Abstract 358: Integrative pathway analysis of molecular profiles of glioblastoma multiforme for predicting patient survival
Authors: Durmaz Arda , Brubaker Douglas , Bebek Gurkan 
Published: May 2015

Abstract
Glioblastoma multiforme (GBM) is the most common form of malignant brain tumor in adults, characterized by median survival of one year and overall poor prognosis. Subtypes based on gene expression signatures have been reported; however, this classification varies based on molecular characteristics of patient samples, and mostly lacks prognostic utility. An important and challenging goal of stratifying patients by molecular markers is to identify functionally relevant sets of genes that are prognostically relevant. In this study we present a new computational approach that integrates multiple datasets to group patients based on their altered pathway characteristics and show prognostic differences within GBM patients (p&lt;0.05). We first enumerate signaling paths from publicly available pathway databases, and calculate a score for signaling pathway segments utilizing gene expression profiles. Patient samples are clustered into groups based on these scores, and Kaplan-Meier curves based on their survival are plotted. Statistical assessment of the survival curves through a Log-Rank Test is calculated to observe prognostic utility of the groups based on pathway segments. We validated our approach on publicly available datasets (TCGA and GSE4271), and showed prognostic differences in patient groups (p&lt;0.05). The two groups we have identified are different than commonly defined two GBM subtypes, namely proneural (PN) and mesenchymal (MES). While some PN tumors might carry features that might favor better prognosis, such as IDH1 mutations, these alterations are not generalizable. We performed gene set enrichment analysis (GSEA) to further investigate the pathway consequences of dysregulated genes in these patient groups, and identified differential enrichment (p&lt;0.01) of various signaling pathways associated with GBM pathogenesis. Recently, Krishna et al. (Cancer Cell, 2013) identified NFKB pathway in aggressive GBM tumors that might have poor prognosis. Furthermore, our analysis identified genes enriched for the longer survivor patient group in PTEN dependent cell cycle arrest and apoptosis pathway, EGF Signaling Pathway, PDGF Signaling Pathway, IL2 Signaling Pathway, and mTOR signaling pathway. While some of key signaling genes (PTEN, PDGFRA, EGFR etc.) included in these pathways were previously associated with GBM subtypes and pathogenesis (Verhaak et al. Cancer Cell. 2010), we provide further utility for their prognostic function within these pathways. These results could serve as a means to improve prognosis and treatment options for future GBM patients as well as to provide opportunities for alternative interventions in multiple other cancers.
Citation Format: Arda Durmaz, Douglas Brubaker, Gurkan Bebek. Integrative pathway analysis of molecular profiles of glioblastoma multiforme for predicting patient survival. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 358. doi:10.1158/1538-7445.AM2014-358