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

Christopher 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 journalArticle

28 Citations (Scopus)

Abstract

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
Volume3
Issue number2
DOIs
StatePublished - Aug 24 2016
Externally publishedYes

Fingerprint

Glioblastoma
Gene Regulatory Networks
Antigen Presentation
Clustered Regularly Interspaced Short Palindromic Repeats
Genes
Genetic Databases
Histocompatibility Antigens Class I
MicroRNAs
Neoplasms
Transcription Factors
Lymphocytes
RNA
Phenotype
Mutation
Survival

Keywords

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

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Cite this

Plaisier, C., O'Brien, S., Bernard, B., Reynolds, S., Simon, Z., Reiss, D. J., ... Paddison, P. J. (2016). Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis. Cell Systems, 3(2), 172-186. https://doi.org/10.1016/j.cels.2016.06.006

Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis. / Plaisier, Christopher; O'Brien, Sofie; Bernard, Brady; Reynolds, Sheila; Simon, Zac; Reiss, David J.; Baliga, Nitin S.; Toledo, Chad M.; Ding, Yu; Paddison, Patrick J.; Toledo, Chad M.; Paddison, Patrick J.

In: Cell Systems, Vol. 3, No. 2, 24.08.2016, p. 172-186.

Research output: Contribution to journalArticle

Plaisier, C, O'Brien, S, Bernard, B, Reynolds, S, Simon, Z, Reiss, DJ, Baliga, NS, Toledo, CM, Ding, Y, Paddison, PJ, Toledo, CM & Paddison, PJ 2016, 'Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis', Cell Systems, vol. 3, no. 2, pp. 172-186. https://doi.org/10.1016/j.cels.2016.06.006
Plaisier, Christopher ; O'Brien, Sofie ; Bernard, Brady ; Reynolds, Sheila ; Simon, Zac ; Reiss, David J. ; Baliga, Nitin S. ; Toledo, Chad M. ; Ding, Yu ; Paddison, Patrick J. ; Toledo, Chad M. ; Paddison, Patrick J. / Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis. In: Cell Systems. 2016 ; Vol. 3, No. 2. pp. 172-186.
@article{11eec16d51964bf8929343e82f899c9b,
title = "Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis",
abstract = "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.",
keywords = "gene regulation, glioblastoma multiforme, glioma, network, systems biology, systems genetics",
author = "Christopher Plaisier and Sofie O'Brien and Brady Bernard and Sheila Reynolds and Zac Simon and Reiss, {David J.} and Baliga, {Nitin S.} and Toledo, {Chad M.} and Yu Ding and Paddison, {Patrick J.} and Toledo, {Chad M.} and Paddison, {Patrick J.}",
year = "2016",
month = "8",
day = "24",
doi = "10.1016/j.cels.2016.06.006",
language = "English (US)",
volume = "3",
pages = "172--186",
journal = "Cell Systems",
issn = "2405-4712",
publisher = "Cell Press",
number = "2",

}

TY - JOUR

T1 - Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis

AU - Plaisier, Christopher

AU - O'Brien, Sofie

AU - Bernard, Brady

AU - Reynolds, Sheila

AU - Simon, Zac

AU - Reiss, David J.

AU - Baliga, Nitin S.

AU - Toledo, Chad M.

AU - Ding, Yu

AU - Paddison, Patrick J.

AU - Toledo, Chad M.

AU - Paddison, Patrick J.

PY - 2016/8/24

Y1 - 2016/8/24

N2 - 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.

AB - 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.

KW - gene regulation

KW - glioblastoma multiforme

KW - glioma

KW - network

KW - systems biology

KW - systems genetics

UR - http://www.scopus.com/inward/record.url?scp=84978811202&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978811202&partnerID=8YFLogxK

U2 - 10.1016/j.cels.2016.06.006

DO - 10.1016/j.cels.2016.06.006

M3 - Article

C2 - 27426982

AN - SCOPUS:84978811202

VL - 3

SP - 172

EP - 186

JO - Cell Systems

JF - Cell Systems

SN - 2405-4712

IS - 2

ER -