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

Overview of attention for article published in BMC Genomics, October 2016
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Title
Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer
Published in
BMC Genomics, October 2016
DOI 10.1186/s12864-016-3149-5
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Student > Bachelor 3 10%
Researcher 3 10%
Student > Master 3 10%
Student > Doctoral Student 2 7%
Other 6 21%
Unknown 6 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 21%
Medicine and Dentistry 5 17%
Biochemistry, Genetics and Molecular Biology 4 14%
Computer Science 3 10%
Neuroscience 3 10%
Other 2 7%
Unknown 6 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 October 2016.
All research outputs
#20,349,664
of 22,896,955 outputs
Outputs from BMC Genomics
#9,295
of 10,673 outputs
Outputs of similar age
#273,131
of 315,882 outputs
Outputs of similar age from BMC Genomics
#183
of 232 outputs
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