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Updated: Aug 15, 2026
Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
Published on: August 4, 2016
Recent advances in cancer fusion transcript detection
Ryley Dorney1,2, Bijay P Dhungel3,4,2, John E J Rasko3,4
1epartment of Molecular & Cell Biology, College of Public Health, Medical & Vet Sciences, James Cook University, Douglas, QLD 4811, Australia.
Abstract:
Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.
Insights
This review clarifies cancer gene fusion terminology and classification. It emphasizes RNA fusions and long-read sequencing for better detection and characterization of novel fusion transcripts.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Gene fusions are key cancer biomarkers and therapeutic targets.
- Previous studies overlooked chromosomal rearrangement-independent fusions and complex structures like fusion-circRNAs.
- A unified classification system for gene and transcript fusions is lacking.
Conclusions:
- Accurate classification and detection of all fusion types are crucial for cancer research.
- Long-read sequencing and advanced bioinformatics offer new avenues for identifying and validating fusion transcripts.
- Further research into the functional roles of novel fusion structures is warranted.
