Abstract 2586: CellMinerCDB: Enabling cross-database exploration of molecular pharmacology data and response determinant discovery in cancer cell lines
Authors: Vinodh N. Rajapakse, Augustin Luna, Chris Sander, William C. Reinhold, Yves Pommier
Journal: Cancer Research (2017)

Abstract Cancer cell line panels are widely used for evaluating drug response across diverse tissue types. A growing set of molecular profiling data complements measurements of chemosensitivity, providing novel avenues for response determinant discovery and clinical translation. Accessing and inter-relating data from different sources is essential for evaluating such determinants, but remains challenging. To enable wider access to cell line pharmacogenomic data, we have developed CellMinerCDB (CellMiner Cross-Database, discover.nci.nih.gov/cellminercdb), a web application integrating data from several widely studied cancer cell line panels, including the NCI-60 (NIH), GDSC (Sanger/MGH), and CCLE/CTRP (Broad). All together, our database spans over 1300 distinct cell lines, 400 clinically relevant cancer drugs, 20,000 experimental compounds, and molecular profiling data, such as gene/protein expression, DNA copy, methylation, and mutational status. Cell line and tested drug overlaps allow cross-database validation of genomic and drug data, and CellMinerCDB simplifies this by transparently matching differently named entities between sources. Data exploration can be additionally restricted to particular tissue types, with individual cell lines annotated to the OncoTree ontology for consistent treatment across sources. A range of analysis tools support interactive data exploration, from 2D plots of drug response and molecular profiling features to exhaustive correlation analyses and multivariate predictive models. We illustrate the power and utility of CellMinerCDB with examples of response determinant discovery and predictive modeling for Top1 and PARP inhibitors. Beginning with established individual determinants, such as SLFN11 mRNA expression, we show how both unbiased and biological knowledge network-based feature selection methods enable iterative refinement of a multivariate genomic signature of drug response. For Top 1 inhibitors, additional predictive features include expression of chromatin remodeling factors and genes modulating apoptosis capacity, while complementary PARP inhibitor response determinants include PARP1 and drug efflux pump expression. Pathway and process-based gene annotations allow biological interpretation of response predictive features. CellMinerCDB also includes ongoing algorithmic work to improve the construction of multivariate predictive models using constraints from biological networks. These approaches bridge a limiting gap in existing methods, which either ignore biological knowledge altogether or are limited to exploration within known pathways and processes. Citation Format: Vinodh N. Rajapakse, Augustin Luna, Chris Sander, William C. Reinhold, Yves Pommier. CellMinerCDB: Enabling cross-database exploration of molecular pharmacology data and response determinant discovery in cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2586. doi:10.1158/1538-7445.AM2017-2586