Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme

Abstract

Accurate individualized breast cancer risk assessment is essential to provide risk–benefit analysis prior to initiating interventions designed to lower breast cancer risk and start surveillance. We have previously shown that a manual adaptation of Claus tables was as accurate as the Tyrer–Cuzick model and more accurate at predicting breast cancer than the Gail, Claus model and Ford models. Here we reassess the manual model with longer follow up and higher numbers of cancers. Calibration of the manual model was assessed using data from 8,824 women attending the family history evaluation and screening programme in Manchester UK, with a mean follow up of 9.71 years. After exclusion of 40 prevalent cancers, 406 incident breast cancers occurred, and 385.1 were predicted (O/E = 1.05, 95 % CI 0.95–1.16) using the manual model. Predictions were close to that of observed cancers in all risk categories and in all age groups, including women in their forties (O/E = 0.99, 95 % CI 0.83–1.16). Manual risk prediction with use of adjusted Claus tables and curves with modest adjustment for hormonal and reproductive factors was a well-calibrated approach to breast cancer risk estimation in a UK family history clinic.

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References

  1. 1.

    Claus EB, Risch N, Thompson WD (1994) Autosomal dominant inheritance of early onset breast cancer: implications for risk prediction. Cancer 73:643–651

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Gail MH, Costantino JP, Bryant J, Croyle R, Freedman L, Helzlsouer K, Vogel V (1999) Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst 91:1829–1846

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Claus EB, Schildkraut JM, Thompson WD, Risch NJ (1996) The genetic attributable risk of breast and ovarian cancer. Cancer 77(11):2318–2324

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Ford D, Easton DF, Stratton M, Narod S, Goldgar D, Devilee P, Bishop DT, Weber B, Lenoir G, Chang-Claude J et al (1998) Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. Am J Hum Genet 62:676–689

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  5. 5.

    Parmigiani G, Berry D, Aguilar O (1998) Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 62(1):145–158

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  6. 6.

    The University of Texas Southwestern Medical Center at Dallas. CancerGene. Available at http://www4.utsouthwestern.edu/breasthealth/cagene/CGdownload.asp

  7. 7.

    Tyrer JP, Duffy SW, Cuzick J (2004) A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 23(7):1111–1130

    PubMed  Article  Google Scholar 

  8. 8.

    Cuzick J, Howell A, Powles T, Forbes J, Baum M (2002) First results from the International breast cancer intervention study (IBIS-I): a randomised prevention trial. Lancet 360:817–824

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Amir E, Evans DG, Shenton A, Lalloo F, Moran A, Boggis C, Wilson M, Howell A (2003) Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme. J Med Genet 40(11):807–814

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  10. 10.

    Cyrillic 3.0 pedigree software. Accessed on March 30, 2004. Details available at http://www.exetersoftware.com/cat/cyrillic/cyrillic.html

  11. 11.

    Evans DGR, Fentiman IS, McPherson K, Asbury D, Ponder BAJ, Howell A (1994) Familial breast cancer. British Med J 308:183–187

    CAS  Article  Google Scholar 

  12. 12.

    Evans DGR, Lalloo F (2002) Risk assessment and management of high risk familial breast cancer. J Med Genet 39:865–871

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  13. 13.

    Lalloo F, Kerr B, Friedman J, Evans DGR (2005) Risk estimation for breast cancer. In: Evans DGR, Kerr B, Lalloo F, Friedman J (eds) Risk assessment and management in cancer genetics. Oxford University Press, Oxford, pp 47–64

    Google Scholar 

  14. 14.

    Evans DG, Shenton A, Woodward E, Lalloo F, Howell A, Maher ER (2008) Penetrance estimates for BRCA1 and BRCA2 based on genetic testing in a clinical cancer genetics service setting. BMC Cancer 8(1):155

    PubMed Central  PubMed  Article  Google Scholar 

  15. 15.

    McIntosh A, Shaw C, Evans G, Turnbull N, Bahar N, Barclay M, Easton D, Emery J, Gray J, Halpin J, Hopwood P, McKay J, Sheppard C, Sibbering M, Watson W, Wailoo A, Hutchinson A (2004 updated 2006) Clinical guidelines and evidence review for the classification and care of women at risk of familial breast cancer. National Collaborating Centre for Primary Care/University of Sheffield, London. NICE guideline CG041. www.nice.org.uk

  16. 16.

    Breslow NE, Day NE (1987) Statistical methods in cancer research Vol II. The design and analysis of cohort studies (IARC) Scientific Publication No 82. International Agency for Research on Cancer, Lyon

  17. 17.

    Collaborative Group on Hormonal Factors in Breast Cancer (2001) Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58 209 women with breast cancer and 101 986 women without the disease. Lancet 358:1389–1399

    Article  Google Scholar 

  18. 18.

    https://pluto.srl.cam.ac.uk/cgi-bin/bd2/v2/bd.cgi. Accessed 9 August 2013

  19. 19.

    Antoniou AC, Cunningham AP, Peto J, Evans DG, Lalloo F, Narod SA, Risch HA, Eyfjord JE, Hopper JL, Southey MC, Olsson H, Johannsson O, Borg A, Pasini B, Radice P, Manoukian S, Eccles DM, Tang N, Olah E, Anton-Culver H, Warner E, Lubinski J, Gronwald J, Gorski B, Tryggvadottir L, Syrjakoski K, Kallioniemi OP, Eerola H, Nevanlinna H, Pharoah PD, Easton DF (2008) The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer 98(12):2015

    CAS  Article  Google Scholar 

  20. 20.

    Norman RP, Evans DG, Easton DF, Young KC (2007) The cost-utility of magnetic resonance imaging for breast cancer in BRCA1 mutation carriers aged 30–49. Eur J Health Econ 8(2):137–144

    PubMed  Article  Google Scholar 

  21. 21.

    Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, Morris E, Pisano E, Schnall M, Sener S, Smith RA, Warner E, Yaffe M, Andrews KS, Russell CA, American Cancer Society Breast Cancer Advisory Group (2007) American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57:75–89

    PubMed  Article  Google Scholar 

  22. 22.

    Evans DGR, Lennard F, Pointon LJ, Ramus SJ, Gayther SA, Sodha N, Kwan-Lim GE, Leach MO, Warren R, Thompson D, Easton DF, Eeles R, On behalf of The UK study of MRI screening for breast cancer in women at high risk (MARIBS) (2009) Eligibility for MRI screening in the UK: effect of strict selection criteria and anonymous DNA testing on breast cancer incidence in the MARIBS study. Cancer Epid Biomarkers Prev 18(7):2123–2131

    CAS  Article  Google Scholar 

  23. 23.

    Ozanne EM, Drohan B, Bosinoff P, Semine A, Jellinek M, Cronin C, Millham F, Dowd D, Rourke T, Block C, Hughes KS (2013) Which risk model to use? Clinical implications of the ACS MRI screening guidelines. Cancer Epidemiol Biomarkers Prev 22(1):146–149

    PubMed  Article  Google Scholar 

  24. 24.

    Jacobi CE, de Bock GH, Siegerink B, van Asperen CJ (2009) Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose? Breast Cancer Res Treat 115(2):381–390

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Quante AS, Whittemore AS, Shriver T, Strauch K, Terry MB (2012) Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance. Breast Cancer Res 14(6):R144

    PubMed  Article  Google Scholar 

  26. 26.

    van Asperen CJ, Jonker MA, Jacobi CE, van Diemen-Homan JE, Bakker E, Breuning MH, van Houwelingen JC, de Bock GH (2004) Risk estimation for healthy women from breast cancer families: new insights and new strategies. Cancer Epidemiol Biomarkers Prev 13(1):87–93

    PubMed  Article  Google Scholar 

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Acknowledgments

This article presents independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (Reference Number RP-PG-0707-10031: “Improvement in risk prediction, early detection and prevention of breast cancer”). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We acknowledge the support of the Manchester Biomedical Research Centre and the Genesis Breast Cancer Prevention Appeal. We would like to thank the study radiologists: Prof. Caroline Boggis, Prof. Anil Jain, Dr. YY Lim, Dr. Emma Hurley, Dr. Soujanya Gadde and Dr. Mary Wilson; the breast physicians Dr. Sally Bundred and Dr. Nicky Barr; and the advanced radiographer practitioners Elizabeth Lord, Rita Borgen and Jill Johnson, for mammography reporting. This study was funded By NIHR as part of the FH-risk study.

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The authors declare no conflict of interest.

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Correspondence to D. Gareth R. Evans.

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Evans, D.G.R., Ingham, S., Dawe, S. et al. Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme. Familial Cancer 13, 189–196 (2014). https://doi.org/10.1007/s10689-013-9694-z

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Keywords

  • Breast cancer
  • Risk estimation
  • Prospective
  • Claus
  • Tyrer–Cuzick