Rationale and Background: Cytotoxic and targeted agents are a critical component of cancer therapy. For some cancers, such as testicular cancer and certain blood cancers, chemotherapy cures most patients. Despite these select successes, intrinsic and acquired drug resistance remains a major clinical problem. Due to the genomics revolution, our understanding of the common genomic alterations in cancer is now quite extensive. Observational cancer genomics has opened up a world of potential therapeutic targets in nave and relapsed tumors. There is now an imperative to prioritize drug targets and to map and block routes of drug resistance. Genomics identifies driver mutations in cancer enabling us to create new-targeted drugs, although single agent targeted therapies frequently eventually fail because a subset of tumor cells escape. Additionally, there are many genes critical for cancer cell survival and drug response that are never mutated. Understanding such non-oncogene addictions in cancer therapy is critical. We need better ways of functionally mapping out cancer cell response and resistance to anti-cancer drugs in order to develop rational ways of blocking cancer cell escape from therapy. In the Weissman lab, I have created new functional genomics tools to explore drug response in cancer therapy. Using a nuclease-inactivate form of the CRISPR effector protein Cas9 (dCas9), we can target transcription activator (CRISPRa) or repressor (CRISPRi) domains to specific DNA sequences to robustly and specifically control expression of individual transcripts encoded by the human genome. We have generated genome-scale CRISPRi/a libraries and shown that when combined CRISPRi/a screens yield a more complete view of processes controlling cell growth, differentiation, and response to toxins. Objective: I am proposing to use our CRISPRi/a functional genomic platform to study drug response in cancer therapy. To complete our functional genomic toolset, I am also proposing to develop a CRISPRi/a genetic-interaction (GI) mapping platform. GI maps are a powerful method of identifying synthetic-lethal gene pairs, which are ideal drug targets in cancer therapy. I propose a period of mentored training to apply our new CRISPR functional genomics platform to determine mechanisms of cellular response to new classes of anti-cancer drugs and crucially to learn how to construct and analyze a GI map. My primary mentor, Jonathan Weissman, is a world expert in genetic- interaction mapping and his lab is the perfect place for me to learn the technical and analytical skills I need to be successful in this aim. I have chosen to use CRISPRi/a to characterize HSP70 inhibitors and K-Ras(G12C) inhibitors, two new exciting classes of anti-cancer drugs that were created by my co-mentors Jason Gestwicki and Kevan Shokat. I am embedded in a community at UCSF with a deep and successful history of studying protein chaperones and Ras. A period of training by my co-mentors who are both chemists in principles of anti-cancer drug design will uniquely enable me to use our CRISPR functional genomics platform as a tool for studying anti-cancer drugs. This work will serve as a general template for using CRISPRi/a to study oncogene (Ras) and non- oncogene (protein chaperones) addictions in cancer. To achieve these goals, I propose the following specific aims: (1) Determine the mechanism of cellular responses to HSP70 chaperone inhibitors (2) Determine the mechanism of cellular response to K-Ras(G12C) inhibitors (3) Implement a CRISPRi/a genetic-interaction (GI) mapping platform. In pursuit of these aims, I will acquire the knowledge base, analytical skills and unique perspective to launch my independent research career as a cancer biologist.
Conventional and targeted drugs are a critical component of cancer therapy; however, intrinsic and acquired drug resistance remains a major clinical problem. We need better ways of mapping out response to anti-cancer drugs in order to develop a plan for blocking cancer cell escape from therapy. I have developed a new technology for identifying genes that control cancer cell survival and now am proposing to use this technology to characterize cancer cell response to two new types of anti-cancer drugs.
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