Cancer cells survive and thrive under extremely stressful conditions, such as inadequate supply of oxygen and nutrients, genomic instability and proteome imbalances. My central hypothesis is that many cancer cells adapt stress response pathways and become uniquely dependent on them. Individual stress response factors have been characterized and some are currently being investigated as drug targets in cancer. However, we lack a systematic description of the complex, redundant organization of individual factors in a stress response net- work. Understanding this network would enable us to characterize cancer-specific adaptations and vulnerabilities that can be exploited for targeted therapies. To gain this depth of insight, new systematic approaches are called for. As a postdoc in the Weissman lab, I have co-developed a technology platform for systematic map- ping of genetic interactions in mammalian cells. Genetic interactions measure how the phenotype of one mutation is modified by a second mutation. A strong synergistic effect of inactivating two genes simultaneously is referred to as """"""""synthetic lethality"""""""". Synthetic lethal genes are ideal targets of effective combination therapies that pre-empt drug resistance. Systematic genetic interaction maps reveal gene functions and cellular path- ways. My long-term goal is to use innovative systematic approaches to understand the complexity of stress network adaptations in cancer and exploit their therapeutic potential. This application focuses on multiple myeloma (MM) as a paradigm. MM cells are characterized by constitutive activation of a stress response, the un- folded protein response. Despite recent advances in MM drug therapy, nearly all patients develop resistance and relapse. The development of better combination therapies for MM is thus an unmet clinical need. In preliminary studies with our platform in MM cell lines, we identified novel genetic vulnerabilities in the stress response network. Expression levels of several of these genes are prognostic of survival in MM and other cancer patients. The overall objective of this application is to determine interactions between these and other vulnerabilities in MM, and to validate the therapeutic potential of targeting these synergistic vulnerabilities in MM and other cancers. I propose the following specific aims: (1) Characterize stress-related and intrinsic vulnerabilities in multiple myeloma and other blood cancer cells (2) Test the role of stress-related genes in determining drug sensitivity and resistance in patient cells (3) Validate the therapeutic potential of targeting the stress response network in multiple myeloma mouse models. This project will provide the basis to test new combination therapies and biomarkers for MM in clinical trials, and pave the way for broad application of our genetic interaction mapping approach to a wide variety of cancers. Through the proposed research and training activities, I will acquire the necessary skills to launch my career as an independent scientist, and lay the foundation of my research program, in which I plan to use innovative approaches to elucidate cancer biology and identify new therapeutic strategies to improve human health.

Public Health Relevance

Cancer is a leading cause of death in the United States, and although targeted cancer drugs have significantly improved therapy, most cancers develop drug resistance, which can likely be pre-empted only by rational combination therapies. To identify the ideal combinatorial drug targets for a given cancer with a specific genetic background, we have developed a technology platform to construct systematic genetic interaction maps in mammalian cells. This project will pioneer the use of systematic genetic interaction maps in cancer research to reveal therapeutically relevant combination therapy targets in the stress response network of multiple myeloma, the second most common cancer of the blood, which is currently incurable.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Career Transition Award (K99)
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Study Section
Subcommittee G - Education (NCI)
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Schmidt, Michael K
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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