Project 4 (Hartmann)

项目 4（哈特曼）

• 批准号: 8757104
• 负责人: JAMES Newell INGLE
• 金额: $ 44.23万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-22 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8757104
• 关键词: Aging-Related Process; Anatomy; Atypia; Atypical hyperplasia; Benign; Biological Markers; Biopsy; Breast; Breast Cancer Model; Breast Cancer Risk Assessment Tool; Breast Carcinogenesis; Breast Diseases; Calibration; Cervix Uteri; Clinic; Clinical; Clinical Management; Collaborations; Colon; Cytologic Atypia; Development; Diagnosis; Disease model; Disease susceptibility; Dissection; Early Diagnosis; Elements; Endometrium; Epidemiologic Factors; Epidemiology; Epithelial; Esophagus; Event; Extracellular Matrix; Fostering; Funding; Histologic; Histology; Immunohistochemistry; In Situ; Incidence; Individual; Institute of Medicine (U.S.); Lobular; Lobule; Malignant Neoplasms; Mammary Gland Parenchyma; Mammographic Density; Measures; Mediator of activation protein; Mesenchymal; Modeling; Molecular; Palpable; Peptide Hydrolases; Physiological Processes; Population; Postmenopause; Predisposition; Prevention; Prevention strategy; Reporting; Research; Risk; Risk Assessment; Series; Stratification; Subgroup; Techniques; Terminal Ductal Lobular Unit; Testing; Tissues; Transcript; Woman; Women' s Group; Work; age related; base; cancer risk; candidate marker; case control; cohort; disorder risk; high risk; improved; insight; malignant breast neoplasm; molecular marker; normal aging; novel; surveillance strategy

Most of the 250,000 U.S. women diagnosed annually with in situ or invasive breast cancer (BC) were not recognized as being at increased risk for the disease. Both the NCI and Institute of Medicine have identified accurate individualized risk assessment as central to improving early detection and prevention. The most widely used risk prediction model for BC, the Gail model, generally performs well in predicting BC risk across groups of women, but is limited when predicting individualized risk. In general, the cancers for which we can predict susceptibility best are those where the at-risk tissue can be examined, e.g., cervix, colon, endometrium. In breast, benign tissue is available for risk assessment from women who have had a benign biopsy. Women with so-called benign breast disease (BBD), numbering 1-2 million/year In the US alone, are a common and clinically important group, with a known increased risk of BC. About 25% of women with BC report having had a prior benign biopsy. Our purpose with this project is to develop a risk prediction model for women with BBD. We hypothesize that features in benign breast tissue, in particular from subgroups known to be at increased risk for BC, can help to identify those women who are at highest likelihood of progression of BC. Two recognized higher-risk groups within BBD are women with atypia, and women in whom normal, age-related regression (or involution) of breast lobules has not occurred. Utilizing a cohort of over 11,000 women with BBD at the Mayo Clinic, we will identify novel BC-predictive biomarkers obtained from transcriptional profiling of women with atypia who either did or did not progress to BC and from dissection of the mechanisms underlying lobular involution, a physiological process which we recently found to be associated with decreased cancer risk. Using a newly-developed technique, we will also quantitate the specific extent of lobular involution that has occurred In these women to generate a continuous risk feature. Then in a nested case: control series within our BBD cohort, we will build a risk prediction model that incorporates the top predicting elements from histology, clinical-epidemiologic features, mammographic density, quantitation of involution and molecular biomarkers. Finally, building upon a strong collaboration fostered by Breast SPORE developmental funds, we will assess our BC risk model using the Nashville BBD cohort supported by the Vanderbilt Breast SPORE. Altogether these studies will test the ability to use tissue based features to enhance risk prediction of BC and potentially provide insights about important early events in breast carcinogenesis.

在每年被诊断为原位或浸润性乳腺癌（BC）的25万名美国妇女中， 被认为是在增加患病的风险。NCI和医学研究所都将准确的个体化风险评估确定为改善早期发现和预防的核心。最广泛使用的BC风险预测模型Gail模型在预测不同女性群体的BC风险方面表现良好，但在预测个体风险时受到限制。一般来说，我们可以最好地预测易感性的癌症是那些可以检查风险组织的癌症，例如，子宫颈结肠子宫内膜在乳房，良性组织是可用于风险评估的妇女谁有一个良性活检。患有所谓的良性乳腺疾病（BBD）的女性，仅在美国每年就有1-2百万人，是一个常见的临床重要群体，已知BC的风险增加。大约25%的BC女性报告曾有过良性活检。我们这个项目的目的是为患有BBD的女性开发一个风险预测模型。我们假设良性乳腺组织的特征，特别是来自已知BC风险增加的亚组的特征，可以帮助识别那些BC进展可能性最高的女性。BBD中两个公认的高风险组是患有乳腺癌的女性和未发生正常的、与年龄相关的乳腺小叶退化（或退化）的女性。利用马约诊所的11，000多名BBD女性患者的队列，我们将鉴定新的BC预测生物标志物，这些生物标志物是从患有乳腺癌的女性的转录谱中获得的，这些女性要么进展到BC，要么没有进展到BC，并且从小叶退化的潜在机制的解剖中获得，小叶退化是我们最近发现与癌症风险降低相关的生理过程。使用一种新开发的技术，我们还将量化这些妇女发生的小叶退化的具体程度，以产生连续的风险特征。然后在嵌套的情况下：在我们的BBD队列中的对照系列中，我们将建立一个风险预测模型，该模型结合了来自组织学、临床流行病学特征、乳腺X线摄影密度、退化定量和分子生物标志物的顶级预测元素。最后，在Breast SPORE发展基金促进的强有力合作的基础上，我们将使用范德比尔特Breast SPORE支持的纳什维尔BBD队列评估我们的BC风险模型。总之，这些研究将测试使用基于组织的特征来增强BC风险预测的能力，并可能提供有关乳腺癌发生中重要早期事件的见解。