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结合基于面板和基于全转录基因组的基因融合检测,通过长读测序测序

Karleena Rybacki¹, Feng Xu², Hannah M Deutsch³

¹Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

Cell reports methods

|July 22, 2025

概括

这项研究引入了一个新的工作流程,用于检测癌症中的基因融合 (GF),使用长读序列. 该方法结合了目标面板和全转录组分析,以更快,更全面地识别GF.

科学领域:

基因组学就是基因组学.
癌症研究癌症研究
生物信息学是一种生物信息学.

背景情况:

基因融合 (GFs) 是各种癌症的关键驱动因素.
准确检测GF对于有针对性的治疗至关重要.
目前的方法可能在灵敏度和范围上有局限性.

研究的目的:

开发和验证一个全面的基因融合检测和分析工作流程.
结合针对性的基于面板和全转录组的长读序列测序,以增强GF发现.
改善处理时间,并在具有挑战性的癌症病例中识别新型GF.

主要方法:

适应一个短读癌症融合面板的长读测序 (牛津纳米孔技术).
基于面板的长读序列的应用,用于已知的GF检测.
对面板阴性质瘤样本的全转录组长读测序分析.
为GF分析开发量身定制的计算管道.

主要成果:

使用适应面板和长读序列,成功检测已知的GF,缩短了周转时间.
在24个面板阴性质瘤样本中识别了20个新型候选GF.
对所有已识别的候选GF进行实验验证.
证明了长读序列用于GF检测的兼容性.

结论:

提出的工作流集成基于面板和全转录组的长读序列为全面的GF检测.
这种方法可以快速准确地识别癌症中已知和新型基因融合.
这种工作流在临床上具有挑战性的病例中是有效的,包括面板负样本.

关键词:

CP:癌症生物学癌症生物学科普:遗传学遗传学牛津纳米孔技术公司计算管道的计算管道基因融合基因融合长时间阅读序列排序.转录组分析转录组分析

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