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: 8295500
• 负责人: BRUCE S KRISTAL
• 金额: $ 41.65万
• 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/8295500
• 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. PUBLIC HEALTH RELEVANCE: Retrospective analysis of plasma and sera repositories is central to many epidemiological investigations and thus to our knowledge about major public health issues such as the links between diet and disease. Quality issues, such as sample contamination and alteration during processing and degradation during shipping can affect assay outcomes, but are poorly understood and needed systematic markers are lacking. We will leverage our existing plasma datasets to identify such biomarkers/profiles so as to reduce artifact associated noise and increase the accuracy, power and precision of the many biomarker studies now being contemplated and performed on these samples.

描述（由申请人提供）：我们将解决评估血浆生物样本完整性及其对下游分析和结果测量（例如风险预测）的影响的问题。我们假设，在样本和变量水平上识别并校正抽取后、分析前的变异性应该会增加信噪比，从而提高在特征明确的人群样本上进行的许多生物标志物研究的功效和精度。我们的方法将使我们能够利用美国国立卫生研究院过去和正在进行的六项生化分析研究中建立的资源（数据、专业知识、平台），以相对较低的成本解决这个问题。我们已经有了红细胞污染以及由于延迟处理而导致的样品降解的候选标记，这是使用中央处理的研究所固有的。这些方法与解决剩下的两个问题（白细胞和血小板污染）所需的方法非常密切相关，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。目标： 目标 1：利用已建立的代谢组学、蛋白质组学和信息学方法来识别样品降解的生物标志物，以及分别识别红细胞、白细胞或血小板成分血浆污染的生物标志物。目标 2：以数学方式重新分析护士健康研究 (NHS) 以及 OMNIHEART 和 CALERIE Clinical 中嵌套样本的现有代谢组学（基于 HPLC-Coularray）和蛋白质组学数据（基于 UPLC-LTQ-Orbitrap）试验目的是：（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；以及； (ii) 评估这些研究中样品降解的程度和分布以及红细胞、白细胞或血小板成分的血浆污染水平的分布目标 3：确定变量和/或观察排除的影响根据预先确定的生物标志物的研究结果，确定原因（即红细胞污染）并增加切点（例如...0.01%、0.03%、0.1%、0.3%...等）热量摄入来预测乳腺癌和 II 型糖尿病的 rik（NHS 内嵌套的 750/1000 配对病例对照对），从而确定生物样本完整性直接对暴露分类（即饮食）和最终产品预测的影响。目标 4：从结构上鉴定感兴趣的代谢组学和蛋白质组学生物标志物标记，使标记能够传播到其他群体并以电子方式发布生物标志物签名。 公共卫生相关性：血浆和血清库的回顾性分析对于许多流行病学调查至关重要，因此对于我们了解饮食与疾病之间的联系等重大公共卫生问题至关重要。质量问题，例如样品污染和加工过程中的改变以及运输过程中的降解，可能会影响测定结果，但人们对此知之甚少，并且缺乏所需的系统标记。我们将利用现有的血浆数据集来识别此类生物标志物/概况，以减少伪影相关噪声并提高目前正在考虑和对这些样本进行的许多生物标志物研究的准确性、功效和精确度。