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Research Grants Awarded
Computationally Derived Estrogen Profiles for Breast Cancer Risk Stratification
Background: It is well-established that estrogen plays a role in breast cancer development. Prior to menopause, menstrual cycling is responsible for the majority of a woman?s exposure to estrogen. However, measurement of daily or even monthly estrogen serum levels through multiple cycles over many years is not practical in human populations. As an alternative, this study proposes the application of computer simulation to quantify variation in cyclic estrogen levels from menstrual cycling. Hypothesis: We hypothesize that simulation of hormone variation from menstrual cycling (including the effect of pregnancy and peri-menopause) will reveal distinct estrogen exposure profiles. These profiles, in addition to genotype, will enable the creation of a more accurate model (compared to traditional risk models) for breast cancer risk prediction in post-menopausal women. Specific Aims: (1) Develop a simulation based system to represent menstrual cycling of estrogen. (2) Construct hormone-exposure based stratifications from simulation results. (3) Create a breast cancer risk model based upon: cumulative hormone exposure and SNPs in estrogen metabolism-related genes. Compare to traditional breast cancer risk assessments (i.e. Gail model). Study Design: We will develop a simulation model of menstrual cycle driven estrogen cycling. The model will be fitted to published serum estrogen levels in normally cylcing women and women in early and late peri-menopause. Sensitivity analysis will be conducted to quantify normal variation and the effect of known estrogen metabolism related SNPs. Stratifications will be based upon this analysis. These stratifications, in addition to genotype, will be used to build a model to predict breast cancer risk in a small set of women (n = 600, 300 cases, 300 controls), for which complete information on their reproductive history and their menstrual cycle is available. Relevance: Successful completion of this project will allow us to assess variation in cyclic estrogen exposure through a woman?s reproductive years based upon lifestyle and genotype. Linking estrogen exposure patterns to breast cancer risk will enable greater precision in creating individual breast cancer risk profiles for women at menopause. Successful application of this technique to breast cancer will open up a new investigative tool into other breast cancer risk factors and to menopause?s impact on other diseases including ? cardiovascular disease, other cancers, diabetes, and osteoporosis, among many others.
Breast cancer is a complex yet common disease - approximately 1 in 8 women in the United States are predicted to develop breast cancer over the course of their lifetime. Risks for developing this disease are not well understood at the level of the individual. Although it is known that certain life history events (i.e. first menstrual cycle at age less than 12, first pregnancy after age 35) increase the risk of breast cancer, not all woman with known risk factors develop breast cancer. One major risk factor for breast cancer is high life-time exposure to estrogen. Most of a woman's estrogen exposure comes from menstrual cycling, and is thus variable. Quantifying a woman's exposure to estrogen would greatly aid development of superior risk models for breast cancer. However, it is not feasible to track a woman?s day to day exposure to estrogen over multiple years. Hence, we propose to develop a computational model to simulate estrogen production from menstrual cycling. In this way, we can quantify individual variability in estrogen exposure based upon life history (i.e. pregnancy) and genetics (variation in genes that code for enzymes that create and break down estrogen in the body). Risk profiles will then be developed based upon simulation results and based upon lifestyle and genetic information from women with breast cancer. We envision the risk model developed over the course of this project will be used by women at the time of menopause to more accurately predict their risk for breast cancer. This information will empower women to make health care choices appropriate for their individual risk. A deeper understanding of how estrogen modulates breast cancer risk may also lead to new hypotheses for breast cancer research. Successful development of this simulation methodology will be a powerful new tool in assessing breast cancer risk.