Deciphering Epithelial-Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

Abstract

Epithelial-mesenchymal transition (EMT), the process by which epithelial cells can convert into motile mesenchymal cells, plays an important role in development and wound healing but is also involved in cancer progression. It is increasingly recognized that EMT is a dynamic process involving multiple intermediate or "hybrid" phenotypes rather than an "all-or-none" process. However, the role of EMT in various cancer hallmarks, including metastasis, is debated. Given the complexity of EMT regulation, computational modeling has proven to be an invaluable tool for cancer research, i.e., to resolve apparent conflicts in experimental data and to guide experiments by generating testable hypotheses. In this review, we provide an overview of computational modeling efforts that have been applied to regulation of EMT in the context of cancer progression and its associated tumor characteristics. Moreover, we identify possibilities to bridge different modeling approaches and point out outstanding questions in which computational modeling can contribute to advance our understanding of pathological EMT.

Keywords: cancer progression; cell metabolism; cell migration; computational modeling; epithelial–mesenchymal transition; stemness.

Figure 1

Models of the epithelial–mesenchymal transition core regulatory network and their behavior. (A,B) Network graphs of the cascading bistable switches (CBS) model by Tian et al. (25) and Zhang et al. (26) (A) and of the ternary chimera switch (TCS) model by Lu et al. (27) (B). (C,D) Bifurcation diagrams corresponding to the CBS model (C) and TCS model (D) [(B,D) based on Ref. (27); (A,C) based on Ref. (26)].

Figure 2

Extensions of the core regulatory models and their behavior. (A–H) Model schemes (A,C,E,F) and corresponding bifurcation diagrams (B,D,G,H) of various extensions of the ternary chimera switch (TCS) and cascading bistable switches (CBS) models: the CBS-OVOL model (A,B), the TCS-OVOL model [(C,D); dashed line in panel (C) indicates inhibition of miR-200 by OVOL which occurs in prostate cancer but not in breast cancer], the TCS-Grainyhead-like transcription factor 2 model (E,G), and the TCS-miR145 model (F,H). Bifurcation diagrams of the unmodified TCS model are shown in orange and of modified TCS models in blue. TGFβ ranges are highlighted for which there exists a stable hybrid E/M state (light yellow) and for which this is the only stable state (dark yellow). Note that in the extensions of the TCS model, this model is first simplified by considering SNAIL as input, which can be done because the tristability in that model is fully determined by the miR-200/ZEB module [(A,B) based on Ref. (39); (C–H) based on Ref. (43, 44)].

Figure 3

Boolean modeling of epithelial–mesenchymal transition (EMT). (A) Boolean model with 70 nodes and 135 edges by Steinway et al. (57). The following network elements are highlighted: upstream regulators (dark gray), EMT transcription factors (light gray), and elements included in the cascading bistable switches and ternary chimera switch core regulatory models (orange border) or their extensions (blue border) [based on Ref. (57)]. (B) One of the probable transition paths from the epithelial to mesenchymal phenotype in a reduced, but functionally similar EMT model [cf. Figure 5A in Ref. (57)].

Figure 4

Scheme of epithelial–mesenchymal transition (EMT) and associated cancer characteristics. The size of the arrows indicates the amount of modeling studies focused on the association of EMT with the particular cancer characteristic [based on by Ref. (90)].