Hybrid Epithelial-Mesenchymal Phenotypes Are Controlled by Microenvironmental Factors

Vulvo-vaginal epithelial tumors in mares: A preliminary investigation on epithelial-mesenchymal transition and tumor-immune microenvironment

Vulvo-vaginal epithelial tumors are uncommon in mares, and data on the epithelial-to-mesenchymal transition (EMT) and the tumor-immune microenvironment (TIME) are still lacking. This is a study investigating the equus caballus papillomavirus type 2 (EcPV2) infection state as well as the EMT process and the tumor microenvironment in vulvo-vaginal preneoplastic/ benign (8/22) or malignant (14/22) epithelial lesions in mares. To do this, histopathological, immunohistochemical, transcriptomic, in situ hybridization, and correlation analyses were carried out. Immunohistochemistry quantification showed that cytoplasmic E-cadherin and β-catenin expression as well as nuclear β-catenin expression were features of malignant lesions, while benign/preneoplastic lesions were mainly characterized by membranous E-cadherin and β-catenin expression. Despite this, there were no differences between benign and malignant equine vulvo-vaginal lesions in the expression of downstream genes involved in the canonical and noncanonical wnt/β-catenin pathways. In addition, malignant lesions were characterized by a lower number of cells with cytoplasmic cytokeratin expression as well as a slightly higher cytoplasmic vimentin immunolabeling. The TIME of malignant lesions was characterized by more numerous CD204+ M2-polarized macrophages. Altogether, our results support the hypothesis that some actors in TIME such as CD204+ M2-polarized macrophages may favor the EMT process in equine vulvo-vaginal malignant lesions providing new insights for future investigations in the field of equine EcPV2-induced genital neoplastic lesions.

Cusp bifurcation in a metastatic regulatory network

Understanding the potential for cancers to metastasize is still relatively unknown. While many predictive methods may use deep learning or stochastic processes, we highlight a long standing mathematical concept that may be useful for modeling metastatic breast cancer systems. Ordinary differential equations (ODEs) can model cell state transitions by considering the pertinent environmental variables as well as the paths systems take over time. Bifurcation theory is a branch of dynamical systems which studies changes in the behavior of an ODE system while one or more parameters are varied. Many studies have applied concepts in one-parameter bifurcation theory to model biological network dynamics, and cell division. However, studies of two-parameter bifurcations are much more rare. Two-parameter bifurcations have not been studied in metastatic systems. Here we show how a specific two-parameter bifurcation phenomenon called a cusp bifurcation separates two qualitatively different metastatic cell state transitions modalities and propose a new perspective on defining such transitions based on mathematical theory. We hope the observations and verification methods detailed here may help in the understanding of metastatic potential from a basic biological perspective and in clinical settings.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Understanding and leveraging phenotypic plasticity during metastasis formation

Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis at the cell population level, and its treatment consequences.

Phenotypic and molecular characterization of novel pulmonary adenocarcinoma cell lines established from a dog

Canine pulmonary adenocarcinoma (PAC) resembles human lung tumors in never-smokers, but it is rarer than human pulmonary adenocarcinoma. Therefore, research on canine PAC is challenging. In the present study, we successfully established various novel canine PAC cell lines from a single lesion in a dog, including two parent cell lines and fourteen cloned cell lines, and characterized their cellular properties in vitro. Several of these cell lines showed epithelial-mesenchymal transition (EMT)-like and/or cancer stem cell (CSCs)-like phenotypes. We additionally assessed the sensitivity of the cells to vinorelbine in vitro. Three clonal lines, two of which showed EMT- and CSC-like phenotypes, were resistant to vinorelbine. Furthermore, we evaluated the expression and activation status of EGFR, HER2, and Ras signaling factors. The findings indicated that the cell lines we established preserved the expression and activation of these factors to varying extents. These novel canine PAC cell lines can be utilized in future research for understanding the pathogenesis and development of treatments for canine PAC.

Common Attractors in Multiple Boolean Networks

Analyzing multiple networks is important to understand relevant features among different networks. Although many studies have been conducted for that purpose, not much attention has been paid to the analysis of attractors (i.e., steady states) in multiple networks. Therefore, we study common attractors and similar attractors in multiple networks to uncover hidden similarities and differences among networks using Boolean networks (BNs), where BNs have been used as a mathematical model of genetic networks and neural networks. We define three problems on detecting common attractors and similar attractors, and theoretically analyze the expected number of such objects for random BNs, where we assume that given networks have the same set of nodes (i.e., genes). We also present four methods for solving these problems. Computational experiments on randomly generated BNs are performed to demonstrate the efficiency of our proposed methods. In addition, experiments on a practical biological system, a BN model of the TGF- β signaling pathway, are performed. The result suggests that common attractors and similar attractors are useful for exploring tumor heterogeneity and homogeneity in eight cancers.

Reducing Boolean networks with backward equivalence

BACKGROUND

Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis.

RESULTS

We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance.

CONCLUSIONS

BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.

Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments

The malignant tumor is a multi-etiological, systemic and complex disease characterized by uncontrolled cell proliferation and distant metastasis. Anticancer treatments including adjuvant therapies and targeted therapies are effective in eliminating cancer cells but in a limited number of patients. Increasing evidence suggests that the extracellular matrix (ECM) plays an important role in tumor development through changes in macromolecule components, degradation enzymes and stiffness. These variations are under the control of cellular components in tumor tissue via the aberrant activation of signaling pathways, the interaction of the ECM components to multiple surface receptors, and mechanical impact. Additionally, the ECM shaped by cancer regulates immune cells which results in an immune suppressive microenvironment and hinders the efficacy of immunotherapies. Thus, the ECM acts as a barrier to protect cancer from treatments and supports tumor progression. Nevertheless, the profound regulatory network of the ECM remodeling hampers the design of individualized antitumor treatment. Here, we elaborate on the composition of the malignant ECM, and discuss the specific mechanisms of the ECM remodeling. Precisely, we highlight the impact of the ECM remodeling on tumor development, including proliferation, anoikis, metastasis, angiogenesis, lymphangiogenesis, and immune escape. Finally, we emphasize ECM "normalization" as a potential strategy for anti-malignant treatment.

Activation of an actin signaling pathway in pre-malignant mammary epithelial cells by P-cadherin is essential for transformation

Alterations in the expression or function of cell adhesion molecules have been implicated in all steps of tumor progression. Among those, P-cadherin is highly enriched in basal-like breast carcinomas, playing a central role in cancer cell self-renewal, collective cell migration and invasion. To establish a clinically relevant platform for functional exploration of P-cadherin effectors in vivo, we generated a humanized P-cadherin Drosophila model. We report that actin nucleators, Mrtf and Srf, are main P-cadherin effectors in fly. We validated these findings in a human mammary epithelial cell line with conditional activation of the SRC oncogene. We show that, prior to promoting malignant phenotypes, SRC induces a transient increase in P-cadherin expression, which correlates with MRTF-A accumulation, its nuclear translocation and the upregulation of SRF target genes. Moreover, knocking down P-cadherin, or preventing F-actin polymerization, impairs SRF transcriptional activity. Furthermore, blocking MRTF-A nuclear translocation hampers proliferation, self-renewal and invasion. Thus, in addition to sustaining malignant phenotypes, P-cadherin can also play a major role in the early stages of breast carcinogenesis by promoting a transient boost of MRTF-A-SRF signaling through actin regulation.

An Overview of Epithelial-to-Mesenchymal Transition and Mesenchymal-to-Epithelial Transition in Canine Tumors: How Far Have We Come?

Historically, pre-clinical and clinical studies in human medicine have provided new insights, pushing forward the contemporary knowledge. The new results represented a motivation for investigators in specific fields of veterinary medicine, who addressed the same research topics from different perspectives in studies based on experimental and spontaneous animal disease models. The study of different pheno-genotypic contexts contributes to the confirmation of translational models of pathologic mechanisms. This review provides an overview of EMT and MET processes in both human and canine species. While human medicine rapidly advances, having a large amount of information available, veterinary medicine is not at the same level. This situation should provide motivation for the veterinary medicine research field, to apply the knowledge on humans to research in pets. By merging the knowledge of these two disciplines, better and faster results can be achieved, thus improving human and canine health.

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

SNAIL driven by a feed forward loop motif promotes TGFβinduced epithelial to mesenchymal transition

Epithelial to Mesenchymal Transition (EMT) plays an important role in tissue regeneration, embryonic development, and cancer metastasis. Several signaling pathways are known to regulate EMT, among which the modulation of TGFβ(Transforming Growth Factor-β) induced EMT is crucial in several cancer types. Several mathematical models were built to explore the role of core regulatory circuit of ZEB/miR-200, SNAIL/miR-34 double negative feedback loops in modulating TGFβinduced EMT. Different emergent behavior including tristability, irreversible switching, existence of hybrid EMT states were inferred though these models. Some studies have explored the role of TGFβreceptor activation, SMADs nucleocytoplasmic shuttling and complex formation. Recent experiments have revealed that MDM2 along with SMAD complex regulates SNAIL expression driven EMT. Encouraged by this, in the present study we developed a mathematical model for p53/MDM2 dependent TGFβinduced EMT regulation. Inclusion of p53 brings in an additional mechanistic perspective in exploring the EM transition. The network formulated comprises a C1FFL moderating SNAIL expression involving MDM2 and SMAD complex, which functions as a noise filter and persistent detector. The C1FFL was also observed to operate as a coincidence detector driving the SNAIL dependent downstream signaling into phenotypic switching decision. Systems modelling and analysis of the devised network, displayed interesting dynamic behavior, systems response to various inputs stimulus, providing a better understanding of p53/MDM2 dependent TGF-βinduced Epithelial to Mesenchymal Transition.

The Wnt pathway can stabilize hybrid phenotypes in the epithelial-mesenchymal transition: A logical modeling approach

The Wnt pathway is important to regulate a variety of biochemical functions and can contribute to cancer development through its influence on the epithelial-mesenchymal transition (EMT). Multiple circuits have been reported to participate in the regulation of the Wnt signaling, however, the way these circuits coordinately regulate this signaling is still unclear. Moreover, the mechanisms responsible for the appearance of hybrid phenotypes (cells presenting both E and M features) are not well determined. The hybrid phenotype can present much higher metastatic potential than the mesenchymal phenotype. In this study, we propose a Boolean model of the Wnt pathway signaling contemplating recent published biochemical information on hepatocarcinoma. The model presents good coherence with experimental data for perturbed and wild-type cases. With the model, we propose two new molecular circuits involving several molecules that can stabilize hybrid states during the EMT. Moreover, we found that the two well studied circuits, AKT1/β-catenin and SNAIL1/miR-34, can cooperate with the predicted ones to favor the stabilization of the hybrid states. These findings highlight some possible unrecognized mechanisms during Wnt signaling and may provide alternative therapeutic strategies to control cancer metastatization.

Discovery of Proteins Responsible for Resistance to Three Chemotherapy Drugs in Breast Cancer Cells Using Proteomics and Bioinformatics Analysis

Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs in breast cancer are very limited. In this study, we employed proteomics and bioinformatics approaches on chemoresistant breast cancer cell lines to understand the underlying resistance mechanisms that resulted from doxorubicin (DR), paclitaxel (PR), and tamoxifen (TAR). In total, 10,385 proteins were identified and quantified from three TMT 6-plex and one TMT 10-plex experiments. Bioinformatics analysis showed that Notch signaling, immune response, and protein re-localization processes were uniquely associated with DR, PR, and TAR resistance, respectively. In addition, proteomic signatures related to drug resistance were identified as potential targets of many FDA-approved drugs. Furthermore, we identified potential prognostic proteins with significant effects on overall survival. Representatively, PLXNB2 expression was associated with a highly significant increase in risk, and downregulation of ACOX3 was correlated with a worse overall survival rate. Consequently, our study provides new insights into the proteomic aspects of the distinct mechanisms underlying chemoresistance in breast cancer.

SYNCRIP Modulates the Epithelial-Mesenchymal Transition in Hepatocytes and HCC Cells

Heterogeneous nuclear ribonucleoproteins (hnRNPs) control gene expression by acting at multiple levels and are often deregulated in epithelial tumors; however, their roles in the fine regulation of cellular reprogramming, specifically in epithelial–mesenchymal transition (EMT), remain largely unknown. Here, we focused on the hnRNP-Q (also known as SYNCRIP), showing by molecular analysis that in hepatocytes it acts as a “mesenchymal” gene, being induced by TGFβ and modulating the EMT. SYNCRIP silencing limits the induction of the mesenchymal program and maintains the epithelial phenotype. Notably, in HCC invasive cells, SYNCRIP knockdown induces a mesenchymal–epithelial transition (MET), negatively regulating their mesenchymal phenotype and significantly impairing their migratory capacity. In exploring possible molecular mechanisms underlying these observations, we identified a set of miRNAs (i.e., miR-181-a1-3p, miR-181-b1-3p, miR-122-5p, miR-200a-5p, and miR-let7g-5p), previously shown to exert pro- or anti-EMT activities, significantly impacted by SYNCRIP interference during EMT/MET dynamics and gathered insights, suggesting the possible involvement of this RNA binding protein in their transcriptional regulation.

ELK1 Promotes Epithelial-Mesenchymal Transition and the Progression of Lung Adenocarcinoma by Upregulating B7-H3

In previous studies, we found that B7 homolog 3 (B7-H3) was highly expressed in lung adenocarcinoma (LUAD) and promoted epithelial-to-mesenchymal transition (EMT) of LUAD cells. However, the underlying molecular mechanism is unclear. This study is aimed at evaluating the role of Ets-like protein 1 (ELK1) as a transcriptional regulator of B7-H3 for mediating the development and progression of LUAD in vitro and in vivo. We confirmed that ELK1 is highly expressed in LUAD and is associated with poor patient prognosis. ELK1 was found to promote proliferation, invasion, migration, and EMT of LUAD cells through in vivo and in vitro experiments. In terms of mechanism, ELK1 binds to the B7-H3 promoter region and induces the upregulation of B7-H3 in LUAD. Our data suggest that ELK1 plays an important role in the development of LUAD and could be used as a prognostic marker and therapeutic target for LUAD.

KLF4 Induces Mesenchymal-Epithelial Transition (MET) by Suppressing Multiple EMT-Inducing Transcription Factors

Epithelial-Mesenchymal Plasticity (EMP) refers to reversible dynamic processes where cells can transition from epithelial to mesenchymal (EMT) or from mesenchymal to epithelial (MET) phenotypes. Both these processes are modulated by multiple transcription factors acting in concert. While EMT-inducing transcription factors (TFs)-TWIST1/2, ZEB1/2, SNAIL1/2/3, GSC, and FOXC2-are well-characterized, the MET-inducing TFs are relatively poorly understood (OVOL1/2 and GRHL1/2). Here, using mechanism-based mathematical modeling, we show that transcription factor KLF4 can delay the onset of EMT by suppressing multiple EMT-TFs. Our simulations suggest that KLF4 overexpression can promote a phenotypic shift toward a more epithelial state, an observation suggested by the negative correlation of KLF4 with EMT-TFs and with transcriptomic-based EMT scoring metrics in cancer cell lines. We also show that the influence of KLF4 in modulating the EMT dynamics can be strengthened by its ability to inhibit cell-state transitions at the epigenetic level. Thus, KLF4 can inhibit EMT through multiple parallel paths and can act as a putative MET-TF. KLF4 associates with the patient survival metrics across multiple cancers in a context-specific manner, highlighting the complex association of EMP with patient survival.

An Update on Molecular Pathways Regulating Vasculogenic Mimicry in Human Osteosarcoma and Their Role in Canine Oncology

Canine tumors are valuable comparative models for human counterparts, especially to explore novel biomarkers and to understand pathways and processes involved in metastasis. Vasculogenic mimicry (VM) is a unique property of malignant cancer cells which promote metastasis. Thus, it represents an opportunity to investigate both the molecular mechanisms and the therapeutic targets of a crucial phenotypic malignant switch. Although this biological process has been largely investigated in different human cancer types, including osteosarcoma, it is still largely unknown in veterinary pathology, where it has been mainly explored in canine mammary tumors. The presence of VM in human osteosarcoma is associated with poor clinical outcome, reduced patient survival, and increased risk of metastasis and it shares the main pathways involved in other type of human tumors. This review illustrates the main findings concerning the VM process in human osteosarcoma, search for the related current knowledge in canine pathology and oncology, and potential involvement of multiple pathways in VM formation, in order to provide a basis for future investigations on VM in canine tumors.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Assembling Disease Networks From Causal Interaction Resources

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.

Protein tyrosine phosphatases in cell adhesion

Adhesive structures between cells and with the surrounding matrix are essential for the development of multicellular organisms. In addition to providing mechanical integrity, they are key signalling centres providing feedback on the extracellular environment to the cell interior, and vice versa. During development, mitosis and repair, cell adhesions must undergo extensive remodelling. Post-translational modifications of proteins within these complexes serve as switches for activity. Tyrosine phosphorylation is an important modification in cell adhesion that is dynamically regulated by the protein tyrosine phosphatases (PTPs) and protein tyrosine kinases. Several PTPs are implicated in the assembly and maintenance of cell adhesions, however, their signalling functions remain poorly defined. The PTPs can act by directly dephosphorylating adhesive complex components or function as scaffolds. In this review, we will focus on human PTPs and discuss their individual roles in major adhesion complexes, as well as Hippo signalling. We have collated PTP interactome and cell adhesome datasets, which reveal extensive connections between PTPs and cell adhesions that are relatively unexplored. Finally, we reflect on the dysregulation of PTPs and cell adhesions in disease.

Simulation of multiple microenvironments shows a pivot role of RPTPs on the control of Epithelial-to-Mesenchymal Transition

Epithelial-to-Mesenchymal Transition (EMT) is a natural and reversible process involved in embryogenesis, wound healing and thought to participate in the process of metastasis. Multiple signals from the microenvironment have been reported to drive EMT. However, the tight control of this process on physiological scenarios and how it is disrupted during cancer progression is not fully understood. Here, we analysed a regulatory network of EMT accounting for 10 key microenvironment signals focusing on the impact of two cell contact signals on the reversibility of EMT and the stability of resulting phenotypes. The analysis showed that the microenvironment is not enough for stabilizing Hybrid and Amoeboid-like phenotypes, requiring intracellular de-regulations as reported during cancer progression. Our simulations demonstrated that RPTP activation by cell contacts have the potential to inhibit the process of EMT and trigger its reversibility under tissue growth and chronic inflammation scenarios. Simulations also showed that hypoxia inhibits the capacity of RPTPs to control EMT. Our analysis further provided a theoretical explanation for the observed correlation between hypoxia and metastasis under chronic inflammation, and predicted that de-regulations in FAT4 signalling may promote Hybrid stabilization. Taken together, we propose a natural control mechanism of EMT that supports the idea that EMT is tightly regulated by the microenvironment.

Role of αVβ3 in Prostate Cancer: Metastasis Initiator and Important Therapeutic Target

In prostate cancer, distant organ metastasis is the leading cause of patient death. Although the mechanism of malignant tumor metastasis is unclear, studies have confirmed that integrin αVβ3 plays an important role in this process. In prostate cancer, αVβ3 mediates adhesion, invasion, immune escape and neovascularization through interactions with different ligands. Among these ligands and in addition to proteins that are directly related to tumor invasion, other proteins that contain the RGD structure could also bind to αVβ3 and cause a number of biological effects. In this article, we summarized the ligand and downstream proteins related to αVβ3-mediated prostate cancer metastasis as well as some diagnostic and therapeutic measures targeting αVβ3.