||5R01CA228357-02 Interpret this number
||Boston University Medical Campus
||Improving Breast Cancer Risk Prediction for African American Women: Consideration of Estrogen Receptor Subtype-Specific Risk Factors
Improved breast cancer risk prediction is of critical importance for African American (AA) women in view of
their younger age at diagnosis, higher incidence of the most aggressive subtypes (e.g., estrogen receptor
negative (ER-) breast cancer), and 40% higher breast cancer mortality compared with white women. Several
models, including the well-known Breast Cancer Risk Assessment Tool (Gail model), have been developed,
largely in white populations, to assess a woman’s absolute risk of breast cancer; they have been used to
identify high-risk women for supplemental screening, preventive treatment, and enrollment in chemoprevention
trials. Only two prediction models have been developed specifically for AA women and both have low
discriminatory accuracy. Recent research by our group and others indicates distinct etiologic pathways for
breast cancer subtypes defined by ER status in that associations with parity, breastfeeding, postmenopausal
hormone use, and body size differ by ER status. For example, high parity is associated with reduced risk of
ER+ cancer and with increased risk of ER- cancer. The relatively poor discriminatory accuracy of breast
cancer risk prediction models in AA women may reflect the failure to properly consider tumor subtypes. This is
a lesser concern in other ethnic groups in which the vast majority of cases are ER+, whereas up to a third of
AA cases are ER-. In a novel approach, we will first estimate ER specific relative risk estimates from analyses
of pooled data from AA women in three population-based case-control studies, including 1382 ER- and 2275
ER+ breast cancer cases, as well as 3341 controls. We will then use those estimates, together with SEER
age-incidence rates for ER+ and ER- breast cancer in AA women to estimate baseline age-specific hazard
rates for ER+ and ER- cancer. Finally, we will combine relative risks and baseline hazards, taking into account
competing risks, to estimate the probability of developing the first of either ER+ or ER- breast cancer over a
pre-specified time interval given a woman’s age and risk factors. Performance of the ER specific models and
the overall tool for predicting any breast cancer will be tested in prospective cohort data from the Black
Women’s Health Study (BWHS), based on occurrence of 703 ER- and 1502 ER+ cases. Existing risk
prediction models for AA women will also be applied to the prospective BWHS data in order to compare their
performance with performance of our new tool. Although genome-wide genotyping is not yet a part of each
patient’s medical records, to set the stage for such genotyping in the future, in a second aim we will add SNPs
associated with breast cancer subtypes identified in GWAS and fine-mapping of AA women to the models and
evaluate changes in performance. Improved breast cancer risk prediction models in AA women will lead to
earlier detection and treatment of high risk women, and thereby to reduced breast cancer mortality.
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