Gene Fusion Detection in Cancer Transcriptomics

Gene fusions, arising from chromosomal rearrangements or aberrant splicing events, generate chimeric transcripts that can drive oncogenesis through novel protein products or altered gene regulation. The precise identification of these fusion events within the transcriptome has become pivotal for both research and clinical decision making. Advances in next-generation sequencing (NGS) technologies, particularly RNA sequencing (RNA-seq), have transformed fusion detection by offering high sensitivity, the ability to survey the entire transcriptome and the opportunity to discover novel, low-abundance fusion transcripts. A variety of approaches are now employed, ranging from unbiased whole-transcriptome sequencing and targeted enrichment panels to complementary non-sequencing methods such as fluorescence in situ hybridisation and immunohistochemistry. Bioinformatic pipelines incorporate read-mapping algorithms and de novo assembly strategies, followed by rigorous filtering to distinguish true gene fusions from artefacts. Despite notable improvements in throughput and accuracy, challenges remain in standardising analysis workflows, minimising false positives, and integrating fusion detection into routine diagnostics worldwide. Successful implementation of fusion detection has enabled the identification of actionable targets, refined tumour classification and guided the deployment of fusion-directed therapies, underscoring its global significance in precision oncology.

Research from Nature Portfolio

Recent studies have shown that targeted RNA sequencing can significantly improve clinical fusion detection by combining laboratory optimisation with tailored bioinformatic analyses. Laboratory protocols using capture probes enrich fusion junctions, while software tools assess sensitivity and quantitation against spike-in standards. In a clinical cohort, this approach raised the diagnostic yield of fusion gene identification from under two-thirds to three-quarters of cases compared with conventional assays. In addition to fusion discovery, the assay enables concurrent measurement of gene expression levels and profiling of immune-receptor repertoires, offering a multifaceted view of tumour biology and potential immune-based biomarkers.

Gene Fusion Detection in Cancer Transcriptomics publication trend

The graph below shows the total number of articles in gene fusion detection in cancer transcriptomics across all publications each year (not limited to Nature Index journals).

Technical terms

Gene fusion: A hybrid gene formed when two separate genomic loci are joined, producing a chimeric transcript and potentially a novel fusion protein.

Transcriptomics: The large-scale study of all RNA molecules expressed in a cell or tissue at a given time.

RNA sequencing (RNA-seq): A high-throughput technique that sequences cDNA to quantify and characterise the transcriptome, including fusion transcripts.

Chimeric RNA: An RNA molecule composed of sequences from two distinct parental genes, often resulting from fusion events.

Fusion calling: The bioinformatic process of detecting gene fusions from sequencing data, using algorithms to align reads, reconstruct junctions and filter artefacts.

Targeted RNA sequencing: A focused sequencing approach that uses probe or primer sets to enrich and sequence specific regions of interest, such as known fusion breakpoints, increasing sensitivity and reducing cost.

References

1. Oncogenic SLC2A11–MIF fusion protein interacts with polypyrimidine tract binding protein 1 to facilitate bladder cancer proliferation and metastasis by regulating mRNA stability. MedComm (2024).
2. Prognostic value of structural variants in early breast cancer patients. npj Breast Cancer (2024).
3. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biology (2019).
4. Diagnosis of fusion genes using targeted RNA sequencing. Nature Communications (2019).