Salk Institute for Biological Studies, La Jolla, CA, USA.
Department of Biochemistry and Molecular Biology, College of Medicine, The Pennsylvania State University, Hershey, PA, USA.
Nat Genet. 2018 Oct;50(10):1388-1398. doi: 10.1038/s41588-018-0195-8. Epub 2018 Sep 10.
Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.
结构变异(SV)可以通过多种机制促进肿瘤发生。尽管它们很重要,但在癌症基因组中识别 SV 仍然具有挑战性。在这里,我们提出了一个框架,该框架集成了光学作图、高通量染色体构象捕获(Hi-C)和全基因组测序,以系统地检测各种正常或癌症样本和细胞系中的 SV。我们确定了每种方法的独特优势,并证明只有综合方法才能全面识别基因组中的 SV。通过将 Hi-C 和光学作图相结合,我们解决了复杂的 SV,并将多个 SV 事件相至单倍型。此外,我们观察到广泛的结构变异事件影响非编码序列的功能,包括远端调控序列的缺失、DNA 复制时间的改变以及新型三维染色质结构域的产生。我们的结果表明,非编码 SV 可能是癌症基因组中被低估的突变驱动因素。