Project 4 (Hartmann)

在每年被诊断为原位或浸润性乳腺癌(BC)的25万名美国女性中，大多数人没有 被认为有更高的患病风险。NCI和医学研究所都已确定准确的个体化风险评估是改进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，在预测女性群体的BC风险方面通常表现良好，但在预测个性化风险时受到限制。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高危组织的癌症，例如宫颈癌、结肠癌、子宫内膜。在乳房，良性组织可用于接受良性活检的女性的风险评估。患有所谓的良性乳房疾病(BBD)的女性，仅在美国每年就有100-200万人，是一个常见的、具有临床重要意义的群体，已知患良性乳房疾病的风险增加。大约25%的患有BC的妇女报告说以前有过良性活检。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，特别是已知的BC风险增加亚组的特征，可以帮助识别那些BC进展可能性最高的女性。BBD中两个公认的高危群体是患有非典型性的女性，以及没有发生正常的、与年龄相关的乳腺小叶退化(或退缩)的女性。利用梅奥诊所11,000多名患有BBD的女性队列，我们将识别新的BC预测生物标记物，这些生物标记物是从患有非典型性疾病的女性的转录图谱中获得的，这些女性要么进展到BC，要么没有进展到BC，并通过解剖小叶退化的机制获得，这是我们最近发现与降低癌症风险相关的一个生理过程。使用一种新开发的技术，我们还将量化这些女性发生的小叶退缩的具体程度，以产生持续的风险特征。然后，在我们的BBD队列中的嵌套病例：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、乳房X光检查密度、退缩量化和分子生物标志物的顶级预测因素。最后，在乳房孢子发展基金促进的强大合作的基础上，我们将使用范德比尔特乳房孢子支持的纳什维尔BBD队列来评估我们的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，并潜在地提供关于乳腺癌发生的重要早期事件的见解。

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.