Abstract
BackgroundEpithelial 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.MethodsWe 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.ResultsOf 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.ConclusionsFuture 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 materialThe online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users.
Highlights
Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths)
The dose response data for all 11 drugs across the 10 EOC cell lines can be found in Additional file 3: Figure S3, Additional file 4: Figure S4, Additional file 5: Figure S5, Additional file 6: Figure S6, Additional file 7: Figure S7, Additional file 8: Figure S8, Additional file 9: Figure S9, Additional file 10: Figure S10, Additional file 11: Figure S11, Additional file 12: Figure S12 and Additional file 13: Figure S13. In this manuscript we have presented a bioinformatics approach for connectivity mapping based on clinical outcomes collected on a large patient population followed by functional validation of the identified drugs
The strengths of this approach are: non-reliance on a signature determined based on a small number of cancer cell lines; use clinically relevant outcomes that are directly tied to response; large clinical studies that provide the ability to look at overlap between Connectivity Mapping (CMAP) determined drugs; functional validation to confirm the ability of the discovered drugs to kill ovarian cancer cells
Summary
Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The Connectivity Map ( known as “CMAP”) is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules analyzed using pattern-matching algorithms that discover relationships between the drugs, gene expression changes, and the phenotypes. This computational approach has greatly facilitated drug screening studies, as CMAP contains more than 7000 gene expression profiles for approximately 1300 compounds (https://www.broadinstitute.org/ cmap/). The candidate drugs are those with the highest absolute scores
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