Drug and disease signature integration identifies synergistic combinations in glioblastoma.

Abstract:

: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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Stathias V,Jermakowicz AM,Maloof ME,Forlin M,Walters W,Suter RK,Durante MA,Williams SL,Harbour JW,Volmar CH,Lyons NJ,Wahlestedt C,Graham RM,Ivan ME,Komotar RJ,Sarkaria JN,Subramanian A,Golub TR,Schürer SC,Ayad NG

doi

10.1038/s41467-018-07659-z

subject

Has Abstract

pub_date

2018-12-14 00:00:00

pages

5315

issue

1

issn

2041-1723

pii

10.1038/s41467-018-07659-z

journal_volume

9

pub_type

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