DATA SHARING AND REPRODUCIBLE CLINICAL GENETIC TESTING: SUCCESSES AND CHALLENGES

Variant reclassification and clinical implications

Genomic technologies have transformed clinical genetic testing, underlining the importance of accurate molecular genetic diagnoses. Variant classification, ranging from benign to pathogenic, is fundamental to these tests. However, variant reclassification, the process of reassigning the pathogenicity of variants over time, poses challenges to diagnostic legitimacy. This review explores the medical and scientific literature available on variant reclassification, focusing on its clinical implications.Variant reclassification is driven by accruing evidence from diverse sources, leading to variant reclassification frequency ranging from 3.6% to 58.8%. Recent studies have shown that significant changes can occur when reviewing variant classifications within 1 year after initial classification, illustrating the importance of early, accurate variant assignation for clinical care.Variants of uncertain significance (VUS) are particularly problematic. They lack clear categorisation but have influenced patient treatment despite recommendations against it. Addressing VUS reclassification is essential to enhance the credibility of genetic testing and the clinical impact. Factors affecting reclassification include standardised guidelines, clinical phenotype-genotype correlations through deep phenotyping and ancestry studies, large-scale databases and bioinformatics tools. As genomic databases grow and knowledge advances, reclassification rates are expected to change, reducing discordance in future classifications.Variant reclassification affects patient diagnosis, precision therapy and family screening. The exact patient impact is yet unknown. Understanding influencing factors and adopting standardised guidelines are vital for precise molecular genetic diagnoses, ensuring optimal patient care and minimising clinical risk.

Sources of discordance among germ-line variant classifications in ClinVar

PurposeClinVar is increasingly used as a resource for both genetic variant interpretation and clinical practice. However, controversies exist regarding the consistency of classifications in ClinVar, and questions remain about how best to use these data. Our study systematically examined ClinVar to identify common sources of discordance and thus inform ongoing practices.MethodsWe analyzed variants that had multiple classifications in ClinVar, excluding benign polymorphisms. Classifications were categorized by potential actionability and pathogenicity. Consensus interpretations were calculated for each variant, and the properties of the discordant outlier classifications were summarized.ResultsOur study included 74,065 classifications of 27,224 unique variants in 1,713 genes. We found that (i) concordance rates differed among clinical areas and variant types; (ii) clinical testing methods had much higher concordance than basic literature curation and research efforts; (iii) older classifications had greater discordance than newer ones; and (iv) low-penetrance variants had particularly high discordance.ConclusionRecent variant classifications from clinical testing laboratories have high overall concordance in many (but not all) clinical areas. ClinVar can be a reliable resource supporting variant interpretation, quality assessment, and clinical practice when factors uncovered in this study are taken into account. Ongoing improvements to ClinVar may make it easier to use, particularly for nonexpert users.

Consistency of BRCA1 and BRCA2 Variant Classifications Among Clinical Diagnostic Laboratories

BACKGROUND

Genetic tests of the cancer predisposition genes BRCA1 and BRCA2 inform significant clinical decisions for both physicians and patients. Most uncovered variants are benign, and determining which few are pathogenic (disease-causing) is sometimes challenging and can potentially be inconsistent among laboratories. The ClinVar database makes de-identified clinical variant classifications from multiple laboratories publicly available for comparison and review, per recommendations of the American Medical Association (AMA), the American College of Medical Genetics (ACMG), the National Society for Genetic Counselors (NSGC), and other organizations.

METHODS

Classifications of more than 2000 BRCA1/2 variants in ClinVar representing approximately 22,000 patients were dichotomized as clinically actionable or not actionable and compared across up to seven laboratories. The properties of these variants and classification differences were investigated in detail.

RESULTS

Per-variant concordance was 98.5% (CI 97.9%-99.0%). All discordant variants were rare; thus, per patient concordance was estimated to be higher: 99.7%. ClinVar facilitated resolution of many of the discordant variants, and concordance increased to 99.0% per variant and 99.8% per patient when reclassified (but not yet resubmitted) variants and submission errors were addressed. Most of the remaining discordances appeared to involve either legitimate differences in expert judgment regarding particular scientific evidence, or were classifications that predated availability of important scientific evidence.

CONCLUSIONS

Significant classification disagreements among the professional clinical laboratories represented in ClinVar are infrequent yet important. The unrestricted sharing of clinical genetic data allows detailed interlaboratory quality control and peer review, as exemplified by this study.

METHODS TO ENSURE THE REPRODUCIBILITY OF BIOMEDICAL RESEARCH

Science is not done in a vacuum - across fields of biomedicine, scientists have built on previous research and used data published in previous papers. A mainstay of scientific inquiry is the publication of one's research and recognition for this work is given in the form of citations and notoriety - ideally given in proportion to the quality of the work. Academic incentives, however, may encourage individual researchers to prioritize career ambitions over scientific truth. Recently, the New England Journal of Medicine published a commentary calling scientists who repurpose data "research parasites" who misuse data generated by others to demonstrate alternative hypotheses. In our opinion, the concept of data hoarding not only runs contrary to the spirit of, but also hinders scientific progress. Scientific research is meant to seek objective truth, rather than promote a personal agenda, and the only way to do so is through maximum transparency and reproducibility, no matter who is using the data….