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.