Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants

Purpose To improve breast cancer risk stratification to enable more targeted early detection/prevention strategies that will better balance risks and benefits of population screening programmes. Methods 9362 of 57,902 women in the Predicting-Risk-Of-Cancer-At-Screening (PROCAS) study who were unaffected by breast cancer at study entry and provided DNA for a polygenic risk score (PRS). The PRS was analysed alongside mammographic density (density-residual-DR) and standard risk factors (Tyrer-Cuzick-model) to assess future risk of breast cancer based on tumour stage receptor expression and pathology. Results 195 prospective incident breast cancers had a prediction based on TC/DR/PRS which was informative for subsequent breast cancer overall [IQ-OR 2.25 (95% CI 1.89–2.68)] with excellent calibration-(0.99). The model performed particularly well in predicting higher stage stage 2+ IQ-OR 2.69 (95% CI 2.02–3.60) and ER + BCs (IQ-OR 2.36 (95% CI 1.93–2.89)). DR was most predictive for HER2+ and stage 2+ cancers but did not discriminate as well between poor and extremely good prognosis BC as either Tyrer-Cuzick or PRS. In contrast, PRS gave the highest OR for incident stage 2+ cancers, [IQR-OR 1.79 (95% CI 1.30–2.46)]. Conclusions A combined approach using Tyrer-Cuzick/DR/PRS provides accurate risk stratification, particularly for poor prognosis cancers. This provides support for reducing the screening interval in high-risk women and increasing the screening interval in low-risk women defined by this model.