A molecular portrait of epithelial–mesenchymal plasticity in prostate cancer associated with clinical outcome

Stylianou, Nataly; Lehman, Melanie; Wang, Chenwei; Fard, Atefeh Taherian; Rockstroh, Anja; Fazli, Ladan; Jovanovic, Lidija; Ward, Micheal; Sadowski, Martin C.; Kashyap, Abhishek S.; Buttyan, Ralph; Gleave, Martin; Westbrook, Thomas F.; Williams, Elizabeth D.; Gunter, Jennifer H.; Nelson, Colleen C.; Hollier, Brett G.

doi:10.1038/s41388-018-0488-5

Cited by 87 publications

(105 citation statements)

References 66 publications

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%

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%

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

“…Recent studies have shown that cancer cells can also stably exist in a hybrid epithelial-mesenchymal state wherein they co-express epithelial and mesenchymal markers Pastushenko et al, 2018). Cancer cells within a solid tumor exhibit widespread heterogeneity in the expression of epithelial and mesenchymal markers and can exhibit an epithelial (E), a mesenchymal (M), or one or more hybrid epithelial-mesenchymal (hybrid E / M) phenotype(s) (Hong et al, 2018;Kim et al, 2013;Pereira et al, 2015;Stylianou et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Mechanism for Epithelial-Mesenchymal Heterogeneity in a Population of Cancer Cells

Tripathi

Levine

Jolly

2019

Preprint

View full text Add to dashboard Cite

Epithelial-mesenchymal heterogeneity, wherein cells within the same tumor can exhibit an epithelial, a mesenchymal, or one or more hybrid epithelial-mesenchymal phenotype(s), has been observed across cancer types and implicated in metastatic aggressiveness. Here, we have used computational modeling to show that this heterogeneity can emerge from the noise in the partitioning of RNAs and proteins among the daughter cells during cancer cell division. Our model captures the population-level behavior of murine prostate cancer cells, the hysteresis in the dynamics of epithelial-mesenchymal plasticity, and how hybrid phenotype-promoting factors alter the phenotypic composition of a population. We further used the model to describe the implications of heterogeneity for therapeutics. By linking the dynamics of an intracellular regulatory circuit to the phenotypic composition of a population, the study contributes towards understanding how non-genetic heterogeneity can be generated and propagated from a small, homogeneous population, and towards therapeutic targeting of cancer cell heterogeneity.

show abstract

“…Though the examples depicted are not complete, it is obvious that many nodes of this signaling network may participate in one or more mechanisms of cellular learning. Purple arrows highlight those nodes, which have already been identified as part of the mechanisms inducing epithelial-mesenchymal transition memory [44][45][46]. All of these nodes possess one or more features identified as potential mechanisms of cellular learning in our paper.…”

Section: Key Figurementioning

confidence: 80%

“…Several observations showed that epithelial-mesenchymal transition has a molecular memory [44][45][46]. Purple arrows in Figure 4 point to those nodes, which have already been identified as participants in these processes.…”

Section: Learning Of Signaling Networkmentioning

confidence: 93%

Learning of Signaling Networks: Molecular Mechanisms

Csermely

Kunsic

Mendik

et al. 2020

Trends in Biochemical Sciences

View full text Add to dashboard Cite

Molecular processes of neuronal learning have been well-described. However, learning mechanisms of non-neuronal cells have not been fully understood at the molecular level. Here, we discuss molecular mechanisms of cellular learning, including conformational memory of intrinsically disordered proteins and prions, signaling cascades, protein translocation, RNAs (microRNA and lncRNA), and chromatin memory. We hypothesize that these processes constitute the learning of signaling networks and correspond to a generalized Hebbian learning process of single, non-neuronal cells, and discuss how cellular learning may open novel directions in drug design and inspire new artificial intelligence methods. Highlights--Besides the well-known learning processes of neurons, non-neuronal single cells are able to learn and show a more robust (and often faster) adaptive response when the same stimulus is repeated.--Known examples of cellular learning are sensitization-or habituation-type responses.--Several molecular mechanisms of neuronal learning, such as conformational memory, protein translocation, signaling cascades, microRNAs, lncRNAs and chromatin memory, also participate in learning of non-neuronal, single cells.--We propose that these molecular mechanisms form the integrative memory of signaling networks and display a generalized Hebbian learning process by increasing those edge weights through which the signal has been propagated. Learning of non-neural cells: Adaptive Molecular Responses Observed when the same Stimulus is RepeatedMolecular mechanisms of neuronal learning have been well-established in the past decades [1]. However, we know relatively little about the molecular details of learning mechanisms of non-neuronal cells. We define cellular learning as an adaptive response to a simple stimulus observed when the same stimulus is repeated in a short time -as compared to the duration of the cell cycle of the given cell. We note that it is crucial to discriminate between molecular changes which are indeed adaptive, and those which are fortuitous by-products of other, co-* Correspondence: csermely.peter@med.semmelweis-univ.hu (P. Csermely)

show abstract

A molecular portrait of epithelial–mesenchymal plasticity in prostate cancer associated with clinical outcome

Cited by 87 publications

References 66 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

A Mechanism for Epithelial-Mesenchymal Heterogeneity in a Population of Cancer Cells

Learning of Signaling Networks: Molecular Mechanisms

Contact Info

Product

Resources

About