Examining go-or-grow using fluorescent cell-cycle indicators and cell cycle-inhibiting drugs

Vittadello, Sean T.; McCue, Scott W.; Gunasingh, Gency; Haass, Nikolas K.; Simpson, Matthew J.

doi:10.1101/797142

Cited by 11 publications

(14 citation statements)

References 35 publications

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“…Such noise in the distribution of molecules may affect cell-fate and drive non-genetic heterogeneity [28][29][30], leading to different phenotypic distributions in terms of EMT [3]. While EMT is believed to repress the cell cycle [76,77], this association remains controversial [78]. Thus, noise in the partitioning of parent cell biomolecules among the daughter cells can further alter the subpopulation structure, and may underlie different bimodal distributions of surface CDH1 expression seen in breast cancer cell lines [52].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Epithelial-mesenchymal transition (EMT) and its reverse process mesenchymalepithelial 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Mathematical models of a dichotomy between proliferation and migration are numerous [27][28][29], but whether the two phenotypes are indeed mutually exclusive remains controversial [30]. Our efforts to use mathematical modeling to inform what cost high-ploidy cells (goers) pay for their robustness builds upon these prior works.…”

Section: Overall Model Designmentioning

confidence: 99%

“…A special case applies when the energy diffusion coefficient is very large relative to cell movement, in which case neither chemotaxis nor haptotaxis occurs. All these energetic contingencies determine whether phenotypic differences between goers and growers manifest as such, and explain why non-proliferative arrested cells can have the same motility as cycling cells [30]. A future extension will be to integrate our model of the potential cost of high ploidy with existing Markov models of the robustness benefits it can provide against deleterious mutations [22,31,32].…”

Section: Overall Model Designmentioning

confidence: 99%

Invasion of homogeneous and polyploid populations in nutrient-limiting environments

Kimmel

Dane

Heiser

et al. 2020

Preprint

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Breast cancer progresses in a multistep process from primary tumor growth and stroma invasion to metastasis. Progression is accompanied by a switch to an invasive cell phenotype. Nutrient-limiting environments exhibit chemotaxis with aggressive morphologies characteristic of invasion. The mTOR pathway senses essential nutrients, informing the cell to respond with either increased chemotaxis and nutrient uptake or cell cycle progression. Randomized clinical trials have shown that mTOR inhibitors (mTOR-I) improve the outcome of metastatic breast cancer patients. However, there are considerable differences between and within tumors that impact the effectiveness of mTOR-I, including differences in access to nutrients. It is unknown how co-existing cells differ in their response to nutrient limitations and how this impacts invasion of the metapopulation as a whole. We integrate modeling with microenvironmental perturbations data to investigate invasion in nutrient-limiting environments inhabited by one or two cancer cell subpopulations. Hereby subpopulations are defined by their energy efficiency and chemotactic ability. We calculate the invasion-distance traveled by a homogeneous population. For heterogeneous populations, our results suggest that an imbalance between nutrient efficacy and chemotactic superiority accelerates invasion. Such imbalance will segregate the two populations spatially and only one type will dominate at the invasion front. Only if these two phenotypes are balanced do the two populations compete for the same space, which decelerates invasion. We investigate ploidy as a candidate biomarker of this phenotypic heterogeneity to discern circumstances when inhibiting chemotaxis amplifies innternal competition and decelerates tumor progression, from circumstances that render clinical consequences of chemotactic inhibition unfavorable. Significance: A better understanding of the nature of the double-edged sword of high ploidy is a prerequisite to personalize combinationtherapies with cytotoxic drugs and inhibitors of signal transduction pathways such as MTOR-Is.

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“…Given suitable initial conditions, r(x, 0) and g(x, 0), and parameter values θ, Equations Despite the abundance of experimental data in Figure 1 and the apparent simplicity of Equations (3)-(4), it is unclear whether these experimental data are sufficient to provide reasonable estimates of the four parameters: θ = (D r , D g , k r , k g ). Therefore, we also consider a simpler model whereby we set D r = D g = D, implying that cells in G1 phase diffuse at the same rate as cells in S/G2/M phase [42]. This simpler model is characterised by just three unknown parameters: θ = (D, k r , k g ).…”

Section: Mathematical Modelmentioning

confidence: 99%

Practical parameter identifiability for spatiotemporal models of cell invasion

Simpson

Baker

Vittadello

et al. 2019

Preprint

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We examine the practical identifiability of parameters in a spatiotemporal reaction-diffusion model of a scratch assay where we have access to a relatively large amount of quantitative experimental data. The experiment involves fluorescent cell cycle labels so that the cell cycle is tracked in real time. This provides spatial information about the position of individual cells as well as temporal information about the cell cycle phase of each cell. Cell cycle labelling is incorporated into the reaction-diffusion model by considering the total population to be composed of two interacting subpopulations. Practical identifiability is examined using a Bayesian Markov Chain Monte Carlo (MCMC) framework, confirming that the parameters are identifiable when we assume that the diffusivities of the two subpopulations are identical, but that the parameters are practically non-identifiable when we allow the diffusivities to be distinct. We also assess identifiability using a profile likelihood approach, and show that this provides similar results to MCMC with the advantage of being an order of magnitude faster to compute. Despite the profile likelihood being relatively underutilised in the mathematical biology literature, we suggest that it ought to be adopted as a screening tool to provide a rapid assessment of practical identifiability before more computationally cumbersome MCMC computations are attempted.

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Examining go-or-grow using fluorescent cell-cycle indicators and cell cycle-inhibiting drugs

Cited by 11 publications

References 35 publications

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

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

Invasion of homogeneous and polyploid populations in nutrient-limiting environments

Practical parameter identifiability for spatiotemporal models of cell invasion

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