ArticlePloS one2010
Automated network analysis identifies core pathways in glioblastoma.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 217 papers, 2 of them syntheses that pooled it.
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Who cites it
217 citing papers in PubMed, 2 syntheses or guidelines pooled it, 352 citations in OpenAlex.
- The Emerging World of TCR-T Cell Trials Against Cancer: A Systematic Review.Technology in cancer research & treatment · 2019Pooled it
- Computational Identification of Novel Stage-Specific Biomarkers in Colorectal Cancer Progression.PloS one · 2016Pooled it
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- Target-Based Anticancer Indole Derivatives for the Development of Anti-Glioblastoma Agents.Molecules (Basel, Switzerland) · 2023Review
- A network-based method for identifying cancer driver genes based on node control centrality.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Graph algorithms for predicting subcellular localization at the pathway levelPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
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- Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients.PeerJ · 2022Article
- Use of multimodality imaging, histology, and treatment feasibility to characterize a transgenicFrontiers in oncology · 2022Article
157 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundGlioblastoma 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
findingsIn 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.
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.