Improving Breast Cancer Risk Prediction for African American Women: Consideration of Estrogen Receptor Subtype-Specific Risk Factors

项目摘要 改善乳腺癌风险预测对非洲裔美国人（AA）女性至关重要， 他们诊断时的年龄较小，最具侵袭性的亚型的发生率较高（例如，雌激素受体 阴性（ER-）乳腺癌），乳腺癌死亡率比白色女性高40%。几 已经开发了一些模型，包括众所周知的乳腺癌风险评估工具（Gail模型）， 主要是在白色人群中，以评估妇女患乳腺癌的绝对风险;他们已经习惯于 确定高危妇女进行补充筛查、预防性治疗和参加化学预防 审判只有两个预测模型已经开发出专门为AA妇女，都有低 辨别准确度我们小组和其他人最近的研究表明， 根据ER状态定义的乳腺癌亚型与产次、母乳喂养、绝经后 激素使用和身体大小因ER状态而异。例如，高生育率与降低生育风险有关。 ER+癌症和ER-癌症的风险增加。乳腺癌的判别准确率相对较差 AA妇女的癌症风险预测模型可能反映了未能正确考虑肿瘤亚型。这是 在其他少数民族中，绝大多数病例为ER+，而高达三分之一的 AA病例为ER-。在一种新的方法中，我们将首先从分析中估计ER特异性相对风险估计值 来自三项基于人群的病例对照研究中AA女性的汇总数据，包括1382 ER-和2275 ER+乳腺癌病例以及3341例对照。然后，我们将使用这些估计，以及SEER AA女性中ER+和ER-乳腺癌的年龄发病率，以估计基线年龄特异性风险 ER+和ER-癌症的比率。最后，我们将结合联合收割机的相对风险和基线危害，考虑到 竞争性风险，以估计在一年内发生ER+或ER-乳腺癌的可能性。 根据女性的年龄和风险因素预先规定的时间间隔。ER特定型号的性能， 预测任何乳腺癌的整体工具将在黑人的前瞻性队列数据中进行测试。 妇女健康研究（BWHS），基于703例ER-和1502例ER+病例。现有风险 AA妇女的预测模型也将应用于前瞻性BWHS数据，以比较其 性能与我们的新工具的性能。虽然全基因组基因分型还不是每个基因组的一部分， 患者的医疗记录，为将来的基因分型奠定基础，我们的第二个目标是增加SNP 与GWAS中确定的乳腺癌亚型以及AA女性与模型的精细映射相关， 评估性能的变化。改善AA女性的乳腺癌风险预测模型将导致 及早发现和治疗高危妇女，从而降低乳腺癌死亡率。

PROJECT SUMMARY 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.