Inferring tree models for oncogenesis from comparative genome hybridization data
Inferring tree models for oncogenesis from comparative genome hybridization data
复制标题
DOI：
10.1089/cmb.1999.6.37
复制
发表时间：
1999-03-01
影响因子：
1.7
通讯作者：
Schäffer, AA
中科院分区：
文献类型：
--
作者：
Desper, R;Jiang, F;Schäffer, AA
比较基因组杂交 (CGH) 是一种测量肿瘤细胞染色体区域增加和丢失的实验室方法。人们相信，肿瘤细胞中 DNA 的获得和损失并非完全随机发生，而是部分通过某种因果关系发生。将肿瘤进展与 DNA 获得和丢失的发生联系起来的模型对于寻找癌症基因和癌症诊断可能非常有用。我们为从 CGH 数据集推断肿瘤进展模型奠定了一些数学基础。我们考虑一类比为结直肠癌开发的路径模型更通用的树模型，我们基于图中最大权重分支的思想推导了树模型推理算法，并且我们表明，在合理的假设下，我们的算法推断出正确的树。我们已经在软件中实现了我们的方法，并用肾癌的 CGH 数据集进行了说明。
Comparative genome hybridization (CGH) is a laboratory method to measure gains and losses of chromosomal regions in tumor cells. It is believed that DNA gains and losses in tumor cells do not occur entirely at random, but partly through some flow of causality. Models that relate tumor progression to the occurrence of DNA gains and losses could be very useful in hunting cancer genes and in cancer diagnosis. We lay some mathematical foundations for inferring a model of tumor progression from a CGH data set. We consider a class of tree models that are more general than a path model that has been developed for colorectal cancer, We derive a tree model inference algorithm based on the idea of a maximum-weight branching in a graph, and we show that under plausible assumptions our algorithm infers the correct tree. We have implemented our methods in software, and we illustrate with a CGH data set for renal cancer.