Abstract 65: An integrated bioinformatics approach for identifying patient-specific gene networks and novel therapeutic targets in glioblastoma

Abstract

Abstract The purpose of our approach is to identify highly relevant and patient specific gene networks from next generation sequencing data and use the resulting gene combinations to identify potential novel therapeutic targets. Glioblastoma multiforme (GBM) is the most common and aggressive malignant brain tumor. Despite medical advances in the field, the median survival time is still below 2 years since recurrence is nearly universal. Thus, the discovery of novel and specific molecular targets is needed. As with all cancers, GBM arises due to complex alterations in a patient's genome. Identifying patient-specific differentially expressed genes (DEGs) though, can impose many difficulties given the discordance of different analysis methods when the sample size is low. We used the data on the large number of GBM patients within TCGA (The Cancer Genome Atlas) as filter for identifying variations in expression. With the help of our algorithm, we produced enriched single patient RNAseq DEG lists and we calculated a hypergeometric probability and a correlation coefficient for every gene pair on these lists. By using the most significant of these pairs, we generated gene association networks. To further validate the biological relevance of the gene pairs, we looked into the presence of these networks in other types of cancer and searched for published experimental data supporting the connections of our networks. In order to assess the therapeutic potential of these genes, we used complimentary experiments involving GBM cell lines recorded through the LINCS (Library of Integrated Network-based Cell Signatures) database. This study identifies a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM, and capitalizes on the LINCS database to assess the potential of novel therapeutic targets. Citation Format: Vasileios Stathias, Chiara Pastori, Ricardo Komotar, Ming Zhang, Stephan Schürer, Jennifer Clarke, Nagi G. Ayad. An integrated bioinformatics approach for identifying patient-specific gene networks and novel therapeutic targets in glioblastoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 65. doi:10.1158/1538-7445.AM2015-65