Abstract 4853: A bioinformatics approach to drug discovery: patient based connectivity mapping
Authors: Brooke L. Fridley, Rama Raghavan, Gottfried E. Konecny, Chen Wang, Ellen L. Goode, Harsh B. Pathak, Stephen Hyter
Journal: Cancer Research (2015)

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, even with modern chemotherapy, most patients relapse and die with the five-year overall survival around 45%. In addition, those patients that initially respond to taxane-platinum therapy eventually develop platinum-resistant tumors and relapse. Thus, finding novel therapeutics for treating EOC is essential. One approach that has been used widely in cancer drug discovery is Connectivity Mapping (CMAP) using gene expression data and drug phenotype data assessed on cancer cell lines (CCL) (i.e. “sensitive” or “resistant” based on dose-response drug data). However, recent publications have highlighted some issues with the use of these standard CCL sets that are often used in these drug screening studies. Therefore, instead of CMAP analysis based on gene expression signatures developed on drug response data collected on a small set of EOC cell lines, we took a novel CMAP approach based on information collected on EOC patients and clinical endpoints. That is, we determined the gene expression signature (e.g., set of genes) that were associated with time to recurrence in data collected on EOC patients within TCGA (n = 518) and a Mayo Clinic study (n = 474). The set of genes associated with time to recurrence (with and without adjustment for additional clinical covariates) was completed separately for TCGA and Mayo Clinic studies, restricting all analyses to EOC tumor of high-grade serous histology. Each of these 4 sets of genes, where genes with hazard ratios (HR) &amp;gt; 1 coded as “positively” associated and genes with HR &amp;lt; 1 coded as “negatively” associated with the phenotype, were inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature “matches” the “reference” signature. The drugs that overlapped between the CMAP analyses on 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, we a priori hypothesized that 5 of these drugs would kill EOC cells (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG), for which all 5 showed an ability to kill EOC cells in vitro (cell lines were treated using serial dilutions of the drugs (20-0 μM) for 72 h followed by measuring cell viability using the CellTiter-Blue assay). Further research is on-going to understand the functional mechanism by which these candidate drugs might treat EOC and to apply this bioinformatics approach to other cancer drug screening studies. Note: This abstract was not presented at the meeting. Citation Format: Brooke L. Fridley, Rama Raghavan, Gottfried E. Konecny, Chen Wang, Ellen L. Goode, Harsh B. Pathak, Stephen Hyter. A bioinformatics approach to drug discovery: patient based connectivity mapping. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4853. doi:10.1158/1538-7445.AM2015-4853