Drug and disease signature integration identifies synergistic combinations in glioblastoma

• DOI: 10.1038/s41467-018-07659-z
• Publication Date: 2018-12-10T12:17:42Z
• Authors: Vasileios Stathias; Anna M. Jermakowicz; Marie E. Maloof; Michele Forlin; Winston Walters; Robert K. Suter; Michael A. Durante; Sion L. Williams; J. William Harbour; Claude-Henry Volmar; Nicholas J. Lyons; Claes Wahlestedt; Regina M. Graham; Michael E. Ivan; Ricardo J. Komotar; Jann N. Sarkaria; Aravind Subramanian; Todd R. Golub; Stephan C. Schürer; Nagi G. Ayad
• Keywords: 0301 basic medicine; Science; Datasets as Topic; Article; 03 medical and health sciences; Cell Line, Tumor; Drug Discovery; Humans; Gene Regulatory Networks; Cell Proliferation; Gene Library; United States Food and Drug Administration; Gene Expression Profiling; Q; Computational Biology; Drug Synergism; United States; 3. Good health; Gene Expression Regulation, Neoplastic; Drug Combinations; Treatment Outcome; Multigene Family; Drug Screening Assays, Antitumor; Glioblastoma; Transcriptome

AbstractGlioblastoma (GBM) is the most common primary adult brain tumor. Despite extensive efforts, the median survival for GBM patients is approximately 14 months. GBM therapy could benefit greatly from patient-specific targeted therapies that maximize treatment efficacy. Here we report a platform termed SynergySeq to identify drug combinations for the treatment of GBM by integrating information from The Cancer Genome Atlas (TCGA) and the Library of Integrated Network-Based Cellular Signatures (LINCS). We identify differentially expressed genes in GBM samples and devise a consensus gene expression signature for each compound using LINCS L1000 transcriptional profiling data. The SynergySeq platform computes disease discordance and drug concordance to identify combinations of FDA-approved drugs that induce a synergistic response in GBM. Collectively, our studies demonstrate that combining disease-specific gene expression signatures with LINCS small molecule perturbagen-response signatures can identify preclinical combinations for GBM, which can potentially be tested in humans.