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A comprehensive liquid biopsy platform for detection and prognostication in early stage breast cancer

用于早期乳腺癌检测和预测的综合液体活检平台

基本信息

批准号：
10001470
负责人：
Ashutosh Agarwal
金额：
$ 42.27万
依托单位：
UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-13 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10001470
关键词：
Address Advanced Malignant Neoplasm Algorithmic Analysis Algorithms Area Basic Science Biological Biology Blood Blood specimen Breast Cancer Patient Caliber Cancer Patient Cells Cellular Morphology Cessation of life Clinical Clinical Management Clinical Pathology Clinical Research Collaborations Collection Competence Computer software Data Detection Diagnosis Diagnostic Disease Early Intervention Engineering Evaluation Fibroblasts Fluorescence Funding Mechanisms Health Hematopoietic Neoplasms Human Image Imaging problem Incidence Individual Institutes Laboratories Malignant Neoplasms Microscope Microscopy Minnesota Neoplasm Circulating Cells Neoplasm Metastasis Oncology Outcome Pathologist Patients Play Population Process Recurrence Reporting Research Research Personnel Resolution Resources Risk Role Sampling Scanning Serum Shapes Solid Neoplasm Surface Technology Time Training Universities Whole Blood analytical tool arm automated analysis base cancer cell cancer cell subtype clinical Diagnosis deep learning deep learning algorithm expectation experience follow-up high resolution imaging imaging modality improved individual patient industry partner liquid biopsy malignant breast neoplasm multidisciplinary neoplastic cell new technology novel novel strategies novel therapeutics outcome prediction personalized medicine prognostic prognostic index prognostic tool response screening success tool treatment planning treatment response tumor tumor microenvironment tumor progression virtual

项目摘要

Project Summary We seek to develop robust platforms for capturing, enumerating and analyzing circulating tumor cells and associated cancer cells from the blood of cancer patients. We will leverage our previously developed precisely engineered microfilter platform that allows effective separation and collection of viable CTC from patient blood. In parallel, we have developed novel imaging methods perfectly suited for rapid analyses for circulating tumor cells, clusters and associated cancer cell subtypes (such as Cancer Associated Fibroblasts, or CAF). We propose to integrate these two novel technologies to create a robust diagnostic tool for clinical pathology as well as basic research that enables capture, enumeration, and analyses of circulating tumor cells and associated cancer cells. Our multidisciplinary team proposes the following Specific Aims: 1) Build and validate the technology to rapidly acquire CTC/CAF images using Fourier Ptychographic Microscopy and evaluate the images with a deep learning algorithm 2) Evaluation of the integrated platform for using CTC/CAF in predicting recurrence/survival in early stage breast cancer patients For these studies in early stage breast cancer, we have robust collaborations with industry partners in Circulogix Inc (Microfilter technology for cell capture), ClearBridge Inc. (FPM imaging of the microfilter surface), and Google Software (Deep learning algorithm for analyzing FPM images of microfilter). The ability to reliably and rapidly detect and analyze CTC and associated cancer cells in early stage breast cancer patients would deliver a major new tool for oncology research, a platform for screening of novel drugs, and a critical advance in precision cancer management.

项目摘要我们寻求开发用于捕获、计数和分析循环肿瘤细胞的强大平台，从癌症患者的血液中提取相关的癌细胞。我们将利用我们以前开发的精确工程微过滤器平台，允许从患者血液中有效分离和收集活CTC。与此同时，我们已经开发出非常适合循环肿瘤快速分析的新型成像方法细胞、簇和相关的癌细胞亚型（例如癌症相关成纤维细胞或CAF）。我们建议整合这两种新技术，为临床病理学创建一个强大的诊断工具以及能够捕获、计数和分析循环肿瘤细胞的基础研究，相关的癌细胞。我们的多学科团队提出了以下具体目标： 1)建立并验证使用傅立叶快速获取CTC/CAF图像的技术重叠关联使用深度学习算法对图像进行显微镜检查和评估 2)CTC/CAF联合平台早期预测复发/生存的评价乳腺癌患者对于这些早期乳腺癌的研究，我们与行业合作伙伴进行了强有力的合作， Circulogix Inc（用于细胞捕获的微滤器技术）、ClearBridge Inc. (FPM微过滤器表面的成像）， Google Software（用于分析微过滤器FPM图像的深度学习算法）。能够可靠、快速地检测和分析早期乳腺癌中的CTC和相关癌细胞癌症患者将为肿瘤学研究提供一个重要的新工具，一个筛选新药的平台，也是精准癌症管理的重要进展