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 journalArticle

4 Citations (Scopus)

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.

Original languageEnglish (US)
Article number5315
JournalNature Communications
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2018

Fingerprint

Glioblastoma
drugs
signatures
Gene expression
Genes
Pharmaceutical Preparations
gene expression
Drug Combinations
Tumors
therapy
Transcriptome
Brain
Libraries
platforms
Molecules
genome
Genomic Library
Atlases
genes
brain

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Cite this

Stathias, V., Jermakowicz, A. M., Maloof, M. E., Forlin, M., Walters, W., Suter, R. K., ... Ayad, N. G. (2018). Drug and disease signature integration identifies synergistic combinations in glioblastoma. Nature Communications, 9(1), [5315]. https://doi.org/10.1038/s41467-018-07659-z

Drug and disease signature integration identifies synergistic combinations in glioblastoma. / 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.

In: Nature Communications, Vol. 9, No. 1, 5315, 01.12.2018.

Research output: Contribution to journalArticle

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 2018, 'Drug and disease signature integration identifies synergistic combinations in glioblastoma', Nature Communications, vol. 9, no. 1, 5315. https://doi.org/10.1038/s41467-018-07659-z
Stathias V, Jermakowicz AM, Maloof ME, Forlin M, Walters W, Suter RK et al. Drug and disease signature integration identifies synergistic combinations in glioblastoma. Nature Communications. 2018 Dec 1;9(1). 5315. https://doi.org/10.1038/s41467-018-07659-z
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. / Drug and disease signature integration identifies synergistic combinations in glioblastoma. In: Nature Communications. 2018 ; Vol. 9, No. 1.
@article{94cc035f504340ac86320f7aabba25f8,
title = "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.",
author = "Vasileios Stathias and Jermakowicz, {Anna M.} and Maloof, {Marie E.} and Michele Forlin and Winston Walters and Suter, {Robert K.} and Durante, {Michael A.} and Williams, {Sion L.} and Harbour, {J. William} and Volmar, {Claude Henry} and Lyons, {Nicholas J.} and Claes Wahlestedt and Graham, {Regina M.} and Ivan, {Michael E.} and Komotar, {Ricardo J.} and Sarkaria, {Jann N} and Aravind Subramanian and Golub, {Todd R.} and Sch{\"u}rer, {Stephan C.} and Ayad, {Nagi G.}",
year = "2018",
month = "12",
day = "1",
doi = "10.1038/s41467-018-07659-z",
language = "English (US)",
volume = "9",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

TY - JOUR

T1 - Drug and disease signature integration identifies synergistic combinations in glioblastoma

AU - Stathias, Vasileios

AU - Jermakowicz, Anna M.

AU - Maloof, Marie E.

AU - Forlin, Michele

AU - Walters, Winston

AU - Suter, Robert K.

AU - Durante, Michael A.

AU - Williams, Sion L.

AU - Harbour, J. William

AU - Volmar, Claude Henry

AU - Lyons, Nicholas J.

AU - Wahlestedt, Claes

AU - Graham, Regina M.

AU - Ivan, Michael E.

AU - Komotar, Ricardo J.

AU - Sarkaria, Jann N

AU - Subramanian, Aravind

AU - Golub, Todd R.

AU - Schürer, Stephan C.

AU - Ayad, Nagi G.

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85058482758&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058482758&partnerID=8YFLogxK

U2 - 10.1038/s41467-018-07659-z

DO - 10.1038/s41467-018-07659-z

M3 - Article

VL - 9

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 5315

ER -