EXPRESSION BASED MARKERS FOR BREAST CANCER DETECTION

用于乳腺癌检测的基于表达的标记物

• 批准号: 6377758
• 负责人: Jeffrey R. Marks
• 金额: $ 41.24万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-30 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6377758
• 关键词: antibody formation; biomarker; breast neoplasm /cancer diagnosis; cancer information system; cell line; circulating neoplastic cell; clinical research; cooperative study; diagnosis design /evaluation; female; flow cytometry; gene expression; genetic library; human subject; immunomagnetic separation; in situ hybridization; mammary epithelium; molecular oncology; northern blottings; polymerase chain reaction; telomerase; tissue resource /registry; women's health

This proposal will attempt to identify expression-based markers for breast cancer and develop assays using these markers to detect circulating breast cancer cells. Our application has been developed as a collaborative effort between the Duke Breast Cancer Program and Abbott Diagnostic Breast Cancer Venture Group and will utilize the considerable strengths of each institution. The basic approach for marker identification takes advantage of ongoing (and separately funded) efforts using powerful new genomics tools. At Duke, SAGE (serial analysis of gene expression) libraries of breast cancer and normal breast epithelium are being constructed as part of CGAP. Also at Duke, a series of 20 primary breast cancers will be analyzed using high-density Affymetrix expression chips (30,000 genes/ESTs will be sampled). Finally, at Abbott Diagnostics, markers have already been identified by using the Incyte Corporation expression libraries. These three sources of expression data will be compared electronically and the most promising markers that are expressed at high levels in a tissue or cancer specific manner will be identified. After the in silico analysis, the lead markers will be put through a validation algorithm that will include northern blotting, multiple tissue RNA dot blots, RT-PCR, and in situ cytohybridization. A panel of breast cell lines and a series of 50 primary breast cancers will be used to determine how commonly expressed these markers are in the target tissue. After these initial validation steps, a series of reconstitution experiments will be performed by spiking normal whole blood with known numbers of breast cancer cells, derived from cell lines and malignant effusions. Epithelial cells will be purified using magnetic bead/antibody technology and the resulting purified samples will be analyzed by flow cytometry, histology, and quantitative RT-PCR. Towards increasing sensitivity, a separate and parallel screen will focus on markers that are highly overexpressed in a subset of breast cancers. PCR multiplexing approaches will be explored in this context and will also include using telomerase gene (TERT) expression as a cancer specific marker in conjunction with other lead markers. Antibodies to the most promising candidates will be developed, particularly for gene products that may be secreted or presented on the cell surface. Finally, a collection of whole blood and serum samples will be obtained from women newly diagnosed with breast cancer at Duke. This bank will constitute a primary validation set for markers and approaches that have proven to be the most robust.

该提案将尝试识别基于表达的乳腺癌标记物，并开发使用这些标记物检测循环乳腺癌细胞的检测方法。 我们的应用程序是杜克乳腺癌计划和雅培诊断乳腺癌风险投资集团合作开发的，并将利用每个机构的巨大优势。标记物识别的基本方法利用了使用强大的新基因组学工具正在进行的（并且单独资助的）努力。 杜克大学正在构建乳腺癌和正常乳腺上皮的 SAGE（基因表达系列分析）文库，作为 CGAP 的一部分。 同样在杜克大学，将使用高密度 Affymetrix 表达芯片对一系列 20 个原发性乳腺癌进行分析（将采样 30,000 个基因/EST）。 最后，在 Abbott Diagnostics，标记已经通过使用 Incyte Corporation 表达库进行了鉴定。 这三种表达数据来源将以电子方式进行比较，并且将鉴定出以组织或癌症特异性方式高水平表达的最有希望的标记物。 计算机分析后，先导标记将通过验证算法，其中包括 Northern 印迹、多组织 RNA 点印迹、RT-PCR 和原位细胞杂交。 一组乳腺细胞系和一系列 50 种原发性乳腺癌将用于确定这些标记物在目标组织中表达的普遍程度。在这些初步验证步骤之后，将通过在正常全血中掺入已知数量的来自细胞系和恶性渗出物的乳腺癌细胞来进行一系列重建实验。 将使用磁珠/抗体技术纯化上皮细胞，并通过流式细胞术、组织学和定量 RT-PCR 分析所得纯化样品。 为了提高敏感性，一个单独的并行筛选将重点关注在乳腺癌子集中高度过度表达的标记物。 PCR 多重方法将在此背景下进行探索，还将包括使用端粒酶基因 (TERT) 表达作为癌症特异性标记物与其他先导标记物结合使用。 将开发针对最有希望的候选者的抗体，特别是针对可能分泌或呈现在细胞表面上的基因产物。 最后，将从杜克大学新诊断出患有乳腺癌的女性中采集全血和血清样本。 该库将为已被证明最可靠的标记和方法构成主要验证集。