Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution

Karacosta, Loukia Georgiou; Anchang, Benedict; Ignatiadis, Nikolaos; Kimmey, Samuel C.; Benson, Jalen; Shrager, Joseph B.; Tibshirani, Robert; Bendall, Sean C.; Plevritis, Sylvia K.

doi:10.1101/570341

Cited by 30 publications

(37 citation statements)

References 61 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EMT involves a multitude of changes at both molecular and morphological levels. Various attempts to characterize the spectrum of EMT at molecular and/or morphological levels have been made recently, enabled by latest developments in multiplex imaging, single-cell RNA-seq and inducible systems (Mandal et al, 2016;Pastushenko et al, 2018;Stylianou et al, 2018;Cook and Vanderhyden, 2019;Devaraj and Bose, 2019;Karacosta et al, 2019;Wang et al, 2019b;Watanabe et al, 2019). These approaches have highlighted the dynamical nature of EMT in driving cancer progression in patients (Jolly and Celia-Terrassa, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…EMT is a multidimensional, nonlinear process that involves changes in a compendium of molecular and morphological traits, such as altered cell polarity, partial or complete loss of cell-cell adhesion, and increased migration and invasion. Cells may take different routes in this multidimensional landscape as effectively captured by recent high-throughput dynamic approaches (Karacosta et al, 2019;Watanabe et al, 2019). Initially thought of as binary, EMT is now considered as a complex process involving one or more hybrid epithelial/mesenchymal (E/M) states (Jolly and Celia-Terrassa, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative study of transcriptomics-based scoring metrics for the epithelial-hybrid-mesenchymal spectrum

Chakraborty

George

Tripathi

et al. 2020

Preprint

View full text Add to dashboard Cite

The Epithelial-mesenchymal transition (EMT) is a cellular process implicated in embryonic development, wound healing, and pathological conditions such as cancer metastasis and fibrosis. Cancer cells undergoing EMT exhibit enhanced aggressive behavior characterized by drug resistance, tumor-initiation potential, and the ability to evade immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells stably acquire one or more hybrid epithelial/ mesenchymal (E/M) phenotypes which often can be more aggressive than purely epithelial or mesenchymal cell populations. Thus, the EMT status of cancer cells can prove to be a critical estimate of patient prognosis. Recent attempts have employed different transcriptomics signatures to quantify EMT status in cell lines and patient tumors. However, a comprehensive comparison of these methods, including their accuracy in identifying cells in the hybrid E/M phenotype(s), is lacking. Here, we compare three distinct metrics that score EMT on a continuum, based on the transcriptomics signature of individual samples. Our results demonstrate that these methods exhibit good concordance among themselves in quantifying the extent of EMT in a given sample. Moreover, scoring EMT using any of the three methods discerned that cells undergo varying extents of EMT across tumor types. Separately, our analysis also identified tumor types with maximum variability in terms of EMT and associated an enrichment of hybrid E/M signatures in these samples. Moreover, we also found that the multinomial logistic regression (MLR) based metric was capable of distinguishing between 'pure' individual hybrid E/M vs. mixtures of epithelial (E) and mesenchymal (M) cells. Our results, thus, suggest that while any of the three methods can indicate a generic trend in the EMT status of a given cell, the MLR method has two additional advantages: a) it uses a small number of predictors to calculate the EMT score, and b) it can predict from the transcriptomic signature of a population whether it is comprised of 'pure' hybrid E/M cells at the single-cell level or is instead an ensemble of E and M cell subpopulations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative study of transcriptomics-based scoring metrics for the epithelial-hybrid-mesenchymal spectrum

Chakraborty

George

Tripathi

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…With an increasing appreciation of the nonlinear dynamics of EMT/MET at the single-cell level [51][52][53] and its implications for metastatic aggressiveness [9], questions regarding the degree of reversibility/irreversibility of EMT/MET in different contexts and the molecular determinants of these processes have gained importance. Recent studies have illustrated that the trajectories taken by individual cells in the high-dimensional molecular and/or morphological landscape of EMP en route to EMT and MET may be different [51,54]; thus, MET cannot be simply thought of as a mirror image of EMT. There may be molecular and/or morphological changes happening at different stages of EMT/MET to varying degrees, hence making it difficult to identify the molecular mechanisms that may render the dynamics of EMT/MET as reversible or irreversible.…”

Section: Discussionmentioning

confidence: 99%

“…The dynamics of EMT has been studied much more in detail as compared to that of MET [51][52][53][54][55][56]; therefore, it is not surprising that irreversible EMT has been reported more frequently. The degree of reversibility of EMT has been proposed to be largely a function of the timescale of EMT induction and corresponding epigenetic changes.…”

Section: Discussionmentioning

confidence: 99%

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

Jia

Tripathi

Chakraborty

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Thereafter, many different partial EMT states were proposed [8, 9, 15–17]. More and more experimental data shows a different number of partial EMT states in various cancer cell lines [18–23]. Recently, several partial EMT phenotypes were found during cancer metastasis in vivo in a skin cancer mouse model [24, 25] and prostate cancer [26].…”

Section: Introductionmentioning

confidence: 99%

A Plausible Accelerating Function of Intermediate States in Cancer Metastasis

Goetz

Melendez-Alvarez

Chen

et al. 2019

Preprint

View full text Add to dashboard Cite

Epithelial-to-mesenchymal transition (EMT) is a fundamental cellular process and plays an essential role in development, tissue regeneration, and cancer metastasis. Interestingly, EMT is not a binary process but instead proceeds with multiple partial intermediate states. However, the functions of these intermediate states are not fully understood. Here, we focus on a general question about how the number of partial EMT states affects cell transformation. First, by fitting a hidden Markov model of EMT with experimental data, we propose a statistical mechanism for EMT in which many unobservable microstates may exist within one of the observable macrostates. Furthermore, we find that increasing the number of intermediate states can accelerate the EMT process and that adding parallel paths or transition layers accelerates the process even further. Last, a stabilized intermediate state traps cells in one partial EMT state. This work advances our understanding of the dynamics and functions of EMT plasticity during cancer metastasis. KeywordsCancer cell plasticity, partial EMT, microstate, macrostate, intermediate state, Markov 1 process, stabilized state. 2 Introduction 3 Epithelial-to-mesenchymal transition (EMT) is a fundamental cellular process in which 4 polarized epithelial cells lose various cell-cell junctions and adhesion and gain migratory 5 and invasive properties to become mesenchymal cells [1, 2]. EMT is very important in 6 embryonic development, tumorigenesis, metastasis, tumour stemness, and therapy 7 resistance [3, 4]. Remarkably, EMT is not a binary process but proceeds with multiple 8 partial intermediate states, collectively known as partial or hybrid EMT states [3, 5-11].9The partial EMT state retains some characteristics of epithelium but also shows 10 features of mesenchymal cells [12][13][14]. One partial EMT state was predicted through 11 mathematical modeling of the EMT core regulatory network and was verified with 12 quantitative experiments by our previous works [5, 6]. Thereafter, many different partial 13 EMT states were proposed [8, 9,[15][16][17]. More and more experimental data shows a 14 different number of partial EMT states in various cancer cell lines [18-23]. Recently, 15 r 1, 2019 1/26 several partial EMT phenotypes were found during cancer metastasis in vivo in a skin 16 cancer mouse model [24, 25] and prostate cancer [26]. While many partial EMT states 17 have been found, their functions are still not fully understood during cancer 18 metastasis [4, 27-29]. 19 Currently, the function of partial EMT states has being studied in the context of 20 coupling with other cellular processes. For example, acquisition of stem-like properties 21 dictates its coupling with cancer stemness [11, 30-34], circulating tumor cells 22 (CTCs) [35, 36], and drug resistance [37]. Thus, the partial EMT cells hold the highest 23 metastatic potential. As well, partial EMT, instead of full EMT, is found to be critical 24 for renal fibrosis [38-40]. There are many potential couplings of partial EMT and other...

show abstract

Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution

Cited by 30 publications

References 61 publications

Comparative study of transcriptomics-based scoring metrics for the epithelial-hybrid-mesenchymal spectrum

Comparative study of transcriptomics-based scoring metrics for the epithelial-hybrid-mesenchymal spectrum

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

A Plausible Accelerating Function of Intermediate States in Cancer Metastasis

Contact Info

Product

Resources

About