A Prostate Cancer Dependency Map to Identify Tumor Subtype-Specific Vulnerabilities

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PROJECT SUMMARY/ABSTRACT Despite the advances in our knowledge of cancer biology and the approval of new cancer drugs by the Food and Drug Administration (FDA), very few patients with solid tumors that have spread to distant sites (metastases) are cured of their disease. This is true of metastatic prostate cancer (mPC). Consequently, substantial research is directed toward identifying new targets and treatments. New technologies which we and others have developed, now provide unprecedented approaches for deeply characterizing tumors on a comprehensive scale. Studies using these approaches have determined that mPC is comprised of multiple distinct subtypes – a subset of these are defined by specific genomic alterations in oncogenes and genes that normally function to suppress tumor growth. Others are defined by the phenotype – characteristics that influence function and behavior. Notably, a number of these subtypes are now known to have different vulnerabilities to particular therapies – knowledge that underlies a more precision approach for treatment. However, for most subtypes we do not currently have knowledge of their particular vulnerabilities nor do we have therapeutics that can halt their growth/progression. Powerful high-throughput approaches to dissect the function of every gene in a particular cancer genome have been deployed to construct cancer dependency maps (DEPMAP) in many cancer types. These approaches are highly ‘discovery driven’ as they approach the problem of identifying cancer vulnerabilities in a systematic rather than hypothesis-driven fashion. However, prostate cancer is largely excluded from these International projects due to the very limited number of mPC models available to conduct these large scale experiments. In this proposal, we aim to identify new treatment avenues for advanced lethal prostate cancer. We will use multiple new models of metastatic prostate cancer that we have developed and characterized to identify PC subtype specific dependencies using genome-wide loss-of-function screens. In parallel, we will evaluate growth inhibitory effects of drug libraries of approved agents as well as compounds in development. Combinations of agents that exploit genomic dependencies will be tested in vivo against a panel of patient derived xenograft (PDX) lines with and without the predicted vulnerability.