Objectives: Genetic testing for the breast and ovarian cancer susceptibility genes BRCA1 and BRCA2 has important implications for the clinical management of people found to carry a mutation. 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 carry pathogenic mutations. Several algorithms that predict the likelihood of carrying a BRCA1 or a BRCA2 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 and/or BRCA2 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 (ie, was well calibrated). BOADICEA also provided the best discrimination, being significantly better (p<0.05) than all models except BRCAPRO (area under the receiver operating characteristic curve statistics: BOADICEA = 0.77, BRCAPRO = 0.76, IBIS = 0.74, Manchester = 0.75, Myriad = 0.72). All models underpredicted the number of BRCA1 and BRCA2 mutations in the low estimated risk category.
Conclusions: Carrier prediction algorithms provide a rational basis for counselling individuals likely to carry BRCA1 or BRCA2 mutations. Their widespread use would improve equity of access and the cost-effectiveness of genetic testing.
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Funding: This study was supported by a grant from the UK Department of Health. PDPP is Cancer Research UK Senior Clinical Research Fellow. DFE is a Cancer Research UK principal research fellow. ACA is funded by CR-UK.
Competing interests: None.