Reduced Breast and Ovarian Cancer Through Targeted Genetic Testing: Estimates Using the NEEMO Microsimulation Model

Area of Science:

• Population genetics and cancer epidemiology
• Health services research and modeling
• Genomic medicine and personalized oncology

Background:

• Genetic testing for hereditary breast and ovarian cancer (HBOC) is crucial for early detection and management.
• Existing simulation models often oversimplify genetic testing uptake and risk management.
• Accurate modeling requires incorporating family dynamics and adherence to cancer risk management.

Purpose of the Study:

• To develop an adaptable and validated microsimulation model (NEEMO) for evaluating HBOC genetic testing strategies in Australia.
• To accurately reflect current genetic testing practices, family-based predictive testing, and cancer risk management adherence.
• To compare clinical outcomes of different genetic testing scenarios for breast cancer patients and their relatives.

Main Methods:

• Developed the Population Genetic Testing Model (NEEMO), a population-level microsimulation with five-generation family structures.
• Incorporated monogenic and polygenic risk, clinical genetic services, screening, and risk-reducing surgeries.
• Compared four scenarios: no testing, current practice, optimized referral, and universal testing for breast cancer patients.

Main Results:

• NEEMO accurately simulated genetic testing utilization, cancer incidence, pathology, and survival.
• Predictive testing uptake in relatives aligned with existing data.
• Optimized genetic referral and expanded testing reduced breast cancer by 9.3% and ovarian cancer by 4.1% in relatives.

Conclusions:

• NEEMO is a validated and adaptable tool for evaluating genetic testing strategies in real-world settings.
• The model accurately captures uptake of clinical and predictive genetic testing and cancer risk management.
• Findings highlight the importance of optimizing genetic testing referrals for improved clinical and cost-effectiveness.