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Breast Cancer Research and Treatment
Published in: Breast Cancer Research and Treatment 1/2019

Open Access 01-07-2019 | Breast Cancer | Clinical trial

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

Authors: D. Gareth R. Evans, Elaine F. Harkness, Adam R. Brentnall, Elke M. van Veen, Susan M. Astley, Helen Byers, Sarah Sampson, Jake Southworth, Paula Stavrinos, Sacha J. Howell, Anthony J. Maxwell, Anthony Howell, William G. Newman, Jack Cuzick

Published in: Breast Cancer Research and Treatment | Issue 1/2019

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Abstract

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.
Appendix
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Metadata
Title
Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants
Authors
D. Gareth R. Evans
Elaine F. Harkness
Adam R. Brentnall
Elke M. van Veen
Susan M. Astley
Helen Byers
Sarah Sampson
Jake Southworth
Paula Stavrinos
Sacha J. Howell
Anthony J. Maxwell
Anthony Howell
William G. Newman
Jack Cuzick
Publication date
01-07-2019
Publisher
Springer US
Published in
Breast Cancer Research and Treatment / Issue 1/2019
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-019-05210-2

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