RESUMEN
PURPOSE: To report BRCA1 and BRCA2 (BRCA1/2) variant reassessments and reclassifications between 2012 and 2017 at the Advanced Molecular Diagnostics Laboratory (AMDL) in Toronto, Canada, which provides BRCA1/2 testing for patients in Ontario, and to compare AMDL variant classifications with submissions in ClinVar. METHODS: Variants were assessed using a standardized variant assessment tool based on the American College of Medical Genetics and Genomics/Association for Molecular Pathology's guidelines and tracked in an in-house database. Variants were shared through the Canadian Open Genetics Repository and submitted to ClinVar for comparison against other laboratories. RESULTS: AMDL identified 1209 BRCA1/2 variants between 2012 and 2017. During this period, 32.9% (398/1209) of variants were reassessed and 12.4% (150/1209) were reclassified. The majority of reclassified variants were downgraded (112/150, 74.7%). Of the reclassified variants, 63.3% (95/150) were reclassified to benign, 20.7% (31/150) to likely benign, 10.0% (15/150) to variant of uncertain significance, 2.0% (3/150) to likely pathogenic, and 4.0% (6/150) to pathogenic. Discordant ClinVar submissions were found for 40.4% (488/1209) of variants. CONCLUSION: BRCA1/2 variants may be reclassified over time. Reclassification presents ethical and practical challenges related to recontacting patients. Data sharing is essential to improve variant interpretation, to help patients receive appropriate care based on their genetic results.
Asunto(s)
Proteína BRCA1/genética , Proteína BRCA2/genética , Neoplasias de la Mama/genética , Proteína BRCA1/clasificación , Proteína BRCA2/clasificación , Neoplasias de la Mama/clasificación , Bases de Datos Genéticas , Femenino , Predisposición Genética a la Enfermedad , Pruebas Genéticas/métodos , Pruebas Genéticas/normas , Variación Genética/genética , Genómica , Humanos , Difusión de la InformaciónRESUMEN
INTRODUCTION: Classification of variants of unknown significance (VUS) in the breast cancer genes BRCA1 and BRCA2 changes with accumulating evidence for clinical relevance. In most cases down-staging towards neutral variants without clinical significance is possible. METHODS: We searched the database of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) for changes in classification of genetic variants as an update to our earlier publication on genetic variants in the Centre of Dresden. Changes between 2015 and 2017 were recorded. RESULTS: In the group of variants of unclassified significance (VUS, Class 3, uncertain), only changes of classification towards neutral genetic variants were noted. In BRCA1, 25% of the Class 3 variants (n = 2/8) changed to Class 2 (likely benign) and Class 1 (benign). In BRCA2, in 50% of the Class 3 variants (n = 16/32), a change to Class 2 (n = 10/16) or Class 1 (n = 6/16) was observed. No change in classification was noted in Class 4 (likely pathogenic) and Class 5 (pathogenic) genetic variants in both genes. No up-staging from Class 1, Class 2 or Class 3 to more clinical significance was observed. CONCLUSION: All variants with a change in classification in our cohort were down-staged towards no clinical significance by a panel of experts of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC). Prevention in families with Class 3 variants should be based on pedigree based risks and should not be guided by the presence of a VUS.
Asunto(s)
Proteína BRCA1/clasificación , Proteína BRCA1/genética , Proteína BRCA2/clasificación , Proteína BRCA2/genética , Neoplasias de la Mama/genética , Genes BRCA1 , Genes BRCA2 , Variación Genética , Neoplasias Ováricas/genética , Femenino , Predisposición Genética a la Enfermedad , Alemania , HumanosRESUMEN
MOTIVATION: We introduce simple graphical classification and prediction tools for tumor status using gene-expression profiles. They are based on two dimension estimation techniques sliced average variance estimation (SAVE) and sliced inverse regression (SIR). Both SAVE and SIR are used to infer on the dimension of the classification problem and obtain linear combinations of genes that contain sufficient information to predict class membership, such as tumor type. Plots of the estimated directions as well as numerical thresholds estimated from the plots are used to predict tumor classes in cDNA microarrays and the performance of the class predictors is assessed by cross-validation. A microarray simulation study is carried out to compare the power and predictive accuracy of the two methods. RESULTS: The methods are applied to cDNA microarray data on BRCA1 and BRCA2 mutation carriers as well as sporadic tumors from Hedenfalk et al. (2001). All samples are correctly classified.
