Novel Multiplex Biomarker Discovery Methods To Predict Breast Cancer Risk After A Benign Biopsy

项目摘要 美国有100多万女性被诊断出患有经活检证实的良性乳腺疾病(BBD) 每年一次。BBD与BC风险的增加有关，从最轻的1.5-2.0倍到BBD的4倍不等 最严重的类别。然而，在BBD类别中，个体风险差异很大。此前，我们 开发了BBD-BC模型，该模型基于自我报告的因素提供个人风险估计，详细说明 BBD程度和严重程度的特征，以及对BBD退化(萎缩和消失)的评估 周围的组织结构称为末梢导管小叶单位(TDLU)，大多数BC前体都来自于TDLU 站起来。TDLU退化程度的增加与较低的BC风险相关，有限的数据表明 BBD活检组织中TDLU的生物标记物分析可以为BC风险提供全面的信息，并可能根据BC亚型提供信息； 然而，这项研究一直受到技术挑战的限制。我们假设，通过运用一部小说 定量多重蛋白质测量技术，纳米串数字空间轮廓(DSP)，以 来自BBD活检组织的TDLU的大核心组织微阵列(LC-TMA)我们可以识别预后 预测BC风险的生物标记物小组。与传统的数字信号处理器相比，数字信号处理器具有潜在的优势 免疫组织化学，包括改进的定量和30或更多的多重分析 每个组织切片都有标记，从而减少了组织利用率。作为原则证明，我们的目标是在技术上 验证Mayo BBD队列中的候选进步标记，其中包括&gt；14,000名女性 &gt；1200人在平均15年的随访期内患上了BC。我们将为以下目标构建3个TDLU的LC-TMA 在BC之前的50例BBD活检中，每一例都与来自50例BBD活检的可比LC-TMA匹配 但这并没有取得进展。我们将应用DSP到上皮和淋巴细胞区域来评估多个候选者 单个组织切片中的进展标记。我们将进行常规的免疫组织化学对 相同的标记，就像每个部分有一个标记。我们将在LC-TMA中包括BC细胞系，其中标记是 通过免疫印迹法测量。我们将比较方法内部和方法之间的一致性(DSP与 免疫组织化学)，免疫印迹和确定预测BC风险的候选标记物。然后，我们 将围绕BC和BC本身准备类似TDLU的TMA，并评估预测的顶级标记 评估这些标记物是否可能与肿瘤亚型相关。组合数字信号处理器 使用LC-TMA将能够高效且不耗尽地分析多个样品中的多个标记 含有TDLU的组织，这是显微镜下的。这一变革性的提案旨在开发和验证 利用结合DSP和LC-TMAS的经济有效的方法来定量TDLU中的蛋白质，该方法 将能够识别和验证BBD的候选预后生物标记物。改善了BC风险 对患有BBD的女性进行预测将有助于精确的管理。

Project Abstract More than one million women in the U.S. are diagnosed with biopsy proven benign breast disease (BBD) annually. BBD is associated with increases in BC risk, ranging from 1.5-2.0 times for least severe to fourfold for most severe categories. However, individual risk varies considerably within BBD categories. Previously, we developed the BBD-BC model, which provides individual risk estimates based on self-reported factors, detailed characteristics of BBD extent and severity, and assessment of involution (shrinkage and disappearance) of surrounding histologic structures termed terminal duct lobular units (TDLUs) from which most BC precursors arise. Increased levels of involution of TDLUs are associated with lower BC risk and limited data suggest that biomarker analysis of TDLUs in BBD biopsies may inform BC risk overall and potentially by BC subtype; however, this research has been limited by technical challenges. We hypothesize that by applying a novel quantitative multiplex protein measurement technology, NanoString Digital Spatial Profiling (DSP) to large core tissue microarrays (LC-TMAs) of TDLUs from BBD biopsies we can identify a prognostic biomarker panel to predict BC risk. DSP offers potential advantages over conventional immunohistochemistry, including improved quantitation and multiplexed analysis of 30 or more markers per tissue section, thus reducing tissue utilization. As proof-of-principle, we aim to technically validate candidate progression markers in the Mayo BBD cohort, which includes >14,000 women of whom >1,200 developed BC in follow-up over an average of ~15 years. We will construct LC-TMAs of 3 TDLUs for each of 50 BBD biopsies that preceded BC matched to comparable LC-TMAS derived from 50 BBD biopsies that did not progress. We will apply DSP to regions of epithelium and lymphocytes to assess multiple candidate progression markers in single tissue sections. We will perform conventional immunohistochemistry for the same markers, as one marker per section. We will include BC cell lines in LC-TMAs in which markers were measured by immunoblotting. We will compare agreement within and between methods (DSP vs. immunohistochemistry) and to immunoblotting and identify candidate markers that predict BC risk. Then, we will prepare similar TMAs of TDLUs surrounding BC and BC itself and evaluate the top markers that predicted BBD progression to assess whether these markers might be associated with tumor subtype. Combining DSP with LC-TMAs will enable analysis of multiple markers in multiple samples, efficiently and without exhausting tissues containing TDLUs, which are microscopic. This transformative proposal aims to develop and validate a method for quantitating protein in TDLUs using a cost-effective approach combining DSP and LC-TMAs, which will enable the identification and validation of candidate prognostic biomarkers for BBD. Improved BC risk prediction for women with BBD would facilitate precision management.