Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling - Archive ouverte HAL
Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2023

Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling

Résumé

Glucose is a primary energy source for cancer cells. Several lines of evidence support the idea that monocarboxylate transporters, such as MCT1, elicit metabolic reprogramming of cancer cells in glucose-poor environments, allowing them to reuse lactate, a byproduct of glucose metabolism, as an alternative energy source with serious consequences for disease progression. We employ a synergistic experimental and mathematical modelling approach to explore the evolutionary processes at the root of cancer cell adaptation to glucose deprivation, with particular focus on the mechanisms underlying the increase in MCT1 expression observed in glucose-deprived aggressive cancer cells. Data from in vitro experiments on breast cancer cells are used to inform and calibrate a mathematical model that comprises a partial integro-differential equation for the dynamics of a population of cancer cells structured by the level of MCT1 expression. Analytical and numerical results of this model indicate that environment-induced changes in MCT1 expression mediated by lactate-associated signalling pathways enable a prompt adaptive response of glucose-deprived cancer cells, whilst spontaneous changes due to non-genetic instability create the substrate for environmental selection to act upon, speeding up the selective sweep underlying cancer cell adaptation to glucose deprivation, and may constitute a long-term bet-hedging mechanism.
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Dates et versions

hal-03947209 , version 1 (19-01-2023)
hal-03947209 , version 2 (24-01-2024)

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  • HAL Id : hal-03947209 , version 1

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Jérôme Denis, Nathalie Ferrand, Tommaso Lorenzi, Michéle Sabbah, Chiara Villa, et al.. Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling. 2023. ⟨hal-03947209v1⟩
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