A review study in Med-X highlights recent advancements in technologies used to detect gene fusions. Looking at whole exome sequencing data from 268 tumor samples, the researchers identified 13,698 gene events. Of these, 38 percent were gene-gene fusions, 28 percent were gene-intergenic fusions, and 34 percent were classified as intergenic-intergenic.
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In their review, the researchers analyzed the efficiency of multiple gene fusion detection methods, offering the following insights:
Fluorescence in situ hybridization (FISH), considered the gold standard for DNA-level analysis, is highly sensitive for detecting DNA-level rearrangements but cannot identify specific fusion partners.
Reverse transcription-polymerase chain reaction (RT-PCR) is effective for detecting known fusions but struggles with new or complex ones.
Next-generation sequencing (NGS), especially RNA-based NGS, offers comprehensive insights and excels at identifying rare and new fusions.
Electrochemiluminescence (ECL) is highly sensitive and has been used to detect specific fusions like BCR-ABL in leukemia, though it requires further development for broader use.
To enhance detection accuracy, computational tools like STAR-Fusion and FusionCatcher analyze RNA sequencing data to identify fusion events. AI, particularly deep learning, further improves accuracy by predicting fusion locations and their potential role in cancer progression. Despite these advances, the report suggests that challenges still remain, including high costs and issues with RNA sample quality, particularly in degraded specimens. Emerging innovations, such as multiplex PCR and hybrid-capture NGS, are addressing these issues by improving precision and reducing costs.
With continued improvements in technology and integration of AI, the authors conclude, the field is moving toward more effective and accessible solutions for cancer care.
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