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A new predictive model accurately identifies Asian ovarian cancer patients likely to carry BRCA pathogenic variants (PVs). This targeted approach optimizes genetic testing in resource-limited settings, improving cost-effectiveness and accessibility.

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Area of Science:

  • Oncology
  • Genetics
  • Epidemiology

Background:

  • Germline BRCA1/2 testing is crucial for ovarian cancer management and family testing.
  • High costs of genetic testing limit its feasibility in resource-limited settings.
  • Existing predictive models for BRCA variants are primarily Western-derived and not validated for Asian ovarian cancer populations.

Purpose of the Study:

  • To develop and validate a predictive model for identifying pathogenic BRCA1/2 variants (PVs) in Asian ovarian cancer patients.
  • To assess the cost-effectiveness of a model-based targeted testing strategy compared to universal testing.

Main Methods:

  • A multi-center study included 1,126 Asian ovarian cancer patients, with 147 identified as BRCA PV carriers.
  • A predictive model was developed using demographic, clinical, and reproductive factors.
  • Model performance was evaluated for discrimination, calibration, and accuracy, alongside cost-benefit analysis.

Main Results:

  • The final model achieved an area under the curve of 0.80, indicating strong discriminatory power.
  • Key predictors included age at diagnosis, ethnicity, personal/family cancer history, and clinicopathological features.
  • The model demonstrated 77% accuracy, significantly outperforming universal testing (13%), reducing costs, and decreasing testing by 15% while maintaining 100% sensitivity.

Conclusions:

  • A targeted genetic testing strategy using a predictive model is a feasible and scalable alternative to universal testing in resource-limited settings.
  • This approach enhances the efficiency and equity of BRCA testing for Asian ovarian cancer patients.