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

doi:10.1038/s41467-018-07659-z

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 journal › Article › peer-review

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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 language	English (US)
Article number	5315
Journal	Nature communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-07659-z
State	Published - Dec 1 2018
Externally published	Yes

ASJC Scopus subject areas

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

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10.1038/s41467-018-07659-z

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Stathias, V., Jermakowicz, A. M., Maloof, M. E., Forlin, M., Walters, W., Suter, R. K., Durante, M. A., Williams, S. L., Harbour, J. W., Volmar, C. H., Lyons, N. J., Wahlestedt, C., Graham, R. M., Ivan, M. E., Komotar, R. J., Sarkaria, J. N., Subramanian, A., Golub, T. R., Schürer, S. C., & Ayad, N. G. (2018). Drug and disease signature integration identifies synergistic combinations in glioblastoma. Nature communications, 9(1), Article 5315. https://doi.org/10.1038/s41467-018-07659-z

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

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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.}",

note = "Funding Information: We acknowledge funding from the NIH via grants R56102590 to N.G.A., U54CA189205 and U24TR002278 (Illuminating the Druggable Genome Knowledge Management Center, IDG-KMC, and Resource Dissemination and Outreach Center, IDG-RDOC) and U54HL127624 (BD2K LINCS Data Coordination and Integration Center, DCIC) to S.C. S. The Illuminating the Druggable Genome (IDG), Library of integrated Network-based Cellular Signatures (LINCS) and Big Data to Knowledge (BD2K) programs are funded by the trans-NIH Common Fund. IDG-KMC, IDG-RDOC and BD2K-LINCS DCIC are administered by the NCI, NCATS, and NHLBI, respectively. A.S. and T.R.G. acknowledge support from U54HG006093. R.M.G. acknowledges funding support from the Mystic Force Foundation. The Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource is supported by Mayo Clinic, the Mayo SPORE in Brain Cancer CA108961and NIH grant R24 NS92940. We gratefully acknowledge single cell library preparation and next generation sequencing services provide by the Onco-Genomics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami. SLW is supported by R21CA216227. Publisher Copyright: {\textcopyright} 2018, The Author(s).",

year = "2018",

month = dec,

day = "1",

doi = "10.1038/s41467-018-07659-z",

language = "English (US)",

volume = "9",

journal = "Nature communications",

issn = "2041-1723",

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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.

N1 - Funding Information: We acknowledge funding from the NIH via grants R56102590 to N.G.A., U54CA189205 and U24TR002278 (Illuminating the Druggable Genome Knowledge Management Center, IDG-KMC, and Resource Dissemination and Outreach Center, IDG-RDOC) and U54HL127624 (BD2K LINCS Data Coordination and Integration Center, DCIC) to S.C. S. The Illuminating the Druggable Genome (IDG), Library of integrated Network-based Cellular Signatures (LINCS) and Big Data to Knowledge (BD2K) programs are funded by the trans-NIH Common Fund. IDG-KMC, IDG-RDOC and BD2K-LINCS DCIC are administered by the NCI, NCATS, and NHLBI, respectively. A.S. and T.R.G. acknowledge support from U54HG006093. R.M.G. acknowledges funding support from the Mystic Force Foundation. The Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource is supported by Mayo Clinic, the Mayo SPORE in Brain Cancer CA108961and NIH grant R24 NS92940. We gratefully acknowledge single cell library preparation and next generation sequencing services provide by the Onco-Genomics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami. SLW is supported by R21CA216227. Publisher Copyright: © 2018, The Author(s).

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.

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Drug and disease signature integration identifies synergistic combinations in glioblastoma

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