Improving Breast Cancer Risk Prediction for Women with Benign Breast Disease

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和医学研究所都已经确定 准确的个性化风险评估是改进早期发现和预防的核心。最多的 广泛使用的BC风险预测模型，即Gail模型，在预测BC风险方面总体上表现良好 妇女群体，但在预测个性化风险时是有限的。总体而言，我们可以治疗的癌症 预测敏感性最好的是那些可以检查高危组织的地方，例如宫颈、结肠、 子宫内膜。在乳房，良性组织可用于风险评估，来自曾接受良性乳房手术的女性。 活组织检查。患有所谓的良性乳腺疾病(BBD)的女性，仅在美国每年就有100-200万人， 一种常见且临床重要的群体，已知患BC的风险增加。约25%的患有BC的女性 先前有良性活检的报告。我们这个项目的目的是开发一个风险预测模型。 对于患有BBD的女性来说。我们假设良性乳腺组织的特征，特别是来自亚群的特征 已知的BC风险增加，可以帮助识别那些最有可能患上BC的女性 公元前的进展。BBD中两个公认的高危群体是患有非典型性的女性和 谁的乳房小叶没有发生正常的、与年龄相关的退化(或退化)。利用一组 在梅奥诊所，超过11,000名患有BBD的女性，我们将识别获得的新的BC预测生物标志物 来自患有非典型性疾病的女性的转录图谱，她们要么进展到BC，要么从 解剖小叶退缩的机制，这是我们最近发现的一个生理过程 与降低癌症风险有关。使用一种新开发的技术，我们还将对 在这些妇女中发生的小叶退缩的具体程度会产生持续的风险特征。 然后，在嵌套情况下：我们的BBD队列中的控制系列，我们将构建一个风险预测模型，该模型 结合了来自组织学、临床流行病学特征、乳房X光检查的顶级预测因素 密度、内卷和分子生物标记物的定量。最后，在强大的协作基础上 在乳房孢子发展基金的支持下，我们将使用纳什维尔BBD来评估我们的BC风险模型 由范德比尔特乳房孢子支持的队列。总之，这些研究将测试使用组织的能力 基于增强BC风险预测的功能，并潜在地提供对重要早期事件的洞察 在乳腺癌的发生中起作用。

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.