Abstract P4-08-02: Integration of transcriptomic, proteomic and drug response data in triple negative breast cancer cell lines and PDX models

Abstract

Abstract Drug response screens on panels of cell lines aimed at identifying markers of sensitivity or resistance have been limited in their successes. Unfortunately, the recent release of many such studies has been accompanied by concerns surrounding reproducibility. Since then, several publications have addressed these concerns by pointing out sources of variability and by suggesting better experimental methods as well as more robust analytical approaches. In the presented profiling effort, we integrated the latest advances in drug response measurement and focused on data diversity and quality rather than on breadth. We selected 32 breast cancer cell lines with a strong bias towards triple negative lines as well as 4 cell lines established from relevant patient-derived xenografts. We used high content microscopy to assay the phenotypic responses of the cell lines to a panel of 34 drugs made up largely of kinase inhibitors currently in the clinic along with some standard of care chemotherapeutics. The microscopy based drug response assay allowed us to measure drug potency, and to quantify the efficacy of the drugs in terms of growth inhibition and cell death. For the same cell lines, we used RNAseq to measure basal mRNA expression levels and shotgun mass spectrometry to measure endogenous protein levels. The completeness and controlled conditions under which these data sets were collected provide confidence in their integration. The complementarity of these multi-omics data has allowed us to address questions about the landscape of, particularly triple negative, breast cancer cell lines. Such questions include: where do the patient-derived lines lay among the established cell lines? and how different are the landscapes defined by drug response phenotypes, mRNA expression, and protein levels? We used network-based algorithms to identify eigenstates of signaling pathways related to genomic events, and further explored these states in the TCGA data. At the level of drug response, we have focused on important questions related to the clinical use of kinase inhibitors. In particular, we compared various CDK inhibitors in an effort to identify markers that are informative of response potency and efficacy. We have also looked at variability of the responses of the cell lines studied to multiple PI3K inhibitors that either target specific isoforms or all isoforms. Overall the data set that has been collected is a valuable resource for understanding drug response in triple negative breast cancer, and the transcriptomic and proteomic factors that influence it. Citation Format: Mills CE, Hafner MA, Sorger PK. Integration of transcriptomic, proteomic and drug response data in triple negative breast cancer cell lines and PDX models [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-08-02.