Research Papers:

Developing a genetic signature to predict drug response in ovarian cancer

Stephen Hyter, Jeff Hirst, Harsh Pathak, Ziyan Y. Pessetto, Devin C. Koestler, Rama Raghavan, Dong Pei and Andrew K. Godwin _

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Oncotarget. 2018; 9:14828-14848. https://doi.org/10.18632/oncotarget.23663

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Stephen Hyter1, Jeff Hirst1, Harsh Pathak1,2, Ziyan Y. Pessetto1, Devin C. Koestler2,3, Rama Raghavan3, Dong Pei2,3 and Andrew K. Godwin1,2

1Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, USA

2University of Kansas Cancer Center, University of Kansas Medical Center, Kansas City, KS, USA

3Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA

Correspondence to:

Andrew K. Godwin, email: [email protected]

Keywords: auranofin; AUY922; ovarian; gene signature; TCGA

Received: September 01, 2017    Accepted: December 13, 2017    Epub: December 26, 2017    Published: March 13, 2018


There is a lack of personalized treatment options for women with recurrent platinum-resistant ovarian cancer. Outside of bevacizumab and a group of poly ADP-ribose polymerase inhibitors, few options are available to women that relapse. We propose that efficacious drug combinations can be determined via molecular characterization of ovarian tumors along with pre-established pharmacogenomic profiles of repurposed compounds. To that end, we selectively performed multiple two-drug combination treatments in ovarian cancer cell lines that included reactive oxygen species inducers and HSP90 inhibitors. This allowed us to select cell lines that exhibit disparate phenotypes of proliferative inhibition to a specific drug combination of auranofin and AUY922. We profiled altered mechanistic responses from these agents in both reactive oxygen species and HSP90 pathways, as well as investigated PRKCI and lncRNA expression in ovarian cancer cell line models. Generation of dual multi-gene panels implicated in resistance or sensitivity to this drug combination was produced using RNA sequencing data and the validity of the resistant signature was examined using high-density RT-qPCR. Finally, data mining for the prevalence of these signatures in a large-scale clinical study alluded to the prevalence of resistant genes in ovarian tumor biology. Our results demonstrate that high-throughput viability screens paired with reliable in silico data can promote the discovery of effective, personalized therapeutic options for a currently untreatable disease.

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