Gsteiger Sandro, Morgenthaler Stephan
Institute of Mathematics, Swiss Federal Institute of Technology, Lausanne, Switzerland.
Theor Biol Med Model. 2008 Jul 21;5:13. doi: 10.1186/1742-4682-5-13.
Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite mixture models, for which we prove identifiability. These models are applied to human lung cancer data from several birth cohorts. Maximum likelihood estimation does not perform well in this application due to the heavy censoring in our data. We thus use analytic graduation instead. Very good fits are achieved for models that combine a small high risk group with a large group that is quasi immune.
癌症发生通常被描述为一个多阶段过程，在此过程中干细胞通过一系列突变转化为癌细胞。在本文中，我们考虑通过混合建模对多阶段癌症发生模型进行扩展。这种方法使我们能够以生物学上有意义的方式描述群体异质性。我们专注于有限混合模型，并证明了其可识别性。这些模型被应用于来自几个出生队列的人类肺癌数据。由于我们的数据存在严重删失，最大似然估计在该应用中表现不佳。因此我们改用解析修匀法。对于将一个小的高风险组与一个几乎免疫的大组相结合的模型，拟合效果非常好。
