In this application, we articulate the vision for a center that will integrate recent computational and experimental advances in cancer systems biology toward the genome-wide prioritization of therapeutic targets using high-throughput screening approaches. The objective is the elucidation of individual and synergistic master regulators of tumor progression and drug- resistance and the identification of their chemical modulators. We address these aims in three specific tumor-related phenotypes for which we have developed detailed computational and experimental models, including (a) Progression from Follicular Lymphoma (FL) to Diffuse Large B Cell Lymphoma (DLBCL) (b) Glucocorticoid resistance in T-lineage Acute Lymphoblastic Leukemia (T-ALL), and (c) the mesenchymal phenotype of Glioblastoma (GBM) associated with the worst clinical outcome in patients with HGG. We will use existing, computationally-inferred molecular interaction networks to prioritize candidate master regulators of tumorigenesis, tumor aggressiveness, and resistance to standard therapeutic agents. These will be further prioritized by positive- and negative-selection genome-wide RNAi screens, using a computational evidence-integration approach. High- ranking targets will be validated in vitro using siRNA/shRNA silencing and then profiled to identify specific expression markers for their inhibition. Then, small molecule screens will be performed to identify compounds that are selectively lethal to tumor cells because of direct or indirect inactivation of the selected high-ranking targets. Other classes of compounds are those that may cooperate with partial knockdown of high priority targets either because they are functionally linked to those targets or because they bind these targets directly. Finally, identified compounds will be tested in vivo using both primary GBM-derived stem-like cells injected intra-cranially in immunodeficient mice or established mouse models of human cancer (DLBCL and T-ALL).
Recent advances in the integration of computational and experimental methods for the understanding of cancer biology are creating unique opportunities to improve our ability to identify biomarkers for early diagnosis and prevention, therapeutic targets that are highly specific to a cancer type, and compounds that inhibit these targets. Investigators at the Herbert Irving Comprehensive Cancer Center have pioneered these type of approaches an implemented a completely integrated computational-experimental approach to the study of cancer called Cancer Systems Biology. In this application, we articulate the vision of a center that will use cancer systems biology tools to identify and prioritize candidate therapeutic targets and to design highly specific molecular screening approaches for the identification of small molecules that inhibit these targets. In particular, we have shown that Master Regulators of tumor progression and chemotherapy resistance can be identified both computationally and experimentally and that these genes provide ideal targets for therapy, either individually or in combination, i.e. using more than one drug. We propose to study three tumor progression and chemotherapy resistance problems, including (a) the mesenchymal signature of Glioblastoma (GBM) associated with the worst clinical outcome in patients with High-Grade Gliomas (HGG), (b) the transformation of Follicular Lymphoma (FL) into Diffuse Large B Cell Lymphoma (DLBCL), and (c) Glucocorticoid resistance in T cell Acute Lymphoblastic Leukemia (T-ALL).
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