Targeted and global proteomic strategies for early breast cancer detection

描述(由申请人提供)：我们建议与由U24机制资助的其他研究人员合作，评估蛋白质组技术，这些技术将能够通过应用基于血液的测试来早期检测几种肿瘤类型。我们的团队由LBNL、加州大学旧金山分校、巴克研究所、MD安德森医院和不列颠哥伦比亚大学的科学家组成，拥有该项目所需的广泛专业知识。我们将重点关注乳腺癌。首先，我们将研究全球策略和基于靶向质谱学(MS)的方法，以开发用于在小鼠血浆中识别人乳腺癌细胞蛋白质特征的最佳工作流程。全球战略将利用强调量化比较的多种工作流程。有针对性的方法将重点放在由异常RNA剪接引起的癌症特异性蛋白质上。候选生物标记物将使用反相蛋白质阵列进行验证。表观组学将产生所需的抗体。该项目的下一阶段将使用人类临床样本。具体地说，我们将应用一种类似的、优化的方法来分析预期从乳腺癌患者(n=200)中收集的血浆样本。对照样本将从患有良性乳腺疾病的健康妇女和类风湿性关节炎妇女那里获得，以说明与炎症过程相关的蛋白质的贡献。我们的候选方法针对剪接体和翻译后修饰(例如糖基化、磷酸化、蛋白分解和氧化损伤)，剪接体将使用血浆捐赠者的乳腺癌活检组织进行分析。我们还包括一项计划，以建立一种系统的方法来标准化蛋白质组学协议和小组之间的数据分析，该小组利用可治愈的能力来分析在许多平台上产生的质谱图，以及我们将用于挖掘这些大型数据集的稳健的统计方法。还讨论了RFA的其他几个要素。例如，我们总结了我们的仪器的分析能力和我们共享项目产生的资源的战略，包括生物标本、协议、数据、软件工具和智力资源。最后，我们设想我们的团队将与其他CPTAC团队和NCI一起，为研究社区开发方法、工具和参考样本，使基于MS的癌症生物标记物发现成为现实。

DESCRIPTION (provided by applicant): We propose to work cooperatively with other investigators funded by this U24 mechanism to evaluate proteomic technologies that will enable the early detection of several tumor types through the application of blood-based tests. Our group, consisting of scientists at LBNL, UCSF, The Buck Institute, MD Anderson Hospital, and the University of British Columbia, has the broad expertise that this project requires. We will focus on breast cancer. Initially, we will examine both global strategies and targeted mass spectrometry (MS)-based approaches to develop optimal workflows for the identification of protein signatures of human breast cancer cells in murine plasma. The global strategies will utilize multiple workflows that emphasize quantitative comparisons. The targeted approaches will focus on cancer-specific proteins that result from aberrant RNA splicing. Candidate biomarkers will be validated using reverse phase protein arrays. The requisite antibodies will be generated by Epitomics. The next phase of the project will employ human clinical samples. Specifically, we will apply an analogous, optimized approach for analyzing plasma samples that will be prospectively collected from breast cancer patients (n = 200). Control samples will be obtained from healthy women with benign breast disease and from women with rheumatoid arthritis to account for the contribution of proteins associated with inflammatory processes. Our candidate approach targets both the spliceome, which will be profiled using breast cancer biopsies from the plasma donors, and posttranslational modifications (e.g., glycosylation, phosphorylation, proteolysis and oxidative damage). We also include a plan for establishing a systematic way to standardize proteomic protocols and data analysis among the groups that exploits curability to analyze mass spectra generated on numerous platforms and the robust statistical methods we will employ to mine these large data sets. Several other elements of the RFA are also addressed. For example, we summarize the analysis capacity of our instruments and our strategy for sharing project-generated resources including biological specimens, protocols, data, software tools, and intellectual resources. In the end, we envision that our group, in conjunction with the other CPTAC teams and the NCI, will develop methods, tools and reference samples for the research community that will make the promise of MS-based cancer biomarker discovery a reality.