Literature DB >> 8589222

Are two mutations sufficient to cause cancer? Some generalizations of the two-mutation model of carcinogenesis of Moolgavkar, Venzon, and Knudson, and of the multistage model of Armitage and Doll.

M P Little1.   

Abstract

Some generalizations of the two-mutation carcinogenesis model of Moolgavkar, Venzon, and Knudson (to allow for an arbitrary number of mutational stages) and of the multistage model of Armitage and Doll are shown to have the property that, at least in the case when the parameters of the model are eventually constant, the excess relative and absolute risks following changes in any of the parameters will eventually tend to zero. It is also shown that when the parameters governing the processes of cell division, death, or additional mutation at the penultimate stage are subject to perturbations, there are relatively large fluctuations in the hazard function for carcinogenesis for either model, which start almost as soon as the parameters are changed. For this reason it appears that without some extra stochastic "stage" appended (such as might be provided by consideration of the process of development of a malignant clone clone from a single malignant cell) the two-mutation model is not well able to describe the pattern of excess risk for solid cancers that is often seen after exposure to ionizing radiation, although leukemia may be better fitted by the two-mutation model in this respect. An examination of the results of perturbing various of the parameters for models that require three or more mutations provides indications that these models are easier to reconcile with the results from a body of epidemiological data relating to solid cancers.

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Year:  1995        PMID: 8589222

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  27 in total

1.  Fitting the two-stage model of carcinogenesis to nested case-control data on the Colorado Plateau uranium miners: dependence on data assumptions.

Authors:  Richard G E Haylock; Colin R Muirhead
Journal:  Radiat Environ Biophys       Date:  2003-11-15       Impact factor: 1.925

2.  Protective bystander effects simulated with the state-vector model.

Authors:  Helmut Schöllnberger; Peter M Eckl
Journal:  Dose Response       Date:  2007-06-26       Impact factor: 2.658

Review 3.  Issues in Interpreting Epidemiologic Studies of Populations Exposed to Low-Dose, High-Energy Photon Radiation.

Authors:  Ethel S Gilbert; Mark P Little; Dale L Preston; Daniel O Stram
Journal:  J Natl Cancer Inst Monogr       Date:  2020-07-01

4.  Impact of tumor progression on cancer incidence curves.

Authors:  E Georg Luebeck; Kit Curtius; Jihyoun Jeon; William D Hazelton
Journal:  Cancer Res       Date:  2012-10-10       Impact factor: 12.701

5.  New stochastic carcinogenesis model with covariates: an approach involving intracellular barrier mechanisms.

Authors:  Igor Akushevich; Galina Veremeyeva; Julia Kravchenko; Svetlana Ukraintseva; Konstantin Arbeev; Alexander V Akleyev; Anatoly I Yashin
Journal:  Math Biosci       Date:  2011-12-17       Impact factor: 2.144

6.  Modeling lung cancer risk in case-control studies using a new dose metric of smoking.

Authors:  Sally W Thurston; Geoffrey Liu; David P Miller; David C Christiani
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2005-10       Impact factor: 4.254

7.  Parameter identifiability and redundancy: theoretical considerations.

Authors:  Mark P Little; Wolfgang F Heidenreich; Guangquan Li
Journal:  PLoS One       Date:  2010-01-27       Impact factor: 3.240

Review 8.  Cancer models, genomic instability and somatic cellular Darwinian evolution.

Authors:  Mark P Little
Journal:  Biol Direct       Date:  2010-04-20       Impact factor: 4.540

9.  Chapter 13: CISNET lung models: comparison of model assumptions and model structures.

Authors:  Pamela M McMahon; William D Hazelton; Marek Kimmel; Lauren D Clarke
Journal:  Risk Anal       Date:  2012-07       Impact factor: 4.000

10.  Parameter identifiability and redundancy in a general class of stochastic carcinogenesis models.

Authors:  Mark P Little; Wolfgang F Heidenreich; Guangquan Li
Journal:  PLoS One       Date:  2009-12-31       Impact factor: 3.240

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