A proliferation gradient in three‐dimensional colonies of cultured human glioma cells

Carlsson, Jörgen

doi:10.1002/ijc.2910200120

Cited by 55 publications

(18 citation statements)

References 13 publications

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“…These radioisotopic methods of determining the labeling index or fraction of cells in S phase have been described by many investigators (Durand 1976;Carlsson 1977;Yuhas and Li 1978). The percent-labeled-mitoses analyses necessary to determine the rates of progress through the various phases of the cell cycle, however, have been carried out for only a limited number of spheroid systems (Durand 1976;Wibe et al 1981).…”

Section: Methodsmentioning

confidence: 98%

Growth and Cellular Characteristics of Multicell Spheroids

Sutherland

Durand

1984

Recent Results in Cancer Research

143

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Section: Methodsmentioning

confidence: 98%

Growth and Cellular Characteristics of Multicell Spheroids

Sutherland

Durand

1984

Recent Results in Cancer Research

143

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“…Our choice of the functional forms for these rates is moti-vated in the following way. The time taken by cells to replicate their DNA places a physical limit on the rate at which they divide (Carlsson, 1977). Thus as the oxygen concentration increases the cell proliferation rate will increase until it reaches this maximum value.…”

Section: The Mathematical Modelmentioning

confidence: 99%

Estimating the Selective Advantage of Mutant p53 Tumour Cells to Repeated Rounds of Hypoxia

Gammack

Byrne

Lewis

2001

Bulletin of Mathematical Biology

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The tumour suppressor gene, p53, plays an important role in tumour development. Under low levels of oxygen (hypoxia), cells expressing wild-type p53 undergo programmed cell death (apoptosis), whereas cells expressing mutations in the p53 gene may survive and express angiogenic growth factors that stimulate tumour vascularization. Given that cells expressing mutations in the p53 gene have been observed in many forms of human tumour, it is important to understand how both wild-type and mutant cells react to hypoxic conditions. In this paper a mathematical model is presented to investigate the effects of alternating periods of hypoxia and normoxia (normal oxygen levels) on a population of wild-type and mutant p53 tumour cells. The model consists of three coupled ordinary differential equations that describe the densities of the two cell types and the oxygen concentration and, as such, may describe the growth of avascular tumours in vitro and/or in vivo. Numerical and analytical techniques are used to determine how changes in the system parameters influence the time at which mutant cells become dominant within the population. A feedback mechanism, which switches off the oxygen supply when the total cell density exceeds a threshold value, is introduced into the model to investigate the impact that vessel collapse (and the associated hypoxia) has on the time at which the mutant cells become dominant within vascular tumours growing in vivo. Using the model we can predict the time it takes for a subpopulation of mutant p53 tumour cells to become the dominant population within either an avascular tumour or a localized region of a vascular tumour. Based on independent experimental results, our model suggests that the mutant population becomes dominant more quickly in vivo than in vitro (12 days vs 17 days).

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“…Cell mitosis is proportional to substrates present [47]. As mitosis occurs, an appropriate amount of water from the interstitial fluid is converted into cell mass.…”

Section: Quick-guide To Equations and Assumptionsmentioning

confidence: 99%

Prediction of Drug Response in Breast Cancer Using Integrative Experimental/Computational Modeling

Edgerton²,

et al. 2009

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Nearly 30% of women with early-stage breast cancer develop recurrent disease attributed to resistance to systemic therapy. Prevailing models of chemotherapy failure describe three resistant phenotypes: cells with alterations in transmembrane drug transport, increased detoxification and repair pathways, and alterations leading to failure of apoptosis. Proliferative activity correlates with tumor sensitivity. Cell-cycle status, controlling proliferation, depends on local concentration of oxygen and nutrients. Although physiologic resistance due to diffusion gradients of these substances and drugs is a recognized phenomenon, it has been difficult to quantify its role with any accuracy that can be exploited clinically. We implement a mathematical model of tumor drug response that hypothesizes specific functional relationships linking tumor growth and regression to the underlying phenotype. The model incorporates the effects of local drug, oxygen, and nutrient concentrations within the three-dimensional tumor volume, and includes the experimentally observed resistant phenotypes of individual cells. We conclude that this integrative method, tightly coupling computational modeling with biological data, enhances the value of knowledge gained from current pharmacokinetic measurements, and, further, that such an approach could predict resistance based on specific tumor properties and thus improve treatment outcome. [Cancer Res 2009;69(10):4484-92]

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A proliferation gradient in three‐dimensional colonies of cultured human glioma cells

Cited by 55 publications

References 13 publications

Growth and Cellular Characteristics of Multicell Spheroids

Growth and Cellular Characteristics of Multicell Spheroids

Estimating the Selective Advantage of Mutant p53 Tumour Cells to Repeated Rounds of Hypoxia

Prediction of Drug Response in Breast Cancer Using Integrative Experimental/Computational Modeling

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