Abstract LB-247: Querying the RAS genomic network with siRNAs and and flow cytometry: Automatic, multidimensional phenotyping of 135 cancer cell lines by Gaussian mixture fitting and expectation maximization

Abstract

Abstract To discover novel therapeutic modalities and genomic predictors of response, large screen utilizing small molecules or sh/siRNA are performed on increasingly large collections of cancer cell lines. However these screens suffer from two main limitations: 1) the off-target effects of the probes 2) the coarse measurement of the cellular response that cannot distinguish between different outcomes such as proliferation block and apoptosis. Here we profile 50 lung cancer cell lines using highly specific combinations of siRNAs against effector nodes of KRAS, and measured several characteristics of phenotypic response by flow cytometry including viability, level of reactive oxygen species and cell membrane integrity. For each assay [node-cell line], typically 25,000 events were measured. We often observed multi modal distributions following node silencing. We used the expectation maximization algorithm to fit the different cell populations induced by the gene silencing. This allows us to automatically extract the proportion of dying cells, and also provides estimates of the cell growth impairment. Assessment of replicates shows that our results are highly reproducible, provide an accurate estimate of the proportion of dying cells, and reveal the complexity of multi-modal response of KRAS cancer cell lines to perturbation of key signaling nodes. Using the genomic profiling and pharmaceutical screen of the same cell lines, we reveal the association of specific node silencing with 1) the basal genomic state of the cell lines 2) the activity of a panel of drugs with the goal of identifying new targeting modalities and patient selection strategies for KRAS mutant cancers. Citation Format: Arnaud Amzallag, Tina L. Yuan, Rachel Bagni, Ming Yi, Robert Stephens, Sridhar Ramawamy, Frank McCormick, Cyril H. Benes. Querying the RAS genomic network with siRNAs and and flow cytometry: Automatic, multidimensional phenotyping of 135 cancer cell lines by Gaussian mixture fitting and expectation maximization. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-247. doi:10.1158/1538-7445.AM2015-LB-247