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一个结合多种类型基因组不稳定性并拟合结肠癌数据的随机致癌模型。

A stochastic carcinogenesis model incorporating multiple types of genomic instability fitted to colon cancer data.

作者信息

Little Mark P, Vineis Paolo, Li Guangquan

机构信息

Department of Epidemiology and Public Health, Imperial College Faculty of Medicine, London W21PG, UK.

出版信息

J Theor Biol. 2008 Sep 21;254(2):229-38. doi: 10.1016/j.jtbi.2008.05.027. Epub 2008 May 29.

Abstract

A generalization of the two-mutation stochastic carcinogenesis model of Moolgavkar, Venzon and Knudson and certain models constructed by Little [Little, M.P. (1995). 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. Biometrics 51, 1278-1291] and Little and Wright [Little, M.P., Wright, E.G. (2003). A stochastic carcinogenesis model incorporating genomic instability fitted to colon cancer data. Math. Biosci. 183, 111-134] is developed; the model incorporates multiple types of progressive genomic instability and an arbitrary number of mutational stages. The model is fitted to US Caucasian colon cancer incidence data. On the basis of the comparison of fits to the population-based data, there is little evidence to support the hypothesis that the model with more than one type of genomic instability fits better than models with a single type of genomic instability. Given the good fit of the model to this large dataset, it is unlikely that further information on presence of genomic instability or of types of genomic instability can be extracted from age-incidence data by extensions of this model.

摘要

开发了Moolgavkar、Venzon和Knudson的双突变随机致癌模型以及Little [Little, M.P. (1995年)。两个突变足以引发癌症吗?Moolgavkar、Venzon和Knudson的双突变致癌模型以及Armitage和Doll的多阶段模型的一些推广。生物统计学51, 1278 - 1291] 以及Little和Wright [Little, M.P., Wright, E.G. (2003年)。一个纳入基因组不稳定性的随机致癌模型,拟合结肠癌数据。数学生物科学183, 111 - 134] 构建的某些模型的推广;该模型纳入了多种类型的渐进性基因组不稳定性和任意数量的突变阶段。该模型拟合了美国白种人结肠癌发病率数据。基于对基于人群数据拟合情况的比较,几乎没有证据支持这样的假设,即具有不止一种类型基因组不稳定性的模型比具有单一类型基因组不稳定性的模型拟合得更好。鉴于该模型对这个大型数据集的良好拟合,通过扩展此模型从年龄 - 发病率数据中提取关于基因组不稳定性的存在或基因组不稳定性类型的进一步信息不太可能。

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