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Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer

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. Unfortunatel...

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Autores principales: Raghavan, Rama, Hyter, Stephen, Pathak, Harsh B., Godwin, Andrew K., Konecny, Gottfried, Wang, Chen, Goode, Ellen L., Fridley, Brooke L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069875/
https://www.ncbi.nlm.nih.gov/pubmed/27756228
http://dx.doi.org/10.1186/s12864-016-3149-5
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author Raghavan, Rama
Hyter, Stephen
Pathak, Harsh B.
Godwin, Andrew K.
Konecny, Gottfried
Wang, Chen
Goode, Ellen L.
Fridley, Brooke L.
author_facet Raghavan, Rama
Hyter, Stephen
Pathak, Harsh B.
Godwin, Andrew K.
Konecny, Gottfried
Wang, Chen
Goode, Ellen L.
Fridley, Brooke L.
author_sort Raghavan, Rama
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-50698752016-10-24 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. BMC Genomics Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-19 /pmc/articles/PMC5069875/ /pubmed/27756228 http://dx.doi.org/10.1186/s12864-016-3149-5 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
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
title Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer
title_full Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer
title_fullStr Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer
title_full_unstemmed Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer
title_short Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer
title_sort drug discovery using clinical outcome-based connectivity mapping: application to ovarian cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069875/
https://www.ncbi.nlm.nih.gov/pubmed/27756228
http://dx.doi.org/10.1186/s12864-016-3149-5
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