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Cell. 2014 Oct 9;159(2):402-14. doi: 10.1016/j.cell.2014.09.021.

Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks.

Author information

1
Department of Systems Biology, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA; Department of Genetics and Development, Columbia University, 701 West 168th Street, New York, NY 10032, USA.
2
Department of Systems Biology, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA.
3
Department of Genetics and Development, Columbia University, 701 West 168th Street, New York, NY 10032, USA.
4
Cancer & Cell Biology Division, TGen, 445N 5th Street, Phoenix, AZ 85004, USA.
5
Department of Systems Biology, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA.
6
Department of Pathology, M.D. Anderson Cancer Center, University of Texas, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
7
Adult Brain Tumor Centre, Ontario Cancer Institute, University of Toronto, 610 University Avenue, Toronto, ON M5G 2M9, Canada.
8
Department of Systems Biology, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA; Department of Genetics and Development, Columbia University, 701 West 168th Street, New York, NY 10032, USA; Department of Medicine, Columbia University, 630 West 168th Street, New York, NY 10032, USA; Department of Urology, Herbert Irving Pavilion, Columbia University, 161 Fort Washington Avenue, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA.
9
Department of Systems Biology, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA; Department of Biomedical Informatics, Biochemistry & Molecular Biophysics, and Institute for Cancer Genetics, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA. Electronic address: califano@c2b2.columbia.edu.

Abstract

Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a framework for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. We tested this framework by identifying the genetic determinants of the mesenchymal subtype of glioblastoma. Our analysis uncovered KLHL9 deletions as upstream activators of two previously established master regulators of the subtype, C/EBPβ and C/EBPδ. Rescue of KLHL9 expression induced proteasomal degradation of C/EBP proteins, abrogated the mesenchymal signature, and reduced tumor viability in vitro and in vivo. Deletions of KLHL9 were confirmed in > 50% of mesenchymal cases in an independent cohort, thus representing the most frequent genetic determinant of the subtype. The method generalized to study other human diseases, including breast cancer and Alzheimer's disease.

PMID:
25303533
PMCID:
PMC4194029
DOI:
10.1016/j.cell.2014.09.021
[Indexed for MEDLINE]
Free PMC Article

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