Molecular Predictors of Mammographic Density

描述（由申请人提供）：与乳房X线摄影密度最低的妇女相比，乳房X线摄影密度最高的妇女患乳腺癌的风险增加4至6倍。研究所涉及的潜在机制的数据很少。人们普遍认为乳房X线摄影密度反映了雌激素的累积暴露量，这一假设受到了来自我们小组和其他小组的数据的质疑。迄今为止，大多数研究都集中在绝经后妇女，其雌激素水平和乳房密度都低于绝经前妇女。乳腺摄影密度可能比绝经后水平更能反映绝经前激素水平，这是非常合理的。此外，许多其他激素，包括胰岛素样生长因子-1和维生素D，以及雌激素代谢产物也与乳腺增生和癌变有关，并可能与乳腺密度有关。在我提交的第一份R 01报告中，我建议通过关注绝经前妇女的循环激素水平和尿雌激素代谢产物来探索乳腺摄影密度与乳腺癌相关的潜在机制。具体而言，我们将：（1）评估雌激素、雄激素、催乳素、性激素结合球蛋白、维生素D和胰岛素样生长因子-1的循环水平与乳腺摄影密度之间的关系;（2）检查尿雌激素代谢产物与乳腺摄影密度之间的关系;（3）确定激素生物标志物和乳腺摄影密度是否是乳腺癌风险的独立预测因子。这些目标将提供补充信息，将有助于阐明乳腺摄影密度的生物学。我们将在一项乳腺癌病例对照研究中收集乳房X线照片，并测量妇女的乳房密度，该研究嵌套在护士健康研究II中，血液样本可用，激素测定将作为另一个资助项目的一部分进行。这项病例对照研究的独特之处在于性类固醇激素在月经周期的早期卵泡期和中期黄体期都进行了测量。我们小组最近的工作强调了在特定阶段的激素水平和乳腺癌风险之间观察到的重要差异。到目前为止，没有其他研究已经检查了阶段特异性类固醇与乳腺摄影密度的关系。我们预计将对大约636例乳腺癌病例（361例有定时样本）和2例匹配对照进行乳腺摄影密度和激素测量。生物标志物和乳腺密度的测量可能有助于识别乳腺癌风险特别高的女性。这是一个有效的研究设计，以确定激素生物标志物和乳腺摄影密度之间的关联，以及两者对乳腺癌风险的影响。 公共卫生相关性：乳房X线密度和循环激素水平都是乳腺癌风险的公认强有力的预测因子。我们建议前瞻性地研究绝经前循环和尿液生物标志物水平与乳腺摄影密度之间的关系，并确定这些生物标志物和乳腺密度是否能独立预测乳腺癌风险。了解两者之间的关系以及它们如何影响乳腺癌风险将有助于风险预测，并可能有助于识别乳腺癌风险特别高的女性。

DESCRIPTION (provided by applicant): Women with the highest mammographic density are at a four- to six-fold increased risk of breast cancer as compared with women with the lowest mammographic density. The data examining the underlying mechanisms involved are sparse. The widely hypothesized belief that mammographic density reflects cumulative exposure to estrogens has come under question with data from our own group and others. Most studies to date have focused on postmenopausal women, whose levels of estrogens and breast density are both lower than those of premenopausal women. It is highly plausible that mammographic density may be more reflective of premenopausal hormone levels than postmenopausal levels. In addition, a number of other hormones including insulin-like growth factor-1 and vitamin D, and estrogen metabolites have also been implicated in breast proliferation and carcinogenesis and may be associated with breast density. In my first R01 submission, I propose to explore the underlying mechanism by which mammographic density is related to breast cancer, by focusing on circulating hormone levels and urinary estrogen metabolites in premenopausal women. Specifically, we will: (1) Assess the relationship between circulating levels of estrogens, androgens, prolactin, sex hormone binding globulin, vitamin D and insulin-like growth factor-1, and mammographic density; (2) Examine the association between urinary estrogen metabolites in relation to mammographic density; and (3) Determine if hormonal biomarkers and mammographic density are independent predictors of breast cancer risk. These aims will provide complementary information that will help elucidate the biology of mammographic density. We will collect film mammograms and measure breast density among women in a breast cancer case- control study nested within the Nurses' Health Study II for whom blood samples are available and hormone assays will be conducted as part of another funded project. This case-control study is unique in that sex steroid hormones have been measured in both the early follicular and mid-luteal phases of the menstrual cycle. Recent work from our group highlights important differences observed between phase-specific hormone levels and breast cancer risk. To date, no other study has examined phase-specific sex steroids in relation to mammographic density. We expect to have mammographic density as well as hormone measurements on approximately 636 breast cancer cases (361 with timed samples) and 2 matched controls per case. Measurements of both biomarkers and breast density may help to identify women at particularly high risk of breast cancer. This is an efficient study design to determine the association between hormonal biomarkers and mammographic density and the effects of both on breast cancer risk. PUBLIC HEALTH RELEVANCE: Mammographic density and circulating hormone levels are both well-established strong predictors of breast cancer risk. We propose to prospectively examine the association between premenopausal levels of circulating and urinary biomarkers and mammographic density, and determine if these biomarkers and breast density independently predict breast cancer risk. Understanding the relation between the two and how they influence breast cancer risk will be useful in risk prediction and may help to identify women at particularly high risk of breast cancer.