Abstract
To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.
Highlights
Glioblastoma multiforme is the most common primary brain tumor in adults and, the most fatal
Subnetwork Signature Discovery We began by using glioblastoma multiforme (GBM) patient information and microarray data from The Cancer Genome Atlas [6] (TCGA) as compiled by Verhaak et al [7]
CRANE, an established method for mining molecular networks [5], successfully identified several subnetworks that were informative in separating short-term (STS) from long-term survivors (LTS) using TCGA mRNA data
Summary
Glioblastoma multiforme is the most common primary brain tumor in adults and, the most fatal. While GBMs are categorized histologically, the nature of the disease leads to significant variability in both tumor classification and patient outcome. To define the disease and simultaneously reveal the etiology, an unbiased search for ‘‘molecular signatures’’ of GBM has been undertaken by several groups [1,2], resulting in a variety of GBM markers which, have modest overlap. Given the large degree of molecular heterogeneity of GBMs, analysis of thousands of patient samples may be required to identify comprehensive gene sets by conventional statistical approaches [3] Suggestions that these myriad lists can be integrated via a systems-level analysis, e.g. using molecular networks to find consensus marker sets [4], may help to simplify the observed heterogeneity. An individual gene can affect the algorithmic contribution of a neighboring gene when they coexist in pathways or networks that act to integrate molecular heterogeneity
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