Literature DB >> 2807642

Quiescence as an explanation of Gompertzian tumor growth.

M Gyllenberg1, G F Webb.   

Abstract

In this paper we propose a mathematical model for the growth of solid tumors which employs quiescence as a mechanism to explain characteristic Gompertz-type growth curves. The model distinguishes between two types of cells within the tumor, proliferating and quiescent. Empirical data strongly suggest that the larger the tumor, the more likely it is that a proliferating cell becomes quiescent and the more unlikely it is that a quiescent cell reenters the proliferating cycle. These facts are taken as the basic assumptions of the model. It is shown that these assumptions imply diminishing of the growth fraction (i.e. proportion of proliferating cells), a phenomenon found in most tumors. Three qualitatively different cases are analyzed in detail and illustrated by examples. In the case of a tumor forming a necrotic center the model predicts that the tumor grows monotonically to its ultimate size according to a typical S-shaped Gompertz curve, and that the growth fraction tends to zero. In the case of true quiescence, where the dormant cells retain their capability of becoming proliferating, we distinguish between two types of tumors: one in which only proliferating cells can die and one in which there is mortality among quiescent cells, too. In the first case the predicted tumor growth occurs in the early stages in a way that is very similar to that of tumors forming a necrotic center; the growth fraction still tends to zero, but ultimately the tumor grows without bound. In the second case the tumor grows to a finite limit depending only on the vital rates, while the growth fraction decreases to a strictly positive value.

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Year:  1989        PMID: 2807642

Source DB:  PubMed          Journal:  Growth Dev Aging        ISSN: 1041-1232


  25 in total

1.  Gompertzian growth pattern correlated with phenotypic organization of colon carcinoma, malignant glioma and non-small cell lung carcinoma cell lines.

Authors:  M A A Castro; F Klamt; V A Grieneisen; I Grivicich; J C F Moreira
Journal:  Cell Prolif       Date:  2003-04       Impact factor: 6.831

2.  A nonlinear structured population model of tumor growth with quiescence.

Authors:  M Gyllenberg; G F Webb
Journal:  J Math Biol       Date:  1990       Impact factor: 2.259

3.  A resource-based model of microbial quiescence.

Authors:  Tufail Malik; Hal Smith
Journal:  J Math Biol       Date:  2006-05-06       Impact factor: 2.259

4.  Parameter non-identifiability of the Gyllenberg-Webb ODE model.

Authors:  Niklas Hartung
Journal:  J Math Biol       Date:  2013-08-30       Impact factor: 2.259

5.  Using Fractal Geometry and Universal Growth Curves as Diagnostics for Comparing Tumor Vasculature and Metabolic Rate With Healthy Tissue and for Predicting Responses to Drug Therapies.

Authors:  Van M Savage; Alexander B Herman; Geoffrey B West; Kevin Leu
Journal:  Discrete Continuous Dyn Syst Ser B       Date:  2013-06       Impact factor: 1.327

6.  Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data.

Authors:  David A Hormuth; Jared A Weis; Stephanie L Barnes; Michael I Miga; Erin C Rericha; Vito Quaranta; Thomas E Yankeelov
Journal:  Phys Biol       Date:  2015-06-04       Impact factor: 2.583

7.  Evaluation of total hepatocellular cancer lifespan, including both clinically evident and preclinical development, using combined network phenotyping strategy and fisher information analysis.

Authors:  Petr Pančoška; Lubomír Skála; Jaroslav Nešetřil; Brian I Carr
Journal:  Semin Oncol       Date:  2015-01-05       Impact factor: 4.929

8.  Growth curve analysis of asymptomatic and symptomatic meningiomas.

Authors:  Satoshi Nakasu; Yoko Nakasu; Tadateru Fukami; Junya Jito; Kazuhiko Nozaki
Journal:  J Neurooncol       Date:  2010-08-05       Impact factor: 4.130

Review 9.  Breast cancer stem cells-research opportunities utilizing mathematical modeling.

Authors:  Rina Ashkenazi; Trachette L Jackson; Gabriela Dontu; Max S Wicha
Journal:  Stem Cell Rev       Date:  2007-06       Impact factor: 5.739

10.  Modelling the growth of solid tumours and incorporating a method for their classification using nonlinear elasticity theory.

Authors:  M A Chaplain; B D Sleeman
Journal:  J Math Biol       Date:  1993       Impact factor: 2.259

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