Predicting the likelihood of carrying a BRCA1 or BRCA2 mutation: validation of BOADICEA, BRCAPRO, IBIS, Myriad and the Manchester scoring system using data from UK genetics clinics

基因检测 突变 宜必思 接收机工作特性 医学 遗传学 计算机科学 生物 机器学习 基因 古生物学
作者
Antonis C. Antoniou,Rachel Hardy,Lisa Walker,D. Gareth Evans,Andrew Shenton,Rosalind Eeles,Susan Shanley,Gabriella Pichert,Louise Izatt,Steven J Rose,Gillian Douglas,Diana Eccles,Patrick J. Morrison,Jessica Scott,Ron Zimmern,Douglas F. Easton,Paul D.P. Pharoah
出处
期刊:Journal of Medical Genetics [BMJ]
卷期号:45 (7): 425-431 被引量:175
标识
DOI:10.1136/jmg.2007.056556
摘要

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.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.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.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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