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Automated network analysis identifies core pathways in glioblastoma - PubMed

  • ️Fri Jan 01 2010

Automated network analysis identifies core pathways in glioblastoma

Ethan Cerami et al. PLoS One. 2010.

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Overview of the network approach for identifying oncogenic processes and candidate driver genes in GBM.

We began by creating a literature curated Human Interaction Network (HIN) derived from protein-protein interactions and signaling pathways (A), and assembling genomic alterations in GBM (B). We then extracted a GBM-specific network of altered genes (C), which was then partitioned into network modules (D). We assessed the level of connectivity seen within the GBM network by using (E1) a global null model to compare the size of the largest component in the observed network v. networks arising from randomly selected gene sets; and (E2) a local null model to compare network modularity of the observed network to locally rewired networks.

Figure 2
Figure 2. Network modules identified in GBM.

(A) Modules are densely connected sets of altered genes that may reflect oncogenic processes. A total of 10 modules were identified, the largest of which are shown. Linker genes, indicated in red, are not altered in GBM, but are statistically enriched for connections to GBM-altered genes. (B) The observed modularity of the GBM network (0.519) is compared with 1000 randomly rewired networks (average 0.296, standard deviation 0.058). This results in a z-score, or scaled modularity score, of 3.84.

Figure 3
Figure 3. Automated network analysis approach is in close agreement with previous manually curated pathway analysis approach.

The original pathway analysis of TCGA glioblastoma datasets was derived by mapping observed gene alterations onto a manually curated GBM-specific network, based on the glioblastoma literature. This non-algorithmic analysis identified driver alterations in the p53, RB and PI3K pathways. Our automated network analysis approach is in close agreement with these results (top: P53/Rb; bottom: PI3K). The one main exception is that network analysis does not identify NF1 as a participant in the PI3K module. Additional candidate driver genes identified by network analysis, including AGAP2, are identified and annotated on the right. Percentage values after each newly identified candidate driver indicate percent of cases with genetic alterations (sequence mutations, homozygous deletions, or multi-copy amplifications) across the 84 TCGA GBM cases analyzed.

Figure 4
Figure 4. Network analysis identifies three additional altered modules, including the DCTN2 module, which is involved in microtubule organization.

Each of the altered modules is implicated by homozygous deletions or multi-copy amplifications across the 84 analyzed GBM cases. Each module is annotated with Gene Ontology enrichment, chromosome location, statistical significance of copy number alteration against a background model of random aberrations, as determined by RAE copy number analysis; assessment of correlation between copy number and mRNA expression, and genomic signature across 84 GBM cases.

Figure 5
Figure 5. BioPAX to binary interaction mapping: example mapping rule.

To integrate complex signaling pathway data into our Human Interaction Network (HIN), we have developed a set of rules for mapping subgraphs of biochemical networks to binary interactions. An example rule for mapping state changes, such as phosphorylation events, is shown.

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References

    1. Holland EC. Glioblastoma multiforme: the terminator. Proc Natl Acad Sci U S A. 2000;97:6242–6244. - PMC - PubMed
    1. Furnari FB, Fenton T, Bachoo RM, Mukasa A, Stommel JM, et al. Malignant astrocytic glioma: genetics, biology, and paths to treatment. Genes Dev. 2007;21:2683–2710. - PubMed
    1. Ohgaki H, Kleihues P. Genetic pathways to primary and secondary glioblastoma. Am J Pathol. 2007;170:1445–1453. - PMC - PubMed
    1. TCGA Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455:1061–1068. - PMC - PubMed
    1. Mischel PS, Cloughesy TF. Targeted molecular therapy of gbm. Brain Pathol. 2003;13:52–61. - PMC - PubMed

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