Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is transforming growth factor beta (TGFβ, which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We have constructed an EMT network of 70 nodes and 135 edges by integrating the signaling pathways involved in developmental EMT and known dysregulations in invasive HCC. We then used discrete dynamic modeling to understand the dynamics of the EMT network driven by TGFβ. Our network model recapitulates known dysregulations during the induction of EMT and predicts the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We show, across multiple murine (P2E and P2M) and human HCC cell lines (Huh7, PLC/PRF/5, HLE, and HLF), that the TGFβ signaling axis is a conserved driver of mesenchymal phenotype HCC and confirm that Wnt and SHH signaling are induced in these cell lines. Furthermore, we identify by network analysis eight regulatory feedback motifs that stabilize the EMT process and show that these motifs involve cross-talk among multiple major pathways. Our model will be useful in identifying potential therapeutic targets for the suppression of EMT, invasion and metastasis in HCC.

Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is transforming growth factor beta (TGFβ), which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We aim to identify network perturbations that suppress TGFβ-driven EMT, with the goal of suppressing invasive properties of cancer cells. We use a systems-level Boolean dynamic model of EMT to systematically screen individual and combination perturbations (inhibition or constitutive activation of up to four nodes). We use a recently developed network control approach to understand the mechanism through which the combinatorial interventions suppress EMT. We test the results of our in silico analysis using siRNA. Our model predicts that targeting key elements of feedback loops in combination with the SMAD complex is more effective than suppressing the SMAD complex alone. We demonstrate experimentally that expression of a majority of these elements is enriched in mesenchymal relative to epithelial phenotype HCC cell lines. An siRNA screen of the predicted combinations confirms that many targeting strategies suppress TGFβ-driven EMT measured by E-cadherin expression and cell migration. Our analysis reveals that some perturbations give rise to hybrid states intermediate to the epithelial and mesenchymal states. Our results indicate that EMT is driven by an interconnected signaling network and many apparently successful single interventions may lead to steady states that are in-between epithelial and mesenchymal states. As these putative hybrid or partial EMT states may retain invasive properties, our results suggest that combinatorial therapies are necessary to fully suppress invasive properties of tumor cells.

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Alpha-difluoromethylornithine (DFMO) inhibits the protooncogene ornithine decarboxylase (ODC) and is known to induce cell cycle arrest. However, the effect of DFMO on human neuroblastoma (NB) cells and the exact mechanism of DFMO-induced cell death are largely unknown. Treatment with DFMO in combination with SAM486A, an S-adenosylmethionine decarboxylase (AdoMetDC) inhibitor, has been shown to enhance polyamine pool depletion. Therefore, we analysed the mechanism of action of DFMO and/or SAM486A in two established MYCN-amplified human NB cell lines. DFMO and SAM486A caused rapid cell growth inhibition, polyamine depletion, and G 1 cell cycle arrest without apoptosis in cell lines LAN-1 and NMB-7. These effects were enhanced with combined inhibitors and largely prevented by cotreatment with exogenous polyamines. The G 1 cell cycle arrest was concomitant with an increase in cyclin-dependent kinase inhibitor p27 Kip1 . In a similar fashion, DFMO and DFMO/SAM486A inhibited the phosphorylation of the G 1 /S transition-regulating retinoblastoma protein Rb at residues Ser795 and Ser807/811. Moreover, we observed a dramatic decrease in MYCN protein levels. Overexpression of MYCN induces an aggressive NB phenotype with malignant behavior. We show for the first time that DFMO and SAM486A induce G 1 cell cycle arrest in NB cells through p27 Kip1 and Rb hypophosphorylation.