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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA179044-01A1
Application #
8632597
Study Section
Special Emphasis Panel (ZRG1-BDCN-W (02))
Program Officer
Li, Jerry
Project Start
2013-12-10
Project End
2018-11-30
Budget Start
2013-12-10
Budget End
2014-11-30
Support Year
1
Fiscal Year
2014
Total Cost
$459,630
Indirect Cost
$172,361
Name
Columbia University (N.Y.)
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Yamamoto, Kenta; Wang, Jiguang; Sprinzen, Lisa et al. (2016) Kinase-dead ATM protein is highly oncogenic and can be preferentially targeted by Topo-isomerase I inhibitors. Elife 5:
Del Giudice, Ilaria; Marinelli, Marilisa; Wang, Jiguang et al. (2016) Inter- and intra-patient clonal and subclonal heterogeneity of chronic lymphocytic leukaemia: evidences from circulating and lymph nodal compartments. Br J Haematol 172:371-83
Ceccarelli, Michele; Barthel, Floris P; Malta, Tathiane M et al. (2016) Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell 164:550-63
Pefanis, Evangelos; Wang, Jiguang; Rothschild, Gerson et al. (2015) RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity. Cell 161:774-89
Crescenzo, Ramona; Abate, Francesco; Lasorsa, Elena et al. (2015) Convergent mutations and kinase fusions lead to oncogenic STAT3 activation in anaplastic large cell lymphoma. Cancer Cell 27:516-32
Melamed, Rachel D; Emmett, Kevin J; Madubata, Chioma et al. (2015) Genetic similarity between cancers and comorbid Mendelian diseases identifies candidate driver genes. Nat Commun 6:7033
Melamed, Rachel D; Wang, Jiguang; Iavarone, Antonio et al. (2015) An information theoretic method to identify combinations of genomic alterations that promote glioblastoma. J Mol Cell Biol 7:203-13
Di Stefano, Anna Luisa; Fucci, Alessandra; Frattini, Veronique et al. (2015) Detection, Characterization, and Inhibition of FGFR-TACC Fusions in IDH Wild-type Glioma. Clin Cancer Res 21:3307-17
Pefanis, Evangelos; Wang, Jiguang; Rothschild, Gerson et al. (2014) Noncoding RNA transcription targets AID to divergently transcribed loci in B cells. Nature 514:389-93
Wang, Jiguang; Khiabanian, Hossein; Rossi, Davide et al. (2014) Tumor evolutionary directed graphs and the history of chronic lymphocytic leukemia. Elife 3:

Showing the most recent 10 out of 13 publications