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

Abstract

We present a Monte Carlo simulation model that reproduces US invasive breast cancer incidence and mortality trends from 1975 to 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, predict 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, and death age and cause of death (breast cancer v. 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 article, predicted mortality outcomes are compared assuming proportional v. nonproportional 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 nonproportional hazards effects for ER-positive cases, the predicted breast cancer mortality rates closely match the SEER mortality trends from 1975 to 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.

Keywords: SEER; breast cancer incidence trends; breast cancer mortality trends; nonproportional hazards; time-dependent treatment effects.

Figure 1

Flowchart of the updated Monte Carlo algorithm per simulated individual. Orange parallelograms indicate inputs that have been modified. ER, estrogen receptor; HER2, human epidermal growth factor 2; MHT, menopausal hormonal therapy; TVDT, tumor volume doubling time.

Figure 2

Annual contribution of treatment to reducing the hazard of death from breast cancer for cases detected between 1990 and 2010 by estrogen receptor (ER) status and age (<50, >50). The black curves are the estimates assuming proportional hazards, and the colored curves are the hazard estimates using our nonproportional hazard assumption (blue for ER-positive cases and red for ER-negative cases). HR, hazard ratio; TX, treatment.

Figure 3

Comparison of estimated and Surveillance, Epidemiology, and End Results (SEER) survival curves (first column) and annual hazards (second column) by estrogen receptor (ER) status for cases detected at age 50 years or older between 1990 and 2010: (A) breast cancer survival under proportional v. nonproportional treatment effects and (B) breast cancer survival for alternative treatment and screening scenarios assuming nonproportional hazards for ER-positive disease and proportional hazards for ER-negative disease.

Figure 4

Age-adjusted (to the 2000 US standard population) rates (per 100,000) for women aged 30 to 79 years at diagnosis (incidence) or at death (mortality) from 1975 to 2010 (overall) or from 1995 to 2010 (ER specific), SEER (solid curves) v. simulation model (dashed curves): (A) breast cancer incidence, (B) breast cancer mortality under nonproportional hazards assumption (for ER-positive cases), and (C) breast cancer mortality under proportional hazards assumption. ER, estrogen receptor; NoSCR + NoTX, no screening and no treatment; NoSCR + TX, no screening and treatment; SCR + NoTX, screening and no treatment; SCR + TX, screening and treatment; SEER, Surveillance, Epidemiology, and End Results.