A combined clinical and genetic model for predicting risk of ovarian cancer. Issue 1 (27th October 2022)

• Record Type: Journal Article
• Authors: Dite, Gillian S.; Spaeth, Erika; Murphy, Nicholas M.; Allman, Richard
• Is Part Of: European journal of cancer prevention. Volume 32:Issue 1(2023)
• Page Start: 57
• Page End: 64
• Publication Date: 2022-10-27
• Subjects: absolute risk -- clinical risk -- ovarian cancer -- polygenic risk -- risk prediction; Cancer -- Prevention -- Periodicals; Neoplasms -- etiology -- Periodicals; Neoplasms -- prevention & control -- Periodicals; Cancer -- Prevention; Periodicals; 616.994052
• DOI: 10.1097/CEJ.0000000000000771
• Languages: English
• ISSNs: 0959-8278

Abstract

Abstract : Objective: Women with a family history of ovarian cancer or a pathogenic or likely pathogenic gene variant are at high risk of the disease, but very few women have these risk factors. We assessed whether a combined polygenic and clinical risk score could predict risk of ovarian cancer in population-based women who would otherwise be considered as being at average risk. Methods: We used the UK Biobank to conduct a prospective cohort study assessing the performance of 10-year ovarian cancer risks based on a polygenic risk score, a clinical risk score and a combined risk score. We used Cox regression to assess association, Harrell's C-index to assess discrimination and Poisson regression to assess calibration. Results: The combined risk model performed best and problems with calibration were overcome by recalibrating the model, which then had a hazard ratio per quintile of risk of 1.338 [95% confidence interval (CI), 1.152–1.553], a Harrell's C-index of 0.663 (95% CI, 0.629–0.698) and overall calibration of 1.000 (95% CI, 0.874–1.145). In the refined model with estimates based on the entire dataset, women in the top quintile of 10-year risk were at 1.387 (95% CI, 1.086–1.688) times increased risk, while women in the top quintile of full-lifetime risk were at 1.527 (95% CI, 1.187–1.866) times increased risk compared with the population. Conclusion: Identification of women who are at high risk of ovarian cancer can allow healthcare providers and patients to engage in joint decision-making discussions around the risks and benefits of screening options or risk-reducing surgery.