For this month’s Customer Success blog, I decided to revisit some of my favorite studies that had previously been featured in years past. At the time we first highlighted the below publications, some of them were in pre-print and have since been accepted and published. What they all have in common is the implementation of our VSClinical software, which saved these researchers valuable analysis time and provided them with reliable, quality data. See how Golden Helix’s VSClinical can support your quest to enable precision medicine!
With more and more clinics utilizing genomic strategies as part of their diagnostic tool kit, it has become increasingly important that diagnostic laboratories improve the efficiency of their variant interpretations. Clinical exome and genome sequencing strategies have proven to display advantages over other diagnostic techniques as they provide the capability to identify previously undetected pathogenic variants. This is a broader approach than relying on custom gene panels or single-gene analysis since they can include genes not previously surveyed. However, the speed of variant interpretation presents a significant challenge when a lab is adopting NGS as a clinical diagnostic offering. A team from the University of Manchester in the UK recently used VarSeq’s VSClinical functionality to demonstrate how the semi-automated generation of personalized gene panels can increase the accuracy and efficiency when performing the task of variant analysis as well as provide a cost-effective solution for offering genetic variant interpretation in clinical settings. In this study, the team assessed a clinician-led and phenotype-based approach for virtual gene panel generation to analyze targeted clinical exome sequencing data in patients with a presumed rare monogenic disease. The data generated in their investigation provides evidence that utilizing personalized virtual gene panels is a sustainable approach for targeted clinical exome sequencing in patients with rare diseases. Additionally, they demonstrated that using semi-automated prioritization of previously reported variants could increase the efficiency of the analysis workload. The team is optimistic their approach will pave the way for a deeper adoption of genomic analysis strategies for personalized medicine in clinical settings.
Molina-Ramírez, L. P., Kyle, C., Ellingford, J. M., Wright, R., Taylor, A., Bhaskar, S. S., Campbell, C., Jackson, H., Fairclough, A., Rousseau, A., Burghel, G. J., Dutton, L., Banka, S., Briggs, T. A., Clayton-Smith, J., Douzgou, S., Jones, E. A., Kingston, H. M., Kerr, B., Ealing, J., … Gokhale, D. (2022). Personalized virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders. Journal of medical genetics, 59(4), 393–398. https://doi.org/10.1136/jmedgenet-2020-107303
MITO-FIND: A study in 390 patients to determine a diagnostic strategy for mitochondrial disease
A group of geneticists, doctors, and analysts from Pediatrics, Medical Genetics, Radiology, and Biochemistry & Molecular Biology at the University of Calgary collaborated to determine a diagnostic strategy for mitochondrial disease. These diseases have many clinical features involving many symptoms, including neurologic, muscular, cardiac, hepatic, visual, and auditory symptoms. As most patients prefer a non-invasive approach and the technical challenges associated with tissue biopsy, diagnosing mitochondrial disease proves challenging. NGS technology has made it easier to identify the clinically relevant variants that may lead to disease, and the researchers wanted to illustrate this. They evaluated the ability of their traditional diagnostic pathway (metabolite analysis, tissue neuropathology, and respiratory chain enzyme activity) in 390 patients. The traditional diagnostic pathway provided a diagnosis of mitochondrial disease in 115 patients (29.50%). Analysis of mtDNA, tissue neuropathology, skin electron microscopy, respiratory chain enzyme analysis using inhibitor assays, blue native polyacrylamide gel electrophoresis were all statistically significant in distinguishing patients between a mitochondrial and non-mitochondrial diagnosis. From these 390 patients who underwent traditional analysis, they recruited 116 patients for the NGS part of the study (36 patients who had a mitochondrial diagnosis (MITO) and 80 patients who had no diagnosis (No-Dx)). In the group of 36 MITO patients, nuclear whole-exome sequencing (nWES) provided a second diagnosis in 2 cases that already had a pathogenic variant in mtDNA and a revised diagnosis (GLUL) in one case that had abnormal pathology but no pathogenic mtDNA variant. Their results showed that a non-invasive, bigenomic sequencing (BGS) approach (using both a nWES and optimized mtDNA analysis to include large deletions) should be the first step in investigating mitochondrial diseases. However, there may still be a role for tissue biopsy in unsolved cases or when the diagnosis is still unclear after NGS studies.
Kerr M, Hume S, Omar F, Koo D, Barnes H, Khan M, Aman S, Wei XC, Alfuhaid H, McDonald R, McDonald L, Newell C, Sparkes R, Hittel D, Khan A. MITO-FIND: A study in 390 patients to determine a diagnostic strategy for mitochondrial disease. Mol Genet Metab. 2020 Sep-Oct;131(1-2):66-82. doi: 10.1016/j.ymgme.2020.08.009. Epub 2020 Sep 18. PMID: 32980267.
Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing are urgently required. Most prior research has been based on women selected for high-risk features and more data is needed to make inferences about breast cancer risk for women unselected for family history, an important consideration of population screening. We tested 1464 women diagnosed with breast cancer and 862 age-matched controls participating in the Australian Breast Cancer Family Study (ABCFS), and 6549 healthy, older Australian women enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) study for rare germline variants using a 24-gene-panel. Odds ratios (ORs) were estimated using unconditional logistic regression adjusted for age and other potential confounders. We identified pathogenic variants in 11.1% of the ABCFS cases, 3.7% of the ABCFS controls and 2.2% of the ASPREE (control) participants. The estimated breast cancer OR [95% confidence interval] was 5.3 [2.1–16.2] for BRCA1, 4.0 [1.9–9.1] for BRCA2, 3.4 [1.4–8.4] for ATM and 4.3 [1.0–17.0] for PALB2. Our findings provide a population-based perspective to gene-panel testing for breast cancer predisposition and opportunities to improve predictors for identifying women who carry pathogenic variants in breast cancer predisposition genes.
Southey, M.C., Dowty, J.G., Riaz, M. et al. Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing. npj Breast Cancer 7, 153 (2021). https://doi.org/10.1038/s41523-021-00360-3
I hope you enjoyed this trip down memory lane! It is always inspiring to me to read the groundbreaking work that is being done in labs around the world and I hope it inspired you as well.