结构变异通过插入破坏正常的基因功能来驱动肿瘤发生,倒置,易位,和拷贝数的变化,包括删除和重复。检测结构变异对于揭示它们在肿瘤发展中的作用至关重要。临床结果,个性化治疗。目前,大多数研究依赖于来自下一代测序的短读数数据,这些数据与参考基因组对齐,以确定是否和,如果是,发生结构变体的地方。然而,通过短阅读测序发现结构变异是具有挑战性的,主要是因为重复序列区域的定位困难。光学基因组作图(OGM)是用于成像和组装长DNA链以检测结构变异的最新技术。为了更彻底地在人类基因组中捕获结构变异景观,我们开发了一种结合BionanoOGM和Illumina全基因组测序的集成流程,并将其应用于29例儿科B-ALL患者的样本.OGM的添加使我们能够识别511个缺失,506插入,93个重复/增益,和145个否则在短读数据中丢失的易位。此外,我们发现了几个新的基因融合体,其表达通过RNA测序证实。我们的结果突出了整合OGM和短阅读检测方法的好处,以获得对遗传变异的全面分析,可以帮助临床诊断,提供新的治疗靶点,并改善由结构变化驱动的癌症的个性化医疗。
Structural variants drive tumorigenesis by disrupting normal gene function through insertions, inversions, translocations, and copy number changes, including deletions and duplications. Detecting structural variants is crucial for revealing their roles in tumor development, clinical outcomes, and personalized therapy. Presently, most studies rely on short-read data from next-generation sequencing that aligns back to a reference genome to determine if and, if so, where a structural variant occurs. However, structural variant discovery by short-read sequencing is challenging, primarily because of the difficulty in mapping regions of repetitive sequences. Optical genome mapping (OGM) is a recent technology used for imaging and assembling long DNA strands to detect structural variations. To capture the structural variant landscape more thoroughly in the human genome, we developed an integrated pipeline that combines Bionano OGM and Illumina whole-genome sequencing and applied it to samples from 29 pediatric B-ALL patients. The addition of OGM allowed us to identify 511 deletions, 506 insertions, 93 duplications/gains, and 145 translocations that were otherwise missed in the short-read data. Moreover, we identified several novel gene fusions, the expression of which was confirmed by RNA sequencing. Our results highlight the benefit of integrating OGM and short-read detection methods to obtain a comprehensive analysis of genetic variation that can aid in clinical diagnosis, provide new therapeutic targets, and improve personalized medicine in cancers driven by structural variation.