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 DunningAndrew Lee, Antonis C. Antoniou, Rosa Legood, Usha Menon, Ian Jacobs, Ranjit Manchanda

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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. View Full-Text
Original languageEnglish
Article number1241
Number of pages21
JournalCancers
Volume12
Issue number5
DOIs
Publication statusPublished - 15 May 2020

Keywords

  • population genetic testing
  • ovarian cancer risk
  • risk stratification
  • BRCA1
  • BRCA2
  • RAD51C
  • RAD51D
  • BRIP1
  • SNP
  • risk modelling

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