Engler David A, Mohapatra Gayatry, Louis David N, Betensky Rebecca A
Department of Biostatistics, Harvard University, 655 Huntington Avenue, Boston, MA 02115, and Massachusetts General Hospital, Department of Pathology, Charlestown 02129, USA.
Biostatistics. 2006 Jul;7(3):399-421. doi: 10.1093/biostatistics/kxj015. Epub 2006 Jan 9.
DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based comparative genomic hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present. Software for this approach is available at http://www.biostat.harvard.edu/ approximately betensky/papers.html.
DNA序列拷贝数已被证明与癌症的发生和发展有关。基于微阵列的比较基因组杂交技术（aCGH）是一项最新进展，旨在确定全基因组中大量标记处的拷贝数比率。由于染色体和杂交过程中存在实验和生物学变异，目前的方法仅限于对单条染色体进行分析。我们提出了一种更强大的方法，该方法可以整合不同染色体和杂交过程中的信息。我们假设一个高斯混合模型，具有隐马尔可夫依赖结构和随机效应，以考虑肿瘤间的变异以及肿瘤内的克隆变异。为便于计算，我们基于伪似然函数进行估计。该方法可以对每个克隆处基因改变的可能性进行定量评估，并生成图形显示以便于直观解读。我们通过模拟研究和对一个脑肿瘤aCGH数据集的分析来评估该方法的特性。我们表明，在存在肿瘤内克隆变异的情况下，伪似然方法在检测拷贝数改变的小区域以及准确分类变化区域方面均优于现有方法。该方法的软件可在http://www.biostat.harvard.edu/ approximately betensky/papers.html获取。
