Metabolic Plasticity and Epithelial-Mesenchymal Transition

Thomson, Timothy M.; Balcells, Cristina; Cascante, Marta

doi:10.3390/jcm8070967

Cited by 26 publications

(26 citation statements)

References 179 publications

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“…As such, we characterized the EMT spectrum in depth, finding numerous enriched signaling, metabolic, and regulatory features throughout. Amongst these, in late-hybrid EMT states, we observed increased MYC activity and proliferation, as well as potential metabolic rewiring from OXPHOS to glycolysis, which has been implicated in both tumor invasiveness 49,50 and EMT 51,52 .…”

Section: Discussionmentioning

confidence: 92%

Single-cell lineage and transcriptome reconstruction of metastatic cancer reveals selection of aggressive hybrid EMT states

Simeonov

Byrns

Clark

et al. 2020

Preprint

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Metastatic cancer remains largely incurable due to an incomplete understanding of how cancer cells disseminate throughout the body. However, tools for probing metastatic dissemination and associated molecular changes at high resolution are lacking. Here we present multiplexed, activatable, clonal, and subclonal GESTALT (macsGESTALT), an inducible lineage recorder with concurrent single cell readout of transcriptional and phylogenetic information. By integrating multiple copies of combined static barcodes and evolving CRISPR/Cas9 barcodes, macsGESTALT enables clonal tracing and subclonal phylogenetic reconstruction, respectively. High barcode editing and recovery rates produce deep lineage reconstructions, densely annotated with transcriptomic information. Applying macsGESTALT to a mouse model of metastatic pancreatic cancer, we reconstruct dissemination of tens-of-thousands of single cancer cells representing 95 clones and over 6,000 unique subclones across multiple distant sites, e.g. liver and lung metastases. Transcriptionally, cells exist along a continuum of epithelial-to-mesenchymal transition (EMT) in vivo with graded changes in associated signaling, metabolic, and regulatory processes. Lineage analysis reveals that from a majority of non-metastatic, highly epithelial clones, a single dominant clone that has progressed along EMT drives the majority of metastasis. Within this dominant clone a parallel process occurs, where a small number of aggressive subclones drive clonal outgrowth. By precisely mapping subclones along the EMT continuum, we find that size and dissemination gradually increase, peaking at late-hybrid EMT states but precipitously falling once subclones are highly mesenchymal. Late-hybrid EMT states are selected from a predominately epithelial ancestral pool, enabling rapid metastasis but also forcing extensive and continuous population bottlenecking. Notably, late-hybrid gene signatures are associated with decreased survival in human pancreatic cancer, while epithelial, early-hybrid, and highly mesenchymal states are not. Our findings illuminate features of metastasis and EMT with the potential for therapeutic exploitation. Ultimately, macsGESTALT provides a powerful, accessible tool for probing cancer and stem cell biology in vivo.

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Section: Discussionmentioning

confidence: 92%

Single-cell lineage and transcriptome reconstruction of metastatic cancer reveals selection of aggressive hybrid EMT states

Simeonov

Byrns

Clark

et al. 2020

Preprint

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“…The epithelial-mesenchymal transition (EMT) is a cell biological process crucial for various aspects of tumor aggressivenesscancer metastasis , resistance against cell death (Huang et al, 2013), metabolic reprogramming (Thomson et al, 2019), refractory response to chemotherapy and radiotherapy (Kurrey et al, 2009), tumor-initiation potential (Jolly et al, 2014), and immune evasion (Tripathi et al, 2016;Terry et al, 2017)thus eventually affecting patient survival (Tan et al, 2014). EMT is a multidimensional, non-linear 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.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Transcriptomics-Based Scoring Metrics for the Epithelial-Hybrid-Mesenchymal Spectrum

Chakraborty

George

Tripathi

et al. 2020

Front. Bioeng. Biotechnol.

105

106

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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 the immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells can stably acquire one or more hybrid epithelial/mesenchymal (E/M) phenotypes which often can be more aggressive than purely E or M 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 can 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 E and 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.

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“…The Epithelial-mesenchymal transition (EMT) is a cell biological process crucial for various aspects of tumor aggressiveness -cancer metastasis , resistance against cell death (Huang et al, 2013), metabolic reprogramming (Thomson et al, 2019), refractory response to chemotherapy and radiotherapy (Kurrey et al, 2009), tumor-initiation potential (Jolly et al, 2014), and immune evasion (Tripathi et al, 2016;Terry et al, 2017) -thus eventually affecting patient survival (Tan et al, 2014). 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.…”

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

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.

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Metabolic Plasticity and Epithelial-Mesenchymal Transition

Cited by 26 publications

References 179 publications

Single-cell lineage and transcriptome reconstruction of metastatic cancer reveals selection of aggressive hybrid EMT states

Single-cell lineage and transcriptome reconstruction of metastatic cancer reveals selection of aggressive hybrid EMT states

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

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