P4-10-05: Improved Breast Cancer Risk Assessment in Biopsied Women Using the Polyfactorial Model OncoVue®.

Abstract

Abstract Background Regardless of the result, women that have undergone a breast biopsy are considered at increased risk due to the clinical circumstances leading to the need for the procedure. Both the number and outcome of biopsy are considered in the widely used Breast Cancer Risk Assessment Tool (BCRAT) also known as the Gail model. Accurately estimating individualized risk of developing breast cancer is useful for early detection and cancer prevention. Although the BCRAT has been demonstrated to accurately estimate the number of breast cancers likely to emerge in a population of women seeking regular mammography screening, it does not produce accurate individualized risk estimates for patient counseling. We have simultaneously analyzed gene polymorphism and clinical factor data in breast cancer cases and matched controls to develop a polyfactorial risk model (OncoVue) to improve estimation of individual risk. In three independent patient populations, OncoVue has been shown to significantly outperform both the BCRAT and composite risk scores produced by combining GWAS SNP risks with the BCRAT. Here we have characterized the performance of OncoVue in stratifying risk in women that have had one or more breast biopsies. Materials and Methods: Risk scores were analyzed for participants ranging in age from 35 to 89 for a subset of participants that had enrolled in a larger case-control study conducted in six distinct geographic regions of the United States. The current study focused on the analysis of participants that had reported one or more biopsies (cases prior to diagnosis/controls at time of enrollment) amounting to 1265 Caucasian women (537 cases and 728 controls) in a model building set and 303 women in an independent validation set (134 cases and 169 controls). DNAs were genotyped for 22 SNP variants and genotype information was combined with clinical risk factor information to calculate the risk scores for the individual participants. Clinical factor information was also used to calculate BCRAT risk scores. The performance of OncoVue was examined in comparison to the BCRAT alone. Results: For both models, positive likelihood ratios (PLR) were calculated as the proportion of patients with breast cancer with an elevated lifetime risk estimate (≥20%) divided by the proportion of disease-free individuals with an elevated risk estimate. In both the model building and validation sets, OncoVue exhibited approximately a 2.0-fold improvement compared to the BCRAT in more accurately assigning elevated risk estimates to breast cancer cases. In these women that are already considered at increased risk because of a history of biopsy, the observed level of improved performance of OncoVue was similar to that in our previous overall studies. Conclusions: The OncoVue polyfactorial risk model incorporating both genetics and clinical factors improves on individualized breast cancer risk estimation compared to the BCRAT which uses only clinical factors. The performance in biopsied women further supports the potential utility of OncoVue for directing prevention and screening decisions. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-10-05.