Automated Network Analysis Identifies Core Pathways in Glioblastoma

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.

Fyn-phosphorylated PIKE-A binds and inhibits AMPK signaling, blocking its tumor suppressive activity

“…25 Indeed, from an automated network analysis on core pathways of glioma formation, PIKE-A has recently been confirmed as a driver gene of glioblastoma. 40 Previous studies suggest that several signaling pathways may contribute to the oncogenic activity of PIKE-A. For instance, PIKE-A directly binds active Akt and stimulates its kinase activity to mediate cancer cell invasion and survival.…”

A replication study of genome-wide CNV association for hepatic biomarkers identifies nine genes associated with liver function

“…The NetBox software is based on copy number alteration and sequence mutation data, and assemblesaltered genes. It identifies linker genes, connects all altered genes, and then identifies network modules and calculatesnetwork modularity (28,29). Although many meta-analysis methods have been reported (30), none were appropriate for our gene-based CNV data.…”

Tumor stem cells from glioblastoma multiforme

“…Мутантный белок р53 в стволовых клетках мультиформной глио-бластомы аналогичен таковому в нейральных стволо-вых и прогениторных клетках [25]. Изменения в сиг-нальном пути р53 включают мутации и делеции из р53, гомозиготную делецию CDKN2A и амплификации MDM2 и MDM4 [1,26].…”

“…Идентифицированы новые онкогены, участвующие в патогенезе глиобластом, включая AGAP2/CENTG, ко-торые являются активаторами PI3K/AKT/mTOR-сиг-нального пути [26].…”