Dataset Information

Identifying inhibitors of epithelial-mesenchymal transition by connectivity map-based systems approach.

ABSTRACT:

Background

Acquisition of mesenchymal phenotype by epithelial cells by means of epithelial-mesenchymal transition (EMT) is considered as an early event in the multistep process of tumor metastasis. Therefore, inhibition of EMT might be a rational strategy to prevent metastasis.

Methods

Using the global gene expression profile from a cell culture model of transforming growth factor-? (TGF-?)-induced EMT, we identified potential EMT inhibitors. We used a publicly available database (www.broad.mit.edu/cmap) comprising gene expression profiles obtained from multiple different cell lines in response to various drugs to derive negative correlations to EMT gene expression profile using Connectivity Map, a pattern matching tool.

Results

Experimental validation of the identified compounds showed rapamycin as a novel inhibitor of TGF-? signaling along with 17-AAG, a known modulator of TGF-? pathway. Both of these compounds completely blocked EMT and the associated migratory and invasive phenotype. The other identified compound, LY294002, demonstrated a selective inhibition of mesenchymal markers, cell migration and invasion, without affecting the loss of E-cadherin expression or Smad phosphorylation.

Conclusions

Our data reveal that rapamycin is a novel modulator of TGF-? signaling, and along with 17-AAG and LY294002, could be used as therapeutic agent for inhibiting EMT. This study demonstrates the potential of a systems approach in identifying novel modulators of a complex biological process.

SUBMITTER: Reka AK

PROVIDER: S-EPMC3196823 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Publications

Identifying inhibitors of epithelial-mesenchymal transition by connectivity map-based systems approach.

Reka Ajaya Kumar AK Kuick Rork R Kurapati Himabindu H Standiford Theodore J TJ Omenn Gilbert S GS Keshamouni Venkateshwar G VG

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 20111101 11

<h4>Background</h4>Acquisition of mesenchymal phenotype by epithelial cells by means of epithelial-mesenchymal transition (EMT) is considered as an early event in the multistep process of tumor metastasis. Therefore, inhibition of EMT might be a rational strategy to prevent metastasis.<h4>Methods</h4>Using the global gene expression profile from a cell culture model of transforming growth factor-β (TGF-β)-induced EMT, we identified potential EMT inhibitors. We used a publicly available database ...[more]

PMID: 21964532

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Project description:Several epithelial-mesenchymal transition (EMT)-associated genes (EAGs) have been confirmed to correlate with the prognosis of hepatocellular carcinoma (HCC) patients. Herein, we explored the value of EAGs in the prognosis of HCC relying on data from The Cancer Genome Atlas (TCGA) database. A total of 200 EMT-associated genes were downloaded from the Gene set enrichment analysis (GSEA) website. Moreover, 96 differentially expressed EAGs were identified. Using Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we forecasted the potential molecular mechanisms of EAGs. To identify prognostic EAGs, Cox regression was used in developing a prognostic risk model. Then, the Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the prognostic significance of the model. A total of 5 prognostic correlated EAGs (P3H1, SPP1, MMP1, LGALS1, and ITGB5) were screened via Cox regression, which provided the basis for developing a novel prognostic risk model. Based on the risk model, patients were subdivided into high-risk and low-risk groups. The overall survival of the low-risk group was better compared to the high-risk group (P < 0.00001). The ROC curve of the risk model showed a higher AUC (Area under Curve) (AUC = 0.723) compared to other clinical features (AUC ≤ 0.511). A nomogram based on this model was constructed to predict the 1-year, 2-year, and 3-year overall survival rates (OS) of patients. Conclusively, we developed a novel HCC prognostic risk model based on the expression of EAGs, which help advance the prognostic management of HCC patients. Abbreviations: HCC: hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; EMT: epithelial-mesenchymal transition; EAGs: EMT-associated genes; GSEA: gene set enrichment analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; TF: transcription factor; ROC: receiver operating characteristic; K-M: Kaplan-Meier; AUC: the area under the ROC curve; FDR: false discovery rate; TNM: Tumor size/lymph nodes/distance metastasis.

Project description:Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, which is composed of transitions from an epithelial state to intermediate or partial EMT state(s) to a mesenchymal state. Using computational models to predict cell state transitions in a specific experiment is inherently difficult for reasons including model parameter uncertainty and error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGFβ-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept "synthetic" in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGFβ doses associated with the state transitions and reproduces experimentally observed variability in cell state by either shifting a single parameter or generating "populations" of model parameters. We performed synthetic experiments for a wide range of TGFβ doses, investigating different cell steady-state conditions, and conducted parameter studies varying properties of the data-assimilation approach including the time interval between observations and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed and the future cell state predicted in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates the feasibility and utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.

Dataset Information

Identifying inhibitors of epithelial-mesenchymal transition by connectivity map-based systems approach.

Background

Methods

Results

Conclusions

Publications

Identifying inhibitors of epithelial-mesenchymal transition by connectivity map-based systems approach.

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