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High-Throughput Evaluation of Breast Cancer Markers

乳腺癌标志物的高通量评估

基本信息

批准号：
7668503
负责人：
RICHARD C ZANGAR
金额：
$ 40.61万
依托单位：
BATTELLE PACIFIC NORTHWEST LABORATORIES
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-09-26 至 2011-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7668503
关键词：
Accounting Affect Age Antibodies Antigens Archives Biological Assay Biological Markers Blood Proteins Breast Breast Cancer Early Detection Breast Diseases Cancer Control Caring Cessation of life Clinical Clinical Protocols Data Databases Detection Development Diagnostic Neoplasm Staging Disease Ductal Early Detection Research Network Early Diagnosis Enzyme-Linked Immunosorbent Assay Epidemiological Factors Epidemiology Evaluation Fourier transform ion cyclotron resonance Funding Goals High Risk Woman Human Individual Laboratories Lobular Malignant Neoplasms Mammography Mass Spectrum Analysis Measures Medical center Menopausal Status Methodology Methods Microarray Analysis Noninfiltrating Intraductal Carcinoma Pacific Northwest Palpation Peptides Phase Phase III Clinical Trials Plasma Population Procedures Protein Analysis Protein Databases Proteins Proteomics Publishing Reagent Reporting Reproductive History Research Retrospective Studies Sampling Screening procedure Set protein Staging Testing Time Training United States Validation Woman Women&apos s Group base capillary liquid chromatography comparative high risk high throughput analysis improved malignant breast neoplasm new technology nipple aspirate fluid programs protein profiling tumor

项目摘要

DESCRIPTION (provided by applicant): Breast cancer is one of the most prevalent cancer in the United States, resulting in the death of ~43,000 women per year. Current methods for early detection (e.g., mammography and breast exam) of this disease rely on physical means to detect a tumor and are unreliable. Since a number of blood proteins have been reported to be altered in women with breast cancer, a more useful and accurate evaluation of breast cancer could potentially be obtained by an analysis of these proteins. Since breast cancer is a multifaceted disease, it seems likely that analysis of more than one protein will be needed to detect all forms of this disease. In addition, the normal levels of many cancer markers will be affected by age, reproductive history, menopausal status and other epidemiological factors. Therefore, we hypothesize that it will be necessary to use a profile of markers in order to accurately detect breast cancer and that the accuracy of this profile will be improved by accounting for predictable effects of epidemiological factors. In order to test this hypothesis, we will undertake a "phase 1" biomarker discovery analysis using sophisticated proteomics methodologies and leveraging the results from several independently funded studies. Based on results of this first study and other information, we will undertake a "phase 2" analysis of 50 proteins in ~1000 plasma samples using ELISA microarray technology. Finally, we will undertaken an extensive "phase 3" retrospective study with the goal of determining whether a selected subset of plasma markers can be used to predict the presence of breast cancer in a population of high-risk women prior to detection of that disease by conventional methods. Therefore, the proposed research will effectively utilize new technologies to significantly accelerate the pace of biomarker research. The final result of these analyses will be an extensive characterization of a whole profile of protein levels. We will use sufficient numbers of samples to draw statistically valid conclusions about the ability of this biomarker profile to detect the presence of early disease and whether incorporation of epidemiological factors can improve the accuracy of this analysis.

描述（由申请人提供）：乳腺癌是美国最常见的癌症之一，每年导致约43,000名妇女死亡。目前这种疾病的早期检测方法（如乳房x光检查和乳房检查）依赖于物理手段来检测肿瘤，并不可靠。据报道，患有乳腺癌的妇女血液中有许多蛋白质发生了改变，因此，通过分析这些蛋白质，可能会对乳腺癌进行更有用和准确的评估。由于乳腺癌是一种多方面的疾病，似乎需要对不止一种蛋白质进行分析才能检测出这种疾病的所有形式。此外，许多癌症标志物的正常水平会受到年龄、生育史、绝经状况等流行病学因素的影响。因此，我们假设有必要使用标记谱来准确检测乳腺癌，并且通过考虑流行病学因素的可预测影响，这种谱的准确性将得到提高。为了验证这一假设，我们将使用复杂的蛋白质组学方法进行“第一阶段”生物标志物发现分析，并利用几项独立资助的研究的结果。基于第一项研究的结果和其他信息，我们将使用ELISA微阵列技术对约1000份血浆样本中的50种蛋白质进行“第二阶段”分析。最后，我们将进行一项广泛的“3期”回顾性研究，目的是确定在传统方法检测出乳腺癌之前，是否可以使用选定的血浆标志物亚群来预测高危妇女人群中乳腺癌的存在。因此，本研究将有效利用新技术，显著加快生物标志物研究的步伐。这些分析的最终结果将是对整个蛋白质水平剖面的广泛表征。我们将使用足够数量的样本来得出统计上有效的结论，即该生物标志物谱检测早期疾病存在的能力，以及纳入流行病学因素是否可以提高该分析的准确性。