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 α 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.