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

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.

MiR-363 inhibits cisplatin chemoresistance of epithelial ovarian cancer by regulating snail-induced epithelial-mesenchymal transition

“…Epithelial ovarian cancer (EOC) is the most common type and deadly form of high-grade serous OC ( 2 ). The standard treatment for EOC is debulking surgery, followed by platinum-based chemotherapy ( 3 ). Cisplatin, a platinum compound, is one of the most common first-line antitumour agents, which bind to and cross-link DNA in cancer cells ( 4 , 5 ).…”

Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma

“…Over 7000 expression profiles corresponding to 1309 compounds are publicly available from the online CMap database (build02). The microarray data for each of the compounds was downloaded and the DEGs were calculated by comparing treatment and control groups . A fold change of > 2 or < 0.5 was used to identify the DEGs, which were subsequently subjected to KEGG pathway analysis using the procedure followed in a previous report …”

“…The Connectivity Map (CMap; http://www.broadinstitute.org/cmap/) is a resource that uses transcriptional expression data to probe relationships between diseases, cell physiology, and therapeutics. CMap seeks to uncover novel treatments for a variety of diseases, including cancers, and neurological and infectious diseases …”

“…The combination of the enrichment inquiry of crucial cancer‐related genes from TCGA and the CMap analysis indicates that signaling genes may be prospective drug targets for therapy in certain tumors such as ovarian cancer and hepatocellular carcinoma . In the current study, we performed an in silico investigation using CMap analysis of the DEGs gathered from a large population of LUAD patients ( n = 483).…”

Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning

“…Although the gene expression-based high-throughput approach has the potential to transform biomedicine and accelerate drug discovery ( Iorio et al, 2009 ; Wen et al, 2015 ; Gillet et al, 2016 ; Raghavan et al, 2016 ), the usual workflow relies heavily on signatures of differentially expressed genes and, as mentioned above, differential expression profiles may be prone to poor reproducibility. On the other hand, network-based approaches provide an enriched biological rationale by contextualizing pathologically altered molecular nodes into a systemic functional scenario, possibly enhancing the robustness of drug predictions ( Zickenrott et al, 2016 ).…”