Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks
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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.
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One of the greatest challenges is to systematically infer causality in molecular networks with a systems genetics approach.
“One of the greatest challenges is to systematically infer causality in molecular networks with a systems genetics approach168,169.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [168]
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- Authors
- Chen, J. C., Alvarez, M. J., Talos, F., Dhruv, H., Rieckhof, G. E., Iyer, A., Diefes, K. L., Aldape, K., Berens, M., Shen, M. M., & Califano, A.
- Affiliations
- Columbia University (US)
- Translational Genomics Research Institute (US)
- University of Toronto (CA)
- Columbia University Irving Medical Center (US)
- Corresponding author
- Califano, Andrea
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 159
- Issue
- 2
- Pages
- 402-414
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4194029
- OpenAlex
- W2105280376
- Cited by
- 188
- References
- 41
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 13 · 2023: 9 · 2022: 10 · 2021: 12 · 2020: 10 · 2019: 17 · 2018: 26 · 2017: 25 · 2016: 25 · 2015: 29 · 2014: 4
- FWCI
- 11.35
- Type
- journal article
- Language
- en
- Update notices
- erratum (2016-08-11)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Bladder Cancer Advocacy Network
- National Cancer Institute (U54CA121852, RC2CA148308, U01CA168426, U01CA168397, 5U01CA16842603, P01CA154293, P50CA127001)
- National Institute of General Medical Sciences (T32GM082797, T32GM007088)
- National Institute of Neurological Disorders and Stroke (R01NS061776)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- driver mutations, regulatory networks, gene regulatory network inference, mesenchymal subtype, glioblastoma, C/EBPβ, C/EBPδ, proteasomal degradation, breast cancer, Alzheimer's disease
- MeSH terms
- Algorithms, Alzheimer Disease, Animals, Breast Neoplasms, Glioblastoma, Humans, Mutation, Neoplasm Transplantation, Proteins, Quantitative Trait Loci, Proteasome Endopeptidase Complex, Mice, CCAAT-Enhancer-Binding Protein-delta, Gene Regulatory Networks, Ubiquitination, DNA Copy Number Variations, Heterografts
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