Biology subjects

Jolly, M. K.

Publications and source records attributed to Jolly, M. K..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Integrative systems biology approach identified crucial genes and transcription factors associated with gallbladder cancer pathogenesis

Gallbladder cancer (GBC) has a lower incidence rate among the population relative to other cancer types but majorly contributes to the total cancer cases of the biliary tract system. GBC is distinguished from other malignancies due to its high mortality, marked geographical variation and poor prognosis. To date no systemic targeted therapy is available for GBC. The main objective of this study is to determine the molecular signatures correlated with GBC development using integrative system level approaches. We performed analysis of publicly available transcriptomic data to identify differentially regulated genes and pathways. Co-expression network analysis and differential regulatory network analysis identified hub genes and hub transcription factors (TFs) associated with GBC pathogenesis and progression. We then assessed the epithelial-mesenchymal transition (EMT) status of the hub genes using a combination of three scoring methods. The hub genes such as; CDC6, MAPK15, CCNB2, BIRC7, L3MBTL1 identified are regulators of cell cycle components which suggests that cell cycle regulatory genes are significantly linked to GBC pathogenesis and progression.

systems biology↗

bioRxiv2021

Analysis of immune subtypes across the epithelial-mesenchymal plasticity spectrum

Epithelial-mesenchymal plasticity plays a critical role in many solid tumor types as a mediator of metastatic dissemination and treatment resistance. In addition, there is also a growing appreciation that the epithelial/mesenchymal status of a tumor plays a role in immune evasion and immune suppression. A deeper understanding of the immunological features of different tumor types has been facilitated by the availability of large gene expression datasets and the development of methods to deconvolute bulk RNA-Seq data. These resources have generated powerful new ways of characterizing tumors, including classification of immune subtypes based on differential expression of immunological genes. In the present work, we combine scoring algorithms to quantify epithelial-mesenchymal plasticity with immune subtype analysis to understand the relationship between epithelial plasticity and immune subtype across cancers. We find heterogeneity of epithelial-mesenchymal transition (EMT) status both within and between cancer types, with greater heterogeneity in the expression of EMT-related factors than of MET-related factors. We also find that specific immune subtypes have associated EMT scores and differential expression of immune checkpoint markers.

cancer biology↗

bioRxiv2021

Dynamic plasticity within the EMT spectrum, rather than static mesenchymal traits, drives tumor heterogeneity and metastatic progression of breast cancers

The Epithelial-to-Mesenchymal Transition (EMT) is a developmental cellular program frequently coopted by cancer cells1 and is a key contributor to both heterogeneity in solid tumors2-4 and later stage chemo-resistance and metastasis5,6. Rather than being a switch from an epithelial to a mesenchymal state, increasing evidence points to the existence of intermediate EMT states, wherein cells co-express both epithelial and mesenchymal traits7-13. Multiple stable intermediate EMT states possessing unique characteristics exist across the EMT spectrum7,8,14,15, contributing to the complex heterogeneity of tumors to promote metastasis16. While much work has been carried out identifying and characterizing EMT-inducing transcription factors17-20, the transcriptional and epigenetic networks responsible for the stability and maintenance of the midpoints along the EMT spectrum are poorly defined. In addition, there are currently no approaches to identifying and quantifying intermediate EMT subpopulations within patient tumors to evaluate their prognostic significance. Using clonally isolated derivatives of the SUM149PT breast cancer cell line, we systematically interrogate how each EMT state independently contributes to heterogeneity and influences metastatic progression, uncovering the role of RUNX2 in stabilizing certain intermediate states. Using SUM149PT-derived tumors as a training set, we develop an entropy-based model to quantify phenotypic heterogeneity and EMT status. Remarkably, the majority of cell states captured in the SUM149PT model are represented in a selection of patient tumors, laying the foundation for quantification of epithelial-mesenchymal heterogeneity and understanding the role of the intermediate EMT state in tumor progression.

cancer biology↗

bioRxiv2021

A mechanistic model captures the emergence and implications of non-genetic heterogeneity and reversible drug resistance in ER+ breast cancer cells

Resistance to anti-estrogen therapy is an unsolved clinical challenge in successfully treating ER+ breast cancer patients. Acquisition of mutations can confer heritable resistance to cancer cells, enabling their clonal selection to establish a drug-resistant population. Recent studies have demonstrated that cells can tolerate drug treatment without any genetic alterations too; however, the mechanisms and dynamics of such non-genetic adaptation remain elusive. Here, we investigate coupled dynamics of epithelial-mesenchymal transition (EMT) in breast cancer cells and emergence of reversible drug resistance. Our mechanism-based model for the underlying regulatory network reveals that these two axes can drive one another, thus conferring bidirectional plasticity. This network can also enable non-genetic heterogeneity in a population of cells by allowing for six co-existing phenotypes: epithelial-sensitive, mesenchymal-resistant, hybrid E/M-sensitive, hybrid E/M-resistant, mesenchymal-sensitive and epithelial-resistant, with the first two ones being most dominant. Next, in a population dynamics framework, we exemplify the implications of phenotypic plasticity (both drug-induced and intrinsic stochastic switching) and/or non-genetic heterogeneity in promoting population survival in a mixture of sensitive and resistant cells, even in the absence of any cell-cell cooperation. Finally, we propose the potential therapeutic use of MET (mesenchymal-epithelial transition) inducers besides canonical anti-estrogen therapy to limit the emergence of reversible drug resistance. Our results offer mechanistic insights into empirical observations on EMT and drug resistance and illustrate how such dynamical insights can be exploited for better therapeutic designs.

cancer biology↗

bioRxiv2021

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

systems biology↗

bioRxiv2021

A theoretical approach to coupling the epithelial-mesenchymal transition (EMT) to extracellular matrix (ECM) stiffness via LOXL2

The epithelial-mesenchymal transition (EMT) plays a critical role in cancer progression, being responsible in many cases for the onset of the metastatic cascade and being integral in the ability of cells to resist drug treatment. Most studies of EMT focus on its induction via chemical signals such as TGF-{beta} or Notch ligands, but it has become increasingly clear that biomechanical features of the microenvironment such as ECM (extracellular matrix) stiffness can be equally important. Here, we introduce a coupled feedback loop connecting stiffness to the EMT transcription factor ZEB1, which acts via increasing the secretion of LOXL2 that leads to increased cross-linking of collagen fibers in the ECM. This increased cross-linking can effectively increase ECM stiffness and increase ZEB1 levels, thus setting a positive feedback loop between ZEB1 and ECM stiffness. To investigate the impact of this non-cell-autonomous effect, we introduce a computational approach capable of connecting LOXL2 concentration to increased stiffness and thereby to higher ZEB1 levels. Our results indicate that this positive feedback loop, once activated, can effectively lock the cells in a mesenchymal state.

cancer biology↗

bioRxiv2020

Coupled feedback loops involving PAGE4, EMT and Notch signaling can give rise to non-genetic heterogeneity in prostate cancer cells

Non-genetic heterogeneity is emerging to be a crucial factor underlying therapy resistance in multiple cancers. However, the design principles of regulatory networks underlying non-genetic heterogeneity in cancer remain poorly understood. Here, we investigate the coupled dynamics of feedback loops involving a) oscillations in androgen receptor (AR) signaling mediated through an intrinsically disordered protein PAGE4, b) multistability in epithelial-mesenchymal transition (EMT), and c) Notch-Delta-Jagged signaling mediated cell-cell communication, each of which can generate non-genetic heterogeneity through multistability and/or oscillations. Our results show how different coupling strengths between AR and EMT signaling can lead to possible bistability in the levels of AR. These results reveal the emergent dynamics of coupled oscillatory and multi-stable systems and unravel mechanisms by which non-genetic heterogeneity in AR levels can be generated, which can act as a barrier to most existing therapies for prostate cancer patients.

systems biology↗

bioRxiv2020

Multistability and consequent phenotypic plasticity in AMPK-Akt double negative feedback loop in cancer cells

Adaptation and survival of cancer cells to various stress and growth factor conditions is crucial for successful metastasis. A double-negative feedback loop between two serine/threonine kinases AMPK and Akt can regulate the adaptation of breast cancer cells to matrix-deprivation stress. This feedback loop can generate majorly two phenotypes or cell states: matrix detachment-triggered pAMPKhigh/ pAktlow state, and matrix (re)attachment-triggered pAkthigh/ pAMPKlow state. However, whether these two cell states can exhibit phenotypic plasticity and heterogeneity in a given cell population, i.e., whether they can co-exist and undergo spontaneous switching to generate the other subpopulation, remains unclear. Here, we develop a mechanism-based mathematical model that captures the set of experimentally reported interactions among AMPK and Akt. Our simulations suggest that the AMPK-Akt feedback loop can give rise to two co-existing phenotypes (pAkthigh/ pAMPKlow and pAMPKhigh/pAktlow) in specific parameter regimes. Next, to test the model predictions, we segregated these two subpopulations in MDA-MB-231 cells and observed that each of them was capable of switching to another in adherent conditions. Finally, the predicted trends are supported by clinical data analysis of TCGA breast cancer and pan-cancer cohorts that revealed negatively correlated pAMPK and pAkt protein levels. Overall, our integrated computational-experimental approach unravels that AMPK-Akt feedback loop can generate multistability and drive phenotypic switching and heterogeneity in a cancer cell population.

cancer biology↗

bioRxiv2020

Operating principles of circular toggle polygons

Decoding the dynamics of cellular decision-making and cell differentiation is a central question in cell and developmental biology. A common network motif involved in many cell-fate decisions is a mutually inhibitory feedback loop between two self-activating master regulators A and B, also called as toggle switch. Typically, it can allow for three stable states - (high A, low B), (low A, high B) and (medium A, medium B). A toggle triad - three mutually repressing regulators A, B and C, i.e. three toggle switches arranged circularly (between A and B, between B and C, and between A and C) - can allow for six stable states: three single positive and three double positive ones. However, the operating principles of larger toggle polygons, i.e. toggle switches arranged circularly to form a polygon, remain unclear. Here, we simulate using both discrete and continuous methods the dynamics of different sized toggle polygons. We observed a pattern in their steady state frequency depending on whether the polygon was an even or odd numbered one. The even-numbered toggle polygons result in two dominant states with consecutive components of the network expressing alternating high and low levels. The odd-numbered toggle polygons, on the other hand, enable more number of states, usually twice the number of components with the states that follow circular permutation patterns in their composition. Incorporating self-activations preserved these trends while increasing the frequency of multistability in the corresponding network. Our results offer insights into design principles of circular arrangement of regulatory units involved in cell-fate decision making, and can offer design strategies for synthesizing genetic circuits.

systems biology↗

bioRxiv2020

Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer

Phenotypic (non-genetic) heterogeneity has significant implications for development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unravelled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other but members of a team activate one another, forming a toggle switch between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their latent design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.

systems biology↗

bioRxiv2020

Investigating epithelial-mesenchymal heterogeneity of tumors and circulating tumor cells with transcriptomic analysis and biophysical modeling

IntroductionCellular heterogeneity along the Epithelial-Mesenchymal Plasticity (EMP) spectrum is a paramount feature observed in tumors and circulating tumor cells (CTCs). High-throughput techniques now offer unprecedented details on this variability at a single-cell resolution. Yet, there is no current consensus about how EMP in tumors propagates to that in CTCs. To investigate the relationship between EMP associated heterogeneity of tumors and that of CTCs, we integrated transcriptomic analysis and biophysical modeling. MethodsWe apply three EMT (Epithelial-Mesenchymal Transition) scoring metrics to multiple tumor samples and CTC datasets from several cancer types. Moreover, we develop a biophysical model that couples EMT associated phenotypic switching in a primary tumor with cell migration. Finally, we integrate EMT transcriptomic analysis and in silico modeling to evaluate the predictive power of several measurements of tumor aggressiveness, including tumor EMT score, CTC EMT score, fraction of CTC clusters found in circulation, and CTC cluster size distribution. ResultsAnalysis of high-throughput datasets reveals a pronounced heterogeneity without a well-defined relation between EMT traits in tumors and CTCs. Moreover, mathematical modeling predicts different phases where CTCs can be less, equally, or more mesenchymal than primary tumor depending on the dynamics of phenotypic transition and cell migration. Consistently, various datasets of CTC cluster size distribution from different cancer types are fitted onto different regimes of the model. By further constraining the model with experimental measurements of tumor EMT score, CTC EMT score, and fraction of CTC cluster in bloodstream, we show that none of these assays alone can provide sufficient information to predict the other variables. ConclusionsBy integrating analysis of single cell gene expression and in silico modeling, we propose that the relationship between EMT progression in tumors and CTCs can be variable, and in general, predicting one from the other may not be as straightforward as tacitly assumed.

cancer biology↗

bioRxiv2020

Hybrid E/M phenotype(s) and stemness: a mechanistic connection embedded in network topology

Metastasis remains an unsolved clinical challenge. Two crucial features of metastasizing cancer cells are a) their ability to dynamically move along the epithelial-hybrid-mesenchymal spectrum and b) their tumor-initiation potential or stemness. With increasing functional characterization of hybrid epithelial/mesenchymal (E/M) phenotypes along the spectrum, recent in vitro and in vivo studies have suggested an increasing association of hybrid E/M phenotypes with stemness. However, the mechanistic underpinnings enabling this association remain unclear. Here, we develop a mechanism-based mathematical modeling framework that interrogates the emergent nonlinear dynamics of the coupled network modules regulating E/M plasticity (miR-200/ZEB) and stemness (LIN28/let-7). Simulating the dynamics of this coupled network across a large ensemble of parameter sets, we observe that hybrid E/M phenotype(s) are more likely to acquire stemness relative to pure epithelial or mesenchymal states. We also integrate multiple phenotypic stability factors (PSFs) that have been shown to stabilize hybrid E/M phenotypes both in silico and in vitro - such as OVOL1/2, GRHL2, and NRF2 - with this network, and demonstrate that the enrichment of hybrid E/M phenotype(s) with stemness is largely conserved in the presence of these PSFs. Thus, our results offer mechanistic insights into recent experimental observations of hybrid E/M phenotype(s) being essential for tumor-initiation and highlight how this feature is embedded in the underlying topology of interconnected EMT and stemness networks.

cancer biology↗

bioRxiv2020

A computational systems biology approach identifies SLUG as a mediator of partial Epithelial-Mesenchymal Transition (EMT)

Epithelial-mesenchymal plasticity comprises of reversible transitions among epithelial, hybrid epithelial/mesenchymal (E/M) and mesenchymal phenotypes, and underlies various aspects of aggressive tumor progression such as metastasis, therapy resistance and immune evasion. The process of cells attaining one or more hybrid E/M phenotypes is termed as partial EMT. Cells in hybrid E/M phenotype(s) can be more aggressive than those in either fully epithelial or mesenchymal state. Thus, identifying regulators of hybrid E/M phenotypes is essential to decipher the rheostats of phenotypic plasticity and consequent accelerators of metastasis. Here, using a computational systems biology approach, we demonstrate that SLUG (SNAIL2) - an EMT-inducing transcription factor - can inhibit cells from undergoing a complete EMT and thus stabilizing them in hybrid E/M phenotype(s). It expands the parametric range enabling the existence of a hybrid E/M phenotype, thereby behaving as a phenotypic stability factor (PSF). Our simulations suggest that this specific property of SLUG emerges from the topology of the regulatory network it forms with other key regulators of epithelial-mesenchymal plasticity. Clinical data suggests that SLUG associates with worse patient prognosis across multiple carcinomas. Together, our results indicate that SLUG can stabilize hybrid E/M phenotype(s).

cancer biology↗

bioRxiv2020

Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum

Inflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.

cancer biology↗

bioRxiv2020

Multistability in cellular differentiation enabled by a network of three mutually repressing master regulators

Identifying the design principles of complex regulatory networks driving cellular decision-making remains essential to decode embryonic development as well as enhance cellular reprogramming. A well-studied network motif involved in cellular decision-making is a toggle switch - a set of two opposing transcription factors A and B, each of which is a master regulator of a specific cell-fate and can inhibit the activity of the other. A toggle switch can lead to two possible states - (high A, low B) and (low A, high B), and drives the either-or choice between these two cell-fates for a common progenitor cell. However, the principles of coupled toggle switches remains unclear. Here, we investigate the dynamics of three master regulators A, B and C inhibiting each other, thus forming three coupled toggle switches to form a toggle triad. Our simulations show that this toggle triad can lead to co-existence of cells into three differentiated single positive phenotypes - (high A, low B, low C), (low A, high B, low C), and (low A, low B, high C). Moreover, the hybrid or double positive phenotypes - (high A, high B, low C), (low A, high B, high C) and (high A, low B, high C) - can co-exist together with single positive phenotypes. Including self-activation loops on A, B and C can increase the frequency of double positive states. Finally, we apply our results to understand the cellular decision-making in terms of differentiation of naive CD4+ T cells into Th1, Th2 and Th17 states, where hybrid Th1/Th2 and hybrid Th1/Th17 cells have been reported in addition to the Th1, Th2 and Th17 ones. Our results offer novel insights into the design principles of a multistable network topology and provides a framework for synthetic biology to design tristable systems.

systems biology↗

bioRxiv2020

Mechanistic modeling of the SARS-CoV-2 and immune system interplay unravels design principles for diverse clinicopathological outcomes

The disease caused by SARS-CoV-2 is a global pandemic that threatens to bring long-term changes worldwide. Approximately 80% of infected patients are asymptomatic or have mild symptoms such as fever or cough, while rest of the patients have varying degrees of severity of symptoms, with 3-4% mortality rate. Severe symptoms such as pneumonia and Acute Respiratory Distress Syndrome can be caused by tissue damage mostly due to aggravated and unresolved innate and adaptive immune response, often resulting from a cytokine storm. However, the mechanistic underpinnings of such responses remain elusive, with an incomplete understanding of how an intricate interplay among infected cells and cells of innate and adaptive immune system can lead to such diverse clinicopathological outcomes. Here, we use a dynamical systems approach to dissect the emergent nonlinear intra-host dynamics among virally infected cells, the immune response to it and the consequent immunopathology. By mechanistic analysis of cell-cell interactions, we have identified key parameters affecting the diverse clinical phenotypes associated with COVID-19. This minimalistic yet rigorous model can explain the various phenotypes observed across the clinical spectrum of COVID-19, various co-morbidity risk factors such as age and obesity, and the effect of antiviral drugs on different phenotypes. It also reveals how a fine-tuned balance of infected cell killing and resolution of inflammation can lead to infection clearance, while disruptions can drive different severe phenotypes. These results will help further the case of rational selection of drug combinations that can effectively balance viral clearance and minimize tissue damage simultaneously. Significance StatementThe SARS-CoV-2 pandemic has already infected millions of people, and thousands of lives have been lost to it. The pandemic has already tested the limits of our public healthcare systems with a wide spectrum of clinicopathological symptoms and outcomes. The mechanistic underpinnings of the resultant immunopathology caused by the viral infection still remains to be elucidated. Here we propose a minimalistic but rigorous description of the interactions of the virus infected cells and the core components of the immune system that can potentially explain such diversity in the observed clinical outcomes. Our proposed framework could enable a platform to determine the efficacy of various treatment combinations and can contributes a conceptual understanding of dynamics of disease pathogenesis in SARS-CoV-2 infections.

systems biology↗

bioRxiv2020

Gene expression and chromatin accessibility during progressive EMT and MET linked to dynamic CTCF engagement

BackgroundEpithelial-mesenchymal transition (EMT) facilitates cellular movements critical for proper development; however, in a carcinoma, EMT promotes metastatic dissemination. Stable intermediate states (partial-EMT) are increasingly implicated in metastatic dissemination while reversal of EMT, termed mesenchymal-epithelial transition (MET), is increasingly implicated in metastatic colonization. To understand the partial and reversible nature of EMT, we characterized chromatin accessibility dynamics, transcriptome changes, protein expression patterns, as well as E-cadherin expression, localization, and gene-level dynamics in mammary epithelial cells undergoing stepwise reversible EMT. ResultsWhile shorter EMT induction induced internalization of E-cadherin protein, surface expression was recovered upon MET without loss of transcript or bulk protein. Conversely, a longer EMT induced stable repression of E-cadherin indicated by loss of chromatin accessibility and induced global expansion of accessible sites across the genome, facilitated by increased engagement of multiple transcription factor families, including AP-1 and SMAD. We observe enrichment for binding sites for the insulator proteins CTCF and BORIS was significantly diminished in both stemness-enriched partial-EMT and partial-MET states and determined that CTCF repression imparts alterations in some histone covalent modifications concomitant with those observed during TGF{beta}-induced EMT. ConclusionsThese findings are indicative of a major role for chromatin looping and reorganization in plasticity, stemness, and partial EMT phenotypes.

cell biology↗

bioRxiv2020

Epigenetic feedback and stochastic partitioning during cell division can drive resistance to EMT

Epithelial-mesenchymal transition (EMT) and its reverse process mesenchymal-epithelial transition (MET) are central to metastatic aggressiveness and therapy resistance in solid tumors. While molecular determinants of both processes have been extensively characterized, the heterogeneity in the response of tumor cells to EMT and MET inducers has come into focus recently, and has been implicated in the failure of anti-cancer therapies. Recent experimental studies have shown that some cells can undergo an irreversible EMT depending on the duration of exposure to EMT-inducing signals. While the irreversibility of MET, or equivalently, resistance to EMT, has not been studied in as much detail, evidence supporting such behavior is slowly emerging. Here, we identify two possible mechanisms that can underlie resistance of cells to undergo EMT: epigenetic feedback in ZEB1/GRHL2 feedback loop and stochastic partitioning of biomolecules during cell division. Identifying the ZEB1/GRHL2 axis as a key determinant of epithelial-mesenchymal plasticity across many cancer types, we use mechanistic mathematical models to show how GRHL2 can be involved in both the abovementioned processes, thus driving an irreversible MET. Our study highlights how an isogenic population may contain subpopulation with varying degrees of susceptibility or resistance to EMT, and proposes a next set of questions for detailed experimental studies characterizing the irreversibility of MET/resistance to EMT.

cancer biology↗