Meet the new SOPHiA DDM Dx RNAtarget Oncology Solution (ROS).
Cancer management outcomes can strongly benefit from robust and accurate detection of gene fusions. SOPHiA GENETICS developed the SOPHiA DDM Dx RNAtarget Oncology Solution (ROS) to make this possible.
Gene fusions: why detect them?
Gene fusions are “hybrid genes” formed by the fusion of two genes into one and producing a novel protein. These are mostly due to DNA translocations, but also sometimes resulting from DNA insertions, inversions, duplications, or deletions1. Exon-skipping events, the erroneous excision of one or more exons during RNA splicing, also lead to pathologically structured genes. Both play an important role in carcinogenesis1 and, because of this, have been preferential targets of precision medicine as well as of some of the most effective treatments in fusion-positive cancers2,3.
Whether specific rearrangements occur at DNA or RNA level, they can both lead to new proteins with altered functions: in human cancers, more than 10,000 gene fusions creating oncogenic drivers of specific tumors or activators of oncogenic signalling have been identified4. Given the large number of gene fusions to identify, researchers worldwide increasingly need intelligent bioinformatics tools to automate the process – and even more: detect them with suitable technical benefits, like the possibility of customizing gene panels, the use of a minimum input sample, and the chance to optimize the lab workflow using high sensitivity technology. Considering the importance of gene fusions as a diagnostic and prognostic marker for cancer management in the clinical setting5-7, these should be transitioned from the research to patient’s bedside. In this particular context, the precious RNA sample for gene fusion testing is not only a “simple sample” – it represents a unique and individual opportunity for better treatment outcomes8. The right technology choice sets the tone for high-accurate detection of gene fusions and, consequently, for high-precision medicine.
Moreover, the possible association between gene fusion type and tumor phenotype can flag the clinical value of accurate gene fusion detection for:
- targeted drug development in translational research,
- cancer screening,
- cancer diagnosis,
- individual prognosis,
- treatment stratification,
- treatment-response monitoring.
The right technology choice sets the tone for high-accurate detection of gene fusions and, consequently, for high-precision medicine.
How to detect gene fusions: DNA or RNA sequencing?
Multiple approaches have been developed to effectively detect gene fusions at DNA, RNA, and protein level based on their significant clinical relevance. The investigation of gene fusions at the DNA level allows to identify structural chromosomal rearrangements, while looking at RNA uncovers fusions generated during RNA splicing without any chromosomal rearrangement and potentially translated into defective fusion proteins. Gene fusion identification has been traditionally performed by immunohistochemistry, fluorescence in situ hybridization (FISH), or polymerase chain reaction (PCR), but next-generation sequencing (NGS) methods represent the current gold standard6,7. These high-throughput sequencing technologies can identify gene fusions either at DNA or RNA level. NGS has become the technology of choice for fusion detection in solid tumors in agreement with international guidelines7. The simultaneous analysis of detailed (nucleotide-level) and comprehensive (genome-wide) information on the genome or transcriptome (all RNA transcripts) allow searching for the constantly increasing number of fusion genes, which would be otherwise impossible to achieve without high-throughput techniques8,9.
Although DNA-based sequencing technologies perform a comprehensive investigation, their short-read NGS approach currently hinders the proper mapping of several gene fusions. Furthermore, they require a considerable amount of sequencing, storage space, and long computational analysis, which makes them expensive and time-consuming. On the other hand, RNA-based approaches are more cost-effective: less storage space and analysis time are needed since only genetic regions which are transcribed and spliced into mature mRNA are explored; therefore, expressed gene fusions are exclusively detected. Also, RNA-based approaches overcome possible technical limitations of DNA-based methods derived from the localization of fusion breakpoints either within long DNA intronic regions (difficult to capture) or on different sites on DNA and RNA encoding the same fusion because of post-transcriptional and splicing rearrangements.
The success of RNA-based gene fusion detection approaches relies on sample quality and quantity: proper RNA extraction, stability, abundance of mRNA transcribed from the gene of interest, and a sufficient amount of sample can often be challenging9. Technics using small inputs coming from precious biopsy samples are required. Furthermore, algorithms with high gene fusion prediction accuracy are crucial. Finally, the complexity of the analysis, which is computationally demanding, remains a significant challenge10.
Why choose SOPHiA DDM Dx RNAtarget Oncology Solution for gene fusions detection?
SOPHiA GENETICS aimed to solve the current challenges of RNA-based approaches for gene fusion detection by developing the SOPHiA DDM Dx RNAtarget Oncology Solution (ROS).
Here we list seven reasons11 why you should consider SOPHiA DDM Dx ROS for your assays:
1.Novel (partner-agnostic) fusion detection
Compared to a fully-guided amplicon-based approaches, the capture-based design of SOPHiA DDM Dx ROS detects fusion without prior knowledge of the partners, increasing considerably the power of fusion detection and exon-skipping events.
2. RNA minimum input
SOPHiA DDM Dx ROS requires only 10 ng of RNA from formalin-fixed paraffin-embedded (FFPE) or fresh frozen tissues. No fragmentation is required before FFPE samples processing, while a short simple fragmentation is performed on fresh frozen tissues. The solution is also compatible with total nucleic acid (TNA).
3. SNV and expression level assessment in RNA
Together with gene fusions, SOPHiA DDM Dx ROS can detect single nucleotide variants (SNVs) and insertion/deletion mutations (Indels). The SNV/Indels calling performed on RNA indicates expression and, therefore, the related biological relevance of the detected mutations is also provided.
4. High sensitivity
The analytical requirements of SOPHiA DDM Dx ROS have been adapted to balance the solution’s sensitivity and specificity. SOPHiA DDM Dx ROS variant caller identifies PCR duplicates to enhance results accuracy. The analytical requirements were optimized to balance the sensitivity and specificity of our technology. The analytical parameters were carefully chosen to balance the need to remove deamination artifacts and background noise. Furthermore, SNV calling algorithms account for biases due to the low biological expression level of the gene of interest. To achieve global standard results with high sensitivity detection of gene fusions, SOPHiA DDM Dx ROS was designed based on the recent ESMO (Tier I and II) and NCCN international guidelines for lung cancer6. The probabilistic model applied to the data leverages multiple features to deliver results with reduced false positives and high accuracy of fusion calling.
5. Customized gene panel
SOPHiA DDM Dx ROS is easily adaptable to the unique needs of your laboratory. An optimized workflow with a dedicated probe design process and extensive wet lab QC experiments provides you with a ready-to-use panel, reducing the need for additional optimization in the lab.
6. Streamlined protocol
SOPHiA DDM Dx ROS comes with a streamlined 1.5-day workflow that supports the whole analysis and ensures access to record-time results.
7. SOPHiA DDM™ reports: visualization, annotation and streamlined interpretation with OncoPortal™ Plus module
The SOPHiA DDM™ Platform fully supports you in the data analysis and interpretation process: pipelines are adapted to sample type, sequencer, enrichment kit, and chemistry used in your laboratory. Moreover, s streamlined workflow helps you to annotate, interpret, and report relevant NGS variants. The complexity of the analysis is minimized. The intuitive and user-friendly interface as well as its accelerated data visualization and interpretation and its customizable reporting and comprehensive QA report increase the efficiency of the decision-making process. The OncoPortal™ Plus module, gathering the latest scientific evidence and guidelines on the actionability and significance of each genomic alteration, completes the all-around support of the SOPHiA DDM™ Platform to facilitate your interpretation process.
SOPHiA DDM Dx ROS supports your laboratory in gene fusion detection. With its high sensitivity and accuracy, it allows the identification and the expression assessment of novel gene fusions, SNVs and Indels. It can also adapt to your research needs thanks to a fully customizable gene panel. Moreover, a streamlined workflow and the SOPHiA DDM™ Platform support you throughout the whole assay, from the RNA sequencing to the data analysis and interpretation. With SOPHiA DDM Dx ROS, you will maximize the insights from precious biopsy specimens getting highly reliable results even from samples of limited quantity.
1. Mitelman F, Johansson B, Mertens F (eds.) (2016) Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer,.http://cgap.nci.nih.gov/Chromosomes/Mitelman.
2. Taniue K, Akimitsu N. Fusion Genes and RNAs in Cancer Development. Noncoding RNA. 2021 Feb 4;7(1):10.
3. Brien GL, Stegmaier K, Armstrong SA. Targeting chromatin complexes in fusion protein-driven malignancies. Nat Rev Cancer. 2019 May;19(5):255-269.
4. Bruno R, Fontanini G. Next Generation Sequencing for Gene Fusion Analysis in Lung Cancer: A Literature Review. Diagnostics (Basel). 2020 Jul 27;10(8):521.
5. Mertens F, Johansson B, Fioretos T, Mitelman F. The emerging complexity of gene fusions in cancer. Nat Rev Cancer. 2015 Jun;15(6):371-81.
6. National Comprehensive Cancer Network®. NCCN Clinical Practice Guidelines in Oncology. Non-Small cell Lung Cancer. Version 3.2022
7. Mosele F, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Ann Oncol. 2020 Nov;31(11):1491-1505.
8. Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet. 2011 Feb;12(2):87-98.
9. Kaya C, et al. Limitations of Detecting Genetic Variants from the RNA Sequencing Data in Tissue and Fine-Needle Aspiration Samples. Thyroid. 2021 Apr;31(4):589-595.
10. Uhrig S, et al. Accurate and efficient detection of gene fusions from RNA sequencing data. Genome Res. 2021 Mar;31(3):448-460.
11. Data on file