Objectives: Genetic testing for the breast and ovarian cancer susceptibility genes BRCA1 and BRCA2 has important implications for the clinical management of individuals with family history of the disease. However, genetic testing is expensive and may be associated with adverse psychosocial effects. To provide a cost-efficient and clinically appropriate genetic counselling service, genetic testing should be targeted at those individuals most likely to harbour pathogenic mutations. Several algorithms which predict the likelihood of carrying a BRCA1/2 mutation are currently used in clinical practice to identify such individuals.
Design: We evaluated the performance of the carrier prediction algorithms BOADICEA, BRCAPRO, IBIS, the Manchester scoring system and Myriad tables, using 1934 families seen in cancer genetics clinics in the UK in whom an index patient had been screened for BRCA1/2 mutations. The models were evaluated for calibration, discrimination and accuracy of the predictions.
Results: Of the five algorithms, only BOADICEA predicted the overall observed number of mutations detected accurately (i.e. was well calibrated). BOADICEA also provided the best discrimination, being significantly better (P<.05) than all models except BRCAPRO (concordance statistics: BOADICEA:0.77, BRCAPRO:0.76, IBIS:0.74, Manchester:0.75, Myriad:0.72). All models under predicted the number of BRCA1/2 mutations in the low estimated risk category.
Conclusions: Carrier prediction algorithms provide a rational basis for counselling individuals likely to carry BRCA1/2 mutations. Their widespread use would improve equity of access and the cost-effectiveness of genetic testing.
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