The interplay between cell adhesion and environment rigidity in the morphology of tumors

Reis, Adriana Neves dos; Mombach, José C. M.; Walter, Marcelo; Ávila, Leôni F de

doi:10.1016/s0378-4371(02)01821-6

Cited by 15 publications

(10 citation statements)

References 10 publications

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“…Some models account for cell-cell adhesion in the tumor core through surface tension (38,39), but while this is appropriate where the cells are densely populated, it does not readily extend to the sparsely populated invasive zone. Thus to model cell-cell adhesion in the invasive zone, we believe a discrete model (16,17,23,40) would be most suitable.…”

Section: Discussionmentioning

confidence: 99%

A Mathematical Model of Glioblastoma Tumor Spheroid Invasion in a Three-Dimensional In Vitro Experiment

Stein

Demuth

Mobley

et al. 2007

Biophysical Journal

222

217

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Glioblastoma, the most malignant form of brain cancer, is responsible for 23% of primary brain tumors and has extremely poor outcome. Confounding the clinical management of glioblastomas is the extreme local invasiveness of these cancer cells. The mechanisms that govern invasion are poorly understood. To gain insight into glioblastoma invasion, we conducted experiments on the patterns of growth and dispersion of U87 glioblastoma tumor spheroids in a three-dimensional collagen gel. We studied two different cell lines, one with a mutation to the EGFR (U87DeltaEGFR) that is associated with increased malignancy, and one with an endogenous (wild-type) receptor (U87WT). We developed a continuum mathematical model of the dispersion behaviors with the aim of identifying and characterizing discrete cellular mechanisms underlying invasive cell motility. The mathematical model quantitatively reproduces the experimental data, and indicates that the U87WT invasive cells have a stronger directional motility bias away from the spheroid center as well as a faster rate of cell shedding compared to the U87DeltaEGFR cells. The model suggests that differences in tumor cell dispersion may be due to differences in the chemical factors produced by cells, differences in how the two cell lines remodel the gel, or different cell-cell adhesion characteristics.

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Section: Discussionmentioning

confidence: 99%

A Mathematical Model of Glioblastoma Tumor Spheroid Invasion in a Three-Dimensional In Vitro Experiment

Stein

Demuth

Mobley

et al. 2007

Biophysical Journal

222

217

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“…22, issue 2, pages 175-176, by permission of Oxford University Press. Simulation results from dos Reis et al (2003) showing how tumors growing in host tissue environments of low (C) and high (D) "rigidity" can influence compact, non-invasive (C) and fractal, fingering, invasive (D) morphologies. Simulations are carried out to approximately 5000 cells, where cells are represented as interacting particles in a 2-D continuous space with periodic boundary conditions.…”

Section: Discussionmentioning

confidence: 99%

“…While "discrete" in-silico models (e.g., DiMilla et al, 1991, Dickinson & Tranquillo et al, 1993, Kansal et al, 2000a, 2000b, Patel et al, 2001, Ferreira et al, 2002, Turner & Sherratt, 2002, Leyrat et al, 2003, dos Reis et al, 2003, Anderson, 2005 are able to capture individual cell migration and easily incorporate biological rules, such as cell-cell & cell-medium interactions and motion due to chemotaxis and haptotaxis, they are limited to relatively small numbers of cells due to computational cost, among the other deficiencies and oversimplifications introduced by the discrete approach. In contrast, "continuum" models (e.g., Byrne & Chaplain, 1995a, 1995b, 1996a, 1996b, Bellomo & Preziosi, 2000, Cristini et al, 2003, Macklin & Lowengrub, 2005, Frieboes et al, 2006a, Li et al, 2007, Macklin & Lowengrub, 2007, describing tissue matter as a continuum medium rather that discrete individual cells, capture the collective motion of FCCMUs with less computational expense.…”

Section: Model Development Goals and Choicesmentioning

confidence: 99%

“…The notion that formation of fingering protuberances at the tumor-host boundary is primarily due to an intrinsic physical property termed rigidity of the host environment to resist tumor growth has also been computationally examined (dos Reis et al, 2003). Low rigidity allows a tumor to expand through the host environment resulting in a well-defined tumor-parenchyma interface, whereas higher rigidity forces a tumor to grow by invading the host tissue resulting in a fingering morphology (Figure 3, C and D), as predicted by simulation results.…”

Section: Effects Of Cell-cell and Cell-matrix Interactionsmentioning

confidence: 99%

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Predictive oncology: A review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth

et al. 2007

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Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

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“…It is important to consider the effects of cellular adhesion between cells and the extra-cellular matrix (ECM) since the secretion or suppression of adhesion molecules is a possible mechanism for the onset of metastasis [3,29,33]. Mathematical models incorporating adhesion include [12,42,56], while [17,60] explore the relationship between cell -cell adhesion, tumor morphology, hypoxia and disease prognosis.…”

Section: Tumor Cellsmentioning

confidence: 99%

Spatial Tumor‐Immune Modeling

Pillis

Mallet

Radunskaya

2000

Computational and Mathematical Methods in Medicine

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In this paper, we carry out an examination of four mechanisms that can potentially lead to changing morphologies in a growing tumor: variations in nutrient consumption rates, cellular adhesion, excessive consumption of nutrients by tumor cells and immune cell interactions with the tumor. We present numerical simulations using a hybrid PDE-cellular automata (CA) model demonstrating the effects of each mechanism before discussing hypotheses about the contribution of each mechanism to morphology change.

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The interplay between cell adhesion and environment rigidity in the morphology of tumors

Cited by 15 publications

References 10 publications

A Mathematical Model of Glioblastoma Tumor Spheroid Invasion in a Three-Dimensional In Vitro Experiment

A Mathematical Model of Glioblastoma Tumor Spheroid Invasion in a Three-Dimensional In Vitro Experiment

Predictive oncology: A review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth

Spatial Tumor‐Immune Modeling

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