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

项目总结 鉴于以下原因，改进的乳腺癌风险预测对非裔美国人(AA)女性至关重要 他们确诊时年龄较小，最具侵袭性的亚型(例如雌激素受体)的发生率较高 与白人女性相比，乳腺癌死亡率高出40%。几个 已经开发了包括著名的乳腺癌风险评估工具(Gail模型)在内的模型， 主要在白人人群中，用来评估女性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。只有两个预测模型是专门为再生障碍性贫血女性开发的，这两个模型都有低 歧视性的准确性。我们小组和其他人最近的研究表明了不同的病因途径 雌激素受体状态与产次、母乳喂养、绝经后乳腺癌亚型的关系 激素的使用，以及身体大小因ER状态而异。例如，高平价与降低风险相关。 ER+癌症和ER-癌风险增加。乳房判别准确率相对较低 再生障碍性贫血女性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 在绝大多数病例为ER+的其他民族中，人们的担忧较少，而多达三分之一的人 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特定的相对风险估计 在三项基于人群的病例对照研究中来自再生障碍性贫血妇女的汇集数据，包括1382名ER-1和2275名 ER+乳腺癌病例，以及3341例对照。然后，我们将使用这些估计值，以及SEER 再生障碍性贫血妇女ER+和ER-乳腺癌的年龄发病率以评估基线年龄特定风险 ER+和ER-癌的比率。最后，我们将结合相对风险和基线风险，考虑到 相互竞争的风险，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特定型号的性能和 预测乳腺癌的整体工具将在Black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 对再生障碍性贫血妇女的预测模型也将应用于预期的BWHS数据，以便比较她们的 性能与我们的新工具的性能。尽管全基因组基因分型还不是每个 患者的医疗记录，为了为未来的基因分型奠定基础，在第二个目标中，我们将添加SNPs 与GWAS中确定的乳腺癌亚型相关，并将AA女性与模型和 评估性能方面的变化。AA女性乳腺癌风险预测模型的改进将导致 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.