VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing
VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing
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DOI：
10.1101/gr.129684.111
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发表时间：
2012-03-01
期刊：
影响因子：
7
通讯作者：
Wilson, Richard K.
中科院分区：
文献类型：
--
作者：
Koboldt, Daniel C.;Zhang, Qunyuan;Wilson, Richard K.
癌症是一种由遗传变异和突变驱动的疾病。外显子组测序可用于发现数百种肿瘤中的这些变体和突变。在这里，我们提出了一种分析工具，VarScan 2，用于检测肿瘤正常对外显子组数据中的体细胞突变和拷贝数改变（CNA）。与目前大多数方法不同，我们的算法同时读取两个样本的数据;启发式和统计算法检测序列变异并按体细胞状态（种系、体细胞或洛）对其进行分类;而标准化读取深度的比较描绘了相对拷贝数的变化。我们应用这些方法分析了151例高级别卵巢肿瘤的外显子组序列数据，这些肿瘤的特征是癌症基因组图谱（TCGA）的一部分。我们验证了约7790个体细胞编码突变，实现了93%的灵敏度和85%的单核苷酸变异（SNV）检测精度。基于外显子组的CNA分析确定了每个肿瘤平均29个大规模改变和619个局灶性事件。正如我们以前对这些数据的分析一样，我们观察到癌基因的频繁扩增（例如，CCNE 1、MYC）和肿瘤抑制因子（NF 1、PTEN和CDKN 2A）的缺失。我们使用相关矩阵对角分割（CMDS）算法搜索了其他复发性局灶性CNA，该算法确定了影响582个基因的424个重要事件。总之，我们的结果证明了VarScan 2在体细胞突变和CNA检测方面的强大性能，并为卵巢癌遗传改变的前景提供了新的见解。
Cancer is a disease driven by genetic variation and mutation. Exome sequencing can be utilized for discovering these variants and mutations across hundreds of tumors. Here we present an analysis tool, VarScan 2, for the detection of somatic mutations and copy number alterations (CNAs) in exome data from tumor normal pairs. Unlike most current approaches, our algorithm reads data from both samples simultaneously; a heuristic and statistical algorithm detects sequence variants and classifies them by somatic status (germline, somatic, or LOH); while a comparison of normalized read depth delineates relative copy number changes. We apply these methods to the analysis of exome sequence data from 151 high-grade ovarian tumors characterized as part of the Cancer Genome Atlas (TCGA). We validated some 7790 somatic coding mutations, achieving 93% sensitivity and 85% precision for single nucleotide variant (SNV) detection. Exome-based CNA analysis identified 29 large-scale alterations and 619 focal events per tumor on average. As in our previous analysis of these data, we observed frequent amplification of oncogenes (e.g., CCNE1, MYC) and deletion of tumor suppressors (NF1, PTEN, and CDKN2A). We searched for additional recurrent focal CNAs using the correlation matrix diagonal segmentation (CMDS) algorithm, which identified 424 significant events affecting 582 genes. Taken together, our results demonstrate the robust performance of VarScan 2 for somatic mutation and CNA detection and shed new light on the landscape of genetic alterations in ovarian cancer.