Predictive signatures in breast cancer using multiplexed ion beam imaging

使用多重离子束成像预测乳腺癌特征

• 批准号: 9341982
• 负责人: Robert michael Angelo
• 金额: $ 40.13万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-22 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9341982
• 关键词: Address; Adjuvant; American Cancer Society; Antibodies; Antigens; Archives; Back; Behavior; Biological; Biological Assay; Biopsy; Breast; Breast Cancer Cell; Breast biopsy; Cancer Etiology; Cells; Cessation of life; Clinical; Clinical Research; Communities; Complex; Data; Data Analytics; Detection; Diagnosis; Disease; Disease model; Epithelial Cells; Epithelium; Evaluation; Fibroblasts; Formalin; Future; Goals; Histologic; Histology; Human; Image; Image Analysis; Immune; In Situ; Intervention; Isotopes; Knowledge; Label; Lead; Lesion; Malignant Neoplasms; Mammary Gland Parenchyma; Mammography; Medicine; Metadata; Methods; Mind; Modeling; Molecular Profiling; Morbidity - disease rate; Morphology; Multiplexed Ion Beam Imaging; Nature; Noninfiltrating Intraductal Carcinoma; Operative Surgical Procedures; Outcome; Paraffin Embedding; Pathogenesis; Pathologic; Patients; Peroxidases; Phenotype; Physical shape; Population; Predictive Value; Prospective Studies; Proteins; Regimen; Research; Resolution; Resources; Risk; Risk stratification; Role; Sampling; Solid; Spatial Distribution; Specimen; Spectrometry, Mass, Secondary Ion; Staining method; Stains; Stromal Cells; Testing; Therapeutic; Time; Tissue Embedding; Tissue Microarray; Tissues; United States; Visualization software; Woman; Work; aggressive therapy; analytical method; analytical tool; base; breast cancer diagnosis; breast lesion; clinical predictors; cohort; demographics; experimental study; high dimensionality; imaging approach; imaging platform; light microscopy; macrophage; malignant breast neoplasm; metal chelator; neoplastic; neoplastic cell; novel; predictive signature; prevent; programs; prospective; protein expression; public health relevance; repository; screening; tumor; virtual

DESCRIPTION (provided by applicant): Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that comprises approximately 20% of new breast cancer diagnoses each year. Because pathological evaluation of DCIS typically relies on histologic criteria that offer little value in predicting which lesions will progress to invasive breast cancer (IBC), many patients receive unnecessary surgical and adjuvant interventions that often lead to therapy-related complications. To date, most clinical studies examining protein expression of DCIS in breast cancer biopsies have employed immune-peroxidase staining using a single primary antibody, such that each protein is visualized in separate serial biopsy sections. Consequently, despite numerous studies implicating a critical role in the pathogenesis of IBC, multiplexed, quantitative single cell profiles relating the phenotype of breast tumor cells with the surrounding microenvironment have not been described. With this in mind, the work proposed here focuses on using a novel imaging platform that I recently developed, multiplexed ion beam imaging (MIBI), to define new disease models for mapping progressive perturbations in protein expression and tissue histology that correlate with developing DCIS and IBC. Multiplexed ion beam imaging (MIBI) is capable of analyzing up to 100 targets simultaneously and is compatible with standard formalin-fixed, paraffin-embedded (FFPE) tissue specimens, and the most common sample type in clinical repositories worldwide. I will first expand upon recent work to create an antibody panel for characterizing fibroblasts, macrophages, and epithelium in human breast tissue. This panel will be used to interrogate previously constructed tissue microarrays comprised of clinically annotated biopsies of normal breast, DCIS, and IBC. These experiments will, for the first time, simultaneously characterize phenotypic and histologic features of epithelal and stromal components of normal and neoplastic human breast tissue at the subcellular level. These features will then be analyzed in aggregate to construct predictive clinical classifiers that can be used to select optimal therapeutic regimens that prevent progression to IBC while also minimizing treatment-related morbidity. More generally, this work will also establish a novel platform for gaining an unprecedented view into disease pathogenesis that could be easily adapted in future work to risk stratify other pre-invasive lesions.

描述（由申请人提供）：原位导管癌（DCIS）是一种侵入性病变，每年约占新乳腺癌诊断的20％。由于对DCI的病理评估通常依赖于组织学标准，这些标准几乎没有预测哪些病变将发展为侵入性乳腺癌（IBC），因此许多患者接受了不必要的手术和辅助干预措施，通常会导致治疗相关并发症。迄今为止，大多数检查了DCI在乳腺癌活检中DCI的蛋白质表达的临床研究已使用单一原代抗体采用免疫 - 过氧酶染色，以便在单独的连续活检切片中可视化每种蛋白质。因此，尽管尚未描述许多研究在IBC的发病机理中具有关键作用，但尚未描述将乳腺肿瘤细胞表型与周围微环境相关的多重定量单细胞谱。考虑到这一点，这里提出的工作重点是使用我最近开发的新型成像平台（MIBI），以定义新的疾病模型，以绘制与与发育型DCIS和IBC相关的蛋白质表达和组织学中的渐进性扰动。多路复用离子束成像（MIBI）能够同时分析多达100个靶标，并且与标准的福尔马林固定，石蜡包装（FFPE）组织样品兼容，并且是全球临床存储库中最常见的样本类型。我将首先扩展最近的工作，以创建一个抗体面板，以表征人类乳腺组织中的成纤维细胞，巨噬细胞和上皮。该面板将用于审问由正常乳房，DCIS和IBC的临床注释活检组成的先前构建的组织微阵列。这些实验将首次同时表征亚细胞水平正常和肿瘤人类乳腺组织上皮和基质成分的表型和组织学特征。然后将分析这些功能，以构建预测性临床分类器 可用于选择最佳的治疗方案，以防止IBC进展，同时还可以最大程度地减少治疗相关的发病率。更普遍的是，这项工作还将建立一个新的平台，以获得对疾病发病机理的前所未有的观点，这很容易在将来的工作中适应以使其他侵入前病变分层。