Abstract 5941: A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women

Abstract

Abstract Purpose: To evaluate the impact of early detection with screening and primary prevention with risk-reducing medication to provide personalized data that will help identify women who are more likely to benefit from various interventions or combinations of interventions with the least harms. Methods: We adapted the CISNET microsimulation model G-E of breast cancer natural history to evaluate the harms and benefits of annual mammography and risk reducing medication among high-risk women (i.e., 5-year risk greater than or equal to 3%). Model G-E is a discrete event microsimulation model that follows millions of women from birth to death and captures the variability in distributions of each event. Each simulated woman is assigned a cohort-specific life expectancy which is used to select a date of breast cancer death. For this study, we dynamically updated the risk of developing breast cancer for each simulated woman based on her family history, breast density, age and history of biopsy. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, incidence and stage without screening, other cause mortality by age, screening performance (sensitivity/specificity), survival by age, stage, and subtype without treatment, treatment efficacy, and other cause mortality. We compared model outcomes for screening alone vs. screening with a 5-year course of risk reducing medication. We modeled various screening strategies including annual or biennial screening starting at ages 35, 40, 45 and stopping at ages 65 and 74 years. Model outcomes for each strategy included, the benefits of risk-reducing drugs (avoiding breast cancer) and screening (breast cancer stage, breast cancer-specific survival), and harms of screening (false positives, overdiagnosis). We also conducted sensitivity analysis to estimate the effects of uncertainty in model inputs or assumptions on results. Results: We found that risk reducing medication could result in an additional 28% decrease in invasive breast cancer incidence, 20% decrease in stage IV diagnosis, and a 30% decrease in breast cancer death if screening started at age 35 in a high-risk woman. However, potential breast density changes due to risk reducing medication among high-risk women could result in a 19% increase in false positive screening results compared to screening alone. The results varied by the starting age of screening. Conclusions: Simulation modeling is useful in assessing the relative benefits and harms of screening and risk reducing medication in high-risk women. Citation Format: Jinani Jayasekera, Amy Zhao. A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5941.