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Fuscan: a robust DNA fusion caller for targeted sequencing data in cancer diagnostics
Zhaoying Liu1, Siyu Wang1, Si Chen1
1Shanghai Yijian Medical Laboratory Co., Ltd., Shanghai, 201802, China.
Motivation:
Gene fusions resulting from genomic structural variation in somatic cells have been increasingly identified as central events driving oncogenesis. Ultra-deep targeted sequencing of driver fusions informs therapeutic selection in precision oncology. However, most structural variant (SV) callers were primarily architected for whole genome sequencing, failing to resolve the artifacts and alignment errors that drive false-positive calls in high-depth targeted data.
Results:
Here, we describe Fuscan, a robust DNA fusion caller specifically optimized for targeted sequencing data to identify oncogenic drivers. Fuscan improves sensitivity by focusing alignment on targeted driver sequences while simultaneously filtering homologous genomic regions to prevent false-positive partner-gene breakpoints. We performed targeted sequencing on 85 non-small cell lung cancer clinical specimens (comprising tissue and body fluids), four SV reference standards at 0.5% allele frequency, and 282 healthy-control leukocyte samples. We benchmarked Fuscan against established SV callers, achieving an area under the curve (AUC) of 0.992 and demonstrating its robustness in challenging clinical scenarios, including low-tumor-content tissues and liquid biopsies.
Availability And Implementation:
Fuscan is available on our GitHub repository: https://github.com/YJmedLab/Fuscan.
Insights
Fuscan accurately identifies oncogenic gene fusions from targeted sequencing data, improving precision oncology by reducing false positives in complex samples like liquid biopsies.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Somatic gene fusions are key drivers of oncogenesis.
- Targeted sequencing aids therapeutic selection in precision oncology.
- Existing structural variant (SV) callers struggle with targeted sequencing data, producing false positives.
Purpose of the Study:
- To develop Fuscan, a DNA fusion caller optimized for targeted sequencing data.
- To enhance the identification of oncogenic driver fusions.
- To improve the accuracy of fusion detection in clinical samples.
Main Methods:
- Developed Fuscan, a novel DNA fusion caller.
- Focused alignment on targeted driver sequences.
- Filtered homologous genomic regions to prevent false-positive breakpoints.
- Validated Fuscan on non-small cell lung cancer specimens, SV reference standards, and healthy controls.
Main Results:
- Fuscan demonstrated high accuracy, achieving an AUC of 0.992.
- The tool effectively identified oncogenic drivers in targeted sequencing data.
- Fuscan showed robustness in challenging samples, including low-tumor-content tissues and liquid biopsies.
Conclusions:
- Fuscan is a robust and sensitive tool for detecting gene fusions in targeted sequencing data.
- The caller improves the accuracy of oncogenic driver identification for precision oncology.
- Fuscan addresses limitations of existing SV callers in targeted sequencing applications.
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