A breast cancer (BC) risk model incorporating Tyrer-Cuzick version 8 (TCv8) and a polygenic risk score (PRS) for diverse ancestries.

Abstract

557 Background: BC risk assessment is important for guiding personalized screening and risk-reducing interventions. TCv8 is used to estimate BC risk based on age, breast density, family cancer history and other clinical factors. Accuracy may be improved by combining TCv8 with a PRS. We developed and validated a PRS for diverse ancestries based on 149 common genetic variants (PRS-149) comprised of 56 ancestry-informative and 93 BC-associated variants. Here, we describe a BC risk model that combines PRS-149 with TCv8. Methods: Subjects had multigene panel testing for hereditary cancer and were negative for pathogenic variants in known BC susceptibility genes. A combined risk score (CRS), incorporating PRS-149 and TCv8, was developed based on 189,230 women, including 43,444 (23%) with a history of BC. Breast Imaging Reporting and Data System (BI-RADS) breast density measurements were available for 12,363 women. We used multivariable logistic regression to test breast density and PRS-149 for association with risk of BC. An independent test cohort of 6,030 BC-unaffected women with BI-RADS assessment was used to evaluate the effect of PRS-149 on risk stratification. Relative contributions of family history, breast density, other clinical factors in TCv8 and PRS-149 were examined by adding terms sequentially to an ANOVA model. We compared differences in classification of women as high (20%) or low/moderate (20%) remaining lifetime risk according to TCv8 versus CRS. Results: In the development cohort, increased breast density was significantly associated with higher risk of BC (p=3.0x10-6) with an effect size consistent with TCv8. PRS-149 improved BC risk prediction over age, breast density and family history (OR per unit standard deviation: 1.41, 95% CI: 1.37 – 1.46; p: 1.8x10-105). PRS-149 was weakly but significantly correlated with both family history (r=0.09) and breast density (r=0.01). After adjusting for multiple testing, no other factors were correlated with PRS-149. In the independent test cohort, PRS-149 explained 27% of CRS variability after accounting for family history, breast density and other clinical factors. Adding PRS-149 to TCv8 significantly altered risk estimates, with 16.3% (983/6,030) of patients classified differently by CRS versus TCv8. By TCv8 alone, 38.0% (2,289/6,030) of patients were classified as high-risk. Among patients who were high-risk by TCv8, 25.2% (576/2,289) were downgraded by CRS. Conclusions: This is the first BC risk model that includes breast density, family history, and a PRS based on genetically determined ancestry that is validated for diverse populations. Addition of PRS-149 improved risk prediction and substantially modified risk stratification compared to TCv8 alone. Implementation of CRS may therefore lead to improved identification of women who are likely to benefit from increased surveillance and preventive medications.