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Breast Cancer Res Treat. 2019 Jul;176(1):141-148. doi: 10.1007/s10549-019-05210-2. Epub 2019 Apr 2.

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

Evans DGR1,2,3,4,5,6,7, Harkness EF8,9,10,11, Brentnall AR12, van Veen EM13, Astley SM8,9,10,14,11, Byers H13,11, Sampson S8, Southworth J8, Stavrinos P8, Howell SJ8,15,14,11, Maxwell AJ8,9,10,14,11, Howell A8,15,14,11, Newman WG13,16,14, Cuzick J12.

Author information

1
Division of Evolution and Genomic Sciences, Faculty of Biology, Medicine and Health, University of Manchester, MAHSC, Manchester, UK. gareth.evans@mft.nhs.uk.
2
Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, UK. gareth.evans@mft.nhs.uk.
3
The Christie NHS Foundation Trust, Manchester, UK. gareth.evans@mft.nhs.uk.
4
Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust (Central), Manchester, UK. gareth.evans@mft.nhs.uk.
5
Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, UK. gareth.evans@mft.nhs.uk.
6
NIHR Manchester Biomedical Research Centre, Cancer Prevention Early Detection Theme, The Christie NHS Foundation Trust, Manchester, UK. gareth.evans@mft.nhs.uk.
7
Department of Genomic Medicine, Manchester Academic Health Sciences Centre (MAHSC), St Mary's Hospital, University of Manchester, Manchester, M13 9WL, UK. gareth.evans@mft.nhs.uk.
8
Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, UK.
9
Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
10
Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
11
NIHR Manchester Biomedical Research Centre, Cancer Prevention Early Detection Theme, The Christie NHS Foundation Trust, Manchester, UK.
12
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, UK.
13
Division of Evolution and Genomic Sciences, Faculty of Biology, Medicine and Health, University of Manchester, MAHSC, Manchester, UK.
14
Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, UK.
15
The Christie NHS Foundation Trust, Manchester, UK.
16
Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust (Central), Manchester, UK.

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.

KEYWORDS:

Breast cancer; Early detection; Mammographic density; Pathology; Polygenic risk score; SNPs

PMID:
30941651
DOI:
10.1007/s10549-019-05210-2

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