Drug and disease signature integration identifies synergistic combinations in glioblastoma

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

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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.

Original languageEnglish (US)
Article number5315
JournalNature communications
Issue number1
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy


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