Identification of Factors Contributing to Variability in a Blood-Based Gene Expression Test
Identification of Factors Contributing to Variability in a Blood-Based Gene Expression Test
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DOI：
10.1371/journal.pone.0040068
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发表时间：
2012-07-03
期刊：
影响因子：
3.7
通讯作者：
Wingrove, James A.
中科院分区：
文献类型：
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作者：
Elashoff, Michael R.;Nuttall, Rachel;Wingrove, James A.
背景：Corus CAD是一种基于年龄、性别和全血中23个基因表达水平的临床验证测试，其得分(1-40分)与阻塞性冠状动脉疾病的可能性成正比。临床实验室过程变异性在24个月期间使用全血对照进行检查：批次内变异性使用样本重复进行评估；批间变异性作为实验室人员、设备和试剂批次的功能进行检查。方法/结果：为了评估批内变异性，处理了5批次的132个全血对照；使用895个全血对照样本估计了批间变异性。用方差分析检验了4个过程步骤的批间变异性：RNA提取、cDNA合成、将cDNA加到分析板上和qRT-PCR。如果可能，操作员、机器和试剂批次被评估为所有阶段的变量，共11个变量。批内和批间偏差估计分别为0.092和0.059个标准偏差单位(SD)；实验室总偏差估计为0.11CP单位(SD)。在包括所有11个实验室变量的回归模型中，试板批次和cDNAKit批次对变异性的贡献最大(p=0.045；0.009)。总体而言，RNA提取、cDNA合成和qRT-PCR的试剂批次对批间变异的贡献率最大(52.3%)，其次是操作员和机器(分别为18.9%和9.2%)，未解释的变异为19.6%。结论：在研究中，批内变异对总体变异的贡献率最大，而试剂批次对批间变异的贡献最大。
Background: Corus CAD is a clinically validated test based on age, sex, and expression levels of 23 genes in whole blood that provides a score (1-40 points) proportional to the likelihood of obstructive coronary disease. Clinical laboratory process variability was examined using whole blood controls across a 24 month period: Intra-batch variability was assessed using sample replicates; inter-batch variability examined as a function of laboratory personnel, equipment, and reagent lots.Methods/Results: To assess intra-batch variability, five batches of 132 whole blood controls were processed; inter-batch variability was estimated using 895 whole blood control samples. ANOVA was used to examine inter-batch variability at 4 process steps: RNA extraction, cDNA synthesis, cDNA addition to assay plates, and qRT-PCR. Operator, machine, and reagent lots were assessed as variables for all stages if possible, for a total of 11 variables. Intra-and inter-batch variations were estimated to be 0.092 and 0.059 Cp units respectively (SD); total laboratory variation was estimated to be 0.11 Cp units (SD). In a regression model including all 11 laboratory variables, assay plate lot and cDNA kit lot contributed the most to variability (p = 0.045; 0.009 respectively). Overall, reagent lots for RNA extraction, cDNA synthesis, and qRT-PCR contributed the most to inter-batch variance (52.3%), followed by operators and machines (18.9% and 9.2% respectively), leaving 19.6% of the variance unexplained.Conclusion: Intra-batch variability inherent to the PCR process contributed the most to the overall variability in the study while reagent lot showed the largest contribution to inter-batch variability.