weakened efficacy from metabolism plasticity. We established a computational framework to identify new pharmacological agents for mCRPC with heightened glycolysis activity under a tumor microenvironment, followed by in vitro validation. Using our established computational tool, OncoPredict, the likelihood of drug responses was imputed to approximately 2300 agents in each mCRPC tumor from two large clinical patient studies. Drugs predicted to have high sensitivity, either strongly correlated with glycolysis scores or with both glycolysis and oxidative phosphorylation (OXPHOS) scores, were selected. In total, ten drug candidates were finalized. Ivermectin, CNF2024, and P276-00 were chosen for subsequent in vitro validation based on the highest measured dose-response curve AUC associated with glycolysis/OXPHOS in pan-cancer cell lines. By decreasing the input glucose level in culture media to mimic the mCRPC tumor microenvironments, we induced a high-glycolysis condition in PC3 cells and validated the projected better tumor inhibition of all three drugs under this condition (p < 0.0001 for all drugs) after 48 hours of treatment. For biomarker discovery, ivermectin and P276-00 were predicted to be more sensitive to mCRPC tumors with low androgen receptor activities combined with high glycolysis activities (AR(low)Gly(high)). In addition, we integrated a protein-protein interaction network and topological methods to identify drug response predictive biomarkers and prognostic factors for patients with mCRPC. EEF1B2 and CCNA2 were identified as drug response predictive biomarkers for ivermectin and CNF2024, respectively, and prognostic factors for patients with mCRPC. Additionally, DLGAP5, KIF11, and CDK1 were identified as drug response predictive biomarkers for P276-00 and prognostic factors for patients with mCRPC. In conclusion, this study offers new efficacious therapeutics beyond traditional androgen deprivation therapies by precisely targeting mCRPC with high glycolysis.