Computational modeling of epithelial-mesenchymal transformations

Using extracellular matrix derived from sugen-chronic hypoxia lung tissue to study pulmonary arterial hypertension

Pulmonary arterial hypertension has characteristic changes to the mechanical environment, extracellular matrix, and cellular proliferation. In order to develop a culture system to investigate extracellular matrix (ECM) compositional-dependent changes in pulmonary arterial hypertension, we decellularized and characterized protein and lipid profiles from healthy and Sugen-Chronic Hypoxia rat lungs. Significant changes in lipid profiles were observed in intact Sugen-Hypoxia lungs compared with healthy controls. Decellularized lung matrix retained lipids in measurable quantities in both healthy and Sugen-Chronic Hypoxia samples. Proteomics revealed significantly changed proteins associated with pulmonary arterial hypertension in the decellularized Sugen-Chronic Hypoxia lung ECM. We then investigated the potential role of healthy vs. Sugen-Chronic Hypoxia ECM with controlled substrate stiffness to determine if the ECM composition regulated endothelial cell morphology and phenotype. CD117+ rat lung endothelial cell clones were plated on the variable stiffness gels and cellular proliferation, morphology, and gene expression were quantified. Sugen-Chronic Hypoxia ECM on healthy stiffness gels produced significant changes in cellular gene expression levels of Bmp2, Col1α1, Col3α1 and Fn1. The signaling and cell morphology observed at low substrate stiffness suggests early changes to the ECM composition can initiate processes associated with disease progression. These data suggest that Sugen-Chronic Hypoxia ECM can be used to investigate cell-ECM interactions relevant to pulmonary arterial hypertension.

Tumor-mediated immunosuppression and cytokine spreading affects the relation between EMT and PD-L1 status

Epithelial-mesenchymal transition (EMT) and immune resistance mediated by Programmed Death-Ligand 1 (PD-L1) upregulation are established drivers of tumor progression. Their bi-directional crosstalk has been proposed to facilitate tumor immunoevasion, yet the impact of immunosuppression and spatial heterogeneity on the interplay between these processes remains to be characterized. Here we study the role of these factors using mathematical and spatial models. We first designed models incorporating immunosuppressive effects on T cells mediated via PD-L1 and the EMT-inducing cytokine Transforming Growth Factor beta (TGFβ). Our models predict that PD-L1-mediated immunosuppression merely reduces the difference in PD-L1 levels between EMT states, while TGFβ-mediated suppression also causes PD-L1 expression to correlate negatively with TGFβ within each EMT phenotype. We subsequently embedded the models in multi-scale spatial simulations to explicitly describe heterogeneity in cytokine levels and intratumoral heterogeneity. Our multi-scale models show that Interferon gamma (IFNγ)-induced partial EMT of a tumor cell subpopulation can provide some, albeit limited protection to bystander tumor cells. Moreover, our simulations show that the true relationship between EMT status and PD-L1 expression may be hidden at the population level, highlighting the importance of studying EMT and PD-L1 status at the single-cell level. Our findings deepen the understanding of the interactions between EMT and the immune response, which is crucial for developing novel diagnostics and therapeutics for cancer patients.

Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms

Breast cancer stem cells (BCSCs) with the ability to self-renew and differentiate have been identified in primary breast cancer tissues and cell lines. The BCSCs are often resistant to traditional radiation and/or chemotherapies. Previous studies have also shown that successful therapy must eradicate cancer stem cells. The purpose of this paper is to develop a mathematical model with self-feedback mechanism to illustrate the issues regarding the difficulties of absolutely eliminating a breast cancer. In addition, we introduce the mechanism of the epithelial-mesenchymal transition (EMT) to investigate the influence of EMT on the effects of breast cancer growth and treatment. Results indicate that the EMT mechanism facilitates the growth of breast cancer and makes breast cancer more difficult to be cured. Therefore, targeting the signals involved in EMT can halt tumor progression in breast cancer. Finally, we apply the experimental data to carry out numerical simulations and validate our theoretical conclusions.

A multiscale in silico model of endothelial to mesenchymal transformation in a tumor microenvironment

Endothelial to mesenchymal transformation (EndMT) is a process in which endothelial cells gain a mesenchymal-like phenotype in response to mechanobiological signals that results in the remodeling or repair of underlying tissue. While initially associated with embryonic development, this process has since been shown to occur in adult tissue remodeling including wound healing, fibrosis, and cancer. In an attempt to understand the role of EndMT in cancer progression and metastasis, we present a multiscale, three-dimensional, in silico model. The model couples tissue level phenomena such as extracellular matrix remodeling, cellular level phenomena such as migration and proliferation, and chemical transport in the tumor microenvironment to mimic in vitro tissue models of the cancer microenvironment. The model is used to study the presence of EndMT-derived activated fibroblasts (EDAFs) and varying substrate stiffness on tumor cell migration and proliferation. The simulations accurately model the behavior of tumor cells under given conditions. The presence of EDAFs and/or an increase in substrate stiffness resulted in an increase in tumor cell activity. This model lays the foundation of further studies of EDAFs in a tumor microenvironment on a cellular and subcellular physiological level.

Complex Determinants of Epithelial: Mesenchymal Phenotypic Plasticity in Ovarian Cancer

Unlike most epithelial malignancies which metastasize hematogenously, metastasis of epithelial ovarian cancer (EOC) occurs primarily via transcoelomic dissemination, characterized by exfoliation of cells from the primary tumor, avoidance of detachment-induced cell death (anoikis), movement throughout the peritoneal cavity as individual cells and multi-cellular aggregates (MCAs), adhesion to and disruption of the mesothelial lining of the peritoneum, and submesothelial matrix anchoring and proliferation to generate widely disseminated metastases. This exceptional microenvironment is highly permissive for phenotypic plasticity, enabling mesenchymal-to-epithelial (MET) and epithelial-to-mesenchymal (EMT) transitions. In this review, we summarize current knowledge on EOC heterogeneity in an EMT context, outline major regulators of EMT in ovarian cancer, address controversies in EMT and EOC chemoresistance, and highlight computational modeling approaches toward understanding EMT/MET in EOC.

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

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.

A morphospace for synthetic organs and organoids: the possible and the actual

Efforts in evolutionary developmental biology have shed light on how organs are developed and why evolution has selected some structures instead of others. These advances in the understanding of organogenesis along with the most recent techniques of organotypic cultures, tissue bioprinting and synthetic biology provide the tools to hack the physical and genetic constraints in organ development, thus opening new avenues for research in the form of completely designed or merely altered settings. Here we propose a unifying framework that connects the concept of morphospace (i.e. the space of possible structures) with synthetic biology and tissue engineering. We aim for a synthesis that incorporates our understanding of both evolutionary and architectural constraints and can be used as a guide for exploring alternative design principles to build artificial organs and organoids. We present a three-dimensional morphospace incorporating three key features associated to organ and organoid complexity. The axes of this space include the degree of complexity introduced by developmental mechanisms required to build the structure, its potential to store and react to information and the underlying physical state. We suggest that a large fraction of this space is empty, and that the void might offer clues for alternative ways of designing and even inventing new organs.

Computational modeling of cardiac fibroblasts and fibrosis

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

Epithelial to mesenchymal transition-the roles of cell morphology, labile adhesion and junctional coupling

Epithelial to mesenchymal transition (EMT) is a fundamental process during development and disease, including development of the heart valves and tumour metastases. An extended cellular Potts model was implemented to represent the behaviour emerging from autonomous cell morphology, labile adhesion, junctional coupling and cell motility. Computer simulations normally focus on these functional changes independently whereas this model facilitates exploration of the interplay between cell shape changes, adhesion and migration. The simulation model is fitted to an in vitro model of endocardial EMT, and agrees with the finding that Notch signalling increases cell-matrix adhesion in addition to modulating cell-cell adhesion.

Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models

The epithelial-mesenchymal transition (EMT) is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs) are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.

Microenvironmental regulation of epithelial-mesenchymal transitions in cancer

The evolution of the cancer cell into a metastatic entity is the major cause of death in patients with cancer. Activation of the epithelial-to-mesenchymal transition (EMT) endows invasive and metastatic properties upon cancer cells that favor successful colonization of distal target organs. The observation that in many cancers distant metastases resemble the epithelial phenotype of primary tumors has led to speculation that the disseminated tumor cells recruited to the target organs undergo mesenchymal-to-epithelial transition (MET). However, the MET cascade has not been recapitulated in vivo, and the cellular and molecular regulators that promote MET remain unknown. In a recent report, using a model of spontaneous breast cancer, we have shown that bone marrow-derived myeloid progenitor cells in the premetastatic lung secrete the proteoglycan versican, which induces MET of metastatic tumor cells and accelerates metastases. This review summarizes recent progress in MET research, outlines a unique paracrine cross-talk between the microenvironment and the cancer cells, which promotes tumor outgrowth in the metastatic organ, and discusses opportunities for novel antimetastatic approaches for cancer therapy.

Quantitative approaches to cancer stem cells and epithelial-mesenchymal transition

The last decade has witnessed significant advances in the application of mathematical and computational models to biological systems, especially to cancer biology. Here, we present stochastic and deterministic models describing tumour growth based on the cancer stem cell hypothesis, and discuss the application of these models to the epithelial-mesenchymal transition. In particular, we discuss how such quantitative approaches can be used to validate different possible scenarios that can lead to an increase in stem cell activity following induction of epithelial-mesenchymal transition, observed in recent experimental studies on human breast cancer and related cell lines. The utility of comparing mammosphere data to computational mammosphere simulations in elucidating the growth characteristics of mammary (cancer) stem cells is discussed as well.