N04: Breast Cancer Pathology And Stage Are Better Predicted By Risk Stratification Models Including Mammographic Density And Common Genetic Variants

D. G. Evans1, 2, 6, 7, 8, E. Harkness2, 3, 4, A. Brentnall5, E. van Veen1, S. Astley2, 3, 4, 8, H. Byers1, S. Sampson2, J. Southworth2, P. Stavrinos2, S. Howell2, 6, 8, A. Maxwell2, 3, 4, 8, A. Howell2, 6, 8, W. Newman1,7,8, J. Cuzick5

1 – Division of Evolution and Genomic Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. 2 – Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, UK. 3 – Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. 4 -Manchester Academic Health Science Centre, University of Manchester, Manchester, UK. 5 – Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, UK. 6 – The Christie NHS Foundation Trust, Manchester, UK. 7 – Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust (Central), Manchester, UK. 8 – Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, UK.

 

Aim: To better stratify breast cancer risks to enable more targeted early detection/prevention strategies particularly to balance the risks/benefits of population

Method: Data from 9,362 women unaffected by breast cancer at study entry who provided a DNA sample for polygenic-risk-score (PRS) were analysed from the 57,902 women in the PROCAS study. The PRS score was analysed along with mammographic density (density residual-DR) and standard risk factors to assess future risk of breast cancer  pathological type and  hormonal receptor status

Results: For the 195 prospective breast cancers a predictor based on Tyrer-Cuzick/DR/PRS was informative for subsequent cancer overall and more so for stage 2+ cancers and calibrated  (0.99) for  predicting cancers across all risk groups. Although DR was most predictive for HER2+ and stage 2+ cancers it did not discriminate as well between poor prognosis cancers and extremely good prognosis cancers as Tyrer-Cuzick or the PRS, with the PRS providing the highest OR for post-prevalent stage 2+ cancers IQR OR=1.79 (95%CI:1.30-2.46).

Conclusion: A combined approach using Tyrer-Cuzick, mammographic density and a PRS provides accurate risk stratification not only overall but also for worse prognosis cancers. This provides support for reducing screening intervals in the high and increasing them in the low risk groups.

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