Breast Cancer

, Volume 24, Issue 2, pp 238–244

Family history predictors of BRCA1/BRCA2 mutation status among Tunisian breast/ovarian cancer families

  • Aouatef Riahi
  • Mohamel el Ghourabi
  • Asma Fourati
  • Habiba Chaabouni-Bouhamed
Original Article

DOI: 10.1007/s12282-016-0693-4

Cite this article as:
Riahi, A., Ghourabi, M.., Fourati, A. et al. Breast Cancer (2017) 24: 238. doi:10.1007/s12282-016-0693-4

Abstract

Background

With the increasing request for BRCA1/BRCA2 mutation tests, several risk models have been developed to predict the presence of mutation in these genes; in this study, we have developed an efficient BRCA genetic testing strategy.

Method

As first step, to identify predictor variables associated with BRCA status, we have undertaken a cumulative mutation analysis including data from three Tunisian studies. Then, we have developed a logistic regression model for predicting the likelihood of harboring a BRCA mutation. Using receiver operating characteristic curves (ROC), an effective evaluation was performed. A total of 92 Tunisian families were included. Overall, 27 women were positive for BRCA1/BRCA2 deleterious mutations.

Results

Tow recurrent mutations (c.211dupA and c.5266dupC) explained 76 % of BRCA1-related families and three recurrent mutations (c.1310_1313del, c.1542_1547delAAGA and c.7887_7888insA) explained 90 % of BRCA2-related families. Early age at diagnosis of breast cancer, ovarian cancer, bilateral breast cancer were associated with BRCA1, whereas male breast cancer and four or more breast cancer cases in the family were associated with BRCA2. The area under the receiver operating characteristic curve of the risk score was 0.802 (95 % confidence interval = 0.0699–0. 905).

Conclusion

Logistic regression reported particular profiles related to BRCA germline mutation carriers in our population, as well as an efficient prediction model that may be a useful tool for increasing the cost-effectiveness of genetic testing strategy.

Keywords

Breast cancer BRCA1 BRCA2 BRCA predictive models Predictive factors 

Copyright information

© The Japanese Breast Cancer Society 2016

Authors and Affiliations

  • Aouatef Riahi
    • 1
  • Mohamel el Ghourabi
    • 2
  • Asma Fourati
    • 3
  • Habiba Chaabouni-Bouhamed
    • 4
  1. 1.Laboratoire Génétique Humaine, Faculté de Médecine de TunisUniversity Tunis El ManarBardoTunisia
  2. 2.High School of Economic and Commercial Sciences of TunisUniversity of TunisTunisTunisia
  3. 3.Department of ImmunohistocytologySalah Azaiz InstituteTunisTunisia
  4. 4.Department of Hereditary and Congenital DisordersCharles Nicolle HospitalTunisTunisia

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