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

描述（由申请方提供）：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抽取后、分析前变异性应增加信噪比，从而增加在充分表征的人群中对样本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在过去和正在进行的六项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志物，以及由于延迟处理而导致的样本降解，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（白色血细胞和血小板污染）所需的方法非常密切相关，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鉴定样本降解的生物标志物，并分别鉴定红细胞、白色细胞或血小板成分污染血浆的生物标志物。目标二：从数学上重新分析已有的代谢组学（基于HPLC-Coularray）和蛋白质组学数据（基于UPLC-LTQ-Orbitrap-based）从嵌套在护士健康研究（NHS）和OMNIHEART和CALERIE临床试验内的样本中进行分析，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分析间一致性，以及;（ii）评估这些研究中样本降解的程度和分布，以及血浆中红细胞、白色细胞或血小板成分污染水平的分布。为了确定变量和/或观察结果排除原因（即红细胞污染）的影响，增加临界点（例如...... 0.01%，0.03%，0.1%，0.3%……等）的研究结果，以预测乳腺癌和II型糖尿病的风险（750/1000配对病例对照对嵌套在NHS内），从而确定生物标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：从结构上鉴定代谢组学和蛋白质组学生物标志物，使标志物能够传播到其他组，并以电子方式发布生物标志物签名。

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.