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Breast Cancer Research and Treatment

, Volume 139, Issue 3, pp 887–896 | Cite as

Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model

  • Gillian S. Dite
  • Maryam Mahmoodi
  • Adrian Bickerstaffe
  • Fleur Hammet
  • Robert J. Macinnis
  • Helen Tsimiklis
  • James G. Dowty
  • Carmel Apicella
  • Kelly-Anne Phillips
  • Graham G. Giles
  • Melissa C. Southey
  • John L. Hopper
Epidemiology

Abstract

It has been shown that, for women aged 50 years or older, the discriminatory accuracy of the Breast Cancer Risk Prediction Tool (BCRAT) can be modestly improved by the inclusion of information on common single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risk. We aimed to determine whether a similar improvement is seen for earlier onset disease. We used the Australian Breast Cancer Family Registry to study a population-based sample of 962 cases aged 35–59 years, and 463 controls frequency matched for age and for whom genotyping data was available. Overall, the inclusion of data on seven SNPs improved the area under the receiver operating characteristic curve (AUC) from 0.58 (95 % confidence interval [CI] 0.55–0.61) for BCRAT alone to 0.61 (95 % CI 0.58–0.64) for BCRAT and SNP data combined (p < 0.001). For women aged 35–39 years at interview, the corresponding improvement in AUC was from 0.61 (95 % CI 0.56–0.66) to 0.65 (95 % CI 0.60–0.70; p = 0.03), while for women aged 40–49 years at diagnosis, the AUC improved from 0.61 (95 % CI 0.55–0.66) to 0.63 (95 % CI 0.57–0.69; p = 0.04). Using previously used classifications of low, intermediate and high risk, 2.1 % of cases and none of the controls aged 35–39 years, and 10.9 % of cases and 4.0 % of controls aged 40–49 years were classified into a higher risk group. Including information on seven SNPs associated with breast cancer risk, improves the discriminatory accuracy of BCRAT for women aged 35–39 years and 40–49 years. Given, the low absolute risk for women in these age groups, only a small proportion are reclassified into a higher category for predicted 5-year risk of breast cancer.

Keywords

Breast cancer Risk prediction Single nucleotide polymorphism Breast Cancer Risk Assessment Tool 

Notes

Acknowledgments

The ABCFR has been supported in Australia by the National Health and Medical Research Council, the New South Wales Cancer Council, the Victorian Health Promotion Foundation, the Victorian Breast Cancer Research Consortium, Cancer Australia and the National Breast Cancer Foundation. The ABCFR has also been supported by the National Cancer Institute, National Institutes of Health, USA under RFA CA-06-503 and through cooperative agreements with members of the Breast Cancer Family Registry: The University of Melbourne, Australia (U01 CA69638); Fox Chase Cancer Center, USA (U01 CA69631); Huntsman Cancer Institute, USA (U01 CA69446); Colombia University, USA (U01 CA69398); Cancer Prevention Institute of California, USA (U01 CA69417); and Cancer Care Ontario, Canada (U01 CA69467). The content of this article does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centres of the Breast Cancer Family Registry. The mention of trade names, commercial products or organisations does not imply endorsement by the US government or the Breast Cancer Family Registry.

KAP is supported by a National Breast Cancer Foundation Practitioner Fellowship.

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gillian S. Dite
    • 1
  • Maryam Mahmoodi
    • 2
  • Adrian Bickerstaffe
    • 1
  • Fleur Hammet
    • 2
  • Robert J. Macinnis
    • 1
    • 3
  • Helen Tsimiklis
    • 2
  • James G. Dowty
    • 1
  • Carmel Apicella
    • 1
  • Kelly-Anne Phillips
    • 1
    • 4
    • 5
    • 6
  • Graham G. Giles
    • 1
    • 3
  • Melissa C. Southey
    • 2
  • John L. Hopper
    • 1
  1. 1.Centre for Molecular, Environmental, Genetic and Analytic EpidemiologyThe University of MelbourneCarltonAustralia
  2. 2.Genetic Epidemiology Laboratory, Department of PathologyThe University of MelbourneParkvilleAustralia
  3. 3.Cancer Epidemiology CentreCancer Council VictoriaCarltonAustralia
  4. 4.Division of Cancer MedicinePeter MacCallum Cancer CentreEast MelbourneAustralia
  5. 5.Department of MedicineSt. Vincent’s Hospital, The University of MelbourneFitzroyAustralia
  6. 6.Peter MacCallum Department of OncologyThe University of MelbourneEast MelbourneAustralia

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