| Title: | A combined clinical and genetic model for predicting risk of ovarian cancer |
| Journal: | European Journal of Cancer Prevention |
| Published: | 27 Oct 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36503897/ |
| DOI: | https://doi.org/10.1097/cej.0000000000000771 |
Publication 8106
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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.</p>
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.</p>
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.</p>
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.</p>
6 Keywords
- Female
- Health Personnel
- Humans
- Models, Genetic
- Ovarian Neoplasms
- Prospective Studies
4 Authors
- Gillian S. Dite
- Erika Spaeth
- Nicholas M. Murphy
- Richard Allman
1 Application
| Application ID | Title |
|---|---|
| 47401 | Validation of polygenic risk scores, based upon pooled discovery studies, for predisposition to major prevalent diseases affecting both quality-of-life and lifespan and testing for causation. |
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