Pre-test prediction models of BRCA1 or BRCA2 mutation in breast/ovarian families attending familial cancer clinics

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Abstract
Objective: To test whether statistical models developed to calculate pre-test probability of being a BRCA1/2 carrier can differentiate better between the breast/ovarian families to be referred to the DNA test laboratory. Study design: A retrospective analysis was performed in 109 Spanish breast/ovarian families previously screened for germline mutations in both the BRCA1 and BRCA2 genes. Four easy to use logistic regression models originally developed in Spanish (HCSC model), Dutch (LUMC model), Finnish (HUCH model), and North American (U Penn model) families and one model based on empirical data of Frank 2002 were tested. A risk counsellor was asked to assign a subjective pre-test probability for each family. Sensitivity, specificity, negative and positive predictive values, and areas under receiver operator characteristics (ROC) curves were calculated in each case. Correlation between predicted probability and mutation prevalence was tested. All statistical tests were two sided. Results: Overall, the models performed well, improving the performances of a genetic counsellor. The median ROC curve area was 0.80 (range 0.77-0.82). At 100% sensitivity, the median specificity was 30% (range 25-33%). At 92% sensitivity, the median specificity was 42% (range 33.3-54.2%) and the median negative predictive value was 93% (range 89.7-98%). BRCA1 families tended to score higher risk than BRCA2 families in all models tested. Conclusions: All models increased the discrimination power of an experienced risk counsellor, suggesting that their use is valuable in the context of clinical counselling and genetic testing to optimise selection of patients for screening and allowing for more focused management. Models developed in different ethnic populations performed similarly well in a Spanish series of families, suggesting that models targeted to specific populations may not be necessary in all cases. Carrier probability as predicted by the models is consistent with actual prevalence, although in general models tend to underestimate it. Our study suggests that these models may perform differently in populations with a high prevalence of BRCA2 mutations.