Identifying and Targeting Specific Determinants of the AR Cistrome that Drive Prostate Cancer Cell Survival and Proliferation

PROJECT SUMMARY/ABSTRACT The AR may be the earliest known example of a lineage oncogene: a master regulator of cell survival and growth to which neoplastic cells derived from prostate epithelium are addicted. Recognizing this unique feature, concerted efforts have focused on developing therapeutics capable of suppressing AR signaling. Androgen deprivation therapy (ADT) and AR pathway signaling inhibitors (ARSI) produce dramatic responses in the vast majority of patients with metastatic PC (mPC). Unfortunately, these responses are not accompanied by cures, with near universal development of treatment resistance. Fundamentally, the mechanism(s) responsible for the dependence of prostate adenocarcinoma on AR signaling for survival and proliferation is not understood. Yet, understanding the signaling pathway by which the AR activation represses apoptosis and promotes cell proliferations has the potential for advancing new treatment approaches for PC. Further, understanding the AR-mediated survival and growth functions can also identify mechanisms by which PC could effectively bypass a requirement for AR activity through oncogenic processes that subsume AR functions. In this proposal our objective is to test the hypothesis that the AR directly transcriptionally regulates a signaling program that promotes cell survival (anti-apoptosis) and cell division/proliferation. Components of the program may be indirectly regulated through downstream regulators of gene expression and metabolism. AIM 1. Determine the direct and indirect AR-regulated cistromes that comprise the AR program hierarchy. AIM 2. Define the AR regulated survival and growth dependencies in prostate adenocarcinoma AIM 3 Characterize changes in prostate cancer AR regulation dictated by genomic alterations. The project aims interact and integrate with studies planned in Project 2 involving AR splice variants and Project 3 evaluating AR interactions with DNA repair processes, and utilizes key core resources that include biostatistics, preclinical models, and advanced genomics technologies.