Glioblastoma (GBM), the most common malignant brain tumor, remains one of the most challenging forms of cancer to treat. The GBM genome is characterized by numerous genetic aberrations of uncertain pathogenetic significance. More specifically, the abundance of passenger mutations and large regions of copy number alterations has complicated the definition of the landscape of driver mutations in GBM. To study the GBM genome, strategies enabling the distinction of causal genetic alterations from bystander genomic noise are needed as well as the identification of sets of lesions that concur to determine distinct cancer phenotypes and prognostic groups of patients. To address this challenge and uncover new driver genes in human GBM, we developed a computational platform that integrates the analysis of copy number variations, somatic mutations and in-frame gene fusions from a large whole-exome and transcriptome dataset. This proposal will contextualize alterations in genetic networks inferred from human GBM in the natural genetic and cellular environment of a specific tumor and identify the key driving modules on which specific GBM subgroups rely for growth, survival and progression. With this information in hand, we can target the critical alterations with specific drugs, often already available for other typesof diseases. By focusing on GBM, we have been able to make incredible progress along this line in the last two years. Our recent work identified the first example of highly oncogenic and recurrent gene fusions in GBM, target their dependency in a particular tumor subtype, and observe dramatic anti- tumor effects. This integrative oncogenomic approach is systematic and comprehensive and has already identified two mutually exclusive genetic modules that drive different subgroups of the human disease and that we are ready to explore with functional experiments in vitro and in vivo. The paradigm that emerges from the identification of the new modules establishes that, when integrated in our proposal, the computational and experimental plans have the power to functionalize the entire set of genetic tumor modules of GBM. Thus, completion of the proposal will deliver to the brain tumor community the first computational and experimentally validated pipeline for the unbiased reconstruction of distinctive driver modules of any form of human cancer.

Public Health Relevance

Glioblastoma multiforme (GBM) is the most common intrinsic brain tumor and is almost invariably lethal, largely as a result of lack of responsiveness to current therapies. In this proposal we will apply an integrated computational and experimental pipeline to identify and experimentally validate the causal networks of genetic alteration in GBM. Overall this work will elucidate the pathogenesis of human GBM and has the potential to facilitate and accelerate the translation of our genomic knowledge into cancer drugs that will impact patient survival.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BDCN-W (02))
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Li, Jerry
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Columbia University (N.Y.)
Internal Medicine/Medicine
Schools of Medicine
New York
United States
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