Feasibility and acceptability of personalised breast cancer screening (DECIDO study): protocol of a single-arm proof-of-concept trial

Anna Pons-Rodriguez, Carles Forné Izquierdo, Jordi Vilaplana-Mayoral, Inés Cruz-Esteve, Isabel Sánchez-López, Mercè Reñé-Reñé, Cristina Cazorla, Marta Hernández-Andreu, Gisela Galindo-Ortego, Montserrat Llorens Gabandé, Celmira Laza-Vásquez, Pau Balaguer-Llaquet, Montserrat Martínez-Alonso, Montserrat Rué, DECIDO Group, Pau Balaguer-Llaquet, Iván-David Benítez, Alexandra Bertran, Àngels Cardona, Misericòrdia Carles-Lavila, Cristina Cazorla-Sánchez, Núria Codern, Inés Cruz-Esteve, Carles Forné-Izquierdo, Maria José Hernández-Andreu, Edelmir Iglesias, Gisela Galindo-Ortego, Marta Hernández, Celmira Laza-Vásquez, Montserrat Llorens-Gabandé, Montserrat Martínez-Alonso, Maria José Pérez-Lacasta, Hèctor Perpiñán, Anna Pons-Rodríguez, Montserrat Rué, Isabel Sánchez-López, Jordi Vilaplana-Mayoral, Mercè Reñé-Reñé, Anna Pons-Rodriguez, Carles Forné Izquierdo, Jordi Vilaplana-Mayoral, Inés Cruz-Esteve, Isabel Sánchez-López, Mercè Reñé-Reñé, Cristina Cazorla, Marta Hernández-Andreu, Gisela Galindo-Ortego, Montserrat Llorens Gabandé, Celmira Laza-Vásquez, Pau Balaguer-Llaquet, Montserrat Martínez-Alonso, Montserrat Rué, DECIDO Group, Pau Balaguer-Llaquet, Iván-David Benítez, Alexandra Bertran, Àngels Cardona, Misericòrdia Carles-Lavila, Cristina Cazorla-Sánchez, Núria Codern, Inés Cruz-Esteve, Carles Forné-Izquierdo, Maria José Hernández-Andreu, Edelmir Iglesias, Gisela Galindo-Ortego, Marta Hernández, Celmira Laza-Vásquez, Montserrat Llorens-Gabandé, Montserrat Martínez-Alonso, Maria José Pérez-Lacasta, Hèctor Perpiñán, Anna Pons-Rodríguez, Montserrat Rué, Isabel Sánchez-López, Jordi Vilaplana-Mayoral, Mercè Reñé-Reñé

Abstract

Introduction: Personalised cancer screening aims to improve benefits, reduce harms and being more cost-effective than age-based screening. The objective of the DECIDO study is to assess the acceptability and feasibility of offering risk-based personalised breast cancer screening and its integration in regular clinical practice in a National Health System setting.

Methods and analysis: The study is designed as a single-arm proof-of-concept trial. The study sample will include 385 women aged 40-50 years resident in a primary care health area in Spain. The study intervention consists of (1) a baseline visit; (2) breast cancer risk estimation; (3) a second visit for risk communication and screening recommendations based on breast cancer risk and (4) a follow-up to obtain the study outcomes.A polygenic risk score (PRS) will be constructed as a composite likelihood ratio of 83 single nucleotide polymorphisms. The Breast Cancer Surveillance Consortium risk model, including age, race/ethnicity, family history of breast cancer, benign breast disease and breast density will be used to estimate a preliminary 5-year absolute risk of breast cancer. A Bayesian approach will be used to update this risk with the PRS value.The primary outcome measures will be attitude towards, intention to participate in and satisfaction with personalised breast cancer screening. Secondary outcomes will include the proportions of women who accept to participate and who complete the different phases of the study. The exact binomial and the Student's t-test will be used to obtain 95% CIs.

Ethics and dissemination: The study protocol was approved by the Drug Research Ethics Committee of the University Hospital Arnau de Vilanova. The trial will be conducted in compliance with this study protocol, the Declaration of Helsinki and Good Clinical Practice.The results will be published in peer-reviewed scientific journals and disseminated in scientific conferences and media.

Trial registration number: NCT03791008.

Keywords: breast tumours; epidemiology; health policy; public health.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Timeline of the study intervention. SNP, single nucleotide polymorphism.

References

    1. European Commission . European Commission 2015/C421/01 – Non-opposition to a notified concentration. Official Journal of the European Union 2015.
    1. Chowdhury S, Dent T, Pashayan N, et al. . Incorporating genomics into breast and prostate cancer screening: assessing the implications. Genet Med 2013;15:423–32. 10.1038/gim.2012.167
    1. Vilaprinyo E, Forné C, Carles M, et al. . Cost-Effectiveness and harm-benefit analyses of risk-based screening strategies for breast cancer. PLoS One 2014;9:e86858. 10.1371/journal.pone.0086858
    1. Tice JA, Cummings SR, Smith-Bindman R, et al. . Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med 2008;148:337–47. 10.7326/0003-4819-148-5-200803040-00004
    1. Tice JA, Miglioretti DL, Li C-S, et al. . Breast density and benign breast disease: risk assessment to identify women at high risk of breast cancer. J Clin Oncol 2015;33:3137–43. 10.1200/JCO.2015.60.8869
    1. Shieh Y, Eklund M, Madlensky L, et al. . Breast cancer screening in the precision medicine era: risk-based screening in a population-based trial. J Natl Cancer Inst 2017;109:djw290. 10.1093/jnci/djw290
    1. Michailidou K, Lindström S, Dennis J, et al. . Association analysis identifies 65 new breast cancer risk loci. Nature 2017;551:92–4. 10.1038/nature24284
    1. Mavaddat N, Pharoah PDP, Michailidou K, et al. . Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst 2015;107:djv036. 10.1093/jnci/djv036
    1. Vachon CM, Pankratz VS, Scott CG, et al. . The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst 2015;107:1–4. 10.1093/jnci/dju397
    1. Vachon CM, Scott CG, Tamimi RM, et al. . Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk. Breast Cancer Res 2019;21:1–10. 10.1186/s13058-019-1138-8
    1. Yanes T, Young M-A, Meiser B, et al. . Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field. Breast Cancer Res 2020;22:1–10. 10.1186/s13058-020-01260-3
    1. Shieh Y, Hu D, Ma L, et al. . Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones. Breast Cancer Res Treat 2017;166:603–12. 10.1007/s10549-017-4430-2
    1. Román M, Sala M, Domingo L, et al. . Personalized breast cancer screening strategies: a systematic review and quality assessment. PLoS One 2019;14:e0226352. 10.1371/journal.pone.0226352
    1. Evans DGR, Donnelly LS, Harkness EF, et al. . Breast cancer risk feedback to women in the UK NHS breast screening population. Br J Cancer 2016;114:1045–52. 10.1038/bjc.2016.56
    1. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004;23:1111–30. 10.1002/sim.1668
    1. Evans DGR, Harkness EF, Brentnall AR, et al. . 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;176:141–8. 10.1007/s10549-019-05210-2
    1. French DP, Astley S, Brentnall AR, et al. . 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;20:1–14. 10.1186/s12885-020-07054-2
    1. Pérez-Lacasta MJ, Sala M, Perestelo-Pérez L, et al. . Effect of information about the benefits and harms of mammography on women ’ s decision making : The InforMa randomised controlled trial. PLoS One 2019:1–20.
    1. Puig-Vives M, Pollan M, Rue M, et al. . Rapid increase in incidence of breast ductal carcinoma in situ in Girona, Spain 1983-2007. Breast 2012;21:646–51. 10.1016/j.breast.2012.01.014
    1. Martinez-Alonso M, Vilaprinyo E, Marcos-Gragera R, et al. . Breast cancer incidence and overdiagnosis in Catalonia (Spain). Breast Cancer Res 2010;12:R58. 10.1186/bcr2620
    1. Rainey L, Jervaeus A, Donnelly LS, et al. . Women’s perceptions of personalized risk‐based breast cancer screening and prevention: An international focus group study. Psychooncology 2019;28:1056–62. 10.1002/pon.5051
    1. Koitsalu M, Sprangers MAG, Eklund M, et al. . Public interest in and acceptability of the prospect of risk-stratified screening for breast and prostate cancer. Acta Oncol 2016;55:45–51. 10.3109/0284186X.2015.1043024
    1. Rainey L, van der Waal D, Donnelly LS, et al. . Women's decision-making regarding risk-stratified breast cancer screening and prevention from the perspective of international healthcare professionals. PLoS One 2018;13:e0197772. 10.1371/journal.pone.0197772
    1. Puzhko S, Gagnon J, Simard J, et al. . Health professionals’ perspectives on breast cancer risk stratification: understanding evaluation of risk versus screening for disease. Public Health Rev 2019;40:1–19. 10.1186/s40985-019-0111-5
    1. Esquivel-Sada D, Lévesque E, Hagan J, et al. . Envisioning implementation of a personalized approach in breast cancer screening programs: stakeholder perspectives. Healthc Policy 2019;15:39–54. 10.12927/hcpol.2019.26072
    1. Hoffmann TC, Del Mar C. Patients' expectations of the benefits and harms of treatments, screening, and tests: a systematic review. JAMA Intern Med 2015;175:274. 10.1001/jamainternmed.2014.6016
    1. Hoffmann TC, Del Mar C. Clinicians' expectations of the benefits and harms of treatments, screening, and tests: a systematic review. JAMA Intern Med 2017;177:407–19. 10.1001/jamainternmed.2016.8254
    1. Toledo-Chávarri A, Rué M, Codern-Bové N, et al. . A qualitative study on a decision aid for breast cancer screening: views from women and health professionals. Eur J Cancer Care 2017;26:e12660–11. 10.1111/ecc.12660
    1. American College of Radiology . Breast imaging reporting and data system (BI-RADS. 5th ed, 2013.
    1. Kerlikowske K, Scott CG, Mahmoudzadeh AP, et al. . Automated and clinical breast imaging reporting and data system density measures predict risk for screen-detected and interval cancers: a case-control study. Ann Intern Med 2018;168:757–65. 10.7326/M17-3008
    1. Conant EF, Sprague BL, Kontos D. Beyond BI-RADS density: a call for quantification in the breast imaging clinic. Radiology 2018;286:401–4. 10.1148/radiol.2017170644
    1. Sauer S, Gut IG. Genotyping single-nucleotide polymorphisms by matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2002;782:73–87. 10.1016/S1570-0232(02)00692-X
    1. Shieh Y, Hu D, Ma L, et al. . Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast Cancer Res Treat 2016;159:513–25. 10.1007/s10549-016-3953-2
    1. Gail MH, Brinton LA, Byar DP, et al. . Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989;81:1879–86. 10.1093/jnci/81.24.1879
    1. Hersch J, Barratt A, Jansen J, et al. . Use of a decision aid including information on overdetection to support informed choice about breast cancer screening: a randomised controlled trial. Lancet 2015;385:1642–52. 10.1016/S0140-6736(15)60123-4
    1. Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res 2017;17:1–13. 10.1186/s12913-017-2031-8
    1. Carles M, Martínez-Alonso M, Pons A, et al. . The effect of information about the benefits and harms of mammography on women’s decision-making: study protocol for a randomized controlled trial. Trials 2017;18:1–8. 10.1186/s13063-017-2161-7
    1. O’Connor AM. Decisional conflict Scale—user manual 1993. decision aid evaluation measures, 2010. Available:
    1. O’Connor AM. Decision self-efficacy Scale—user manual 1995. decision aid evaluation measures, 2002. Available:
    1. Marteau TM, Bekker H. The development of a six-item short-form of the state scale of the Spielberger State-Trait anxiety inventory (STAI). Br J Clin Psychol 1992;31:301–6. 10.1111/j.2044-8260.1992.tb00997.x
    1. R Core Team . R: a language and environment for statistical computing, 2020. Available:
    1. RStudio Team . RStudio: integrated development environment for R, 2020. Available:

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