Abstract PR8: Network modeling of epithelial-to-mesenchymal transition in liver cancer metastasis
Authors: Steven N. Steinway, Hien Dang, Wei Ding, Carl B. Rountree, Reka Albert
Journal: Cancer Research (2012)

Abstract Hepatocellular carcinoma (HCC) is the third most common cause of cancer death in the world and its incidence is rising in the United States. Over 90% of cancer deaths occur due to tumor metastasis. Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site and establish distant metastases. Cells undergoing EMT lose cell-cell adhesion properties and change cell morphology in order to migrate into the blood stream. Loss of expression of E-cadherin, a cell adhesion protein, is considered the hallmark of EMT, and molecular analyses have revealed complex signaling pathways regulating E-cadherin expression. The hepatocyte growth factor (HGF)/C-met receptor axis is a major EMT inducer and is dysregulated in 40% of HCC. TGF-beta signaling, another important EMT inducer, is dysregulated in 20% of HCC. To systematically understand signaling components that regulate HCC metastasis, we constructed an EMT network (74 nodes, 143 edges) by integrating the signaling pathways involved in developmental EMT and known dysregulations in metastatic HCC. This network was subsequently translated into a predictive, discrete, dynamic model. Using HGF and TGF-beta1 as our EMT inducers and E-cadherin expression as our EMT marker, we reveal that the EMT process, although robust, appears to be targetable through inhibition of a small subset of critical (EMT “driver”) nodes. Our model suggests the following: 1) A critical eight-node transcriptional network that is downstream of major growth signals is necessary for EMT induction. 2) Cross-talk with TGF-beta and Notch signaling pathways exists and coordinated inhibition of both pathways will inhibit HGF-induced EMT. 3) Of the 135 nodes that did not block EMT transmission (termed “passenger” nodes), when combined with other EMT “passengers,” 11 combination knockouts were able to inhibit transmission of the EMT signal. 5) Analysis of the states in the basin of attraction of the “EMT negative” steady states has revealed a critical subnetwork (a strongly connected component of the larger EMT network) for EMT transmission. Initial testing has revealed that Snail1 inhibition blocks TGF-Beta1 induced EMT. Further predictions for nodes that inhibit EMT are currently being tested with a novel in vitro EMT screen. These results reveal network modeling as an important tool for identifying critical mediators in biological processes. Furthermore, we propose network modeling as a tool for rational drug targeting of disease pathways, specifically in liver cancer metastasis. This proffered talk is also presented as Poster A12. Citation Format: Steven N. Steinway, Hien Dang, Wei Ding, Carl B. Rountree, Reka Albert. Network modeling of epithelial-to-mesenchymal transition in liver cancer metastasis [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR8.