Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

Najafi, Annice; Jolly, Mohit Kumar; George, Jason T.

doi:10.1016/j.isci.2023.106964

Cited by 5 publications

(4 citation statements)

References 81 publications

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“…Nonetheless, with limited experimental data and different assays, we were able to narrow down to mechanisms that could explain E-M population dynamics observed in PMC42-LA and HCC38 cells, and establish the necessity of cell-state transition in determining these dynamics. Future longitudinal experimental data using more than one surface or molecular marker to classify phenotypic heterogeneity (for instance, E-cadherin and Vimentin) will facilitate characterizing and isolating the more plastic hybrid E/M phenotypes and their contribution to population dynamics 5,13,[44][45][46][47][48] .…”

Section: Discussionmentioning

confidence: 99%

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Jain,

Kizhuttil,

Nair

et al. 2023

Preprint

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Individual cells in a tumour can be distributed among Epithelial (E) and Mesenchymal (M) cell-states, as characterised by the levels of canonical E and M markers. Even after E and M (E-M) subpopulations are isolated and then cultured independently, E-M heterogeneity can re-equilibrate in each population over time, sometimes regaining the initial distribution of the parental cell population. However, it remains unclear which population-level processes give rise to the dynamical changes in E-M heterogeneity observed experimentally, including 1) differential growth, 2) cell-state switching, and 3) frequency-dependent growth or state-transition rates. Here, we analyse the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that growth differences among E and M subpopulations, with and without any frequency-dependent interactions (cooperation or suppression) among E-M sub-populations, are insufficient to explain the observed population dynamics. This insufficiency is ameliorated by including cell-state transitions, albeit at slow rates, in explaining both PMC42-LA and HCC38 cells data. Further, our models predict that treatment of HCC38 cells with TGFβ signalling and JAK2/3 inhibitors could significantly enhance the transition rates from M state to E state, but does not prevent transitions from E to M. Finally, we devise a selection criterion to identify the next most informative time points for which future experimental data can optimally improve the identifiability of our estimated best fit model parameters. Overall, our study identifies the necessary population-level processes shaping the dynamics of E-M heterogeneity in breast cancer cells.

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Section: Discussionmentioning

confidence: 99%

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Jain,

Kizhuttil,

Nair

et al. 2023

Preprint

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“…COMET relies on a DTW alignment score to a reference flow cytometry data for the identification of the optimal cutoff of highly variable EMT genes (Please refer to Najafi et al. 2023 6 for further information). To visualize the DTW alignment scores to each of the three EMT trajectories and the total score, the code in step 8 can be utilized to generate heatmaps as shown in Figure 4 A. Lastly, the code in steps 9 to 10 can be used to visualize the stochastic trajectories fitted to the time-course data and the inter-state transition rates (the results of this code were run on the A549 sample treated with TGF

from the Cook et al.…”

Section: Expected Outcomesmentioning

confidence: 99%

Protocol for inferring epithelial-to-mesenchymal transition trajectories from single-cell RNA sequencing data using R

Najafi,

Jolly,

George

2024

STAR Protocols

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“…COMET can be utilized to infer the dynamic phenotypic transitions induced by EMP, including EMT, p‐EMT, and MET, to predict the timing and distribution of phenotypic heterogeneity within tumors. 254 Besides, spatial transcriptomics can also analyze RNA levels in a spatial context to reveal tissue heterogeneity. 255 Another recent study employed spatial transcriptomics and single cell datasets to investigate the spatial heterogeneity of EMT and analyzed the various interactions between EMT and NK cells as well as fibroblasts in the tumor microenvironment.…”

Section: Future Perspectives and Emerging Technologiesmentioning

confidence: 99%

Epithelial–mesenchymal plasticity in cancer: signaling pathways and therapeutic targets

Wang,

Xue,

Pang

et al. 2024

MedComm

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Currently, cancer is still a leading cause of human death globally. Tumor deterioration comprises multiple events including metastasis, therapeutic resistance and immune evasion, all of which are tightly related to the phenotypic plasticity especially epithelial–mesenchymal plasticity (EMP). Tumor cells with EMP are manifest in three states as epithelial–mesenchymal transition (EMT), partial EMT, and mesenchymal–epithelial transition, which orchestrate the phenotypic switch and heterogeneity of tumor cells via transcriptional regulation and a series of signaling pathways, including transforming growth factor‐β, Wnt/β‐catenin, and Notch. However, due to the complicated nature of EMP, the diverse process of EMP is still not fully understood. In this review, we systematically conclude the biological background, regulating mechanisms of EMP as well as the role of EMP in therapy response. We also summarize a range of small molecule inhibitors, immune‐related therapeutic approaches, and combination therapies that have been developed to target EMP for the outstanding role of EMP‐driven tumor deterioration. Additionally, we explore the potential technique for EMP‐based tumor mechanistic investigation and therapeutic research, which may burst vigorous prospects. Overall, we elucidate the multifaceted aspects of EMP in tumor progression and suggest a promising direction of cancer treatment based on targeting EMP.

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Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

Cited by 5 publications

References 81 publications

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Protocol for inferring epithelial-to-mesenchymal transition trajectories from single-cell RNA sequencing data using R

Epithelial–mesenchymal plasticity in cancer: signaling pathways and therapeutic targets

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