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

良性活检后预测乳腺癌风险的新型多重生物标志物发现方法

• 批准号: 10021604
• 负责人: MARK E SHERMAN
• 金额: $ 20.5万
• 依托单位: MAYO CLINIC JACKSONVILLE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021604
• 关键词: Age; Agreement; Benign; Biological Assay; Biological Markers; Biopsy; Breast Cancer Model; Breast Cancer Risk Factor; Breast Cancer cell line; Breast Diseases; Calendar; Categories; Cell Cycle; Cell Proliferation; Characteristics; Clinical; Consensus; Data; Detection; Diagnosis; Disease Progression; Epithelial; Epithelium; Estrogens; Genes; Guidelines; Histologic; Immunoblotting; Immunohistochemistry; Individual; Indolent; Lesion; Lobule; Lymphocyte; Measurement; Measures; Methods; Microscopic; Patient Self-Report; Patients; Postmenopause; Preparation; Prognostic Marker; Prolactin; Proteins; RNA; ROC Curve; Research; Risk; Risk Estimate; Sampling; Serum; Severities; Severity of illness; Structure; Technology; Terminal Ductal Lobular Unit; Testing; Time; Tissue Microarray; Tissues; Tumor Subtype; Tumor-infiltrating immune cells; Woman; base; biomarker discovery; biomarker panel; cancer subtypes; candidate marker; candidate validation; cohort; cost; cost effective; digital; exhaust; follow-up; high risk; improved; malignant breast neoplasm; molecular subtypes; multiple omics; nano-string; novel; novel strategies; personalized management; predictive marker; prognostic; progression marker; protein biomarkers; tool; tumor

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.

项目摘要 美国超过 100 万女性被诊断患有活检证实的良性乳腺疾病 (BBD) 每年。 BBD 与 BC 风险增加相关，范围从最不严重的 1.5-2.0 倍到最严重的 4 倍。 最严重的类别。然而，BBD 类别中的个人风险差异很大。此前，我们 开发了 BBD-BC 模型，该模型根据自我报告的因素提供个人风险估计，详细 BBD 范围和严重程度的特征，以及退化（收缩和消失）的评估 周围的组织学结构称为终末导管小叶单位 (TDLU)，大多数 BC 前体均来自该单位 出现。 TDLU 退化水平的增加与较低的 BC 风险相关，有限的数据表明 BBD 活检中 TDLU 的生物标志物分析可能会提示 BC 整体风险，并可能按 BC 亚型分类； 然而，这项研究受到技术挑战的限制。我们假设通过应用小说 定量多重蛋白质测量技术，NanoString 数字空间分析 (DSP) 通过 BBD 活检中 TDLU 的大核心组织微阵列 (LC-TMAs)，我们可以确定预后 预测 BC 风险的生物标志物组合。 DSP 与传统技术相比具有潜在优势 免疫组织化学，包括改进的定量和多重分析 30 或更多 每个组织切片的标记，从而减少组织利用率。作为原理证明，我们的目标是在技术上 验证 Mayo BBD 队列中的候选进展标志物，其中包括超过 14,000 名女性 在平均约 15 年的随访中，超过 1,200 例出现了 BC。我们将构建 3 个 TDLU 的 LC-TMA BC 之前的 50 个 BBD 活检中的每一个都与源自 50 个 BBD 活检的可比 LC-TMAS 相匹配 那没有进展。我们将 DSP 应用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。我们将进行常规免疫组织化学检查 相同的标记，每个部分一个标记。我们将在 LC-TMA 中包含 BC 细胞系，其中标记物 通过免疫印迹测量。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）以及免疫印迹和识别预测 BC 风险的候选标记。然后，我们 将准备围绕 BC 和 BC 本身的 TDLU 的类似 TMA，并评估预测的最高标记 BBD 进展以评估这些标志物是否可能与肿瘤亚型相关。结合DSP 使用 LC-TMAs 将能够高效且不费力地分析多个样品中的多个标记物 含有 TDLU 的组织，这是微观的。这项变革性提案旨在开发和验证 使用结合 DSP 和 LC-TMA 的经济有效的方法定量 TDLU 中的蛋白质的方法，该方法 将能够识别和验证 BBD 候选预后生物标志物。改善 BC 风险 对 BBD 女性的预测将有助于精准管理。