Development of a High-Throughput Three-Dimensional H&E Platform for the Characterization of Breast Cancer Biopsies

高通量三维 H 的开发

• 批准号: 9812203
• 负责人: Michael T Johnson
• 金额: $ 5.5万
• 依托单位: VISIKOL, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9812203
• 关键词: 3-Dimensional; Biological Assay; Biopsy; Cells; Clinical; Confocal Microscopy; Data; Data Set; Development; Dimensions; Disease; Drug resistance; Guidelines; Histopathology; Image; Imaging Device; Label; Legal patent; Libraries; Machine Learning; Mammary Neoplasms; Microscope; Optics; Outcome; Patient-Focused Outcomes; Patients; Sampling; Slide; Technology; Thinness; Tissues; Treatment Protocols; Tumor Tissue; base; breast imaging; commercialization; disease diagnosis; imaging approach; improved; individualized medicine; malignant breast neoplasm; off-patent; precision medicine; prognostic; tumor; tumor heterogeneity; virtual

PROJECT SUMMARY The purpose of this project is to develop a prognostic imaging tool for breast cancer that can be used to predict specific clinical outcomes such as tumor reoccurrence and drug resistance based upon a tumor’s histomorphology features. The current histopathology paradigm for evaluating tissues is focused on diagnosing disease wherein these same tissues if evaluated in their entirety and digitized, provide an opportunity to correlate clinical outcomes to specific tissue features. Through this project, we are developing a high- throughput breast tumor imaging approach that is capable of imaging breast tumor tissue in its entirety and generating virtual H&E optical Z sections in 3D of equivalent quality to traditional H&E sections so that all of the cells within a biopsy are characterized. To achieve this objective, we are combining our patented tissue clearing approach with fluorescent labeling, high-content confocal microscopy and an unbiased machine learning approach. This approach allows for biopsies to be digitized in their entirety and for all of the features and heterogeneity of tumors to be assessed instead of just looking at a few ultra-thin 2D slides. Through the combination of this unique imaging approach with hierarchical agglomerative clustering, specific histomorphological features can be correlated to clinical outcomes using a detailed sample library with corresponding clinical outcome data. The main objectives of this project are to 1) develop a robust 3D H&E labeling approach, 2) demonstrate that tissues can be imaged in 3D using a fluorescent approach to generate “H&E-like” images of equivalent quality to traditional H&E and 3) show that this tissue analysis approach can be transferred to an automated high- content confocal microscope. Additionally, we will show the ability to cluster tissues based on their histomorphological features and will with a small data set of 34 breast tumor biopsies show how these features are correlated to clinical outcomes. If successful, we will develop this proof-of-concept into a robust CLIA 21 CFR Part 11 compliant assay that complies with the ICH guidelines for analytical assays. This assay would ultimately allow clinicians to better predict how a tumor will respond to certain treatments and best tailor a treatment for a specific patient. This type of precision medicine approach will lead to improved patient outcomes and a more efficacious treatment regimen.

项目摘要 本项目的目的是开发一种乳腺癌的预后成像工具， 特定的临床结果，如肿瘤复发和基于肿瘤的耐药性， 组织形态学特征当前用于评价组织的组织病理学范例集中于诊断 如果对这些相同的组织进行整体评估和数字化， 将临床结果与特定组织特征相关联。通过这个项目，我们正在开发一个高- 本发明提供了一种能够对乳腺肿瘤组织整体成像的通量乳腺肿瘤成像方法， 在3D中生成与传统H&E截面质量相当的虚拟H&E光学Z截面， 对活组织检查中的细胞进行表征。为了实现这一目标，我们将我们的专利组织 使用荧光标记、高含量共聚焦显微镜和无偏见机器的清除方法 学习方法这种方法允许活组织检查的全部和所有特征都被数字化 和肿瘤的异质性来评估，而不仅仅是看几张超薄的2D切片。通过 这种独特的成像方法与分层凝聚聚类的结合， 组织形态学特征可以使用详细的样品库与临床结果相关联， 相应的临床结局数据。 该项目的主要目标是：1）开发一种强大的3D H&E标签方法，2）证明， 可以使用荧光方法对组织进行3D成像，以生成同等质量的“H& E样”图像 传统的H&E和3）表明这种组织分析方法可以转移到自动化的高- 内容共聚焦显微镜此外，我们将展示基于组织的聚类能力。 组织形态学特征，并将与一个小的数据集的34个乳腺肿瘤活检显示这些功能 与临床结果相关。如果成功，我们将把这个概念验证发展成一个强大的CLIA 21 符合CFR第11部分的分析方法，符合ICH分析方法指导原则。这项测定将 最终使临床医生能够更好地预测肿瘤对某些治疗的反应， 针对特定患者的治疗。这种类型的精准医疗方法将改善患者 更有效的治疗方案。