Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Abstract

BackgroundIn the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.ObjectivesTo (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.DesignTwo cohort studies assessing breast cancer incidence.SettingHigh-risk FHC and the NHSBSP Greater Manchester, UK.ParticipantsA total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].InterventionsQuestionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.Main outcome measuresBreast cancer incidence.Data sourcesThe NHSBSP; cancer registration.ResultsA total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.ConclusionsRisk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.Future workA pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.FundingThe National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

A case–control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

“…Women were excluded from PROCAS if they had been diagnosed with breast cancer before completing the questionnaire; cancers detected as a result of the screening test were included. In total, 57,902 women were enrolled in the PROCAS study . Unaffected women in the cohort who lived within the smaller defined Withington area (South Manchester) were subsequently invited to participate in an additional risk assessment study using DNA extracted from a saliva sample; all women with breast cancer diagnosed after completion of the questionnaire were invited to provide saliva samples and participate as cases and saliva samples were obtained from 9,956 women…”

“…A study was run within the Greater Manchester National Health Service Breast Screening Program to identify suitable models to assess breast cancer risk in population settings (predicting risk of cancer at screening [PROCAS]). 18 Women aged 47-73 years were invited once and recruited between October 2009 and June 2015 at the time of attendance for mammographic screening. Breast cancer risk factors were selfreported by the women via completion of a two-page paper questionnaire.…”

Limited influence of germline genetic variation on all-cause mortality in women with early onset breast cancer: evidence from gene-based tests, single-marker regression, and whole-genome prediction

“…For example, recent research has suggested that other germline genetic variants may be associated with mortality in women with pathogenic mutations in the genes BRCA2 and TP53 . [38,39] Beyond gene-by-gene interactions, germline variation may have a larger influence on breast cancer mortality in populations that have different background risk factors such as ancestry (this sample was of European ancestry), or country of origin (this sample was recruited from affluent countries). Germline variation may also influence mortality by way of an interaction with treatment [40], which was not consistently available for our participants.…”

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis

“…Previous experience in a research context suggests that embedding stratification in the existing NBSP is feasible [47] but no data exist on the effects of stratification on screening uptake and this is an important topic for further research.…”