Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis

Christopher L. Plaisier, Sofie O'Brien, Brady Bernard, Sheila Reynolds, Zac Simon, David J. Reiss, Nitin S. Baliga, Chad M. Toledo, Yu Ding, Patrick J. Paddison, Chad M. Toledo, Patrick J. Paddison

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


We developed the transcription factor (TF)-target gene database and the Systems Genetics Network Analysis (SYGNAL) pipeline to decipher transcriptional regulatory networks from multi-omic and clinical patient data, and we applied these tools to 422 patients with glioblastoma multiforme (GBM). The resulting gbmSYGNAL network predicted 112 somatically mutated genes or pathways that act through 74 TFs and 37 microRNAs (miRNAs) (67 not previously associated with GBM) to dysregulate 237 distinct co-regulated gene modules associated with patient survival or oncogenic processes. The regulatory predictions were associated to cancer phenotypes using CRISPR-Cas9 and small RNA perturbation studies and also demonstrated GBM specificity. Two pairwise combinations (ETV6-NFKB1 and romidepsin-miR-486-3p) predicted by the gbmSYGNAL network had synergistic anti-proliferative effects. Finally, the network revealed that mutations in NF1 and PIK3CA modulate IRF1-mediated regulation of MHC class I antigen processing and presentation genes to increase tumor lymphocyte infiltration and worsen prognosis. Importantly, SYGNAL is widely applicable for integrating genomic and transcriptomic measurements from other human cohorts.

Original languageEnglish (US)
Pages (from-to)172-186
Number of pages15
JournalCell Systems
Issue number2
StatePublished - Aug 24 2016
Externally publishedYes


  • gene regulation
  • glioblastoma multiforme
  • glioma
  • network
  • systems biology
  • systems genetics

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology


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