For most anti-cancer drugs, relatively little is known about the detailed mechanism of action. Even where targets have been defined, as with FDA-approved and in-clinical-trial drugs, broader off-target effects remain poorly understood. Cancer is a disease that emerges though genetic and epigenetic alterations that perturb molecular networks controlling cell growth, survival, and differentiation. To develop more targeted and efficacious cancer treatments, it is essential to situate and understand drug actions in this networked, systems-level context. We have combined state-of-the-art techniques to organize our large drug compound database (20,602 compounds) into a network of coherent clusters of compounds sharing similar response profiles over the NCI-60 cancer cell lines. The resulting network is highly concordant with the existing understanding of compound class relationships, grouping known mechanism of action drugs into coherent clusters, together with novel compounds sharing similar response profiles. At the same time, the drug cluster network reveals numerous clusters of response profile-related drugs with little or no relation to clusters enriched for known mechanism of action drugs. These drug compound clusters may represent agents that are active against novel targets and pathways. To characterize these potential target pathways, we will applied two complementary analysis approaches to representative 'hub'compounds from each cluster. First, the activity profile of each hub compound will be correlated with molecular profiling data associated with the NCI-60 cell lines (transcript expression, gene copy number, and gene sequence variants), followed by a pathway enrichment analysis for genes with significantly correlated molecular profiles. Second, the elastic net regression algorithm (a machine learning approach) will be applied to learn robust, multifactorial predictors of drug response using the aforementioned molecular profiling data. We will establish the suitability of these approaches by presenting several 'positive control'results based on clusters enriched for known kinase inhibitors and DNA damaging drugs. From this foundation, we will present predicted target pathways and response-related gene sets for several entirely uncharacterized compound clusters. These results will be additionally focused using compound structure-based methods to characterize drug cluster target specificity. Integrating drug structure, activity and molecular profiling data over the widely studied NCI-60 cancer cell lines, we will be able to organize a large drug compound database into functionally related groups, providing a foundational resource for further, focused studies.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Scientific Cores Intramural Research (ZIC)
Project #
Application #
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
National Cancer Institute Division of Basic Sciences
Zip Code
Reinhold, William C; Varma, Sudhir; Sousa, Fabricio et al. (2014) NCI-60 whole exome sequencing and pharmacological CellMiner analyses. PLoS One 9:e101670