NGS Detection of Gene Fusions
What is a fusion gene and why are they important?
A fusion gene is a gene formed by joining parts of two previously independent genes. Fusion genes are caused by translocation, chromosomal inversions, or interstitial deletions and contribute to tumorigenesis, as they produce more active abnormal protein than non-fusion genes. Sarcomas, hematological cancer, prostate cancer, and ovarian cancer are all a result of fusion genes. As such, detection of fusion genes is an important diagnostic and prognostic biomarker and is also an area of interest for biopharmaceutical development.
How are gene fusions detected?
There are several methods of detecting gene fusions that can be used depending on the diagnostic or experimental goals, as shown in the table below. Those techniques can be divided into single gene testing and multiplex testing, conventional and next generation methods, DNA, RNA or protein based.
FISH (Fluorescence in situ hybridization) and other conventional methods have been the go-to technologies for gene fusion detection due to their low cost and high sensitivity; however, their inability to detect more than one mutation per experiment and the ever-growing list of predictive biomarkers have encouraged the development of multiplex techniques.
Next Generation Sequencing (NGS) is one of those techniques, it enables the ability to test for fusion events (both known and novel depending on NGS technologies) and other genetic mutations rapidly and simultaneously, which conserves time, cost, and research samples.
|Technologies||Test type||Level of detection||Strength||Limitation|
|FISH||Single-test||DNA (Detection of gene rearrangements)||High sensitivity, familiar, low cost||Unable to determine exact fusion variant, low multiplexing, low throughput, need an expert pathologist for interpretation, can miss small intrachromosomal rearrangements, interobserver variation|
|IHC||Single-test||Protein (detection of fusion proteins)||High sensitivity, familiar, low cost, high throughput||Low multiplexing, need an expert pathologist for interpretation, interobserver variation, orthogonal techniques may be required to confirm specific fusion variant|
|RT-PCR||Single-test||RNA (detection of fusion transcripts)||High sensitivity, familiar, low cost||Low multiplexing, low RNA quality from FFPE may impede test, misses unknown variants (requires prime pairs specific for known fusion)|
|Nanostring||Multiplex||DNA, RNA and proteins||No retro-transcription or amplification required, high sensitivity and specificity, high throughput, can analyze degraded samples and RNA, DNA, and protein alterations together||Target specific probes only permit detection of known fusion transcripts|
|NGS-Capture||Multiplex||DNA or RNA||Enables detection of known and unknown fusion genes, scalable, supports RNA & DNA gene panels, with DNA input – simultaneous analysis of different gene variants and no RNA purification required||Higher RNA input required in comparison to amplicon-based methods, large DNA intron regions with repetitive sequences can impact performance|
|NGS Amplicon||Multiplex||RNA||Lower RNA input required, effective with small and mid-sized panels, only anchored mPCR enables detection of known and unknown fusion gene events, 5’ and 3’ imbalance evaluation can increase test analytical accuracy||Only supports gene fusion analysis at the RNA level, amplicon primer design can be challenging, mPCR only enables detection of known fusion gene events, PCR bias can impact results|
How does SOPHiA GENETICS support NGS detection of gene fusions?
SOPHiA DDM™ supports gene fusion detection, annotation, interpretation, and reporting for multiple available panels, listed below, without compromising analytical sensitivity. Key biomarkers are covered, including but not limited to ALK, NTRK, FGFR, RET and ROS1 fusions.
The platform features expert-designed and intuitive functionalities:
- User-friendly interface to explore genomic variants without bioinformatics expertise
- Wide ranges of databases to reach a comprehensive variant annotation
- Machine learning- and algorithm supported variant prioritization
- Multiple filtering functionalities to pinpoint the most relevant variants
- Jax-CKB integration through the OncoPortal™ to access therapeutic, diagnostic, and prognostic insights for each variant
- Customizable variant reporting
Product comparison table3
|Addressed diseases||Pan-Cancer||Pan-Cancer||Myelodysplastics syndromes, myeloproliferative neoplasms, and leukemia||Solid tumors such as lung, colorectal, skin, and brain cancers||Solid tumors, including lung, breast and colorectal cancers|
|# of Genes||523||170||30||42||47-gene base panel with full customizability|
|# of Gene Fusions||Known and unknown fusions pairs including 55 fusion partners||Known and unknown fusions pairs including 55 fusion partners||119 fixed fusion pairs||137 fixed fusion pairs|
|Sample type||FFPE||FFPE||Blood and bone marrow||FFPE, fresh-frozen tissue||FFPE, fresh frozen tissue|
|Sample input||40ng DNA, 40ng RNA||40ng DNA, 40ng RNA||200ng DNA, 500Ng RNA||10 ng (50 ng recommended) DNA, 100-200 ng RNA||10 – 200 ng, 50 ng recommended|
|Sequencer Compatibility||Illumina NextSeq®||Illumina NextSeq®||Illumina MiSeq® , llumina NextSeq®||Illumina MiSeq®, Illumina NextSeq®||Illumina MiSeq® , llumina NextSeq® 550|
|Library prep time||Bioinformatic workflow only||Bioinformatic workflow only||2 days for DNA, 6 hours for RNA||1.5 days for DNA, 6 hours for RNA||1.5 days|
|Analysis Time for FASTQ||8 hours*||8 hours*||4 hours*||4 hours*||8h/16 sample batch*|
1 – Adapted from Bruno R, Fontanini G. Next generation sequencing for gene fusion analysis in lung cancer: a literature review. Diagnostics. 2020 Aug;10(8):521.
2,3 – data on file
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