Included under terms of UK Non-commercial Government License.
NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.
Headline
The study found that risk precision in breast cancer can be improved by using information from deoxyribonucleic acid and mammographic breast density, and could be used to stratify screening in the NHS Breast Screening Programme. It may also identify women at greater risk of high-stage cancers so that they can undergo enhanced screening.
Abstract
Background:
In 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.
Objectives:
To (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.
Design:
Two cohort studies assessing breast cancer incidence.
Setting:
High-risk FHC and the NHSBSP Greater Manchester, UK.
Participants:
A 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].
Interventions:
Questionnaires 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 measures:
Breast cancer incidence.
Data sources:
The NHSBSP; cancer registration.
Results:
A 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 in BRCA1 (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.
Conclusions:
Risk 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 work:
A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.
Funding:
The 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.
Contents
- Plain English summary
- Scientific summary
- Chapter 1. Introduction and background
- Chapter 2. Project 1: improvement of risk prediction algorithms for women at high risk of breast cancer
- Chapter 3. Project 2: assessment of predictive value of new genetic variants
- Chapter 4. PROCAS: Predicting Risk of Breast Cancer at Screening
- Introduction
- Methods
- Risk feedback
- Measurement of mammographic density in PROCAS
- Analysis of density data
- Cohort study
- Case–control study
- Density case–control study of film priors using the Manchester Stepwedge and visual assessment score
- The relationship between volumetric and area-based mammographic density to age and hormonal factors
- Ethnicity and mammographic density
- Using breast volume as a surrogate for weight/body mass index
- Repeatability of visual assessment score assessment of mammographic density
- Interobserver variability of visual assessment score assessment and correction method
- Selection of best performing risk prediction model in PROCAS and incorporation of density
- Publications arising from work in Chapter 4
- Chapter 5. Cost-effectiveness
- Aim
- Objectives
- Study 1: systematic review of economic evaluations of breast screening programmes and screening modalities
- Study 2: quantifying the impact of a national breast screening programme on women’s out-of-pocket expenses
- Study 3: preliminary model-based cost-effectiveness analysis of a risk-based screening strategy for breast cancer as part of the NHS Breast Screening Programme
- General discussion
- Implications for policy
- Future research
- Conclusion
- Chapter 6. Patient and public involvement
- Chapter 7. Conclusions
- Acknowledgements
- References
- Appendix 1 PROCAS two-page questionnaire
- Appendix 2 MEDLINE search filter
- Appendix 3 EMBASE search filter
- Appendix 4 NHS Economic Evaluation Database and Health Technology Assessment search filter
- Appendix 5 Examples of parametric distributions potentially fitted to the adjusted lifetime risk of the PROCAS study population
- List of abbreviations
Article history
The research reported in this issue of the journal was funded by PGfAR as project number RP-PG-0707-10031. The contractual start date was in January 2009. The final report began editorial review in September 2014 and was accepted for publication in October 2015. As the funder, the PGfAR programme agreed the research questions and study designs in advance with the investigators. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The PGfAR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.
Declared competing interests of authors
Jack Cuzick reports grants from AstraZeneca for being on the advisory board, and funding of the prevention trial outside the submitted work.
- NLM CatalogRelated NLM Catalog Entries
- What are the benefits and harms of risk stratified screening as part of the NHS breast screening Programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420).[BMC Cancer. 2020]What are the benefits and harms of risk stratified screening as part of the NHS breast screening Programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420).French DP, Astley S, Brentnall AR, Cuzick J, Dobrashian R, Duffy SW, Gorman LS, Harkness EF, Harrison F, Harvie M, et al. BMC Cancer. 2020 Jun 18; 20(1):570. Epub 2020 Jun 18.
- Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants.[Breast Cancer Res Treat. 2019]Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants.Evans DGR, Harkness EF, Brentnall AR, van Veen EM, Astley SM, Byers H, Sampson S, Southworth J, Stavrinos P, Howell SJ, et al. Breast Cancer Res Treat. 2019 Jul; 176(1):141-148. Epub 2019 Apr 2.
- Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction.[JAMA Oncol. 2018]Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction.van Veen EM, Brentnall AR, Byers H, Harkness EF, Astley SM, Sampson S, Howell A, Newman WG, Cuzick J, Evans DGR. JAMA Oncol. 2018 Apr 1; 4(4):476-482.
- Review Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Service Task Force[ 2016]Review Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Service Task ForceMelnikow J, Fenton JJ, Whitlock EP, Miglioretti DL, Weyrich MS, Thompson JH, Shah K. 2016 Jan
- Review Improving pregnancy outcome in obese women: the UK Pregnancies Better Eating and Activity randomised controlled Trial[ 2017]Review Improving pregnancy outcome in obese women: the UK Pregnancies Better Eating and Activity randomised controlled TrialPoston L, Bell R, Briley AL, Godfrey KM, Nelson SM, Oteng-Ntim E, Sandall J, Sanders TAB, Sattar N, Seed PT, et al. 2017 Apr
- Improvement in risk prediction, early detection and prevention of breast cancer ...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
- NADH dehydrogenase subunit 1, partial (mitochondrion) [Artedius corallinus]NADH dehydrogenase subunit 1, partial (mitochondrion) [Artedius corallinus]gi|155543525|gb|ABU24239.1|Protein
Your browsing activity is empty.
Activity recording is turned off.
See more...