Abstract P6-09-04: Accurate Identification of Women at High Risk of Breast Cancer Using OncoVue®
Authors: Jupe ER , Pugh TW , Knowlton NS , DeFreese DC. 
Published: April 2011

Abstract
Background: Accurate estimation of individualized risk of developing breast cancer is critical to making informed clinical decisions regarding surveillance and prevention. OncoVue is a logistic regression model developed from individual genetic and personal factor data from a large case-control study. In three independent validation populations, including a blinded validation, OncoVue has been shown to significantly outperform both the Gail model and composite risk scores produced by combining GWAS SNP risks with the Gail model. Here we have examined the application of the OncoVue risk score to identifying women that exceed a lifetime risk of 20% and would be candidates for surveillance by MRI. 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. As in previous studies, the original assignment of the samples into a discovery set of 4768 Caucasian women (1592 cases and 3176 controls) and two independent validation sets was used. The first validation set consisted of 1137 Caucasian women (376 cases and 761 controls) while the second consisted of 494 African American women (149 cases and 345 controls). DNAs were genotyped for 22 SNP variants and genotype information was combined with personal factors information to calculate the OncoVue risk scores for the individual participants. Personal factor information was used to calculate Gail model risk scores. Results: For both models, positive likelihood ratios (PLR) were calculated as the proportion of patients with breast cancer with an elevated risk estimate (≥20%) divided by the proportion of disease-free individuals with an elevated risk estimate. In both the discovery and validation sets, OncoVue exhibited a 1.6- to 1.8-fold improvement compared to the GM in more accurately assigning elevated risk estimates to breast cancer cases rather than controls. At higher risk thresholds, the fold improvement increasedand exceeded 2.5 in some sample sets. Another measure of clinical utility of a risk assessment test is the ability to correctly place more breast cancer cases at elevated risk. This was examined with a normalized analysis of the number of breast cancer cases assigned elevated risk (≥20%) estimates by OncoVue compared to the Gail model. In these analyses, the percent improvement in number of cases placed at elevated risk by OncoVue ranged from 20 to 33%. Conclusion: OncoVue exhibited significantly improved performance, compared to the Gail model alone, in estimating lifetime individual risk and accurately assigning elevated risk to cases. The improved performance of OncoVue was similar to that observed in previous studies using other risk frames and thresholds. Together these studies demonstrate the improved ability of OncoVue to produce more accurate individualized breast cancer risk estimation.
Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P6-09-04.