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Breast Cancer Res. 2009;11(1):R8. doi: 10.1186/bcr2223. Epub 2009 Feb 6.

A method to assess the clinical significance of unclassified variants in the BRCA1 and BRCA2 genes based on cancer family history.

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  • 1Department of Clinical Genetics, University Hospital Maastricht, Maastricht, the Netherlands.



Unclassified variants (UVs) in the BRCA1/BRCA2 genes are a frequent problem in counseling breast cancer and/or ovarian cancer families. Information about cancer family history is usually available, but has rarely been used to evaluate UVs. The aim of the present study was to identify which is the best combination of clinical parameters that can predict whether a UV is deleterious, to be used for the classification of UVs.


We developed logistic regression models with the best combination of clinical features that distinguished a positive control of BRCA pathogenic variants (115 families) from a negative control population of BRCA variants initially classified as UVs and later considered neutral (38 families).


The models included a combination of BRCAPRO scores, Myriad scores, number of ovarian cancers in the family, the age at diagnosis, and the number of persons with ovarian tumors and/or breast tumors. The areas under the receiver operating characteristic curves were respectively 0.935 and 0.836 for the BRCA1 and BRCA2 models. For each model, the minimum receiver operating characteristic distance (respectively 90% and 78% specificity for BRCA1 and BRCA2) was chosen as the cutoff value to predict which UVs are deleterious from a study population of 12 UVs, present in 59 Dutch families. The p.S1655F, p.R1699W, and p.R1699Q variants in BRCA1 and the p.Y2660D, p.R2784Q, and p.R3052W variants in BRCA2 are classified as deleterious according to our models. The predictions of the p.L246V variant in BRCA1 and of the p.Y42C, p.E462G, p.R2888C, and p.R3052Q variants in BRCA2 are in agreement with published information of them being neutral. The p.R2784W variant in BRCA2 remains uncertain.


The present study shows that these developed models are useful to classify UVs in clinical genetic practice.

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