Population study of personalised ovarian cancer risk prediction for targeted screening and prevention


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Ranjit Manchanda, 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 Antoniou, Rosa Legood, Usha Menon, Ian Jacobs

Abstract

Background

Unselected population-based testing for personalised ovarian cancer (OC) risk assessment combining genetic, epidemiology and 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.

Method

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 use of an optional telephone helpline . 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 incorporating genetic/lifestyle/hormonal OC risk factors. Uptake/acceptability of population genetic testing (PGT) for OC risk stratification, as well as satisfaction, decision-aid and 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 included: feasibility/ acceptability, uptake, decision aid/telephone helpline use, satisfaction/regret, and impact on psychological health/quality of life.

Results

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.

Conclusion

Our 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 or quality of life.

Impact statement

Population-based testing for personalised OC risk stratification is feasible, acceptable, has high satisfaction, reduces cancer worry/risk perception; implementation studies evaluating its impact, clinical efficacy, long-term psychological, and socio-ethical consequences are needed