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401 Population testing and personalised ovarian cancer risk prediction for risk adapted targeted prevention
  1. Faiza Gaba1,
  2. Oleg Blyuss2,
  3. Saskia Sanderson3,
  4. Antonis Antoniou4,
  5. Jatinderpal Kalsi5,
  6. Andrew Wallace6,
  7. Usha Menon7,
  8. Rosa Legood8,
  9. Ian Jacobs9 and
  10. Ranjit Manchanda10
  1. 1Wolfson Institute of Preventative Medicine, Barts Cruk Cancer Centre, Queen Mary University of London; Centre for Cancer Prevention
  2. 2University of Hertfordshire; School of Physics, Astronomy and Mathematics
  3. 3University College London; Department of Behavioural Science and Health
  4. 4University of Cambridge; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care
  5. 5University College London; Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health
  6. 6University of Manchester; Manchester Centre for Genomic Medicine
  7. 7University College London; Medical Research Council Clinical Trials Unit at Ucl, Institute of Clinical Trials and Methodology
  8. 8London School of Hygiene and Tropical Medicine; Department of Health Services Research and Policy
  9. 9University of New South Wales; Department of Women’s Health
  10. 10Wolfson Institute of Preventative Medicine, Barts Cruk Cancer Centre, Queen Mary University of London; Department of Gynaecological Oncology, Barts Health NHS Trust; Centre for Cancer Prevention


Faiza Gaba, Oleg Blyuss, Jatinderpal Kalsi, Saskia Sanderson, Andrew Wallace, Antonis C. Antoniou, Rosa Legood, Usha Menon, Ian Jacobs, & Ranjit Manchanda, on behalf of the PROMISE-FS team

Introduction/Background The current approach to genetic-testing and risk assessment is based on family-history and misses the majority of people at risk. Unselected population-based testing can enable personalised ovarian cancer (OC) risk prediction combining genetic/epidemiology/hormonal data. This permits population risk stratification for risk adapted targeted screening and prevention. Such an intervention study has not previously been undertaken. We aimed to assess the feasibility of OC risk stratification of general population women using a personalised OC risk tool followed by risk management.

Methodology 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) for 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.

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%. The high-risk woman underwent surgical prevention. 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 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. Larger implementation studies evaluating long-term impact and cost effectiveness of this strategy are needed.

Disclosures RM- funding from CRUK & Eve Appeal for thiswork. Funding from Barts Charity, Rosetrees trust outside this work. Honorarium from Astrazeneca & MSD.

IJ, UM- Financial interest in Abcodia, company for development of biomarkers for early detection of cancer. Other authors- No disclosures.

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