Computational systems biology of epithelial-hybrid-mesenchymal transitions
Introduction
Metastasis causes above 90% of all cancer-related deaths. Metastasis involves primary tumor cells leaving the home organ, entering the bloodstream, disseminating throughout the body, and forming secondary tumors (metastases) at distant organs [1]. The ‘metastatic cascade’ is extremely challenging for cancer cells. Therefore, only 0.01% of cells entering the circulation are estimated to be able to form metastases [2].
An intriguing aspect of metastasis emerges from the fact that more than 80% of cancers are carcinomas, i.e. cancers beginning in epithelial organs such as breast, prostate, and lung. Carcinoma cells adhere tightly to their neighbors in highly organized 3-D structures and lack the innate ability to invade surrounding tissue. Standard thinking in the field suggests that to metastasize, the cells shed some epithelial traits of cell polarity and E-cadherin based cell–cell adhesion, and pick up mesenchymal features of migration and invasion 3, 4, 5. After reaching the distant organ, these migratory cells often lose migration and regain cell–cell adhesion, reverting to an epithelial phenotype. Thus, in many cases, these reversible bidirectional transitions among epithelial and mesenchymal phenotypes – EMT (Epithelial–Mesenchymal Transition) and its reverse MET (Mesenchymal–Epithelial Transition) – form the cornerstone of cancer metastasis [6]. We would be remiss in not mentioning the raging controversy within the EMT field regarding recent claims that EMT is dispensable for metastasis but required for emergence of drug resistance 7, 8. These recent reports have raised important issues such as whether these results hold true only for specific genetically engineered mouse models (GEMMs), whether the markers used are truly indicative of EMT, and whether the knockdown of one transcription factor ablated EMT fully 2, 9.
Recent experimental and computational studies have suggested that these transitions are rarely ‘all-or-none’, instead cells can exhibit a spectrum of intermediate or hybrid epithelial/mesenchymal (E/M) phenotypes ∗∗10, 11, ∗∗12, ∗∗13, 14, ∗15, 16, 17. Such hybrid E/M cells can both adhere to their neighbors and migrate, thereby leading to tumor budding and/or clustered migration of Circulating Tumor Cells (CTCs) 18, 19. These clusters, although quite rare, can form up to 50-times more secondary tumors as compared to individually migrating CTCs, suggesting the enhanced metastatic potential of a hybrid E/M phenotype ∗∗20, 21. Furthermore, the presence of clusters in patients can predict poor survival [20]. Therefore, understanding how cells transition among epithelial, mesenchymal and hybrid E/M phenotypes can offer novel insights into halting metastatic progression.
Section snippets
Regulatory networks underlying EMT/MET
EMT can be induced by many signaling pathways such as TGFβ, HGF, EGF, FGF, Wnt, Notch, NF-kB, Hedgehog, JAK/STAT, and Hippo [22], hypoxia [23], and mechanical factors such as extracellular matrix (ECM) density [24]. These signals often activate one or more of EMT-inducing transcription factors (EMT-TFs) such as ZEB1/2, SNAIL1/2, TWIST1, Goosecoid – all of which can, directly or indirectly, repress epithelial genes including E-cadherin – the inhibition of which is considered as a hallmark of EMT
Mechanism-driven ‘bottom-up’ models of EMT
Mechanism-based ‘bottom-up’ models represent experimentally identified interactions among a set of small number of core players that regulate EMT/MET, and characterize the emergent dynamics of the regulatory network. The first two ‘bottom-up’ attempts to model EMT focus on interactions between two microRNA families miR-34, miR-200 and two EMT-TF families ZEB and SNAIL ∗∗10, 11. These small-scale models capture the kinetics of individual reactions in the network, i.e. similar to the
Data-driven ‘top-down’ models of EMT
The data-driven ‘top-down’ models start with omics level data – typically gene expression – and apply various statistical techniques to identify patterns and/or trajectories for these transitions. For instance, Zadran et al. [46] characterize the changes in gene expression profiles for TGFβ1-induced EMT in A549 lung cancer cells on a free energy landscape to predict that EMT proceeds via a stable intermediate state that may correlate with metabolic shifts. Similarly, Chang et al. [47] collected
Conclusion
As discussed above, computational dynamical and statistical models of EMT have contributed significantly by providing quantitative frameworks to characterize the landscape of genetic and biophysical changes associated with EMT in multiple contexts. They have highlighted that EMT is rarely an ‘all-or-none’ process, and yielded specific falsifiable hypotheses that can be tested experimentally to understand regulation of EMT in a more nuanced way. Furthermore, they have provided tractable schemas
Acknowledgements
This work was supported by National Science Foundation (NSF) Center for Theoretical Biological Physics (NSF PHY-1427654) and NSF DMS-1361411.
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2019, Pharmacology and TherapeuticsCitation Excerpt :A cell line derived from this mouse model also contained all 3 subpopulations – E, M, and hybrid E/M – as revealed by flow cytometry analysis (Ruscetti et al., 2016). The subpopulations within the cell line also spontaneously interconverted between the three states, validating the mathematical models that predict the existence of multi-stability in EMT networks and consequent phenotypic switching, even in the absence of an EMT/MET inducer (Burger et al., 2017; Jolly & Levine, 2017). In clinical samples, a hybrid E/M phenotype was reported in prostate cancer CTCs metastases from men with metastatic castration-resistant prostate cancer (CRPC) co-expressing epithelial and mesenchymal biomarkers such as EpCAM, cytokeratins, E-cadherin, N-cadherin, and vimentin (Armstrong et al., 2011).
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