Abstract 977: Phenotypic analysis of single-cell breast cancer inhibition data reveals insights into EMT
Authors: William S. Chen, Nevena Zivanovic, Dana Pe'er, Bernd Bodenmiller, Smita Krishnaswamy
Journal: Cancer Research (2017)

Abstract Background: A leading model of cancer metastasis is epithelial-to-mesenchymal transition (EMT). We sought to determine whether single-cell inhibition data targeting potential mediators of EMT could uncover mechanistic insights into the EMT process. Methods: EMT was artificially induced on Py2T murine breast cancer cells by TGFb treatment. Additionally, a unique drug inhibitor was added to each well of a multiplexed CyTOF experiment. 37 transcription factors and cell surface markers were measured in each cell to assess epithelial and mesenchymal states, SMAD, AKT, and MAPK signaling activity, cell cycle regulation, and apoptosis pathway activation. The final single-cell dataset consisted of 300 inhibition and control conditions (cell populations), which we aimed to characterize in relation to one another with respect to effect on EMT. Analyzing the similarity between drug inhibitions amounts to a novel type of clustering problem that involves computing the similarity between diverse cell populations generated by each inhibitor. Traditional methods for comparing cell populations are not robust to the intra-population heterogeneity we observed amongst cells undergoing EMT. Thus, we developed Phenotypic Earth Mover’s Distance (PhEMD). This method for comparing cell populations leverages the insight that only a limited number of “cell subtypes” (e.g. mesenchymal, epithelial, transitional) are observed in unperturbed and perturbed EMT. By classifying each cell as one of these distinct subtypes using community-detection based clustering, PhEMD represents an inhibition or control condition as its relative abundance of each cell subtype. It then uses Earth Mover’s Distance (EMD) to compare two relative abundance distributions (i.e. heterogeneous cell populations). PhEMD thus derives a single value representing the dissimilarity between two inhibition conditions. Using PhEMD as measure of dissimilarity between each pair of inhibition and control conditions, we constructed an inhibitor-inhibitor graph and used graph clustering to identify groups of inhibitors that had similar effects to one another. Results: PhEMD analysis revealed that MEK, EGFR and Src inhibitors significantly halted EMT by generating far fewer mesenchymal cells and maintaining a large epithelial cell subpopulation. PhEMD also revealed that different mTOR, PI3K, and Akt inhibitors tended to have similar effects to one another and collectively generated a distinct, transitional cell subpopulation with altered pS6 expression. The pS6 dysregulation may be explained by the fact that ribosomal protein S6 is downstream of PI3K/AKT/mTOR, and the intermediate levels of both E-cadherin and vimentin suggest that this subset of cells may have been halted mid-transition. Finally, several Aurora Kinase and CDK inhibitors resulted in a relatively high percentage of apoptotic cells, suggesting these kinases may be important cell cycle regulators in the context of EMT. Citation Format: William S. Chen, Nevena Zivanovic, Dana Pe'er, Bernd Bodenmiller, Smita Krishnaswamy. Phenotypic analysis of single-cell breast cancer inhibition data reveals insights into EMT [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 977. doi:10.1158/1538-7445.AM2017-977