Aziza, Nyimas Aziza

Abstract Background: Optimal early detection and prevention strategies for breast cancer are predicated on our ability to accurately identify women at increased risk for disease. Current breast cancer risk prediction models rely on clinical and epidemiologic features. As demonstrated with other cancers, such as those of the cervix and colon, cancer risk assessment is enhanced when the tissue at risk is examined for evidence of premalignant abnormalities. Here, we present a multivariate model for individualized breast cancer risk prediction that incorporates tissue features identified from biopsies from women with benign breast disease (BBD). Methods: We used our cohort of more than 9000 women diagnosed with BBD at the Mayo Clinic from 1967–1991, and identified a nested case:control series of 377 women who developed cancer (cases) matched with 734 women who did not (controls). We identified a set of variables that either alone or in two-way interactions contributed to a multivariable risk prediction model. We combined this relative risk model with the baseline incidence of breast cancer and the baseline hazard of death from our full BBD cohort to develop a risk prediction tool that computes absolute risk estimates for breast cancer. We calculated 5-year risk predictions for these cases and controls using our model and using the Gail model and compared c-statistics from these risk predictions. We validated our model using an independent set of 378 breast cancer cases and 728 matched controls drawn from our BBD cohort. Results: Cases and controls in the model development data set differed significantly on a number of features when examined individually. Those included in a multivariable relative risk model for breast cancer among women with BBD are shown below. After including interactions between lobular involution, and histologic impression, with age at first live birth/number of children, and degree of involution with sclerosing adenosis, we devised a model that predicts the absolute risk of breast cancer. Predicted five-year risks in the model development series, we observed a c-statistic of 0.66, higher than the c-statistic of 0.61 for the Gail model. With risk prediction estimates from our validation series of cases and matched controls, the c-statistic of our model decreased to 0.59 and the c-statistic for the Gail model was 0.55. Conclusion: We have identified an initial risk prediction model for breast cancer in women with BBD. The identified model includes basic demographic information as well as a number of histologic features of the tissue samples. Its initial performance is promising, providing improved discrimination over the Gail model in our replication data set. Further work is needed to identify features which might provide additional predictive information for breast cancer risk. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-12-03.