PO.MCB08.03 · 分子与细胞生物学
基于长读长杂交捕获对95个已知癌症基因进行靶向,通过简单的生物信息学流程和基于AI的临床解读方案检测大型结构变异和复杂变异
Long-read hybrid-capture based targeting of 95 known cancer genes detects large structural and complex variants with a simple bioinformatics workflow and an AI-based clinical interpretation solution.
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
短读长测序推动了从SNP到小型indel的致癌突变鉴定进展。然而,由于读长较短,传统NGS方法界定跨越多个千碱基的复杂突变的能力有限。另一方面,靶向长读长测序能够表征新型复杂突变,因为长读长可跨越大型基因组区域并在其间进行分相。长读长杂交捕获为捕获含有未解析结构变异的靶向区域提供了探针设计的灵活性。在此,我们描述了QIAseq xHYB长读长遗传性癌症Panel及其化学的开发,用于检测95个已知癌症驱动基因中的大型结构变异。使用QIAseq xHYB长读长遗传性癌症Panel和分析流程来鉴定人类参考DNA已知携带的、参与癌症进展的基因中的大型结构变异。使用酶促长读长片段化制备文库,随后用为长DNA片段优化的探针捕获靶向区域。捕获的DNA使用为长片段快速PCR开发的化学进行扩增,并在PacBio和Oxford Nanopore两个平台上测序。Oxford Nanopore平台还使用了实时自适应采样,以增加测序深度并改善均匀性。所得长读长测序数据被比对,并使用CLC Genomics Workbench Lightspeed模块和QIAGEN的Franklin(一个基于云、AI驱动的平台,整合了世界上首个开放式基因组社区以大规模驱动精准医疗)检测大型结构变异。QIAseq xHYB长读长探针设计靶向整个基因,包括内含子和UTR,以无偏方式捕获基因的扩展区域。酶促片段化产生的文库平均读长为4.5 kb。在一张Revio SMRTcell上对来自一个捕获池的八个QIAseq xHYB遗传性癌症Panel文库进行测序,产生了30X的平均覆盖度,均匀性为超过95%的读长>均值的0.2X。在Oxford Nanopore Minion平台上对相同文库进行测序,产生了12X的平均覆盖度,均匀性为超过90%的读长>均值的0.2X。为增加Minion的测序深度,我们使用实时自适应采样来富集靶向区域,结果在Nanopore平台上8个文库获得了平均30X的覆盖度,以及93%的读长>均值的0.2X。使用CLC Genomics Workbench Lightspeed模块进行的下游分析清晰地鉴定出Coriell参考DNA中预期的大型结构变异,随后由AI驱动的Franklin进行的临床解读,从所研究的结构变异参考DNA中得出了信息丰富的可干预结论。
查看英文原文 English abstract
Short read sequencing has driven progress in identifying cancer-causing mutations ranging from SNPs to small indels. However, the ability to define complex mutations that span multiple kilobases is limited with traditional NGS approaches due to short read lengths. Targeted long-read sequencing, on the other hand, enables characterization of novel, complex mutations as long reads can span and be phased across large genomic regions. Long-read hybrid capture offers probe design flexibility for capturing targeted regions with unresolved structural variants. Here we describe the development of QIAseq xHYB Long Read Hereditary Cancer Panel and chemistry for detection of large structural variants in 95 known cancer driver genes.QIAseq xHYB Long Read Hereditary Cancer Panel and analysis pipelines were used to identify large structural variants in genes involved in cancer progression that human reference DNA were known to harbor. Libraries were prepared using enzymatic long-read fragmentation, after which targeted regions were captured with probes optimized for long DNA fragments. Captured DNA was amplified with chemistry developed for long, fast PCR and sequenced on both PacBio and Oxford Nanopore platforms. Real-time adaptive sampling was also used on the Oxford Nanopore platform to increase sequencing depth and improve uniformity. The resulting long-read sequencing data was mapped and large structural variants detected with CLC Genomics Workbench Lightspeed Module and Franklin by QIAGEN, a cloud-based, AI-powered platform that integrates the world's first open genomic community to power precision medicine at scale.The QIAseq xHYB Long Read probe design targets entire genes, including introns and UTR's, capturing extended regions of genes in an unbiased manner. Enzymatic fragmentation produced libraries with average read lengths of 4.5 kb. Sequencing eight QIAseq xHYB Hereditary Cancer Panel libraries from one capture pool on a Revio SMRTcell yields 30X average coverage, and uniformity greater than 95% of reads > 0.2X of the mean. Sequencing the same libraries on the Oxford Nanopore Minion platform resulted in 12X average coverage, and uniformity greater than 90% of reads > 0.2X of the mean. To increase Minion sequencing depth, we used real-time adaptive sampling to enrich for our target regions, which resulted in average of 30X coverage, and 93% of reads > 0.2X of the mean for 8 libraries on the Nanopore platform. Downstream analysis with CLC Genomics Workbench Lightspeed Module clearly identified the expected large structural variants in Coriell reference DNA, and subsequent clinical interpretation performed by AI-powered Franklin produced informative actionable conclusions from the structural variant reference DNA investigated.
利益披露 Disclosure
N. H. Blewett, None..
M. Zais, None..
J. Zhang, None..
J. Deng, None..
J. DiCarlo, None..
J. Hill, None..
C. Haldrup, None..
M. Fosbrink, None..
J. Shaffer, None.