yang poster 2015

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Prediction of Gene Signatures in TGFB-induced Breast Cancer Metastasis by Markov Chain Model Yang Cong, Shenyi Chen, Stephen T.C. Wong Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, 6670 Berter Ave, Houston, TX 77030 Abstract Introduction TGFB-EMT pathway CDH1 transcription factor selection Expression Signature Prediction Results Acknowledgements NCI Center for Modeling Cancer Development U54 Ca149196 Metastasis is a major cause of patient death in breast cancer. Epithelial-mesenchymal transition (EMT) plays the initiation role in the metastasis cascade. Based on gene profiling data of 1165 breast cancer patients in TCGA, we built a statistical Markov Chain model to predict gene signatures of epithelial and mesenchymal cells. Based on the literature integrated TGFB-induced EMT pathway, we extracted the regulatory mechanism among molecules. We used a recursive algorithm to predict the likelihood of expression signatures and further mapped the prediction with known epithelial and mesenchymal expression patterns. A molecule is regulated by its regulators, and the change of its expression level only depends on the observed expression of all its regulators The loss of the epithelial adhesion molecule E-cadherin (CDH1) is considered as a fundamental event in EMT and an early step in cancer metastasis. EMT can be induced by several growth factors, such as TGFB1, hepatocyte growth factor, and platelet-derived growth factor PDGF. Also EMT can be induced by any one of the following transcription factors alone, such as SNAI1, SNAI2, TWIST1, FOXC1, FOXC2, ZEB1 and ZEB2. A central target of these transcription factors listed above is the repression of E-cadherin gene CDH1. The expression level of CDH1 plays a key role in the cell phenotype. The inhibition of CDH1 expression is the starter of EMT process. Epithelial cells have a high expression level of CDH1, meanwhile mesenchymal cells have a low expression. Hence, we map the expression patterns with cell types based on the expression level of CDH1. Data Processing

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Page 1: Yang Poster 2015

Prediction of Gene Signatures in TGFB-induced Breast Cancer Metastasis by Markov Chain Model

Yang Cong, Shenyi Chen, Stephen T.C. WongDepartment of Systems Medicine and Bioengineering, Houston Methodist Research Institute, 6670 Berter Ave, Houston, TX 77030

Abstract

Introduction

TGFB-EMT pathway

CDH1 transcription factor selection

Expression Signature Prediction

Results

Acknowledgements•NCI Center for Modeling Cancer Development U54 Ca149196

Metastasis is a major cause of patient death in breast cancer. Epithelial-mesenchymal transition (EMT) plays the initiation role in the metastasis cascade. Based on gene profiling data of 1165 breast cancer patients in TCGA, we built a statistical Markov Chain model to predict gene signatures of epithelial and mesenchymal cells. Based on the literature integrated TGFB-induced EMT pathway, we extracted the regulatory mechanism among molecules. We used a recursive algorithm to predict the likelihood of expression signatures and further mapped the prediction with known epithelial and mesenchymal expression patterns.

A molecule is regulated by its regulators, and the change of its expression level only depends on the observed expression of all its regulators

The loss of the epithelial adhesion molecule E-cadherin(CDH1) is considered as a fundamental event in EMT and an early step in cancer metastasis. EMT can be induced by several growth factors, such as TGFB1, hepatocyte growth factor, and platelet-derived growth factor PDGF. Also EMT can be induced by any one of the following transcription factors alone, such as SNAI1, SNAI2, TWIST1, FOXC1, FOXC2, ZEB1 and ZEB2. A central target of these transcription factors listed above is the repression of E-cadherin gene CDH1. The expression level of CDH1 plays a key role in the cell phenotype. The inhibition of CDH1 expression is the starter of EMT process. Epithelial cells have a high expression level of CDH1, meanwhile mesenchymal cells have a low expression. Hence, we map the expression patterns with cell types based on the expression level of CDH1.

Data Processing