Automated Network Analysis Identifies Core Pathways in Glioblastoma

By Ethan G. Cerami, Emek Demir, Nikolaus Schultz, Barry S. Taylor, Christopher Sander. PLoS ONE, 2010

345 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f826825c5f87cd92bdd967d5c8dee8dcba26b0ff

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Citations in the corpus

Computational solutions for omics data (2013, methodology): “In the context of analysing cancer genomes, approaches based on diffusion 148 and shortest paths 149 (Hotnet and Netbox) have been applied to identify subnetworks enriched in recurrently mutated proteins across patients.”
Pathway Commons, a web resource for biological pathway data (2010, background): “Nonetheless, the SIF network is useful for those applications that require pairwise interaction input (2).”
Pathway and Network Analysis of Cancer Genomes (2015, neutral): “NetBox is conceptually similar to ReactomeFIViz and reports functional network modules by identifying clusters of altered genes on a background network derived from databases.”
A human functional protein interaction network and its application to cancer data analysis (2010, methodology): “The edge-betweenness algorithm [75] has been used to find network modules in protein interaction networks [76-78].”
Network enrichment analysis: extension of gene-set enrichment analysis to gene networks (2012, methodology): “This approach was applied, for example, by [19-22].”
Mutual exclusivity analysis identifies oncogenic network modules. (2012, background): “The first network, HRN1, is derived from Cerami et al. (2010), and consists of four curated data sources: Reactome (Matthews et al. 2009), the MSKCC Cancer Cell Map (Memorial Sloan-Kettering Cancer Center 2007), the NCI/ Nature Pathway Interaction Database (Schaefer et al. 2009), and the Human…”