Evolution of cancer-specific molecular requirements for glioblastoma multiforme (

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DESCRIPTION (provided by applicant): In cancer biology it has become evident that requirements for specific gene activities can vary widely across cancer types. These differences presumably arise from context-specific molecular constrains intrinsic to the tissue or cell type of origin and to the process of cancer development itself. Our current lack of knowledge of cancer-specific gene requirements and the processes driving their requirement has hampered development of targeted therapeutic strategies for cancer. However, in the past five years significant progress has been made in the development of culture systems for patient cancers that allow unprecedented access to cancer- evolved molecular pathways and cellular phenotypes. Over the past three years, we have developed a strategy for defining the nature of gene requirements in patient cancer samples. Our approach integrates data from functional genetic screens in patient derived cancer stem cells with network models constructed from "cancer- omics" data sets to make gene requirement predictions. In proof of concept studies for Glioblastoma multiforme (GBM), an incurable form of brain cancer, we have demonstrated the existence of GBM-lethal genes, which when inhibited render patient GBM tumor cells sensitive to cellular stresses that arise as a consequence of cellular transformation. In this application w use this cancer-lethal prediction paradigm to address Provocative Question 8: Why do certain mutational events promote cancer phenotypes in some tissues and not others? We test the hypothesis that GBM-specific requirements for gene activities arise from one of three context-specific constraints: (a) the tissue of origin (i.e., neural-specific activity); (b) a GBM-specific evolution process; or (c) cellular transformation process in general. Our experimental approach will combine data from functional genetic screen in human GBM stem cells (of multiple subtypes) with pre-existing Bayesian network models for GBM and other cancers including, breast, lung, ovarian, and prostate (generated from The Cancer Genome Atlas patient data sets). If successful, these studies will reveal the extent and origin of GBM-specific requirements for gene activities in GBM patient samples. In addition to providing key insight into brain tumor biology, these studies will significantly aid in identifying new targeted therapeutic strategies fo GBM and other cancers with standard of care therapies suffering from poor therapeutic windows. PUBLIC HEALTH RELEVANCE: This application attempts to model variation in the behavior of brain tumors through experimentation with patient-derived cells and manipulation of previously generated molecular data sets for five cancers including brain, breast, lung, ovarian and prostate. If successful, the experimental aims and results will create a new paradigm for the study of cancer biology and therapeutics. In addition to providing key insight into brain tumor biology, these studies will significantly aid in identifying new targeted therapeutic strategies fo brain and other cancers.