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.