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

Faiza Gaba; 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; Ranjit Manchanda

doi:10.3390/cancers12051241

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

Cancers (Basel). 2020 May 15;12(5):1241. doi: 10.3390/cancers12051241.

Authors

Faiza Gaba^{1

2}, Oleg Blyuss^{3

4

5}, Xinting Liu¹, Shivam Goyal¹, Nishant Lahoti¹, Dhivya Chandrasekaran^{1

2}, Margarida Kurzer², Jatinderpal Kalsi⁶, Saskia Sanderson⁷, Anne Lanceley⁶, Munaza Ahmed⁸, Lucy Side⁹, Aleksandra Gentry-Maharaj¹⁰, Yvonne Wallis¹¹, Andrew Wallace¹², Jo Waller¹³, Craig Luccarini¹⁴, Xin Yang¹⁴, Joe Dennis¹⁴, Alison Dunning¹⁴, Andrew Lee¹⁴, Antonis C Antoniou¹⁴, Rosa Legood¹⁵, Usha Menon¹⁰, Ian Jacobs¹⁶, Ranjit Manchanda^{1

2

10}

Affiliations

¹ Wolfson Institute of Preventative Medicine, Barts CRUK Cancer Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.
² Department of Gynaecological Oncology, St Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK.
³ School of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK.
⁴ Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow 119146, Russia.
⁵ Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603098, Russia.
⁶ Department of Women's Cancer, Elizabeth Garrett Anderson Institute for Women's Health, University College London, London WC1E 6AU, UK.
⁷ Department of Behavioural Science and Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK.
⁸ Department Clinical Genetics, North East Thames Regional Genetics Unit, Great Ormond Street Hospital, London WC1N 3JH, UK.
⁹ Department of Clinical Genetics, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK.
¹⁰ Medical Research Council Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, 90 High Holborn, London WC1V 6LJ, UK.
¹¹ West Midlands Regional Genetics Laboratory, Birmingham Women's NHS Foundation Trust, Birmingham B15 2TG, UK.
¹² Manchester Centre for Genomic Medicine, 6th Floor Saint Marys Hospital, Oxford Rd, Manchester M13 9WL, UK.
¹³ Cancer Prevention Group, King's College London, Great Maze Pond, London SE1 9RT, UK.
¹⁴ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK.
¹⁵ Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.
¹⁶ Department of Women's Health, University of New South Wales, Australia, Level 1, Chancellery Building, Sydney 2052, Australia.

Abstract

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.

Keywords: BRCA1; BRCA2; BRIP1; RAD51C; RAD51D; SNP; ovarian cancer risk; population genetic testing; risk modelling; risk stratification.

Abstract

Grants and funding