Abstract 4184: Drug set enrichment analysis : A computational approach to identify functional drug sets (2014)

Abstract Most drug-testing approaches published so far focus on identifying a single drug that shows favorable response and is associated with a known cancer biomarker such as the drug Imatinib in BCR-ABL gene fusion positive cells. We developed and applied drug set enrichment analysis (DSEA) to find enriched patterns or statistically significant similarities (overlaps) between the drug responses of a test sample against a cohort of 182 previously screened cancer samples. The samples studied included established (ATCC) cancer cell lines, drug-resistant cancer cell models, ex-vivo patient cancer cells in primary cultures, including conditionally reprogrammed cancer cells from patients. DSEA is adopting Gene Set Enrichment Analysis statistics commonly used for gene expression analysis to high throughput drug testing data. Our drug screening (Pemovska et al., Cancer Discovery, 2013) was done with a panel of 306 established (FDA-approved) and emerging targeted cancer drugs such as tyrosine-kinase inhibitors (e.g. EGFR, PDGFR, BRAF, MET), S/T-type inhibitors, (e.g. MEK, Plk1, Akt, Aurora, Chk1), and inhibitors of other pathways (HDACs, Hh, BCL2, PI3K, PARP) and many others. The readout was based on viability of cells after a 72 hour culture. The DSEA approach is based on taking the top most sensitive drugs (above a defined sensitivity score cut-off) in an individual cancer sample and then identifying overlapping drug response profiles in previously screened reference samples. Our hypothesis is that the most sensitive drug sets in any given sample tend to show similar response profiles in a cohort of similar samples. We convey the correlations and drug set enrichment analysis results as dendrogram trees, plots and tables with enrichment and significance scores. Interestingly, our results show that clustering of drug sensitivity testing data does not place all cancer cell line samples within well-established subgroups based on biological features or histological origin. We find a similar tendency in ex vivo patient samples. Therefore, comprehensive drug response profiles seen may reveal novel biological data that reflect pharmacologically-relevant, phenotypic cancer cell states. DSEA could also provide novel means to subtype previously poorly characterized cancer samples based on their drug response profiles and thereby in the future facilitate the choice of therapies to patients whose cancers repond in an atypical way as compared to the expectations based on anatomical origin or genomic composition. Citation Format: John Patrick Mpindi, Dimitry Bychkov, Yadav Bhagwan, Disha Malani, Hirasawa Akira, Khalid Saeed, Susanne Hultsch, Sara Kangaspeska, Astrid Murumägi, Caroline A Heckman, Kimmo Porkka, Tero Aittokallio, Krister Wennerberg, Päivi Östling, Olli Kallioniemi. Drug set enrichment analysis : A computational approach to identify functional drug sets. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4184. doi:10.1158/1538-7445.AM2014-4184