Purpose: This modeling group’s goal is to study the effects of breast cancer screening and treatments, such as the identification of the best mammography screening strategy, or the evaluation of the impact of new molecular pathway and genomic tumor profile-targeted treatment paradigms in the adjuvant setting and at disease recurrence. These tasks are critical to informing policy makers when allocating resources and designing public health guidelines and recommendations. The Bayesian Simulation Model, developed at MD Anderson Cancer Center, assembles results from relevant meta-analyses and large-scale databases as the input parameters, and yield outputs to answer various questions of interest through intensive computation.
Overview: The Bayesian Simulation Model simulates populations of four million women with the age distribution that existed in the United States in 1975. The sample size four million is found to be generally sufficient to give accurate parameter estimation. For each virtual woman, the model simulates a natural course of her life separate from the possible occurrence of breast cancer. Moving forward in time, in each year, each woman is diagnosed with breast cancer or not, depending on the disease incidence for women of her age that year, whether she had a screening mammogram that year, and her mammography history. The model keeps track of which women are diagnosed with breast cancer each year and which women die of breast cancer and of other causes. For each virtual woman who is diagnosed with breast cancer, her survival depends her tumor’s characteristics, the mode of detection, and the treatment she received. We compare her simulated breast cancer-specific survival time with her natural lifetime to determine her cause of death (and use the earlier date and cause of death).
The parameters of interest in the model include those that affect the diagnosis of breast cancer and its course once the disease is diagnosed. Treatment parameters include those for the effects of adjuvant treatments, including hormone therapy, trastuzumab, the combination of cyclophosphamide, methotrexate, and fluorouracil (CMF) chemotherapy, anthracycline, and taxanes. For mammography screening, we use a slope parameter to describe the linear changing pattern over time of breast cancer incidence rates in a hypothetical scenario of no screening. Screening-detected breast cancers tend to have earlier stage distributions than clinically detected cases. Besides that, it has been found that even among cases of the same stage, screening-detected cases have better survival. We use two parameters for these “beyond stage-shift" effects for tumor stages I/II and stages III/IV respectively.
We study the age-adjusted U.S. breast cancer incidence and mortality rates from 1975 to 2010, obtained from the Surveillance, Epidemiology, and End Results (SEER) Program. It can be seen that the incidence rates had been mostly increasing over these years, except some of which were decreasing from 2000 to 2010, but the mortality rates had been slightly increasing before 1990 and substantially decreasing after that. What had caused these changing patterns? Potential factors include: environmental and lifestyle changes, mammography screening, the use and then halting of menopausal hormonal therapy (MHT), improvements in chemotherapy over the years, improvements in hormonal therapies for estrogen receptor positive and/or progesterone receptor positive (ER+/PR+, or simply ER+) breast cancer subtypes, and the discovery of trastuzumab for human epidermal growth factor receptor 2 positive (HER2+) breast cancer subtypes. By comparing results from different simulation runs that turn on and off the factors, we are able to explore the combined effects on breast cancer incidence and mortality of these intertwining factors, each with different temporal trends and differential effects on the molecular subtypes of breast cancer. We have evaluated the relative contributions of screening and adjuvant treatments on the overall breast cancer mortality reduction in the US from 1990 to 2000, then updated this analysis to the year 2010 and extended it to breast cancer subtypes based on each ER/HER2 subgroup.
References
- Huang X, Li Y, Song J, Berry D. A Bayesian simulation model for breast cancer screening, incidence, treatment, and mortality. Med Decis Making. 2018 Apr;38(1_suppl):78S-88S. [Abstract]
- Berry DA, Inoue L, Shen Y, Venier J, Cohen D, Bondy M, Theriault R, Munsell MF. Modeling the impact of treatment and screening on U.S. breast cancer mortality: a Bayesian approach. J Natl Cancer Inst Monogr 2006;(36):30-6. [Abstract]
