BiospecimenIntegrity: Assessing Quality and Influence on -Omics-based Analyses

生物样本完整性：评估基于组学的分析的质量和影响

• 批准号: 8842689
• 负责人: BRUCE S KRISTAL
• 金额: $ 43.96万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-02 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8842689
• 关键词: Address; Affect; Agreement; Biochemical; Bioinformatics; Biological Assay; Biological Markers; Blinded; Blood Platelets; Blood specimen; Classification; Clinical; Clinical Trials; Computational algorithm; Computer software; Coupled; Data; Data Set; Detection; Diet; Disease; Energy Intake; Epidemiologic Studies; Epidemiology; Erythrocytes; Exclusion; Floor; Food; Frequencies; Funding; Grant; High Pressure Liquid Chromatography; Human; Individual; Informatics; Investigation; Leukocytes; Link; Metabolic; Modeling; Morphologic artifacts; Noise; Non-Insulin-Dependent Diabetes Mellitus; Nurses' Health Study; Odds Ratio; Outcome; Outcome Measure; Oxidation-Reduction; Pattern Recognition; Peptides; Plasma; Population; Process; Proteomics; Public Health; Publishing; Questionnaires; Rattus; Relative Risks; Resources; Risk; Sampling; Serotyping; Shipping; Ships; Signal Transduction; Site; Time; Training; United States National Institutes of Health; Ursidae Family; base; cancer type; candidate marker; case control; cell type; cohort; cost; feeding; interest; malignant breast neoplasm; metabolomics; multi-electrode arrays; protein metabolite; repository; small molecule

DESCRIPTION (provided by applicant): We will address the issue of assessing plasma biospecimen integrity and its effect on downstream analyses and outcome measures such as risk prediction. We hypothesize that identifying and enabling correction of post-draw, pre-analytical variability at both the sample and variable level should engender increased signal to noise and thus increased power and precision of many biomarker studies being performed on samples in well-characterized human populations. Our approach will give us the ability to leverage the resources (data, expertise, platforms) established in six past and ongoing NIH- funded biochemical profiling studies to bear on this problem at comparatively lower cost. We already have candidate markers for red cell contamination as well as for sample degradation due to delayed processing as is inherent in studies using central processing. These approaches are very closely related to those needed to address the remaining two pieces, on white blood cell and platelet contamination, and we can expand the quantitation approaches that yielded our initial markers by ~10-fold in coverage due to recent software advances. The Aims: Aim 1: To utilize established metabolomics, proteomics, and informatics approaches to identify biomarkers of sample degradation, and, separately, biomarkers of contamination of plasma with constituents of red blood cells, white blood cells, or platelets. Aim 2: To mathematically re-analyze pre-existing metabolomics (HPLC-Coularray-based) and proteomics data (UPLC-LTQ-Orbitrap-based) from samples nested within the Nurses' Health Study (NHS) and within the OMNIHEART and CALERIE Clinical Trials so as: (i) to cross-validate the metabolomics and proteomics data to determine inter-assay agreement concerning sample quality, and; (ii) to assess the extent and distribution of sample degradation and distribution of levels of contamination of plasma with constituents of red blood cells, white blood cells, or platelets across these studies Aim 3: To determine the effect of variable and/or observation exclusion for cause (ie, red blood cell contamination) with incremented cut-points (eg, ...0.01%, 0.03%, 0.1%, 0.3%... etc) on the results of a study of pre-identified biomarkers of caloric intake to predict rik for breast cancer and type II diabetes (750/1000 paired case-control pairs nested within NHS), and thus to determine the influence of biospecimen integrity directly on both exposure classification (ie, diet) and end product prediction. Aim 4: To structurally identify metabolomics and proteomics biomarkers markers of interest to enable marker propagation to other groups and to electronically publish biomarker signatures.

描述(由申请人提供)：我们将解决评估血浆生物样品完整性及其对下游分析和结果测量(如风险预测)的影响的问题。我们假设，在样本和变量水平上识别和校正提取后、分析前的可变性应该会产生更高的信噪比，从而提高在特征良好的人类群体中对样本进行的许多生物标记物研究的能力和精确度。我们的方法将使我们有能力利用在过去和正在进行的NIH资助的六项生化图谱研究中建立的资源(数据、专业知识和平台)，以相对较低的成本解决这个问题。我们已经有了红细胞污染的候选标记物，以及由于延迟处理而导致的样品退化，这是使用中央处理的研究中固有的。这些方法与解决其余两个问题所需的方法密切相关，即白细胞和血小板污染，由于最近的软件进步，我们可以将产生初始标记物的定量方法扩展到原来的10倍。目的：目标1：利用已建立的代谢组学、蛋白质组学和信息学方法，确定样品降解的生物标志物，以及分别识别血浆与红细胞、白细胞或血小板成分污染的生物标志物。目的2：从护士健康研究(NHS)和OMNIHEART和CALERIE临床试验中嵌套的样本中对先前存在的代谢组学(基于高效液相-Coularray)和蛋白质组学数据(基于UPLC-LTQ-Orbitrap)进行数学重新分析，以便：(I)交叉验证代谢组学和蛋白质组学数据，以确定关于样本质量的测定间一致性；(Ii)评估样品降解的程度和分布，以及血浆中红细胞、白细胞或血小板成分的污染水平在这些研究中的分布目标3：确定变量和/或观察排除原因(即红细胞污染)的影响，并增加切点(例如，0.01%、0.03%、0.1%、0.3%)。等)，以预测乳腺癌和II型糖尿病的RIK(750/1000配对病例对照对嵌套在NHS内)的研究结果，从而确定生物样本完整性对暴露分类(即饮食)和最终产品预测的直接影响。目的4：从结构上识别代谢组学和蛋白质组学感兴趣的生物标记物标记，使标记能够传播到其他组并以电子方式发布生物标记物签名。