A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence Survival and Mortality Trends from 1975 to 2010

摘要

We present a Monte Carlo simulation model that reproduces U.S. invasive breast cancer incidence and mortality trends from 1975–2010 as a function of screening and adjuvant treatment. This model was developed for multiple purposes, including to quantify the impact of screening and adjuvant therapy on past and current trends, predicting future trends, and evaluate potential outcomes under hypothetical screening and treatment interventions. The model first generates the life histories of individual breast cancer patients by determining the patient’s age, tumor size, estrogen receptor (ER) status, human epidermal growth factor 2 (HER2) status, SEER (Surveillance Epidemiology and End Results) historic stage, detection mode at time of detection, preclinical tumor course, as well as death age and cause of death (breast cancer versus other causes). The model incorporates common inputs used by the Cancer Intervention and Surveillance Modeling Network (CISNET) including the dissemination patterns for screening mammography, breast cancer survival in the absence of adjuvant therapy, dissemination and efficacy of treatment by ER and HER2-status and death from causes other than breast cancer. In this manuscript, predicted mortality outcomes are compared assuming a proportional versus non-proportional hazards effects of treatment on breast cancer survival. We found that the proportional hazards treatment effects are sufficient for ER-negative disease. However, for ER-positive disease, the treatment effects appear to be higher during the early years following diagnosis and then diminish over time. Using non-proportional hazards effects for ER-positive cases, the predicted breast cancer mortality rates closely match the SEER mortality trends from 1975–2010, particularly after 1995. Our work indicates that population level simulation modeling may have a broader role in assessing the time-dependence of treatment effects.