Improved identification of structural variants (SVs) in cancer can lead to more targeted and effective treatment options as well as advance our basic understanding of the disease and its progression. We performed whole-genome sequencing of the SKBR3 breast cancer cell line and patient-derived tumor and normal organoids from two breast cancer patients using Illumina/10x Genomics, Pacific Biosciences (PacBio), and Oxford Nanopore Technologies (ONT) sequencing. We then inferred SVs and large-scale allele-specific copy number variants (CNVs) using an ensemble of methods. Our findings show that long-read sequencing allows for substantially more accurate and sensitive SV detection, with between 90% and 95% of variants supported by each long-read technology also supported by the other. We also report high accuracy for long reads even at relatively low coverage (25×–30×). Furthermore, we integrated SV and CNV data into a unifying karyotype-graph structure to present a more accurate representation of the mutated cancer genomes. We find hundreds of variants within known cancer-related genes detectable only through long-read sequencing. These findings highlight the need for long-read sequencing of cancer genomes for the precise analysis of their genetic instability.

中文翻译：

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使用单分子测序对乳腺癌基因组中的结构变异进行综合分析。

改进对癌症结构变异 (SV) 的识别可以带来更有针对性和有效的治疗选择，并促进我们对疾病及其进展的基本了解。我们使用 Illumina/10x Genomics、Pacific Biosciences (PacBio) 和 Oxford Nanopore Technologies (ONT) 测序对两名乳腺癌患者的 SKBR3 乳腺癌细胞系和患者来源的肿瘤和正常类器官进行了全基因组测序。然后，我们使用一组方法推断 SV 和大规模等位基因特异性拷贝数变体 (CNV)。我们的研究结果表明，长读长测序可以实现更准确和灵敏的 SV 检测，每种长读长技术支持 90% 到 95% 的变体也得到另一种支持。即使在相对较低的覆盖率（25×–30×）下，我们也报告了长读取的高精度。此外，我们将 SV 和 CNV 数据整合到统一的核型图结构中，以更准确地表示突变的癌症基因组。我们发现已知癌症相关基因中的数百种变异只能通过长读长测序才能检测到。这些发现强调了对癌症基因组进行长读长测序以精确分析其遗传不稳定性的必要性。