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Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention

Wolfson Institute of Preventative Medicine, Barts CRUK Cancer Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
Department of Gynaecological Oncology, St Bartholomew’s Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
School of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow 119146, Russia
Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603098, Russia
Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London WC1E 6AU, UK
Department of Behavioural Science and Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK
Department Clinical Genetics, North East Thames Regional Genetics Unit, Great Ormond Street Hospital, London WC1N 3JH, UK
Department of Clinical Genetics, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
Medical Research Council Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, 90 High Holborn, London WC1V 6LJ, UK
West Midlands Regional Genetics Laboratory, Birmingham Women’s NHS Foundation Trust, Birmingham B15 2TG, UK
Manchester Centre for Genomic Medicine, 6th Floor Saint Marys Hospital, Oxford Rd, Manchester M13 9WL, UK
Cancer Prevention Group, King’s College London, Great Maze Pond, London SE1 9RT, UK
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
Department of Women’s Health, University of New South Wales, Australia, Level 1, Chancellery Building, Sydney 2052, Australia
Author to whom correspondence should be addressed.
Cancers 2020, 12(5), 1241;
Received: 1 April 2020 / Revised: 1 May 2020 / Accepted: 6 May 2020 / Published: 15 May 2020
Unselected population-based personalised ovarian cancer (OC) risk assessment combining genetic/epidemiology/hormonal data has not previously been undertaken. We aimed to perform a feasibility study of OC risk stratification of general population women using a personalised OC risk tool followed by risk management. Volunteers were recruited through London primary care networks. Inclusion criteria: women ≥18 years. Exclusion criteria: prior ovarian/tubal/peritoneal cancer, previous genetic testing for OC genes. Participants accessed an online/web-based decision aid along with optional telephone helpline use. Consenting individuals completed risk assessment and underwent genetic testing (BRCA1/BRCA2/RAD51C/RAD51D/BRIP1, OC susceptibility single-nucleotide polymorphisms). A validated OC risk prediction algorithm provided a personalised OC risk estimate using genetic/lifestyle/hormonal OC risk factors. Population genetic testing (PGT)/OC risk stratification uptake/acceptability, satisfaction, decision aid/telephone helpline use, psychological health and quality of life were assessed using validated/customised questionnaires over six months. Linear-mixed models/contrast tests analysed impact on study outcomes. Main outcomes: feasibility/acceptability, uptake, decision aid/telephone helpline use, satisfaction/regret, and impact on psychological health/quality of life. In total, 123 volunteers (mean age = 48.5 (SD = 15.4) years) used the decision aid, 105 (85%) consented. None fulfilled NHS genetic testing clinical criteria. OC risk stratification revealed 1/103 at ≥10% (high), 0/103 at ≥5%–<10% (intermediate), and 100/103 at <5% (low) lifetime OC risk. Decision aid satisfaction was 92.2%. The telephone helpline use rate was 13% and the questionnaire response rate at six months was 75%. Contrast tests indicated that overall depression (p = 0.30), anxiety (p = 0.10), quality-of-life (p = 0.99), and distress (p = 0.25) levels did not jointly change, while OC worry (p = 0.021) and general cancer risk perception (p = 0.015) decreased over six months. In total, 85.5–98.7% were satisfied with their decision. Findings suggest population-based personalised OC risk stratification is feasible and acceptable, has high satisfaction, reduces cancer worry/risk perception, and does not negatively impact psychological health/quality of life. View Full-Text
Keywords: population genetic testing; ovarian cancer risk; risk stratification; BRCA1; BRCA2; RAD51C; RAD51D; BRIP1; SNP; risk modelling population genetic testing; ovarian cancer risk; risk stratification; BRCA1; BRCA2; RAD51C; RAD51D; BRIP1; SNP; risk modelling
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MDPI and ACS Style

Gaba, F.; Blyuss, O.; Liu, X.; Goyal, S.; Lahoti, N.; Chandrasekaran, D.; Kurzer, M.; Kalsi, J.; Sanderson, S.; Lanceley, A.; Ahmed, M.; Side, L.; Gentry-Maharaj, A.; Wallis, Y.; Wallace, A.; Waller, J.; Luccarini, C.; Yang, X.; Dennis, J.; Dunning, A.; Lee, A.; Antoniou, A.C.; Legood, R.; Menon, U.; Jacobs, I.; Manchanda, R. Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention. Cancers 2020, 12, 1241.

AMA Style

Gaba F, Blyuss O, Liu X, Goyal S, Lahoti N, Chandrasekaran D, Kurzer M, Kalsi J, Sanderson S, Lanceley A, Ahmed M, Side L, Gentry-Maharaj A, Wallis Y, Wallace A, Waller J, Luccarini C, Yang X, Dennis J, Dunning A, Lee A, Antoniou AC, Legood R, Menon U, Jacobs I, Manchanda R. Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention. Cancers. 2020; 12(5):1241.

Chicago/Turabian Style

Gaba, Faiza, Oleg Blyuss, Xinting Liu, Shivam Goyal, Nishant Lahoti, Dhivya Chandrasekaran, Margarida Kurzer, Jatinderpal Kalsi, Saskia Sanderson, Anne Lanceley, Munaza Ahmed, Lucy Side, Aleksandra Gentry-Maharaj, Yvonne Wallis, Andrew Wallace, Jo Waller, Craig Luccarini, Xin Yang, Joe Dennis, Alison Dunning, Andrew Lee, Antonis C. Antoniou, Rosa Legood, Usha Menon, Ian Jacobs, and Ranjit Manchanda. 2020. "Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention" Cancers 12, no. 5: 1241.

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