SENSITIVE, MULTIPLEXED ANALYSIS OF BREAST CANCER MARKERS

这项提议描述了半导体纳米晶体(量子点)作为探针的发展，用于同时检测乳腺细胞转化的多个标志物。量子点是一种新型的发光探针，它具有光稳定、易于多路传输、可以用简单、廉价的光学系统灵敏地检测到的特点。在项目的第一阶段，我们将使用共轭量子点在特征良好的乳腺癌细胞株的切片中检测三个选定的乳腺癌标记物，以在一个简单的模型系统中展示它们的特异性。在第二阶段，我们将对这个系统进行多路传输，加入第四个标记，并将其开发用于组织和组织微阵列。我们将检测三种蛋白标记物(P53、雌激素受体和孕激素受体)的表达。最初，我们将分别对探针的检测特异性和性能进行表征。我们将使用这个模型来比较量子点和传统荧光染料的稳定性和灵敏度。预计这项技术将允许使用这些探针来表征乳腺肿瘤活检组织，最终量子点可以用来更有信心地对任何存在多个标记的疾病进行表型分析。拟议的商业应用：这项研究可能直接导致一种新的产品，使乳腺癌组织切片的诊断和预后评估更敏感、更快、更有信息量。该技术还可直接应用于许多不同类型的肿瘤分析，并可用于组织微阵列，以极大地提高吞吐量并帮助组织切片分析的存档。

This proposal describes the development of semi-conductor nanocrystals (quantum dots) as probes for the specific detection of multiple markers of breast cell transformation simultaneously. Quantum dots are novel luminescent probes that are photo-stable, easily multiplexed and can be sensitively detected with simple, inexpensive optical systems. In phase I of the project we will use conjugated quantum dots to detect three selected breast cancer markers in sections of well-characterized breast cancer cell lines to demonstrate their specificity in a simple model system. In phase II we will multiplex this system, incorporate a fourth marker and develop it for use on tissues and tissue micro-arrays. We will detect the expression of three protein markers (p53, estrogen receptor and progesterone receptor). Initially we will characterize the assay specificity and performance of the probes individually. We will use this model to compare the stability and sensitivity of quantum dots with conventional fluorescent dyes. It is anticipated that this technology will allow the use of these probes to characterize breast tumor biopsies and ultimately quantum dots could be used to more confidently phenotype any disease for which multiple markers exist. PROPOSED COMMERCIAL APPLICATIONS: This research could directly lead to a novel product that allows more sensitive, quicker and more informative diagnostic and prognostic evaluation of breast cancer tissue sections. The technology is also directly applicable to many different kinds of tumor analysis and could be used on tissue microarrays to vastly increase the throughput and aid the archiving of tissue section analysis.