Drug discovery using clinical outcome-based Connectivity Mapping

Application to ovarian cancer

Rama Raghavan, Stephen Hyter, Harsh B. Pathak, Andrew K. Godwin, Gottfried Konecny, Chen Wang, Ellen L Goode, Brooke L. Fridley

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Background: Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients. Methods: We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature "matches" the "reference" signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines. Results: Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability. Conclusions: Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies.

Original languageEnglish (US)
Article number811
JournalBMC Genomics
Volume17
Issue number1
DOIs
StatePublished - Oct 19 2016

Fingerprint

Drug Discovery
Ovarian Neoplasms
Neoplasms
Pharmaceutical Preparations
tanespimycin
Podophyllotoxin
Recurrence
Cell Line
Mitoxantrone
Preclinical Drug Evaluations
Validation Studies
Carboplatin
Paclitaxel
Combination Drug Therapy
Computational Biology
Early Detection of Cancer
Transcriptome
Computer Simulation
Doxorubicin
Genes

Keywords

  • Bioinformatics
  • Connectivity Mapping
  • Drug discovery
  • Gene expression signature
  • Ovarian cancer
  • Time to recurrence

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

Raghavan, R., Hyter, S., Pathak, H. B., Godwin, A. K., Konecny, G., Wang, C., ... Fridley, B. L. (2016). Drug discovery using clinical outcome-based Connectivity Mapping: Application to ovarian cancer. BMC Genomics, 17(1), [811]. https://doi.org/10.1186/s12864-016-3149-5

Drug discovery using clinical outcome-based Connectivity Mapping : Application to ovarian cancer. / Raghavan, Rama; Hyter, Stephen; Pathak, Harsh B.; Godwin, Andrew K.; Konecny, Gottfried; Wang, Chen; Goode, Ellen L; Fridley, Brooke L.

In: BMC Genomics, Vol. 17, No. 1, 811, 19.10.2016.

Research output: Contribution to journalArticle

Raghavan, Rama ; Hyter, Stephen ; Pathak, Harsh B. ; Godwin, Andrew K. ; Konecny, Gottfried ; Wang, Chen ; Goode, Ellen L ; Fridley, Brooke L. / Drug discovery using clinical outcome-based Connectivity Mapping : Application to ovarian cancer. In: BMC Genomics. 2016 ; Vol. 17, No. 1.
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AB - Background: Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients. Methods: We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature "matches" the "reference" signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines. Results: Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability. Conclusions: Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies.

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