Article Text

Download PDFPDF
A new scoring system for the chances of identifying a BRCA1/2 mutation outperforms existing models including BRCAPRO
  1. D G R Evans1,
  2. D M Eccles2,
  3. N Rahman3,
  4. K Young1,
  5. M Bulman1,
  6. E Amir1,
  7. A Shenton1,
  8. A Howell1,
  9. F Lalloo1
  1. 1Academic Unit of Medical Genetics and Regional Genetics Service, St Mary’s Hospital, Manchester M13 0JH, UK
  2. 2Wessex Clinical Genetics Service, Princess Anne Hospital, Coxford Road, Southampton S016 5YA, UK
  3. 3Section of Cancer Genetics, Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey SM2 5NG, UK
  1. Correspondence to:
 Professor D G R Evans
 Consultant Clinical Geneticist, University Department of Medical Genetics and Regional Genetic Service, St. Mary’s Hospital, Hathersage Road, Manchester M13 0JH, UK;


Purpose: To develop a simple scoring system for the likelihood of identifying a BRCA1 or BRCA2 mutation.

Methods: DNA samples from affected subjects from 422 non-Jewish families with a history of breast and/or ovarian cancer were screened for BRCA1 mutations and a subset of 318 was screened for BRCA2 by whole gene screening techniques. Using a combination of results from screening and the family history of mutation negative and positive kindreds, a simple scoring system (Manchester scoring system) was devised to predict pathogenic mutations and particularly to discriminate at the 10% likelihood level. A second separate dataset of 192 samples was subsequently used to test the model’s predictive value. This was further validated on a third set of 258 samples and compared against existing models.

Results: The scoring system includes a cut-off at 10 points for each gene. This equates to >10% probability of a pathogenic mutation in BRCA1 and BRCA2 individually. The Manchester scoring system had the best trade-off between sensitivity and specificity at 10% prediction for the presence of mutations as shown by its highest C-statistic and was far superior to BRCAPRO.

Conclusion: The scoring system is useful in identifying mutations particularly in BRCA2. The algorithm may need modifying to include pathological data when calculating whether to screen for BRCA1 mutations. It is considerably less time-consuming for clinicians than using computer models and if implemented routinely in clinical practice will aid in selecting families most suitable for DNA sampling for diagnostic testing.

  • BRCA1
  • BRCA2
  • breast cancer
  • mutation analysis
  • ovarian cancer
  • triage
  • CSGE, conformation sensitive gel electrophoresis
  • ER, oestrogen receptor
  • FBC, female breast cancer
  • HA, hetero-duplex analysis
  • MBC, male breast cancer
  • PTT, protein truncation test
  • SSCP, single strand conformation polymorphism

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.


  • Conflict of interest: none declared.