拷贝数变异 (CNVs) 占遗传性癌症检测患者致病性变异的 10.8%。
Copy Number Variations (CNVs) Account for 10.8% of Pathogenic Variants in Patients Referred for Hereditary Cancer Testing.
机构信息
Genekor Medical S.A, Athens, Greece;
Genekor Medical S.A, Athens, Greece.
出版信息
Cancer Genomics Proteomics. 2023 Sep-Oct;20(5):448-455. doi: 10.21873/cgp.20396.
BACKGROUND/AIM: Germline copy number variation (CNV) is a type of genetic variant that predisposes significantly to inherited cancers. Today, next-generation sequencing (NGS) technologies have contributed to multi gene panel analysis in clinical practice.
MATERIALS AND METHODS
A total of 2,163 patients were screened for cancer susceptibility, using a solution-based capture method. A panel of 52 genes was used for targeted NGS. The capture-based approach enables computational analysis of CNVs from NGS data. We studied the performance of the CNV module of the commercial software suite SeqPilot (JSI Medical Systems) and of the non-commercial tool panelcn.MOPS. Additionally, we tested the performance of digital multiplex ligation-dependent probe amplification (digitalMLPA).
RESULTS
Pathogenic/likely pathogenic variants (P/LP) were identified in 464 samples (21.5%). CNV accounts for 10.8% (50/464) of pathogenic variants, referring to deletion/duplication of one or more exons of a gene. In patients with breast and ovarian cancer, CNVs accounted for 10.2% and 6.8% of pathogenic variants, respectively. In colorectal cancer patients, CNV accounted for 28.6% of pathogenic/likely pathogenic variants.
CONCLUSION
In silico CNV detection tools provide a viable and cost-effective method to identify CNVs from NGS experiments. CNVs constitute a substantial percentage of P/LP variants, since they represent up to one of every ten P/LP findings identified by NGS multigene analysis; therefore, their evaluation is highly recommended to improve the diagnostic yield of hereditary cancer analysis.
背景/目的：胚系拷贝数变异（CNV）是一种易导致遗传性癌症的遗传变异类型。如今，下一代测序（NGS）技术已在临床实践中促成了多基因面板分析。
材料和方法
使用基于溶液的捕获方法对 2163 名癌症易感性患者进行了筛选。使用了针对 52 个基因的靶向 NGS 面板。基于捕获的方法能够从 NGS 数据中计算分析 CNV。我们研究了商业软件套件 SeqPilot（JSI Medical Systems）的 CNV 模块和非商业工具 panelcn.MOPS 的性能。此外，我们还测试了数字多重连接依赖性探针扩增（digitalMLPA）的性能。
结果
在 464 个样本中发现了致病性/可能致病性变异（P/LP）（21.5%）。CNV 占致病性变异的 10.8%（50/464），指一个或多个基因外显子的缺失/重复。在乳腺癌和卵巢癌患者中，CNV 分别占致病性变异的 10.2%和 6.8%。在结直肠癌患者中，CNV 占致病性/可能致病性变异的 28.6%。
结论
基于计算机的 CNV 检测工具为从 NGS 实验中识别 CNV 提供了一种可行且具有成本效益的方法。CNV 构成了 P/LP 变异的很大一部分，因为它们代表了 NGS 多基因分析所识别的 P/LP 发现中的每十个中的一个；因此，强烈建议评估它们，以提高遗传性癌症分析的诊断产量。
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