IMPORTANCE Variant reclassification is an important component of hereditary cancer genetic testing; however, there are few published data quantifying the prevalence of reclassification. OBJECTIVE Retrospective cohort study of individuals who had genetic testing from 2006 through 2016 at a single commercial laboratory. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort of individuals who had genetic testing between 2006 and 2016 at a single commercial laboratory was assessed. Variants were classified as benign, likely benign, variant of uncertain significance, likely pathogenic, or pathogenic. Retrospective chart reviews were conducted for patients from the University of Texas Southwestern (UTSW) Medical Center. EXPOSURES Hereditary cancer genetic testing. MAIN OUTCOMES AND MEASURES Frequency of and time to amended reports; frequency and types of variant reclassification. RESULTS From 2006 through 2018, 1.45 million individuals (median [interquartile range] age at testing, 49 years [40.69-58.31 years], 95.6% women) had genetic testing, and 56.6% (n = 821 724) had a personal history of cancer. A total of 1.67 million initial tests were reported and 59 955 amended reports were issued due to variant reclassification. Overall, 6.4% (2868 of 44 777) of unique variants were reclassified. Reclassification to a different clinical category was rare among unique variants initially classified as pathogenic or likely pathogenic (0.7%, 61 of 9112) or benign or likely benign (0.2%, 15 of 8995). However, 7.7% (2048 of 26 670) of unique variants of uncertain significance were reclassified: 91.2% (1867 of 2048) were downgraded to benign or likely benign (median time to amended report, 1.17 years), 8.7% (178 of 2048) were upgraded to pathogenic or likely pathogenic variants (median time to amended report, 1.86 years). Because most variants were observed in more than 1 individual, 24.9% (46 890 of 184 327) of all reported variants of uncertain significance were reclassified. CONCLUSIONS AND RELEVANCE Following hereditary cancer genetic testing at a single commercial laboratory, 24.9% of variants of uncertain significance were reclassified, which included both downgrades and upgrades. Further research is needed to assess generalizability of the findings for other laboratories, as well as the clinical consequences of the reclassification as a component of a genetic testing program.
Patients exposed to a surgical safety checklist experience better postoperative outcomes, but this could simply reflect wider quality of care in hospitals where checklist use is routine.
BackgroundThere is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance.ResultsA total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization.ConclusionsThe CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
Genomic information reported as haplotypes rather than genotypes will be increasingly important for personalized medicine. Current technologies generate diploid sequence data that is rarely resolved into its constituent haplotypes. Furthermore, paradigms for thinking about genomic information are based on interpreting genotypes rather than haplotypes. Nevertheless, haplotypes have historically been useful in contexts ranging from population genetics to disease-gene mapping efforts. The main approaches for phasing genomic sequence data are molecular haplotyping, genetic haplotyping, and population-based inference. Long-read sequencing technologies are enabling longer molecular haplotypes, and decreases in the cost of whole-genome sequencing are enabling the sequencing of whole-chromosome genetic haplotypes. Hybrid approaches combining high-throughput short-read assembly with strategic approaches that enable physical or virtual binning of reads into haplotypes are enabling multi-gene haplotypes to be generated from single individuals. These techniques can be further combined with genetic and population approaches. Here, we review advances in whole-genome haplotyping approaches and discuss the importance of haplotypes for genomic medicine. Clinical applications include diagnosis by recognition of compound heterozygosity and by phasing regulatory variation to coding variation. Haplotypes, which are more specific than less complex variants such as single nucleotide variants, also have applications in prognostics and diagnostics, in the analysis of tumors, and in typing tissue for transplantation. Future advances will include technological innovations, the application of standard metrics for evaluating haplotype quality, and the development of databases that link haplotypes to disease.
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