Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma.

in Molecular systems biology by Yunpeng Liu, Ning Shi, Aviv Regev, Shan He, Michael T Hemann

TLDR

  • The study aims to understand why some types of brain cancer, called Glioblastoma multiforme (GBM), are so hard to treat. The study uses a computer program called inTRINSiC to look at how different parts of the cancer work together. The program finds out which parts of the cancer are working differently in different types of GBM. The study also finds out which parts of the cancer are more vulnerable to being treated. The study finds that a protein called MYBL2 is a good target for treatment in one type of GBM.

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.

Overview

  • The study aims to develop a 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. The study uses a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins to identify differential regulatory activity of TFs that shape subtype-specific expression landscapes. The study also aims to infer mechanisms for altered TF behavior in different GBM subtypes and 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.

Comparative Analysis & Findings

  • The study identifies differential regulatory activity of TFs that shape subtype-specific expression landscapes. The study also allows inference of mechanisms for altered TF behavior in different GBM subtypes. The study performs 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.

Implications and Future Directions

  • The study's findings provide insights into the regulatory circuitry underlying GBM heterogeneity and its implications for therapy. The study's computational pipeline, inTRINSiC, can be used to predict genetic dependencies in individual patient tumors and identify drug targets specific for different GBM subtypes. Future research directions could include validation of the in silico predictions using experimental data and development of personalized treatment strategies based on the identified drug targets.
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