Centre for Computational Medicine, Hospital for Sick Children, Toronto, Ontario, Canada.
Division of Pathology, Hospital for Sick Children, Toronto, Ontario, Canada.
J Mol Diagn. 2023 Dec;25(12):921-931. doi: 10.1016/j.jmoldx.2023.09.002. Epub 2023 Sep 23.
Oncogenic fusion genes may be identified from next-generation sequencing data, typically RNA-sequencing. However, in a clinical setting, identifying these alterations is challenging against a background of nonrelevant fusion calls that reduce workflow precision and specificity. Furthermore, although numerous algorithms have been developed to detect fusions in RNA-sequencing, there are variations in their individual sensitivities. Here this problem was addressed by introducing MetaFusion into clinical use. Its utility was illustrated when applied to both whole-transcriptome and targeted sequencing data sets. MetaFusion combines ensemble fusion calls from eight individual fusion-calling algorithms with practice-informed identification of gene fusions that are known to be clinically relevant. In doing so, it allows oncogenic fusions to be identified with near-perfect sensitivity and high precision and specificity, significantly outperforming the individual fusion callers it uses as well as existing clinical-grade software. MetaFusion enhances clinical yield over existing methods and is able to identify fusions that have patient relevance for the purposes of diagnosis, prognosis, and treatment.
致癌融合基因可通过下一代测序数据（通常是 RNA 测序）来鉴定。然而，在临床环境中，在存在降低工作流程精度和特异性的非相关融合调用的背景下，识别这些改变具有挑战性。此外，尽管已经开发了许多算法来检测 RNA 测序中的融合，但它们的个体灵敏度存在差异。本研究通过将 MetaFusion 引入临床应用来解决此问题。当将其应用于全转录组和靶向测序数据集时，说明了其实用性。MetaFusion 将八个独立融合调用算法的融合调用组合在一起，并结合已知具有临床相关性的基因融合的实践信息识别。这样做可以近乎完美的灵敏度和高精度和特异性来识别致癌融合，显著优于它所使用的单个融合调用者以及现有的临床级软件。MetaFusion 提高了现有方法的临床收益，并能够识别与诊断、预后和治疗相关的具有患者相关性的融合。
