Automated network analysis identifies core pathways in glioblastoma

Ethan Cerami, Emek Demir, Nikolaus Schultz, Barry S. Taylor, Chris Sander

Research output: Contribution to journalArticle

224 Citations (Scopus)

Abstract

Background: Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing "driver" mutations from passively selected "passenger" mutations. Principal Findings: In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups. Conclusions: We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.

Original languageEnglish (US)
Article numbere8918
JournalPLoS One
Volume5
Issue number2
DOIs
StatePublished - Feb 12 2010
Externally publishedYes

Fingerprint

Glioblastoma
Electric network analysis
Genes
neoplasms
Neoplasms
Phosphatidylinositol 3-Kinases
Mutation
Tumors
mutation
Atlases
Genome
phosphatidylinositol 3-kinase
Molecular interactions
genome
Protein Kinases
Public Opinion
Brain
Proteins
DNA Sequence Analysis
oncogenes

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Automated network analysis identifies core pathways in glioblastoma. / Cerami, Ethan; Demir, Emek; Schultz, Nikolaus; Taylor, Barry S.; Sander, Chris.

In: PLoS One, Vol. 5, No. 2, e8918, 12.02.2010.

Research output: Contribution to journalArticle

Cerami, Ethan ; Demir, Emek ; Schultz, Nikolaus ; Taylor, Barry S. ; Sander, Chris. / Automated network analysis identifies core pathways in glioblastoma. In: PLoS One. 2010 ; Vol. 5, No. 2.
@article{4dbd1d318d0d41628d97b9adf7235e28,
title = "Automated network analysis identifies core pathways in glioblastoma",
abstract = "Background: Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing {"}driver{"} mutations from passively selected {"}passenger{"} mutations. Principal Findings: In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups. Conclusions: We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.",
author = "Ethan Cerami and Emek Demir and Nikolaus Schultz and Taylor, {Barry S.} and Chris Sander",
year = "2010",
month = "2",
day = "12",
doi = "10.1371/journal.pone.0008918",
language = "English (US)",
volume = "5",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "2",

}

TY - JOUR

T1 - Automated network analysis identifies core pathways in glioblastoma

AU - Cerami, Ethan

AU - Demir, Emek

AU - Schultz, Nikolaus

AU - Taylor, Barry S.

AU - Sander, Chris

PY - 2010/2/12

Y1 - 2010/2/12

N2 - Background: Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing "driver" mutations from passively selected "passenger" mutations. Principal Findings: In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups. Conclusions: We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.

AB - Background: Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing "driver" mutations from passively selected "passenger" mutations. Principal Findings: In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups. Conclusions: We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.

UR - http://www.scopus.com/inward/record.url?scp=77949446630&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77949446630&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0008918

DO - 10.1371/journal.pone.0008918

M3 - Article

C2 - 20169195

AN - SCOPUS:77949446630

VL - 5

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 2

M1 - e8918

ER -