Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA.
Bioinformatics. 2009 Aug 15;25(16):1999-2005. doi: 10.1093/bioinformatics/btp364. Epub 2009 Jun 19.
Integration of various genome-scale measures of molecular alterations is of great interest to researchers aiming to better define disease processes or identify novel targets with clinical utility. Particularly important in cancer are measures of gene copy number DNA methylation. However, copy number variation may bias the measurement of DNA methylation. To investigate possible bias, we analyzed integrated data obtained from 19 head and neck squamous cell carcinoma (HNSCC) tumors and 23 mesothelioma tumors.
Statistical analysis of observational data produced results consistent with those anticipated from theoretical mathematical properties. Average beta value reported by Illumina GoldenGate (a bead-array platform) was significantly smaller than a similar measure constructed from the ratio of average dye intensities. Among CpGs that had only small variations in measured methylation across tumors (filtering out clearly biological methylation signatures), there were no systematic copy number effects on methylation for three and more than four copies; however, one copy led to small systematic negative effects, and no copies led to substantial significant negative effects.
Since mathematical considerations suggest little bias in methylation assayed using bead-arrays, the consistency of observational data with anticipated properties suggests little bias. However, further analysis of systematic copy number effects across CpGs suggest that though there may be little bias when there are copy number gains, small biases may result when one allele is lost, and substantial biases when both alleles are lost. These results suggest that further integration of these measures can be useful for characterizing the biological relationships between these somatic events.
将各种基因组规模的分子改变测量方法整合在一起,对于旨在更好地定义疾病过程或识别具有临床应用价值的新靶点的研究人员来说非常感兴趣。在癌症中,特别重要的是基因拷贝数 DNA 甲基化的测量。然而,拷贝数变异可能会影响 DNA 甲基化的测量。为了研究可能的偏差,我们分析了来自 19 个头颈部鳞状细胞癌 (HNSCC) 肿瘤和 23 个间皮瘤肿瘤的综合数据。
对观测数据的统计分析产生的结果与从理论数学特性预期的结果一致。Illumina GoldenGate(一种珠阵列平台)报告的平均β值明显小于从平均染料强度比构建的类似测量值。在 CpG 中,在肿瘤之间测量的甲基化变化很小(排除明显的生物学甲基化特征),对于三个或更多拷贝,没有系统的拷贝数对甲基化的影响;然而,一个拷贝会导致小的系统负效应,而没有拷贝会导致显著的负效应。
由于数学考虑表明使用珠阵列检测的甲基化几乎没有偏差,观测数据与预期特性的一致性表明偏差很小。然而,对 CpG 中系统拷贝数效应的进一步分析表明,虽然当存在拷贝数增加时可能几乎没有偏差,但当一个等位基因丢失时可能会导致小的偏差,而当两个等位基因丢失时可能会导致显著的偏差。这些结果表明,进一步整合这些措施可以用于描述这些体细胞事件之间的生物学关系。