Drug and disease signature integration identifies synergistic combinations in glioblastoma

• Published in: Nature communications Vol. 9; no. 1; pp. 5315 - 13
• Main Authors: Stathias, Vasileios, Jermakowicz, Anna M., Maloof, Marie E., Forlin, Michele, Walters, Winston, Suter, Robert K., Durante, Michael A., Williams, Sion L., Harbour, J. William, Volmar, Claude-Henry, Lyons, Nicholas J., Wahlestedt, Claes, Graham, Regina M., Ivan, Michael E., Komotar, Ricardo J., Sarkaria, Jann N., Subramanian, Aravind, Golub, Todd R., Schürer, Stephan C., Ayad, Nagi G.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.12.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 13; 38; 38/47; 45; 45/23; 631/114; 631/67/1059; 631/67/69; 64; 64/60; Brain; Brain cancer; Brain tumors; Cancer; Cell Line, Tumor; Cell Proliferation - drug effects; Cellular communication; Computational Biology - methods; Datasets as Topic; Discordance; Drug Combinations; Drug Discovery - methods; Drug Screening Assays, Antitumor; Drug Synergism; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic - drug effects; Gene Library; Gene Regulatory Networks; Genomes; Glioblastoma; Glioblastoma - drug therapy; Glioblastoma - genetics; Humanities and Social Sciences; Humans; multidisciplinary; Multigene Family; Patients; Science; Science (multidisciplinary); Signatures; Transcription; Transcriptome - drug effects; Treatment Outcome; United States; United States Food and Drug Administration - standards; United States
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-07659-z

Glioblastoma (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. Inherent or acquired resistance to treatment of glioblastoma (GBM) is nearly universal. Here, the authors introduce a platform to identify synergistic drug combinations for patient-specific treatment of GBM based on gene expression signatures and small molecule perturbation-response profiles.