Department of Microbiology, Boston University School of Medicine, Boston, Massachusetts, United States of America.
Department of Pathology and Laboratory Medicine, Boston Medical Center, Boston, Massachusetts, United States of America.
PLoS One. 2019 Apr 17;14(4):e0209060. doi: 10.1371/journal.pone.0209060. eCollection 2019.
Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor "S" is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1-5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.
生物标志物是基础和临床研究结果的基础，通过报告宿主反应并深入了解疾病病理生理学。使用研究用 ELISA 试剂盒测量生物标志物是通用的，但试剂盒缺乏标准化和批次间不可预测的变异性，这给长期项目带来了分析挑战。在一项正在进行的为期两年的癌症患者血浆生物标志物测量项目中，一种生物标志物（PF）的对照浓度在 ELISA 试剂盒批次变化后显著降低。全面的运营审查指出，新试剂盒的标准曲线发生了偏移，这一分析变量危及了已经在数百个患者样本上收集的数据。在排除了数据变异性的其他合理因素后，开发了一种计算解决方案，为跨多个 ELISA 试剂盒批次的数据分析提供了一个统一的平台。该解决方案（ELISAtools）是在开放获取的 R 软件中开发的，其中试剂盒之间的变异性被视为批处理效应。为每个标准曲线建模一个定义最佳拟合的参考标准曲线，并计算一个独特的偏移因子“S”，然后相应地调整数据。PF ELISA 试剂盒批次 #1-5 的平均 S 因子范围从-0.086 到 0.735，将控制的批内变异性从 62.4%降低到<9%，在质量控制范围内。为其他四个生物标志物计算的 S 因子提供了一个定量指标，用于在 10 个月的研究期间监测 ELISA 以进行质量控制。可重复的生物标志物测量至关重要，特别是对于具有有价值患者样本的长期项目。使用研究用 ELISA 试剂盒是普遍的，明智地使用这种计算解决方案可以最大限度地提高生物标志物的可重复性。
