Survival Outcomes in Cancer Patients Predicted by a Partial EMT Gene Expression Scoring Metric

Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition

We present the epithelial-to-mesenchymal transition (EMT) from two perspectives: experimental/technological and theoretical. We review the state of the current understanding of the regulatory networks that underlie EMT in three physiological contexts: embryonic development, wound healing, and metastasis. We describe the existing experimental systems and manipulations used to better understand the molecular participants and factors that influence EMT and metastasis. We review the mathematical models of the regulatory networks involved in EMT, with a particular emphasis on the network motifs (such as coupled feedback loops) that can generate intermediate hybrid states between the epithelial and mesenchymal states. Ultimately, the understanding gained about these networks should be translated into methods to control phenotypic outcomes, especially in the context of cancer therapeutic strategies. We present emerging theories of how to drive the dynamics of a network toward a desired dynamical attractor (e.g. an epithelial cell state) and emerging synthetic biology technologies to monitor and control the state of cells.

Regulation of miRNAs by Snail during epithelial-to-mesenchymal transition in HT29 colon cancer cells

Epithelial-to-mesenchymal transition (EMT) in cancer cells, represents early stages of metastasis and is a promising target in colorectal cancer (CRC) therapy. There have been many attempts to identify markers and key pathways induced throughout EMT but the process is complex and depends on the cancer type and tumour microenvironment. Here we used the colon cancer cell line HT29, which stably overexpressed Snail, the key transcription factor in early EMT, as a model for colorectal adenocarcinoma cells with a pro-metastatic phenotype. We investigated miRNA expression regulation during that phenotypic switching. We found that overexpression of Snail in HT29 cells triggered significant changes in individual miRNA levels but did not change the global efficiency of miRNA processing. Snail abundance repressed the expression of miR-192 and miR-194 and increased miR-205, let-7i and SNORD13 levels. These identified changes correlated with the reported transcriptomic alterations in Snail-overexpressing HT29 cells. We also investigated how Snail affected the miRNA content of extracellular vesicles (EVs) released from HT29 cells. Our data suggest that the presence of Snail significantly alters the complex mRNA/miRNA interactions in the early steps of metastasis and also has an impact on the content of EVs released from HT29 cells.

High content screening identifies monensin as an EMT-selective cytotoxic compound

Epithelial-to-mesenchymal transition (EMT) is implicated in cancer metastasis and drug resistance. Specifically targeting cancer cells in an EMT-like state may have therapeutic value. In this study, we developed a cell imaging-based high-content screening protocol to identify EMT-selective cytotoxic compounds. Among the 2,640 compounds tested, salinomycin and monensin, both monovalent cation ionophores, displayed a potent and selective cytotoxic effect against EMT-like cells. The mechanism of action of monensin was further evaluated. Monensin (10 nM) induced apoptosis, cell cycle arrest, and an increase in reactive oxygen species (ROS) production in TEM 4-18 cells. In addition, monensin rapidly induced swelling of Golgi apparatus and perturbed mitochondrial function. These are previously known effects of monensin, albeit occurring at much higher concentrations in the micromolar range. The cytotoxic effect of monensin was not blocked by inhibitors of ferroptosis. To explore the generality of our findings, we evaluated the toxicity of monensin in 24 human cancer cell lines and classified them as resistant or sensitive based on IC₅₀ cutoff of 100 nM. Gene Set Enrichment Analysis identified EMT as the top enriched gene set in the sensitive group. Importantly, increased monensin sensitivity in EMT-like cells is associated with elevated uptake of ³H-monensin compared to resistant cells.

Computational Modeling of the Crosstalk Between Macrophage Polarization and Tumor Cell Plasticity in the Tumor Microenvironment

Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M₁ vs. M₂) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.

Testing the gene expression classification of the EMT spectrum

The epithelial-mesenchymal transition (EMT) plays a central role in cancer metastasis and drug resistance-two persistent clinical challenges. Epithelial cells can undergo a partial or full EMT, attaining either a hybrid epithelial/mesenchymal (E/M) or mesenchymal phenotype, respectively. Recent studies have emphasized that hybrid E/M cells may be more aggressive than their mesenchymal counterparts. However, mechanisms driving hybrid E/M phenotypes remain largely elusive. Here, to better characterize the hybrid E/M phenotype (s) and tumor aggressiveness, we integrate two computational methods-(a) RACIPE-to identify the robust gene expression patterns emerging from the dynamics of a given gene regulatory network, and (b) EMT scoring metric-to calculate the probability that a given gene expression profile displays a hybrid E/M phenotype. We apply the EMT scoring metric to RACIPE-generated gene expression data generated from a core EMT regulatory network and classify the gene expression profiles into relevant categories (epithelial, hybrid E/M, mesenchymal). This categorization is broadly consistent with hierarchical clustering readouts of RACIPE-generated gene expression data. We also show how the EMT scoring metric can be used to distinguish between samples composed of exclusively hybrid E/M cells and those containing mixtures of epithelial and mesenchymal subpopulations using the RACIPE-generated gene expression data.

Gene expression cancer classification using modified K-Nearest Neighbors technique

Gene expression microarray classification is a crucial research field as it has been employed in cancer prediction and diagnosis systems. Gene expression data are composed of dozens of samples characterized by thousands of genes. Hence, an accurate and effective classification of such samples is a challenge. Machine learning techniques have been broadly utilized to build substantial and precise classification models. This paper proposes a new classification technique for gene expression data, which is called Modified k-nearest neighbor (MKNN). MKNN is applied in two scenarios namely; smallest modified KNN (SMKNN) and largest modified KNN (LMKNN). Both implementations are undertaken to enhance the performance of KNN. The key idea is to employ robust neighbors from training data by using a new weighting strategy. Several experiments have been performed on six different gene expression datasets. Experiments have shown that MKNN in its both scenarios outperforms traditional as well as recent ones. MKNN has been compared against (i) KNN, (ii) weighted KNN, (iii) support vector machine (SVM), (iv) fuzzy support vector machine, (v) brain emotional learning (BEL) in terms of classification accuracy, precision, and recall. On the other hand, results show that MKNN introduces smaller testing time than both KNN and weighted KNN.

EMT Transition States during Tumor Progression and Metastasis

Epithelial-mesenchymal transition (EMT) is a process in which epithelial cells acquire mesenchymal features. In cancer, EMT is associated with tumor initiation, invasion, metastasis, and resistance to therapy. Recently, it has been demonstrated that EMT is not a binary process, but occurs through distinct cellular states. Here, we review the recent studies that demonstrate the existence of these different EMT states in cancer and the mechanisms regulating their functions. We discuss the different functional characteristics, such as proliferation, propagation, plasticity, invasion, and metastasis associated with the distinct EMT states. We summarize the role of the transcriptional and epigenetic landscapes, gene regulatory network and their surrounding niche in controlling the transition through the different EMT states.

Hybrid epithelial/mesenchymal phenotypes promote metastasis and therapy resistance across carcinomas

Cancer metastasis and therapy resistance are the major unsolved clinical challenges, and account for nearly all cancer-related deaths. Both metastasis and therapy resistance are fueled by epithelial plasticity, the reversible phenotypic transitions between epithelial and mesenchymal phenotypes, including epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET). EMT and MET have been largely considered as binary processes, where cells detach from the primary tumor as individual units with many, if not all, traits of a mesenchymal cell (EMT) and then convert back to being epithelial (MET). However, recent studies have demonstrated that cells can metastasize in ways alternative to traditional EMT paradigm; for example, they can detach as clusters, and/or occupy one or more stable hybrid epithelial/mesenchymal (E/M) phenotypes that can be the end point of a transition. Such hybrid E/M cells can integrate various epithelial and mesenchymal traits and markers, facilitating collective cell migration. Furthermore, these hybrid E/M cells may possess higher tumor-initiation and metastatic potential as compared to cells on either end of the EMT spectrum. Here, we review in silico, in vitro, in vivo and clinical evidence for the existence of one or more hybrid E/M phenotype(s) in multiple carcinomas, and discuss their implications in tumor-initiation, tumor relapse, therapy resistance, and metastasis. Together, these studies drive the emerging notion that cells in a hybrid E/M phenotype may occupy 'metastatic sweet spot' in multiple subtypes of carcinomas, and pathways linked to this (these) hybrid E/M state(s) may be relevant as prognostic biomarkers as well as a promising therapeutic targets.

Downregulation of FOXK2 is associated with poor prognosis in patients with gastric cancer

Forkhead box (FOX)K2 (FOXK2) is a member of the FOX transcription factor family. It has been suggested previously that FOXK2 is required to suppress tumor growth; however, the exact role of FOXK2 in gastric cancer remains to be elucidated. In the present study, the association between FOXK2 expression and the clinicopathological characteristics of patients with gastric cancer was investigated. The prognostic value of FOXK2 expression and the significance of clinicopathological parameters in the overall survival (OS) and progression-free survival of patients were also determined by survival analysis. To investigate the functional roles of FOXK2, it was downregulated in BGC-823 cells using small interfering (si)RNA, and upregulated using a FOXK2 plasmid. Colony formation, Cell Counting Kit-8 and cell proliferation analyses were conducted to examine the proliferation of gastric cancer cells. Transwell and wound-healing assays were performed to investigate the effect of FOXK2 expression on gastric cancer cell migration and invasion. The clinical data demonstrated that FOXK2 expression was reduced in high-grade gastric cancer tissues, and a low level of FOXK2 expression indicated a poor prognosis. The data obtained from the Human Protein Atlas revealed that patients with gastric cancer and a high level of FOXK2 expression had a longer OS time. The results of colony formation assays, Transwell and wound healing assays demonstrated that FOXK2 repressed the proliferation, invasion and migration of gastric cancer cells, respectively. The findings indicated that FOXK2 may serve as a promising therapeutic target in gastric cancer. Taken together, the findings of the present study demonstrated that FOXK2 functions as a tumor suppressor in gastric cancer; the loss of FOXK2 may induce the growth and invasion of gastric cancer cells.

Interconnected feedback loops among ESRP1, HAS2, and CD44 regulate epithelial-mesenchymal plasticity in cancer

Aberrant activation of epithelial-mesenchymal transition (EMT) in carcinoma cells contributes to increased migration and invasion, metastasis, drug resistance, and tumor-initiating capacity. EMT is not always a binary process; rather, cells may exhibit a hybrid epithelial/mesenchymal (E/M) phenotype. ZEB1-a key transcription factor driving EMT-can both induce and maintain a mesenchymal phenotype. Recent studies have identified two novel autocrine feedback loops utilizing epithelial splicing regulatory protein 1 (ESRP1), hyaluronic acid synthase 2 (HAS2), and CD44 which maintain high levels of ZEB1. However, how the crosstalk between these feedback loops alters the dynamics of epithelial-hybrid-mesenchymal transition remains elusive. Here, using an integrated theoretical-experimental framework, we identify that these feedback loops can enable cells to stably maintain a hybrid E/M phenotype. Moreover, computational analysis identifies the regulation of ESRP1 as a crucial node, a prediction that is validated by experiments showing that knockdown of ESRP1 in stable hybrid E/M H1975 cells drives EMT. Finally, in multiple breast cancer datasets, high levels of ESRP1, ESRP1/HAS2, and ESRP1/ZEB1 correlate with poor prognosis, supporting the relevance of ZEB1/ESRP1 and ZEB1/HAS2 axes in tumor progression. Together, our results unravel how these interconnected feedback loops act in concert to regulate ZEB1 levels and to drive the dynamics of epithelial-hybrid-mesenchymal transition.

Metastatic tumor cells - genotypes and phenotypes

BACKGROUND

Metastasis is the primary cause of mortality in cancer patients. Therefore, elucidating the genetics and epigenetics of metastatic tumor cells and the mechanisms by which tumor cells acquire metastatic properties constitute significant challenges in cancer research.

OBJECTIVE

To summarize the current understandings of the specific genotype and phenotype of the metastatic tumor cells.

METHOD and RESULT

In-depth genetic analysis of tumor cells, especially with advances in the next-generation sequencing, have revealed insights of the genotypes of metastatic tumor cells. Also, studies have shown that the cancer stem cell (CSC) and epithelial to mesenchymal transition (EMT) phenotypes are associated with the metastatic cascade.

CONCLUSION

In this review, we will discuss recent advances in the field by focusing on the genomic instability and phenotypic dynamics of metastatic tumor cells.

An alternative splicing switch in FLNB promotes the mesenchymal cell state in human breast cancer

Alternative splicing of mRNA precursors represents a key gene expression regulatory step and permits the generation of distinct protein products with diverse functions. In a genome-scale expression screen for inducers of the epithelial-to-mesenchymal transition (EMT), we found a striking enrichment of RNA-binding proteins. We validated that QKI and RBFOX1 were necessary and sufficient to induce an intermediate mesenchymal cell state and increased tumorigenicity. Using RNA-seq and eCLIP analysis, we found that QKI and RBFOX1 coordinately regulated the splicing and function of the actin-binding protein FLNB, which plays a causal role in the regulation of EMT. Specifically, the skipping of FLNB exon 30 induced EMT by releasing the FOXC1 transcription factor. Moreover, skipping of FLNB exon 30 is strongly associated with EMT gene signatures in basal-like breast cancer patient samples. These observations identify a specific dysregulation of splicing, which regulates tumor cell plasticity and is frequently observed in human cancer.

As the human body develops, countless cells change from one state into another. Two important cell states are known as epithelial and mesenchymal. Cells in the epithelial state tend to be tightly connected and form barriers, like skin cells. Mesenchymal state cells are loosely organized, move around more and make up connective tissues. Some cells alternate between these states via an epithelial-to-mesenchymal transition (EMT for short) and back again. Without this transition, certain organs would not develop and wounds would not heal. Yet, cancer cells also use this transition to spread to distant sites of the body. Such cancers are often the most aggressive, and therefore the most deadly.

The epithelial-to-mesenchymal transition is dynamically regulated in a reversible manner. For example, the genes for some proteins might only be active in the epithelial state and further reinforce this state by turning on other ‘epithelial genes’. Alternatively, there might be differences in the processing of mRNA molecules – the intermediate molecules between DNA and protein – that result in the production of different proteins in epithelial and mesenchymal cells.

Li, Choi et al. wanted to know which of the thousands of human genes can endow epithelial state cells with mesenchymal characteristics. A better understanding of the switch could help to prevent cancers undergoing an epithelial-to-mesenchymal transition.

From a large-scale experiment in human breast cancer cells, Li, Choi et al. found that a group of proteins that bind and modify mRNA molecules are important for the epithelial-to-mesenchymal transition. Two proteins in particular promoted the transition, most likely by binding to the mRNA of a third protein called FLNB and removing a small piece of it. FLNB normally works to prevent the epithelial-to-mesenchymal transition, but the smaller protein encoded by the shorter mRNA promoted the transition by turning on ‘mesenchymal genes’.

This switching between different FLNB proteins happens in some of the more aggressive breast cancers, which also contain mesenchymal cells. Finding out which FLNB protein is made in a given cancer may provide an indication of its aggressiveness. Also, looking for drugs that can target the mRNA-binding proteins or FLNB may one day lead to new treatments for some of the most aggressive breast cancers.

Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems

Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity of cancer cells, highlighting the kinetics of cancer stem cell and the role of the epithelial mesenchymal transition for metastasis. It appears that the tumor micro-environment plays a crucial role in triggering phenotypic transitions, as we illustrate discussing the challenges posed by macrophages and cancer associated fibroblasts. To disentangle the complexity of environmentally induced phenotypic transitions, there is a growing need for novel advanced algorithms as those proposed in our recent work combining single cell data analysis and numerical simulations of gene regulatory networks. We conclude discussing recent developments in artificial intelligence and its applications to personalized cancer treatment.

Hybrid epithelial/mesenchymal phenotype(s): The 'fittest' for metastasis?

Metastasis is the leading cause of mortality among cancer patients. Dissemination enabled by an epithelial-to-mesenchymal transition (EMT) of carcinoma cells has long been considered to be the predominant mechanism for carcinoma metastasis, based on overexpression studies of many EMT-inducing transcription factors. Individual CTCs - and a binary framework of EMT - have been long considered to be sufficient and necessary condition for metastasis. However, recent studies have shown that collective migration and invasion through tumor buds and clusters of Circulating Tumor Cells (CTCs) as possibly being the prevalent mode of metastasis, although individual CTCs may still contribute to metastasis. These strands and clusters have been proposed to often exhibit a hybrid epithelial/mesenchymal (E/M) phenotype where cells retain epithelial traits of cell-cell adhesion and simultaneously gain mesenchymal characteristics of migration and invasion. To highlight the crucial questions regarding metastasis, we define EMT in a non-binary and context-specific manner, suggest that it can be viewed as a trans-differentiation process, and illustrate the implications of hybrid E/M phenotype(s) and cluster-based dissemination in metastasis.

A mechanism-based computational model to capture the interconnections among epithelial-mesenchymal transition, cancer stem cells and Notch-Jagged signaling

Epithelial-mesenchymal transition (EMT) and cancer stem cell (CSCs) formation are two fundamental and well-studied processes contributing to cancer metastasis and tumor relapse. Cells can undergo a partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype or a complete EMT to attain a mesenchymal one. Similarly, cells can reversibly gain or lose 'stemness'. This plasticity in cell states is modulated by signaling pathways such as Notch. However, the interconnections among the cell states enabled by EMT, CSCs and Notch signaling remain elusive. Here, we devise a computational model to investigate the coupling among the core decision-making circuits for EMT, CSCs and Notch. Our model predicts that hybrid E/M cells are most likely to associate with stem-like traits and enhanced Notch-Jagged signaling – a pathway implicated in therapeutic resistance. Further, we show that the position of the 'stemness window' on the 'EMT axis' is varied by altering the coupling strength between EMT and CSC circuits, and/or modulating Notch signaling. Finally, we analyze the gene expression profile of CSCs from several cancer types and observe a heterogeneous distribution along the 'EMT axis', suggesting that different subsets of CSCs may exist with varying phenotypes along the epithelial-mesenchymal axis. We further investigate therapeutic perturbations such as treatment with metformin, a drug associated with decreased cancer incidence and increased lifespan of patients. Our mechanism-based model explains how metformin can both inhibit EMT and blunt the aggressive potential of CSCs simultaneously, by driving the cells out of a hybrid E/M stem-like state with enhanced Notch-Jagged signaling.

Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models

Mathematical cancer models are immensely powerful tools that are based in part on the fractal nature of biological structures, such as the geometry of the lung. Cancers of the lung provide an opportune model to develop and apply algorithms that capture changes and disease phenotypes. We reviewed mathematical models that have been developed for biological sciences and applied them in the context of small cell lung cancer (SCLC) growth, mutational heterogeneity, and mechanisms of metastasis. The ultimate goal is to develop the stochastic and deterministic nature of this disease, to link this comprehensive set of tools back to its fractalness and to provide a platform for accurate biomarker development. These techniques may be particularly useful in the context of drug development research, such as combination with existing omics approaches. The integration of these tools will be important to further understand the biology of SCLC and ultimately develop novel therapeutics.

Topography of epithelial-mesenchymal plasticity

The transition between epithelial and mesenchymal states has fundamental importance for embryonic development, stem cell reprogramming, and cancer progression. Here, we construct a topographic map underlying epithelial-mesenchymal transitions using a combination of numerical simulations of a Boolean network model and the analysis of bulk and single-cell gene expression data. The map reveals a multitude of metastable hybrid phenotypic states, separating stable epithelial and mesenchymal states, and is reminiscent of the free energy measured in glassy materials and disordered solids. Our work not only elucidates the nature of hybrid mesenchymal/epithelial states but also provides a general strategy to construct a topographic representation of phenotypic plasticity from gene expression data using statistical physics methods.

Triptolide suppresses paraquat induced idiopathic pulmonary fibrosis by inhibiting TGFB1-dependent epithelial mesenchymal transition

Idiopathic pulmonary fibrosis (IPF) and tumor are highly similar to abnormal cell proliferation that damages the body. This malignant cell evolution in a stressful environment closely resembles that of epithelial-mesenchymal transition (EMT). As a popular EMT-inducing factor, TGFβ plays an important role in the progression of multiple diseases. However, the drugs that target TGFB1 are limited. In this study, we found that triptolide (TPL), a Chinese medicine extract, exerts an anti-lung fibrosis effect by inhibiting the EMT of lung epithelial cells. In addition, triptolide directly binds to TGFβ and subsequently increase E-cadherin expression and decrease vimentin expression. In in vivo studies, TPL improves the survival state and inhibits lung fibrosis in mice. In summary, this study revealed the potential therapeutic effect of paraquat induced TPL in lung fibrosis by regulating TGFβ-dependent EMT progression.

[Experience in the management of children with diabetes mellitus]

Aerobic glycolysis, also referred to as the Warburg effect, has been regarded as the dominant metabolic phenotype in cancer cells for a long time. More recently, it has been shown that mitochondria in most tumors are not defective in their ability to carry out oxidative phosphorylation (OXPHOS). Instead, in highly aggressive cancer cells, mitochondrial energy pathways are reprogrammed to meet the challenges of high energy demand, better utilization of available fuels and macromolecular synthesis for rapid cell division and migration. Mitochondrial energy reprogramming is also involved in the regulation of oncogenic pathways via mitochondria-to-nucleus retrograde signaling and post-translational modification of oncoproteins. In addition, neoplastic mitochondria can engage in crosstalk with the tumor microenvironment. For example, signals from cancer-associated fibroblasts can drive tumor mitochondria to utilize OXPHOS, a process known as the reverse Warburg effect. Emerging evidence shows that cancer cells can acquire a hybrid glycolysis/OXPHOS phenotype in which both glycolysis and OXPHOS can be utilized for energy production and biomass synthesis. The hybrid glycolysis/OXPHOS phenotype facilitates metabolic plasticity of cancer cells and may be specifically associated with metastasis and therapy-resistance. Moreover, cancer cells can switch their metabolism phenotypes in response to external stimuli for better survival. Taking into account the metabolic heterogeneity and plasticity of cancer cells, therapies targeting cancer metabolic dependency in principle can be made more effective.