Endogenous hormones and postmenopausal breast cancer: Etiologic insights and improving risk prediction

内源性激素和绝经后乳腺癌：病因学见解和改进风险预测

• 批准号: 9899942
• 负责人: Susan E Hankinson
• 金额: $ 100.35万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9899942
• 关键词: 27-hydroxycholesterol; Affect; Androgens; Benefits and Risks; Binding; Biological Assay; Biological Markers; Blood; Blood specimen; Breast Cancer Risk Factor; Breast Cancer cell line; Breast Carcinogenesis; C-Peptide; Cancer Etiology; Characteristics; Chemoprevention; Clinical; Cohort Studies; Data; Diagnosis; Disease; Documentation; Estradiol; Estrogen receptor positive; Estrogens; Estrone-Sulfate; Etiology; Evaluation; FRAP1 gene; Formalin; Funding; Gene Expression; Gene Mutation; Genetic Risk; Gonadal Steroid Hormones; Grant; Health; High Risk Woman; Hormonal; Hormones; Individual; Insulin; Insulin Receptor; Insulin Resistance; Insulin Signaling Pathway; Lead; Malignant Neoplasms; Mammary Gland Parenchyma; Mammary Neoplasms; Mammographic Density; Mammography; Measurement; Measures; Modeling; Molecular; Molecular Analysis; Mutation; Nurses' Health Study; PIK3CA gene; PIK3CG gene; PTEN gene; Paraffin Embedding; Participant; Pathway interactions; Plasma; Play; Postmenopause; Prevention; Preventive measure; Prolactin; Questionnaires; Research; Risk; Risk Assessment; Risk Factors; Risk Reduction; Role; Sampling; Selective Estrogen Receptor Modulators; Signal Pathway; Signal Transduction; T47D; Time; Translational Research; Tumor Tissue; Woman; Work; base; case control; cohort; cost effectiveness; design; epidemiology study; estrogenic; estrogenic activity; follow-up; improved; insight; insulin secretion; insulin signaling; malignant breast neoplasm; member; novel; practical application; predictive marker; prospective; risk prediction model; risk variant; screening; tumor; working group

We propose to continue our research group’s work to identify and validate hormonal markers that predict risk of invasive breast cancer in postmenopausal women. Although the estrogen pathway plays a critical role in breast carcinogenesis, accruing evidence suggests measurement of the classic estrogens only, as done in prior epidemiologic studies (including our own) may miss a sizeable component of estrogenic activity that is etiologically relevant to risk. Further, the insulin pathway is another critical, but relatively understudied, hormonal pathway that may impact breast cancer risk. Using a prospective nested case-control design, we plan to analyze blood samples collected from the 32,826 participants in the Nurses' Health Study (NHS) who provided a blood sample in 1989-90 and, for 18,743 of these women, a second sample in 1999-2000. Specifically, we propose to evaluate the independent associations of 27-hydroxycholesterol (27HC; a recently identified endogenous selective estrogen receptor modulator) as well as an estrogen bioassay (that assesses estrogen pathway activation) in relation to breast cancer risk. We also will evaluate several new aspects of the relationship between plasma c-peptide (reflecting insulin secretion) and breast cancer risk. With 26 years of follow-up and two blood samples in a subset of women, we will evaluate the importance of timing of c-peptide exposure in breast carcinogenesis. Further, we will assess whether the c-peptide/breast cancer relationship varies by breast tumor expression of insulin receptor or several markers of PI3k/Akt/mTOR pathway activation. Previous work, by not accounting for these tumor molecular characteristics, may have underestimated the etiologic importance of insulin in breast cancer risk and hence opportunities for prevention. We also will evaluate if c-peptide (and secondarily the novel estrogen related markers) improve the Gail and Rosner-Colditz risk prediction models, and validate the addition of multiple biomarkers (i.e., the plasma hormones, a genetic risk score and mammographic density) to these models in the prospective Mayo Mammography Health Study (MMHS) and Melbourne Collaborative Cohort Study (MCCS). The MMHS and MCCS have risk factor and biomarker information that is comparable to that in the NHS. In addition to providing one or two blood samples, NHS cohort members have provided detailed exposure and disease information biennially (including data on most known or probable breast cancer risk factors) since 1976, and, for about 70% of breast cancer cases, we have collected formalin-fixed paraffin embedded tumor tissue, thus allowing molecular analyses of the tumor. The recently funded NHS UM1 grant will provide cohort follow-up, breast cancer documentation, and important covariate data, increasing the cost-effectiveness of the proposed project. This project, by working across the translational research spectrum from discovery of novel hormonal biomarkers to practical application of well confirmed biomarkers in risk prediction models, should add considerable insight into breast cancer etiology and ways to better identify high risk women who may benefit from risk reduction efforts (e.g., chemoprevention).

我们建议继续我们的研究小组的工作，以确定和验证预测风险的激素标志物， 绝经后妇女的浸润性乳腺癌。尽管雌激素途径在 乳腺癌的发生，累积的证据表明，测量的经典雌激素只，如在 之前的流行病学研究（包括我们自己的）可能会错过雌激素活性的很大一部分，即 与风险相关的病因。此外，胰岛素途径是另一个关键的，但相对研究不足， 可能影响乳腺癌风险激素途径。采用前瞻性巢式病例对照设计，我们 计划分析从护士健康研究（NHS）的32，826名参与者中收集的血液样本， 1989- 1990年提供了一份血样，1999-2000年为其中18 743名妇女提供了第二份血样。 具体而言，我们建议评估27-羟基胆固醇（27 HC;最近 鉴定的内源性选择性雌激素受体调节剂）以及雌激素生物测定（评估 雌激素途径激活）与乳腺癌风险的关系。我们还将评估 血浆c肽（反映胰岛素分泌）与乳腺癌风险之间的关系。有着26年 随访和两个血液样本中的一个子集的妇女，我们将评估的重要性，时间的c肽 乳腺癌的发生。此外，我们将评估c肽与乳腺癌的关系是否 胰岛素受体或PI 3 k/Akt/mTOR通路激活的几种标志物的乳腺肿瘤表达不同。 以前的工作，由于没有考虑这些肿瘤分子特征，可能低估了肿瘤的分子生物学特性。 胰岛素在乳腺癌风险中的病因学重要性，因此预防的机会。我们也将 评估c肽（其次是新的雌激素相关标志物）是否改善了Gail和Rosner-Colditz 风险预测模型，并验证多种生物标志物的添加（即，血浆激素，一种基因 风险评分和乳房X线摄影密度）与这些模型进行比较 （MMHS）和墨尔本协作队列研究（MCCS）。MMHS和MCCS有危险因素， 生物标志物信息与NHS中的生物标志物信息相当。除了提供一两个血样外， NHS队列成员每两年提供一次详细的暴露和疾病信息（包括 自1976年以来，大多数已知或可能的乳腺癌风险因素），并且对于大约70%的乳腺癌病例，我们 收集了福尔马林固定的石蜡包埋的肿瘤组织，从而可以对肿瘤进行分子分析。 最近资助的NHS UM 1赠款将提供队列随访，乳腺癌文件，以及重要的 协变量数据，提高拟议项目的成本效益。这个项目，通过在整个 从发现新的激素生物标志物到实际应用的转化研究范围 风险预测模型中已确认的生物标志物，应该会对乳腺癌病因有相当深入的了解， 更好地识别可能从降低风险工作中受益的高危妇女的方法（例如，化学预防）。