A multiscale phenotype-structured partial differential equation model for epidermal melanoma cell populations is developed, first considering subcellular MITF and then spatially uniform and spatially heterogeneous populations, highlighting that phenotype reversibility by individual cells does not imply reversibility of phenotype population distributions.
Abstract
Cancer progression is driven by the ability of cells with identical driver mutations to adopt biologically distinct adaptive phenotypes. Yet the population dynamics implied by intratumour phenotypic heterogeneity is poorly understood. Melanoma is an excellent setting to study phenotype switching, in part because phenotypic identity is conferred by melanocyte inducing transcription factor (MITF) activity. Here we develop a multiscale phenotype-structured partial differential equation model for epidermal melanoma cell populations, first considering subcellular MITF and then spatially uniform and spatially heterogeneous populations. The model admits three stable long-term behaviours: slow growth with proliferative cells and non-cycling differentiated cells; faster expansion, with an invasive core; and rapid growth with oscillatory core dynamics. More broadly, the analysis highlights that phenotype reversibility by individual cells does not imply reversibility of phenotype population distributions. Hence, single-cell properties (e.g., reversibility of invasive capacity) must be extrapolated with caution to populations with coupled cell dynamics.
AP-1 transcription factors have been implicated in cellular plasticity, differentiation-state heterogeneity, and phenotype switching, enabling adaptation to anti-cancer therapies. Although AP-1 states, defined by the combinatorial expression of AP-1 proteins, are heterogeneous within cell populations, only a subset of...
Yonatan N. Degefu, Magda Bujnowska, Douglas G. Baumann et al.· Cell Reports· 0 citations
A mathematical model of tumor growth under fluctuating conditions with stochastic phenotypic epithelial-mesenchymal switch as the main mechanism of adaptation is developed, and non-trivial evolutionary outcomes are revealed, depending on the relative time scales of the underlying processes.
Sanasar G. Babajanyan, Y. Wolf, R. Canevarolo et al.· bioRxiv· 0 citations
Comparing EMT dynamics in lung cancer cells under normal growth conditions and in the presence of TGF-β, a known EMT inducer, finds that untreated cells undergo density-independent EMT, and discusses how the minimal quantitative framework may provide insights into the structure of the Waddington landscape associated wi...
Domenico Caudo, Federica Mulè, Giovanna Peruzzi et al.· Journal of Physics, Conferen...· 0 citations
Phenotypic plasticity, the ability of cells to switch between states, is central to development, differentiation, and therapy resistance. Although it is modeled at several scales, from compartmental ODEs to phenotype-structured PDEs and single-cell stochastic equations, a framework connecting these descriptions is miss...
It is shown that aCAFs, whose expression has been associated with poor prognosis, tend to localize at tumor boundaries, where proximity to tumor cells predicts increased expression of aCAF-associated genes.
Ling-Yi Cai, Tai-Hsien Ou Yang, Dimitris Anastassiou· Frontiers in Immunology· 0 citations
Lineage hierarchies and plasticity regulate development and tissue homeostasis, while diverted lineage dynamics and aberrant phenotypic plasticity are among the causes of incomplete drug response and resistance in cancer. Knowing the dynamics of phenotypically heterogeneous populations is therefore central to understan...
Andrea Piras, F. Galvagno, Letizia Pizzini et al.· Cell Reports Methods· 0 citations
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