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

项目总结 该项目的目的是开发一种可用于预测乳腺癌预后的成像工具。 特定的临床结果，如肿瘤复发和基于肿瘤的耐药性 组织形态特征。目前用于评估组织的组织病理学范式侧重于诊断 如果对这些相同的组织进行整体评估和数字化，这些疾病将提供机会 将临床结果与特定的组织特征相关联。通过这个项目，我们正在开发一种高- 吞吐量乳腺肿瘤成像方法，能够对乳腺肿瘤组织进行整体和 生成与传统H&E截面质量相同的3D虚拟H&E光学Z截面，以便所有 活组织检查中的细胞具有特征性。为了实现这一目标，我们正在结合我们的专利组织 采用荧光标记、高含量共聚焦显微镜和无偏振机的清除方法 学习方法。这种方法允许活检组织的全部数字化和所有特征 以及肿瘤的异质性，而不是仅仅看几张超薄的2D幻灯片。通过 将这一独特的成像方法与分层凝聚聚类相结合， 使用详细的样本库可以将组织形态特征与临床结果相关联 相应的临床结果数据。 该项目的主要目标是1)开发一种稳健的3D H&E标签方法，2)展示 可以使用荧光方法对组织进行3D成像，以生成同等质量的类似H&E的图像 向传统的H&E和3)表明，这种组织分析方法可以转移到自动化的高度- 内容共聚焦显微镜。此外，我们还将展示基于其组织集群的能力 组织形态特征和意志与34个乳腺肿瘤活检的小数据集显示这些特征 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 Part 11标准的化验，符合ICH分析化验指南。这个化验结果会 最终使临床医生能够更好地预测肿瘤对某些治疗的反应，并最好地定制 针对特定病人的治疗。这种类型的精准医疗方法将使患者的病情得到改善 结果和更有效的治疗方案。

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.