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Breast cancer risk prediction models and subsequent tumor characteristics

Breast Cancer volume 27, pages 662–669 (2020)Cite this article

Abstract

Background

A previous study found evidence that a breast cancer risk prediction model preferentially selected for less aggressive tumors in Swedish women. In the US, the Gail model has been widely used and was used for entry criteria in two large breast cancer prevention trials. We assessed if higher risk levels from the Gail model were associated with less aggressive tumor characteristics and if risk levels were predictive of mortality and survival.

Methods

We used questionnaire data from women in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial to calculate Gail risk levels (low < 1.66%; moderate 1.66–2.99%; high ≥ 3.00%). Women aged 55–74 were enrolled between 1993 and 2001 and had detailed information on breast cancer incidence and tumors collected. We calculated breast cancer incidence and mortality rates among all women by risk levels and examined breast cancer survival and tumor characteristics among women diagnosed with breast cancer. We used Chi-squared tests and multivariable logistic regression to assess the association between risk levels and tumor characteristics.

Results

The study population for this analysis included 45,402 women with 1908 cases of breast cancer. Women at high risk were associated with higher risk of breast cancer mortality compared to women with low risk [rate ratio (RR) = 2.29 95% confidence interval (CI) 1.37–3.84)]. Higher risk levels were associated with lobular-type tumors [moderate: adjusted odds ratio (aOR) = 1.57 95% CI 1.13–2.17; high: aOR = 1.78 95% CI 1.25–2.54] but were not associated with any other tumor characteristics or breast cancer survival.

Conclusions

We did not find evidence that higher risk levels from the Gail model are predictive of less aggressive breast cancer tumors.

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Fig. 1

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Acknowledgements

Cancer incidence data have been provided by the Alabama Statewide Cancer Registry, Arizona Cancer Registry, Colorado Central Cancer Registry, District of Columbia Cancer Registry, Georgia Cancer Registry, Hawaii Cancer Registry, Cancer Data Registry of Idaho, Maryland Cancer Registry, Michigan Cancer Surveillance Program, Minnesota Cancer Surveillance System, Missouri Cancer Registry, Nevada Central Cancer Registry, Ohio Cancer Incidence Surveillance System, Pennsylvania Cancer Registry, Texas Cancer Registry, Utah Cancer Registry, Virginia Cancer Registry, and Wisconsin Cancer Reporting System. All are supported in part by funds from the Center for Disease Control and Prevention, National Program for Central Registries, local states or by the National Cancer Institute, Surveillance, Epidemiology, and End Results program. The results reported here and the conclusions derived are the sole responsibility of the authors.

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Work conducted as part of normal job functions at NIH.

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  1. Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20850, USA

    Eric A. Miller, Paul F. Pinsky, Brandy M. Heckman-Stoddard & Lori M. Minasian

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  1. Eric A. Miller
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  2. Paul F. Pinsky
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Correspondence to Eric A. Miller.

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Miller, E.A., Pinsky, P.F., Heckman-Stoddard, B.M. et al. Breast cancer risk prediction models and subsequent tumor characteristics. Breast Cancer 27, 662–669 (2020). https://doi.org/10.1007/s12282-020-01060-9

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  • DOI: https://doi.org/10.1007/s12282-020-01060-9

Keywords

  • Breast cancer
  • Risk prediction
  • Gail model
  • Tumor characteristics