Rationale and Objectives: The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors. Materials and Methods: Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method. Results: The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001). Conclusions: BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.
Bibliographical noteFunding Information:
The authors acknowledge the support of the Wisconsin Genomics Initiative from the state of Wisconsin and support from the National Institutes of Health (grants: R01CA127379 , R01CA127379-03S1 , R01GM097618 , R01LM011028 , R01ES017400 , U54AI117924 , K24CA194251 ). We also acknowledge support from the eMERGE Network ( U01HG004608 ), the UW Institute for Clinical and Translational Research ( UL1TR000427 ), and the UW Carbone Comprehensive Cancer Center ( P30CA014520 ). The sponsors/funders of this study had no role in (1) study design; (2) collection, analysis, or interpretation of data, (3) writing of the report; or (4) decision to submit the article for publication.
- Genetic variants
- Predictive value
- Risk estimation