Combinatorial interventions inhibit TGFβ-driven epithelial-to-mesenchymal transition and support hybrid cellular phenotypes

Abstract

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.

Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity

“…However, we did not identify attractors correlated with the hybrid phenotype. Hybrid EMT phenotypes have been previously reported in NSCLC (47) and lung adenocarcinoma (48), and other groups have recently reported computational modeling of hybrid EMT phenotypes by driving EMT networks with external stimuli (43,49). Additionally, the Boolean modeling approach cannot capture intermediate levels of expression, and therefore attractors corresponding to the hybrid state may not be identifiable using this method.…”

Epithelial/mesenchymal plasticity: how have quantitative mathematical models helped improve our understanding?

“…Multiple open questions related to EMT/MET furnish exciting opportunities for cross‐pollination of ideas among experimental and computational biologists, including (a) ‘How many intermediate states can cells attain en route to EMT and MET?’; (b) ‘What is the genomic, proteomic, and epigenetic signature of these states?’; (c) ‘How symmetric are the dynamics of EMT and MET, and do cells display hysteresis (i.e., cellular memory)?’; and (d) ‘What is the relative stability and relative ‘stemness’ possessed by each of these states?’ As expected, mathematical models encompassing a larger number of EMT/MET regulatory players than considered in the initial models (Lu et al ., ; Tian et al ., ) have suggested multiple intermediate states (Hong et al ., ; Huang et al ., ; Steinway et al ., ), but these predictions remain to be experimentally verified, thus providing impetus for many collaborative efforts.…”

Boolean network modeling in systems pharmacology

“…In essence, the states of nodes in a control set are able to provide insights into the behavior of the system, and perturbing specific nodes of a control set can guide the behavior toward one that is favorable. This stable motif-control methodology has been applied to identify control sets in an epithelial-mesenchymal transition (EMT) network model that drives the system towards an epithelial steady state [105]. Steinway and colleagues identified seven individual targets (all related to E-cadherin transcription), and three targets in combination with SMAD complex inhibition, that were able to suppress EMT.…”