Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data.
Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data.
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DOI：
10.1093/annonc/mdu479
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
Eklund AC
中科院分区：
文献类型：
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作者：
Favero F;Joshi T;Marquard AM;Birkbak NJ;Krzystanek M;Li Q;Szallasi Z;Eklund AC
我们描述了我们的算法和软件，用于确定从肿瘤基因组测序数据的拷贝数配置文件，并发现它相比有利的现有算法，用于相同的目的。肿瘤DNA的外显子组或全基因组深度测序沿着配对的正常DNA可以潜在地提供表征肿瘤的体细胞突变的详细图片。然而，这样的序列数据的分析可能由于肿瘤标本中正常细胞的存在、肿瘤内异质性和原始数据的绝对大小而变得复杂。特别是，单独从外显子组测序数据确定拷贝数变异已被证明是困难的;因此，单核苷酸多态性（SNP）阵列经常用于该任务。最近，已经描述了从肿瘤测序数据估计绝对但非等位基因特异性拷贝数谱的算法。我们开发了Sequenza，这是一个软件包，它使用配对的肿瘤正常DNA测序数据来估计肿瘤细胞结构和倍性，并计算等位基因特异性拷贝数谱和突变谱。我们将Sequenza以及两种先前发表的算法应用于来自癌症基因组图谱的30种肿瘤的外显子组序列数据。我们通过将这些算法的结果与使用匹配的SNP阵列产生的结果进行比较并通过肿瘤的等位基因特异性拷贝数分析（ASCAT）算法进行处理来评估这些算法的性能。Sequenza/外显子组和SNP/ASCAT之间的比较揭示了细胞性（Pearson’s r = 0.90）和倍性估计（r = 0.42，或在手动检查替代溶液后r = 0.94）的强相关性。这种性能明显优于以前发表的算法上级。此外，在模拟正常肿瘤混合物的人工数据中，Sequenza在肿瘤含量低至30%的样品中检测到正确的倍性。Sequenza和基于SNP阵列的拷贝数谱之间的一致性表明，单独的外显子组测序不仅足以鉴定小规模突变，而且足以估计细胞性和推断DNA拷贝数畸变。
We describe our algorithm and software for determining copy number profiles from tumor genome sequencing data, and find that it compares favorably to existing algorithms for the same purpose. Exome or whole-genome deep sequencing of tumor DNA along with paired normal DNA can potentially provide a detailed picture of the somatic mutations that characterize the tumor. However, analysis of such sequence data can be complicated by the presence of normal cells in the tumor specimen, by intratumor heterogeneity, and by the sheer size of the raw data. In particular, determination of copy number variations from exome sequencing data alone has proven difficult; thus, single nucleotide polymorphism (SNP) arrays have often been used for this task. Recently, algorithms to estimate absolute, but not allele-specific, copy number profiles from tumor sequencing data have been described. We developed Sequenza, a software package that uses paired tumor-normal DNA sequencing data to estimate tumor cellularity and ploidy, and to calculate allele-specific copy number profiles and mutation profiles. We applied Sequenza, as well as two previously published algorithms, to exome sequence data from 30 tumors from The Cancer Genome Atlas. We assessed the performance of these algorithms by comparing their results with those generated using matched SNP arrays and processed by the allele-specific copy number analysis of tumors (ASCAT) algorithm. Comparison between Sequenza/exome and SNP/ASCAT revealed strong correlation in cellularity (Pearson's r = 0.90) and ploidy estimates (r = 0.42, or r = 0.94 after manual inspecting alternative solutions). This performance was noticeably superior to previously published algorithms. In addition, in artificial data simulating normal-tumor admixtures, Sequenza detected the correct ploidy in samples with tumor content as low as 30%. The agreement between Sequenza and SNP array-based copy number profiles suggests that exome sequencing alone is sufficient not only for identifying small scale mutations but also for estimating cellularity and inferring DNA copy number aberrations.