Automated Network Analysis Identifies Core Pathways in Glioblastoma

Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
PLoS ONE (Impact Factor: 3.23). 02/2010; 5(2):e8918. DOI: 10.1371/journal.pone.0008918
Source: PubMed


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.
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.
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.

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Available from: Nikolaus Schultz
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    • "PARADIGM (Vaske et al., 2010) is a method for pathway analysis of heterogenic data, which goes a step further by incorporating information on the type of interaction between elements of a pathway. It enables the analysis of low frequency variations in cross-platform datasets thereby supporting the analysis in situations where the disease in each person, or subgroup of people, is caused by different types of variations (for additional methods for integrative network analysis, seeCerami et al., 2010;Wu et al., 2010;Ciriello et al., 2012). Creating a tool that incorporates biological knowledge in the analysis process is highly dependent on the availability of reliable, maintained, and well-structured databases. "
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    • "Among the miRNAs differentially expressed between group 1 and group 3 samples, miR-200c exhibited very high statistical significance (Table 2). Analysis of the TCGA glioblastoma multiforme dataset [22], [23] found that miR-200c expression also shows a statistically significant decrease (p value<1e-5) in samples with high EGFR amplification in this dataset. "
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    • "Although such rare aberrations are not statistically significant on their own, one of the outcomes from recent cancer sequencing studies is the demonstration of extensive mutational heterogeneity in cancer with driver mutations distributed over a large number of genes (Vogelstein et al., 2013). Thus, obtaining a comprehensive view of the mutations that drive cancer requires the analysis of combinations of rare and common mutations in pathways and interaction networks (Cerami et al., 2010; Ciriello et al., 2012; Leiserson et al., 2013; Vandin et al., 2011, 2012; Vaske et al., 2010). "
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