Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing

Abstract

PURPOSE Screening and prevention decisions for women at increased risk of developing breast cancer depend on genetic and clinical factors to estimate risk and select appropriate interventions. Integration of polygenic risk into clinical breast cancer risk estimators can improve discrimination. However, correlated genetic effects must be incorporated carefully to avoid overestimation of risk. MATERIALS AND METHODS A novel Fixed-Stratified method was developed that accounts for confounding when adding a new factor to an established risk model. A combined risk score (CRS) of an 86–single-nucleotide polymorphism polygenic risk score and the Tyrer-Cuzick v7.02 clinical risk estimator was generated with attenuation for confounding by family history. Calibration and discriminatory accuracy of the CRS were evaluated in two independent validation cohorts of women of European ancestry (N = 1,615 and N = 518). Discrimination for remaining lifetime risk was examined by age-adjusted logistic regression. Risk stratification with a 20% risk threshold was compared between CRS and Tyrer-Cuzick in an independent clinical cohort (N = 32,576). RESULTS Simulation studies confirmed that the Fixed-Stratified method produced accurate risk estimation across patients with different family history. In both validation studies, CRS and Tyrer-Cuzick were significantly associated with breast cancer. In an analysis with both CRS and Tyrer-Cuzick as predictors of breast cancer, CRS added significant discrimination independent of that captured by Tyrer-Cuzick ( P < 10−11 in validation 1; P < 10−7 in validation 2). In an independent cohort, 18% of women shifted breast cancer risk categories from their Tyrer-Cuzick–based risk compared with risk estimates by CRS. CONCLUSION Integrating clinical and polygenic factors into a risk model offers more effective risk stratification and supports a personalized genomic approach to breast cancer screening and prevention.

Comprehensive Breast Cancer Risk Assessment for CHEK2 and ATM Pathogenic Variant Carriers Incorporating a Polygenic Risk Score and the Tyrer-Cuzick Model

“…However, it has been previously shown that this potential bias can be avoided by accounting for family history in the logistic regression model. 1 , 16 , 18 , 26 Second, this study used only women of European ancestry. Further studies are required to examine polygenic breast cancer risk for women of non-European ancestry, including PV carriers and noncarriers.…”

“… 21 , 28 Although future work may expand the SNP profiles for commercially available PRSs, this and other recent work show that these risk models provide important clinical information to inform individual patient cancer risks. 15 , 16 , 18 …”

“…This work combined a validated PRS with mutation status and the Tyrer-Cuzick model according to a previously validated methodology. 18 Validation in an independent patient cohort would be beneficial but is currently infeasible because of the rarity of PVs in unbiased study populations. By incorporating polygenic variant-conferred risk for moderate penetrance genes such as CHEK2 and ATM into other clinically accepted risk assessment tools, it is possible to refine short-term and lifetime risk stratification.…”

“…PV-associated and Tyrer-Cuzick–estimated breast cancer risks were combined according to the Fixed-Stratified method that prevents double counting of information from correlated risk factors in a manner equivalent to full multivariable coestimation. 18 A detailed explanation of the statistical equations used is given in the Data Supplement.…”

Information needs on breast cancer genetic and non-genetic risk factors in relatives of women with a BRCA1/2 or PALB2 pathogenic variant

“…Statistical models such as the Tyrer-Cuzick [ 11 ] and the ‘Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA)’ [ 12 ] integrate the PRS and NGRF and can now provide a more precise and accurate BC risk prediction than family history or monogenic testing alone. Based on this estimation, more personalized recommendations, such as increased breast surveillance at a younger age than standard recommendation, can be delivered for BC risk management.…”