4.5 Article

Modelling lung tumour risk in radon-exposed uranium miners using generalizations of the two-mutation model of Moolgavkar, Venzon and Knudson

Journal

INTERNATIONAL JOURNAL OF RADIATION BIOLOGY
Volume 78, Issue 1, Pages 49-68

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/09553000110085797

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Purposes: To model radon-induced lung cancer in uranium miners using a quasi-biological model of carcinogenesis. Materials and methods: Fitting of generalizations of the stochastic two-mutation carcinogenesis model of Moolgavkar, Venzon and Knudson to a case-control dataset nested within the cohort and to the full cohort of lung cancer mortality in the Colorado Plateau uranium miners, taking account of exposure to cigarette smoke and to radon daughters. Results: Models with three mutations gave adequate descriptions of the time and age patterns of radon-daughter-induced excess lung tumour mortality. The overall fit of the two-mutation model to the case-control data was somewhat worse than that of the three-mutation model. For both the optimal two- and three-mutation models radon daughters and cigarette smoke were assumed to act on the first mutation rate. In the optimal two-mutation model, radon daughters also modified the intermediate cell death or differentiation rate. In the optimal three-mutation model, radon daughters modified the second mutation rate. In all models, the action of radon daughters and cigarette smoke was markedly non-linear, particularly in their action on the mutation rates. The optimal two- and three-mutation models fitted to the cohort data were of slightly different form to those fitted to the case-control data. The model fits to the cohort data are preferred to those to the case-control data on grounds of plausibility. Conclusions : Quasi-biological carcinogenesis models with three mutations give adequate descriptions of the time and age patterns of radon-daughter-induced excess lung tumour mortality. The overall fit of the two-mutation model is somewhat worse than that of the three-mutation model.

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