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Transposon Mediated Integration of Plasmid DNA into the Subventricular Zone of Neonatal Mice to Generate Novel Models of Glioblastoma
Published on: February 22, 2015
Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma
Yunpeng Liu1,2,3, Ning Shi4, Aviv Regev1,2,3
1Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Abstract:
Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well-defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype-specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype-specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype.
Insights
Researchers developed a computational tool to uncover Glioblastoma multiforme (GBM) subtype-specific regulatory programs. This approach identified MYBL2 as a potential drug target for the Proneural GBM subtype, offering new avenues for personalized cancer therapy.
Area of Science:
- Computational biology
- Cancer genomics
- Systems biology
Background:
- Glioblastoma multiforme (GBM) is an aggressive brain cancer with limited therapeutic options.
- Molecular subtypes of GBM exist, but their regulatory networks and therapeutic implications are not fully understood.
- Personalized cancer therapy strategies for GBM are needed.
Purpose of the Study:
- To develop a computational pipeline to analyze subtype-specific regulatory programs in GBM.
- To predict genetic dependencies and vulnerabilities in individual GBM patient tumors.
- To identify potential therapeutic targets for distinct GBM subtypes.
Main Methods:
- Development of the Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC) pipeline.
- Construction of a multilayer network integrating transcription factors (TFs), target genes, and signaling proteins.
- Application of in silico perturbation analysis to infer genetic vulnerabilities.
Main Results:
- Accurate identification of differential transcription factor (TF) activity shaping GBM subtype-specific expression.
- Inference of mechanisms underlying altered TF behavior across GBM subtypes.
- Identification of MYBL2 as a drug target specific to the Proneural GBM subtype through in silico analysis.
Conclusions:
- The inTRINSiC pipeline effectively dissects GBM subtype-specific regulatory networks.
- Computational modeling can predict subtype-specific genetic vulnerabilities in GBM.
- MYBL2 represents a promising therapeutic target for Proneural Glioblastoma multiforme.
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