Abstract 1520: Identifying differential dependency networks accounting for response to NEDD8-inhibitor in large-scale cancer cell line data
Authors: Gil Speyer, Harshil Dhruv, Jeff Kiefer, Stuart Schreiber, Paul Clemons, Michael E. Berens, Seungchan Kim
Journal: Cancer Research (2016)

Abstract The Cancer Cell Line Encyclopedia (CCLE) houses molecular profiles of ∼800 human long term cell lines spanning several different histological types of cancer. In addition, Cancer Therapeutics Response Portal (CTRP) provides drug response measurements for 481 small molecules. Integration of these data enables investigation of the molecular correlates of drug response (sensitivity and resistance). In this current effort, we studied the NEDDylation small molecule inhibitor, MLN4924, in the context of genomic data to uncover novel mechanistic correlates of drug response across the panel of cell lines. We recently reported (Jung and Kim 2016 NAR) development of a robust computational method that shows promise to identify novel insights when applied to multi-dimensional data sets as outlined above. The Evaluation of Differential Dependency (EDDY) employs Bayesian networks to represent statistically distinct differences in relationships between genes within a specific biological pathway as queried between two conditions, in this instance, cell lines that are sensitive and those that are non-sensitive to MLN4924. While EDDY has been successfully employed in the analysis of specific diseases such as TCGA adrenocortical carcinoma, its statistical rigor incurred a prohibitive computational load to assess conditional differences across larger datasets. Recent computational enhancements to EDDY enable processing of larger datasets in reasonable time while maintaining sensitivity. The capability of analyzing broader pan-cancer datasets such as CCLE has enabled EDDY to become more capable in identifying general trends across disease subtypes. Specifically, we demonstrate the enhanced EDDY in analysis of MLN4924 response across the CCLE data set combined with CTRP data set. Initial outcomes from EDDY point to both anticipated and unanticipated biological determinants of response. For example, it is noted that specific oncogenic pathways, such as those centered on PIK3CA, appear to show differential dependencies in the sensitive and non-sensitive cell lines. We also observe genes and candidate pathways related to apoptotic mechanisms that may reveal mechanistic insights to predicting drug response. Specifically, genes and pathways associated with certain apoptotic mechanisms around mitochondrial proteins and glutathione peroxidase may serve as unique determinants of drug response. Multidimensional data analyzed by EDDY uncovers candidate mechanisms of vulnerability to specific small molecule inhibitors, which may guide development of predictive models for treatment planning when using agents with highly context-dependent efficacies. Supported by NIH U01CA168397 Citation Format: Gil Speyer, Harshil Dhruv, Jeff Kiefer, Stuart Schreiber, Paul Clemons, Michael E. Berens, Seungchan Kim. Identifying differential dependency networks accounting for response to NEDD8-inhibitor in large-scale cancer cell line data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1520.