Sequencing Strategies for Fusion Gene Detection

• Published in: BioEssays Vol. 42; no. 7; pp. e2000016 - n/a
• Main Authors: Heyer, Erin E., Blackburn, James
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2020
• Subjects: amplicon sequencing; cancer diagnostics; Chromosome rearrangements; Cost analysis; Data analysis; Fluorescence; fusion gene detection; Fusion protein; hybridization capture sequencing; Impact fusion; Innovations; next‐generation sequencing; RNA sequencing; whole genome sequencing; RNA sequencing; whole genome sequencing; next-generation sequencing; cancer diagnostics; hybridization capture sequencing; amplicon sequencing; fusion gene detection
• ISSN: 0265-9247; 1521-1878
• DOI: 10.1002/bies.202000016

Fusion genes formed by chromosomal rearrangements are common drivers of cancer. Recent innovations in the field of next‐generation sequencing (NGS) have seen a dynamic shift from traditional fusion detection approaches, such as visual characterization by fluorescence, to more precise multiplexed methods. There are many different NGS‐based approaches to fusion gene detection and deciding on the most appropriate method can be difficult. Beyond the experimental approach, consideration needs to be given to factors such as the ease of implementation, processing time, associated costs, and the level of expertise required for data analysis. Here, the different NGS‐based methods for fusion gene detection, the basic principles underlying the techniques, and the benefits and limitations of each approach are reviewed. This article concludes with a discussion of how NGS will impact fusion gene detection in a clinical context and from where the next innovations are evolving. Fusion genes formed by chromosomal rearrangements drive cancer development. NGS‐based detection methods can simultaneously screen hundreds of genes, though they require bioinformatic analysis for fusion gene identification. Selecting the most appropriate experimental approach depends on factors such as previous knowledge of the fusion, ease of implementation, time, and cost.