Hepatocellular Carcinoma (HCC) represents the third most common cause of cancer death in the world and the incidence of HCC is rising in the United States. Only 15% of HCC patients are eligible for surgical intervention, and resistance to chemotherapy, high relapse rates and poor prognosis are common features of HCC. Epithelial-to-mesenchymal transition (EMT) is a process initiated during liver development that is utilized by liver cancer cells to initiate metastatic spread. After EMT, liver epithelial cells become motile, mesenchymal cells and lose cell-to-cell adhesion, which allows for the migration from the primary tumor site. Understanding EMT is critical to HCC and other cancer patients as the primary cause of cancer related mortality in 90% of cases is metastatic disease. Recently, we have demonstrated that Hepatocyte Growth Factor (HGF) is a primary initiator of EMT in multiple liver cancer models. HGF is a potent growth factor for hepatocytes and binds to high-affinity receptor c-Met. In HCC, c-Met tyrosine kinase phosphorylation results in down-stream activation of multiple signal cascades, ultimately driving tumor proliferation, survival, invasion, and metastasis. The specific objective of this proposal is to utilize network theory and dynamic modeling with direct in vitro and in vivo laboratory analysis to assess the role of hepatocyte growth factor (HGF) as a primary inducer of EMT and to understand the role of Snail in maintaining the EMT phenotype in metastatic disease. The central hypothesis is that HGF is required for induction of EMT and that Snail is a critical node for maintenance of EMT in liver cancer. As presented in Preliminary Studies, we have demonstrated that HGF- induced EMT results in increased metastasis in vivo. We have validated our murine models using human HCC transcriptome profiles from the NCI. Within metastatic tumors from murine models and human HCC cells, the mesenchymal cancer cells demonstrated up-regulated Zeb1, Zeb2, and Snail, and loss of E-cadherin and microRNA 200b. In an effort to reverse EMT with forced up-regulation of microRNA 200b, we demonstrated a reversal of mesenchymal characteristics in vitro without a decreased metastatic potential in vivo. To better understand these discongruous results, we constructed an EMT signaling network and utilized dynamic Boolean modeling. We identified that Snail is a critical node for maintaining a mesenchymal/metastatic phenotype post EMT. The rationale for the proposed research is that network modeling provides a unique means to identify critical regulators of complex biological processes. Through identification of the critical regulators of EMT in liver cancer metastasis, we will identify the major contributors to metastasis. The proposed work is innovative as we capitalize on network modeling to rationally identify critical nodes within the EMT pathway, and then we propose model verification using human and murine HCC cell lines with in vitro and in vivo assessment of mesenchymal phenotype and metastatic potential. We expect that the proposed model will produce novel critical targets for potential prevention and/or treatment of metastatic HCC.

Public Health Relevance

Epithelial-to-mesenchymal transition (EMT) is a process utilized by liver cancer cells to leave the primary tumor site and establish distant metastases. We plan to utilize a network modeling (systems biology) approach to identify the key regulators of this complex process in liver cancer cells. This research will allow identification of key mediators of EMT, which will be confirmed using cell lines and in vivo mouse studies. These studies will allow future research to focus on targeting these critical regulators to inhibit liver cancer metastasis and prevent liver cancer progression in patients with chronic liver disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
Project #
5F30DK093234-04
Application #
8788014
Study Section
Special Emphasis Panel (ZDK1-GRB-2 (M1))
Program Officer
Podskalny, Judith M,
Project Start
2011-12-01
Project End
2015-11-30
Budget Start
2014-12-01
Budget End
2015-11-30
Support Year
4
Fiscal Year
2015
Total Cost
$44,659
Indirect Cost
Name
Pennsylvania State University
Department
Pediatrics
Type
Schools of Medicine
DUNS #
129348186
City
Hershey
State
PA
Country
United States
Zip Code
17033
Steinway, Steven N; Biggs, Matthew B; Loughran Jr, Thomas P et al. (2015) Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome. PLoS Comput Biol 11:e1004338
Steinway, Steven N; Dang, Hien; You, Hanning et al. (2015) The EGFR/ErbB3 Pathway Acts as a Compensatory Survival Mechanism upon c-Met Inhibition in Human c-Met+ Hepatocellular Carcinoma. PLoS One 10:e0128159
Steinway, Steven Nathaniel; Zañudo, Jorge Gomez Tejeda; Michel, Paul J et al. (2015) Combinatorial interventions inhibit TGF?-driven epithelial-to-mesenchymal transition and support hybrid cellular phenotypes. NPJ Syst Biol Appl 1:15014
Dang, Hien; Steinway, Steven N; Ding, Wei et al. (2015) Induction of tumor initiation is dependent on CD44s in c-Met? hepatocellular carcinoma. BMC Cancer 15:161
Steinway, Steven Nathaniel; Zañudo, Jorge G T; Ding, Wei et al. (2014) Network modeling of TGF? signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint sonic hedgehog and Wnt pathway activation. Cancer Res 74:5963-77
Steinway, Steven Nathaniel; LeBlanc, Francis; Loughran Jr, Thomas P (2014) The pathogenesis and treatment of large granular lymphocyte leukemia. Blood Rev 28:87-94
Steinway, Steven N; Loughran, Thomas P (2013) Targeting IL-15 in large granular lymphocyte leukemia. Expert Rev Clin Immunol 9:405-8
Ding, W; Dang, H; You, H et al. (2012) miR-200b restoration and DNA methyltransferase inhibitor block lung metastasis of mesenchymal-phenotype hepatocellular carcinoma. Oncogenesis 1:e15
Steinway, Steven N; Dannenfelser, Ruth; Laucius, Christopher D et al. (2010) JCoDA: a tool for detecting evolutionary selection. BMC Bioinformatics 11:284