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

Research output: Contribution to journalArticlepeer-review

Authors

  • Yunpeng Liu
  • Ning Shi
  • Aviv Regev
  • Shan He
  • Michael T. Hemann

Colleges, School and Institutes

External organisations

  • Massachusetts Institute of Technology
  • Broad Institute of Harvard and MIT, 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.

Details

Original languageEnglish
Article numbere9506
Pages (from-to)e9506
Number of pages20
JournalMolecular Systems Biology
Volume16
Issue number9
Publication statusPublished - 25 Sep 2020

Keywords

  • cell state plasticity, gene essentiality inference, glioblastoma multiforme, subtype-specific gene regulation, transcription regulatory networks