Skip to main content

Advertisement

SpringerLink
Log in

Population frequencies of pathogenic alleles of BRCA1 and BRCA2: analysis of 173 Danish breast cancer pedigrees using the BOADICEA model

  • Original Article
  • Published:
Familial Cancer Aims and scope Submit manuscript

Abstract

The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) calculates the probability that a woman carries a pathogenic variant in BRCA1 or BRCA2 based on her pedigree and the population frequencies of pathogenic alleles of BRCA1 (0.0006394) and BRCA2 (0.00102) in the United Kingdom (UK). BOADICEA allows the clinician to define the population frequencies of pathogenic alleles of BRCA1 and BRCA2 for other populations but only includes preset values for the Ashkenazy Jewish and Icelandic populations. Among 173 early-onset breast cancer pedigrees in Denmark, BOADICEA discriminated well between carriers and non-carriers of pathogenic variants (area under the receiver operating characteristics curve: 0.81; 95% CI 0.74–0.86) but underestimated the frequency of carriers of pathogenic variants in BRCA1 or BRCA2 as measured by the observed-to-expected ratio (O/E 1.83; 95% CI 1.18–2.84). This reflects findings from older studies of BOADICEA in UK, German, Italian, and Chinese populations, all accounting for the different calibration for different carrier probabilities. To improve the performance of BOADICEA for non-UK populations, we developed a method to derive population frequencies of pathogenic alleles of BRCA1 and BRCA2. Compared to the UK population frequencies, we estimated the Danish population frequencies of pathogenic alleles to be higher for BRCA1 (0.0015; 95% CI 0.00064–0.0034) and lower for BRCA2 (0.00052; 95% CI 0.00018–0.0017) after adjusting for the different calibration of BOADICEA for different carrier probabilities. Incorporating additional population frequencies into BOADICEA could improve its performance for non-UK populations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

BOADICEA:

The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm

BWA v3:

BOADICEA web application version 3

BRCA1:

Breast cancer 1 gene

BRCA2:

Breast cancer 2 gene

ER:

Estrogen receptor

HER2:

Human epidermal growth factor receptor 2

ROC:

Receiver operating characteristics

AUC:

Area under the ROC curve

O/E:

Observed-to-expected ratio

UK:

United Kingdom

References

  1. Peto J, Collins N, Barfoot R et al (1999) Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J Natl Cancer Inst 91(11):943–949

    Article  CAS  PubMed  Google Scholar 

  2. Loman N, Johannsson O, Kristoffersson U, Olsson H, Borg A (2001) Family history of breast and ovarian cancers and BRCA1 and BRCA2 mutations in a population-based series of early-onset breast cancer. J Natl Cancer Inst 93(16):1215–1223

    Article  CAS  PubMed  Google Scholar 

  3. Fackenthal JD, Olopade OI (2007) Breast cancer risk associated with BRCA1 and BRCA2 in diverse populations. Nat Rev Cancer 7(12):937–948. https://doi.org/10.1038/nrc2054

    Article  CAS  PubMed  Google Scholar 

  4. Cintolo-Gonzalez JA, Braun D, Blackford AL et al (2017) Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Res Treat 164(2):263–284. https://doi.org/10.1007/s10549-017-4247-z

    Article  PubMed  Google Scholar 

  5. Bredart A, Kop JL, Antoniou AC et al (2018) Use of the BOADICEA web application in clinical practice: appraisals by clinicians from various countries. Fam Cancer 17(1):31–41. https://doi.org/10.1007/s10689-017-0014-x

    Article  PubMed  Google Scholar 

  6. Lee AJ, Cunningham AP, Kuchenbaecker KB et al (2014) BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer 110(2):535–545. https://doi.org/10.1038/bjc.2013.730

    Article  CAS  PubMed  Google Scholar 

  7. Antoniou AC, Hardy R, Walker L et al (2008) Predicting the likelihood of carrying a BRCA1 or BRCA2 mutation: validation of BOADICEA, BRCAPRO, IBIS, Myriad and the Manchester scoring system using data from UK genetics clinics. J Med Genet 45(7):425–431. https://doi.org/10.1136/jmg.2007.056556

    Article  CAS  PubMed  Google Scholar 

  8. Fischer C, Kuchenbacker K, Engel C et al (2013) Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium. J Med Genet 50(6):360–367. https://doi.org/10.1136/jmedgenet-2012-101415

    Article  CAS  PubMed  Google Scholar 

  9. Stahlbom AK, Johansson H, Liljegren A, von Wachenfeldt A, Arver B (2012) Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer. Fam Cancer 11(1):33–40. https://doi.org/10.1007/s10689-011-9495-1

    Article  PubMed  Google Scholar 

  10. Varesco L, Viassolo V, Viel A et al (2013) Performance of BOADICEA and BRCAPRO genetic models and of empirical criteria based on cancer family history for predicting BRCA mutation carrier probabilities: a retrospective study in a sample of Italian cancer genetics clinics. Breast 22(6):1130–1135. https://doi.org/10.1016/j.breast.2013.07.053

    Article  CAS  PubMed  Google Scholar 

  11. Antoniou AC, Durocher F, Smith P, Simard J, Easton DF, members IBp (2006) BRCA1 and BRCA2 mutation predictions using the BOADICEA and BRCAPRO models and penetrance estimation in high-risk French-Canadian families. Breast Cancer Res 8(1):R3. https://doi.org/10.1186/bcr1365

    Article  CAS  PubMed  Google Scholar 

  12. Kurian AW, Gong GD, John EM et al (2009) Performance of prediction models for BRCA mutation carriage in three racial/ethnic groups: findings from the Northern California Breast Cancer Family Registry. Cancer Epidemiol Biomark Prev 18(4):1084–1091. https://doi.org/10.1158/1055-9965.EPI-08-1090

    Article  CAS  Google Scholar 

  13. Thirthagiri E, Lee SY, Kang P et al (2008) Evaluation of BRCA1 and BRCA2 mutations and risk-prediction models in a typical Asian country (Malaysia) with a relatively low incidence of breast cancer. Breast Cancer Res 10(4):R59. https://doi.org/10.1186/bcr2118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kwong A, Wong CH, Suen DT et al (2012) Accuracy of BRCA1/2 mutation prediction models for different ethnicities and genders: experience in a southern Chinese cohort. World J Surg 36(4):702–713. https://doi.org/10.1007/s00268-011-1406-y

    Article  PubMed  PubMed Central  Google Scholar 

  15. Antoniou AC, Cunningham AP, Peto J et al (2008) The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer 98(8):1457–1466. https://doi.org/10.1038/sj.bjc.6604305

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gerdes AM, Cruger DG, Thomassen M, Kruse TA (2006) Evaluation of two different models to predict BRCA1 and BRCA2 mutations in a cohort of Danish hereditary breast and/or ovarian cancer families. Clin Genet 69(2):171–178. https://doi.org/10.1111/j.1399-0004.2006.00568.x

    Article  PubMed  Google Scholar 

  17. Pedersen IS, Schmidt AY, Bertelsen B et al (2018) A Danish national effort of BRCA1/2 variant classification. Acta Oncol 57(1):159–162. https://doi.org/10.1080/0284186X.2017.1400693

    Article  PubMed  Google Scholar 

  18. Nadji M, Gomez-Fernandez C, Ganjei-Azar P, Morales AR (2005) Immunohistochemistry of estrogen and progesterone receptors reconsidered: experience with 5,993 breast cancers. Am J Clin Pathol 123(1):21–27

    Article  PubMed  Google Scholar 

  19. Rockhill B, Spiegelman D, Byrne C, Hunter DJ, Colditz GA (2001) Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. J Natl Cancer Inst 93(5):358–366

    Article  CAS  PubMed  Google Scholar 

  20. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845

    Article  CAS  PubMed  Google Scholar 

  21. Manchanda R, Legood R, Burnell M et al (2015) Cost-effectiveness of population screening for BRCA mutations in Ashkenazi jewish women compared with family history-based testing. J Natl Cancer Inst 107(1):380. https://doi.org/10.1093/jnci/dju380

    Article  CAS  PubMed  Google Scholar 

  22. Copson ER, Maishman TC, Tapper WJ et al (2018) Germline BRCA mutation and outcome in young-onset breast cancer (POSH): a prospective cohort study. Lancet Oncol 19(2):169–180. https://doi.org/10.1016/S1470-2045(17)30891-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ford D, Easton DF, Peto J (1995) Estimates of the gene frequency of BRCA1 and its contribution to breast and ovarian cancer incidence. Am J Hum Genet 57(6):1457–1462

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Jervis S, Song H, Lee A et al (2015) A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects. J Med Genet 52(7):465–475. https://doi.org/10.1136/jmedgenet-2015-103077

    Article  CAS  PubMed  Google Scholar 

  25. Lilyquist J, LaDuca H, Polley E et al (2017) Frequency of mutations in a large series of clinically ascertained ovarian cancer cases tested on multi-gene panels compared to reference controls. Gynecol Oncol 147(2):375–380. https://doi.org/10.1016/j.ygyno.2017.08.030

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rebbeck TR, Friebel TM, Friedman E et al (2018) Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Hum Mutat 39(5):593–620. https://doi.org/10.1002/humu.23406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rebbeck TR, Mitra N, Wan F et al (2015) Association of type and location of BRCA1 and BRCA2 mutations with risk of breast and ovarian cancer. JAMA 313(13):1347–1361. https://doi.org/10.1001/jama.2014.5985

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Byers H, Wallis Y, van Veen EM et al (2016) Sensitivity of BRCA1/2 testing in high-risk breast/ovarian/male breast cancer families: little contribution of comprehensive RNA/NGS panel testing. Eur J Hum Genet 24(11):1591–1597. https://doi.org/10.1038/ejhg.2016.57

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Antonis Antoniou at the University of Cambridge, UK, for helpful comments and suggestions for the manuscript.

Funding

The study was funded by a cancer research grant administered by Aarhus University Hospital, Denmark. The funder had no role in study design, data collection and analysis, decision to publish, or the preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Clinical Genetics, Aarhus University Hospital, Brendstrupgaardsvej 21C, 8200, Aarhus N, Denmark

    Thorkild Terkelsen, Uffe Birk Jensen & Lone Sunde

  2. Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark

    Lise-Lotte Christensen

  3. Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark

    Deirdre Cronin Fenton & Anne-Bine Skytte

  4. Department of Clinical Genetics, Odense University Hospital, Odense, Denmark

    Mads Thomassen

Authors
  1. Thorkild Terkelsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lise-Lotte Christensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deirdre Cronin Fenton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Uffe Birk Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lone Sunde
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mads Thomassen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anne-Bine Skytte
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TT and ABS designed the study with input from the co-authors. MT and LLC interpreted the sequencing data. TT performed the statistical analyses. TT and ABS wrote the first draft. All authors revised the manuscript for intellectual content, approved the version to be published, and agree to be accountable for all aspects of the work in ensuring that questions related to the integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Thorkild Terkelsen.

Ethics declarations

Conflicts of interest

The authors declare that they had no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 57 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Terkelsen, T., Christensen, LL., Fenton, D.C. et al. Population frequencies of pathogenic alleles of BRCA1 and BRCA2: analysis of 173 Danish breast cancer pedigrees using the BOADICEA model. Familial Cancer 18, 381–388 (2019). https://doi.org/10.1007/s10689-019-00141-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10689-019-00141-9

Keywords

Search

Navigation