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: 8646992
• 负责人: BRUCE S KRISTAL
• 金额: $ 43.73万
• 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/8646992
• 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; 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标本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绘制后、分析前的可变性应该会增加信号对噪声的影响，从而提高许多生物标志物研究的能力和精度，这些研究是在特征良好的人群中进行的。我们的方法将使我们能够利用在过去和正在进行的六个NIH资助的生化分析研究中建立的资源（数据，专业知识，平台），以相对较低的成本来解决这个问题。我们已经有了红细胞污染的候选标记物，以及由于使用中央处理的研究中固有的延迟处理而导致的样品降解。这些方法与解决剩下的白血球和血小板污染所需的方法密切相关，并且由于最近软件的进步，我们可以将产生初始标记的定量方法的覆盖范围扩大约10倍。目的：目的1：利用已建立的代谢组学、蛋白质组学和信息学方法来鉴定样品降解的生物标志物，以及血浆中红细胞、白细胞或血小板成分污染的生物标志物。目标2：从数学上重新分析护士健康研究（NHS）和OMNIHEART和CALERIE临床试验中嵌套的样品中已有的代谢组学（基于hplc - coulararray）和蛋白质组学数据（基于uplc - ltq - orbitrapp），以便：(i)交叉验证代谢组学和蛋白质组学数据，以确定有关样品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污染水平的分布。目的3：通过增加切点（例如……0.01%、0.03%、0.1%、0.3%……）来确定变量和/或观察排除原因（即红细胞污染）的影响。等)对预测乳腺癌和II型糖尿病风险的热量摄入预先识别的生物标志物的研究结果（750/1000对嵌套在NHS内的病例对照），从而确定生物标本完整性直接影响暴露分类（即饮食）和最终产品预测。目标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，使标记物能够传播到其他群体，并以电子方式发布生物标记物签名。