AACR poster Yang

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  1. 1. Yang Cong, Ming Zhan, Stephen T. C. Wong NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering; Houston Methodist Research Institute, Weill Cornel Medical College [1] Mani, S. A. et al. The epithelial-mesenchymal transition generates cells with properties of stem cell. Cell 133, 704-15 (2008). [2] Tam, W. L. et al. Protein Kinase C Is a Central Signaling Node and Therapeutic Target for Breast Cancer Stem Cells. Cancer Cell 24, 347-64 (2013). [3] Liu, S. L. et al. Breast Cancer Stem Cells Transition between Epithelial and Mesenchymal States Reflective of their Normal Counterparts. Stem Cell Reports 2, 78-91 (2014). Liu S. L. et al. showed that breast cancer stem cells display plasticity that enables them to transition between epithelial-like (ALDH+) and mesenchymal-like (CD24-CD44+) states [3]. Out computational model evaluates their breast cancer patients gene profiling data and find some quasi-epithelial and quasi-mesenchymal gene expression patterns with high stability scores. This may indicate the existence of intermediate stage in epithelial- mesenchymal (EMT) and mesenchymal-epithelial (MET) transition. ABSTRACT # 3998 Network-based Identification of Gene Signatures of Epithelial-mesenchymal Transition in Breast Cancer Method 1. Transitions in cell phenotypes from epithelial to mesenchymal states, defined as epithelial- mesenchymal transition (EMT) contributes to tumor progression. 2. Induction of EMT in immortalized human mammary epithelial cells has shown to result in expression of stem-cell markers and acquisition of functional stem-cell properties [1]. Background Results Discussion References Acknowledgement This work is supported by NIH grant U54CA149196 and John S. Dunn Research Foundation.. 1. EMT regulatory pathway was built up based on Ingenuity Knowledge Base. 2. Gene expression profiles of breast cancer patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). 3. Based on gene profiling data, we modeled the gene regulatory mechanism of EMT by a stochastic model. 4. An efficient computational algorithm was developed to identify gene signatures. Our computational model identified and evaluates gene expression patterns of epithelial and mesenchymal cells. The results are confirmed with data from Tam W. L. et al[2]..They transduced genes encoding Snail and Slug into immortalized human mammary epithelial cells (HMELs), resulting EMT-TF-induced mesenchymal cells, phase contrast images and qPCR shown in the left.