Abstract 416: Identification of therapeutic combinations in glioblastoma using personalized gene expression networks

Abstract

Abstract The goal of our study was to identify patient-specific gene expression networks from Glioblastoma Patient-Derived Xenografts (PDXs) and determine novel therapeutic compound combinations using those networks. Glioblastoma is the most common malignant primary adult brain tumor with a standard of care consisting of maximal surgical resection followed by radiotherapy and adjuvant temozolomide (TMZ) chemotherapy. However, despite medical advances in the field, recurrence is almost universal, suggesting the need for more personalized and targeted therapeutic approaches. For this, we obtained, transcriptional data from Glioblastoma PDXs and used them to identify their respective differentially expressed genes. Patient-specific gene expression networks were then created and their biological relevance was supplemented by integrating them with TCGA Glioblastoma transcriptional data. In order to identify compound combinations specific for those networks, we used the extensive chemical perturbation signatures from the Library of Network-based Cellular Signatures (LINCS). From the large number of L1000 transcriptional data we extracted gene expression signatures that were indicative of specific LINCS compounds. We then compared those signatures to the patient-specific networks in order to prioritize compound combinations that were inducing discordant transcriptional changes in distinct sub-networks of the PDXs transcriptome. The most orthogonal compound combinations were then chosen and used in in-vitro cell viability assays of Glioblastoma PDXs in order to evaluate their effectiveness. The above process can be used to prioritize compound combinations with potential therapeutic effect in a patient-specific manner. Citation Format: Vasileios Stathias, Michele Forlin, Bryce Allen, Stephan Schürer, Nagi G. Ayad. Identification of therapeutic combinations in glioblastoma using personalized gene expression networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 416. doi:10.1158/1538-7445.AM2017-416