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25万名美国妇女中， 被认为是在增加患病的风险。国家癌症研究所和医学研究所都发现 准确的个性化风险评估是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模型通常在预测BC风险方面表现良好。 女性群体，但在预测个体风险时有限。总的来说，我们可以治疗的癌症 预测易感性最好的是那些可以检查风险组织的组织，例如，子宫颈，结肠， 子宫内膜在乳房，良性组织可用于风险评估的妇女谁有良性 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， 一个常见的和临床上重要的群体，具有已知的BC风险增加。大约25%的女性患有BC 报告之前进行过良性活检。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，特别是来自亚组的特征， 已知患有BC的风险增加，可以帮助确定那些最有可能发生BC的女性。 BC的发展。在BBD中，两个公认的高风险群体是患有子宫内膜异位症的妇女和患有子宫内膜异位症的妇女。 未发生正常的、与年龄相关的乳腺小叶退化（或退化）。利用一群 在马约诊所的11，000多名BBD女性中，我们将鉴定获得的新的BC预测生物标志物， 从患有乳腺癌的妇女的转录谱分析中，无论她们是否进展到BC， 解剖小叶退化的机制，这是我们最近发现的一个生理过程， 与降低癌症风险有关。使用新开发的技术，我们还将定量 在这些妇女中发生的特定程度的小叶退化产生了持续的风险特征。 然后在嵌套的情况下：在我们的BBD队列中的对照系列，我们将建立一个风险预测模型， 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。最后，在强有力的合作的基础上 在Breast SPORE发展基金的支持下，我们将使用纳什维尔BBD评估我们的BC风险模型 由范德比尔特乳腺孢子支持的队列。总之，这些研究将测试使用组织的能力， 基于功能的功能可增强BC的风险预测，并可能提供有关重要早期事件的见解 在乳腺癌发生中的作用

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.