A probability model for predicting BRCA1 and BRCA2 mutations in breast and breast-ovarian cancer families

Br J Cancer. 2001 Mar 2;84(5):704-8. doi: 10.1054/bjoc.2000.1626.


Germline mutations in BRCA1 and BRCA2 genes predispose to hereditary breast and ovarian cancer. Our aim was to find associations between the clinical characteristics and positive mutation status in 148 breast cancer families in order to predict the probability of finding a BRCA mutation in a family. Several factors were associated with mutations in univariate analysis, whereas in multivariate analysis (logistic regression with backward selection) only the age of the youngest breast cancer patient and the number of ovarian cancer cases in a family were independent predictors of BRCA mutations. A logistic model was devised to estimate the probability for a family of harbouring a mutation in either BRCA1 or BRCA2. Altogether, 63 out of 148 families (43%) and 28 out of 29 (97%) mutation carrier families obtained probabilities over 10%. The mean probability was 55% for mutation-positive families and 11% for mutation-negative families. The models by Couch et al (1997) and Shattuck-Eidens et al (1997) previously designed for BRCA1 were also tested for their applicability to distinguish carrier families with mutations in either gene. The probability model should be a useful tool in genetic counselling and focusing the mutation analyses, and thus increasing also the cost-effectiveness of the genetic screening.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • BRCA2 Protein
  • Breast Neoplasms / genetics*
  • Cohort Studies
  • Family Health
  • Feasibility Studies
  • Female
  • Forecasting
  • Genes, BRCA1*
  • Genetic Predisposition to Disease
  • Germ-Line Mutation*
  • Humans
  • Middle Aged
  • Models, Statistical*
  • Neoplasm Proteins / genetics*
  • Ovarian Neoplasms / genetics*
  • Probability
  • Risk Factors
  • Transcription Factors / genetics*


  • BRCA2 Protein
  • Neoplasm Proteins
  • Transcription Factors