PO.BCS01.12 · 生物信息与计算
利用外显子组测序数据检测微拷贝数变异的生物信息学方法
Bioinformatic method for the detection of micro copy number variations with exome sequencing data
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
短片段拷贝数变异（CNV）在体细胞癌症基因组图谱中占据很大比例。对全基因组泛癌分析（PCAWG）联盟的CNV分析显示，拷贝数缺失和重复的大小呈多峰分布，其中一个主要峰位于约1kb处[1]。利用长读长测序（LRS）对包括CNV在内的结构变异（SV）进行灵敏度分析显示，多种SV检测工具得出的SV长度中位数介于133bp至1kbp之间[2]。然而，现有的用于短读长测序数据（SRS）的CNV分析工具基于分箱（binning）方法，这限制了对这些短片段CNV的可靠检测。外显子组测序数据还带来了额外的挑战，即由于不同基因组位置的捕获效率不同，导致读取深度不均匀。因此，我们开发了一种方法，利用每个基因组位置可获得的读取深度数据，从外显子组测序数据中发现短片段CNV，即微CNV（Micro CNV）。我们采用了一种称为自适应窗口延伸（adaptive window extension）的方法，即延伸窗口以识别读取深度显著偏离对照样本的基因组片段。随后，我们通过在碱基水平空间中搜索最优评分来精细调整变异边界。我们在模拟数据中评估了该方法，其中我们局部模拟了12个包含知名癌症基因（包括抑癌基因和癌基因）的基因组位点。在90%纯度的肿瘤中，对于捕获区域内短至300bp（相当于变异涉及1-2个外显子）的2倍拷贝数缺失和重复，我们能够获得超过0.9的中位灵敏度。在50%纯度下，对于捕获区域内短至1kbp的模拟变异，观察到类似的性能，同时保持高特异性。鉴于目前germ-line CNV（100%纯度）在权衡特异性后的灵敏检测下限为3个外显子（捕获区域内约750bp）[3]，我们相信我们的工具即使在较低纯度环境下也能呈现出具有竞争力的结果，并且当应用于更大的癌症外显子组队列时，将发现更多的癌症驱动基因和可干预基因。
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1. Li, Y., et al., Patterns of somatic structural variation in human cancer genomes. Nature, 2020. 578 (7793): p. 112-121.
2. Liu, L., et al., Performance of somatic structural variant calling in lung cancer using Oxford Nanopore sequencing technology. BMC Genomics, 2024. 25 (1): p. 898.
3. Babadi, M., et al., GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data. Nat Genet, 2023. 55 (9): p. 1589-1597.
查看英文原文 English abstract
Short-sized copy number variants (CNVs) account for a large proportion of the somatic cancer genome landscape. Analysis of CNVs from Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium showed that the sizes of copy number deletions and duplications have multimodal distributions with one of the major modes centering around 1kb [1]. Sensitive profiling of structural variants (SVs), which include CNVs, with long-read sequencing (LRS), showed that the median SV lengths range from 133bp to 1kbp by multiple SV callers [2]. However, existing CNV analysis tools for short-read sequencing data (SRS) are based on a binning approach, which limits the reliable detection of these short-sized CNVs. Exome sequencing data poses additional challenges of uneven read depth due to capturing efficiency differing between genomic positions. Therefore, we developed a methodology that uses read depth data available at every genomic position to discover short-sized CNVs, or Micro CNVs, from exome sequencing data. We employed an approach we named adaptive window extension, in which we extended the windows to identify genomic segments with read depths significantly deviating from control samples. Then, we fine-tuned the variant boundaries by searching for an optimal score in the base-level space. We evaluated our approach in our simulated data where we locally simulated 12 genomic loci containing well-known cancer genes, both tumor suppressors and oncogenes. We were able to obtain median sensitivity over 0.9 for 2-fold copy number deletions and duplications in 90% purity tumors as short as 300bp over the captured region, which corresponds to 1-2 exons involved in the variation. At 50% purity, similar performance was observed for simulated variants as short as 1kbp over the captured region, retaining high specificity. Given the current lower limit of sensitive detection of germ-line CNV (of 100% purity) is 3 exons (~750bp in the captured region) after comprising specificity [3], we believe our tool presents competitive results even in the lower purity settings and, when applied to a larger cancer exome cohort, will discover additional cancer driver genes and actionable genes.
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1. Li, Y., et al., Patterns of somatic structural variation in human cancer genomes. Nature, 2020. 578 (7793): p. 112-121.
2. Liu, L., et al., Performance of somatic structural variant calling in lung cancer using Oxford Nanopore sequencing technology. BMC Genomics, 2024. 25 (1): p. 898.
3. Babadi, M., et al., GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data. Nat Genet, 2023. 55 (9): p. 1589-1597.
利益披露 Disclosure
J. Lee, None..
H. Choi, None..
D. Hayes, None.