Clinical and Experimental Pharmacology Group, Paterson Institute for Cancer Research, Manchester Academic Health Science Centre, Christie Hospital, University of Manchester, Wilmslow Road, Manchester M20 4BX, United Kingdom.
J Proteome Res. 2012 Apr 6;11(4):2103-13. doi: 10.1021/pr200636x. Epub 2012 Mar 12.
A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes>2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.
建立了一种基于质谱的血浆生物标志物发现工作流程，以促进生物标志物的发现。来自健康志愿者或胰腺癌患者的血浆分别用 8 重同位素标签标记，通过二维反相色谱分离，并进行 MALDI ToF/ToF 质谱分析。使用基于 q 值的统计方法处理数据，以最大化蛋白质定量和鉴定。计算技术（重复样本之间）和生物学变异（个体之间和个体内部），从而能够进行功效分析。使用来自健康志愿者的样本进行了先验功效分析，以确定用于稳健生物标志物识别所需的样本量。随后使用涉及胰腺癌患者的真实临床设置进行了事后功效分析来验证结果。这表明，每组 6 个样本（例如，治疗前与治疗后）可能为大多数变化>2 倍的蛋白质提供足够的统计功效。参考标准允许直接比较多个实验之间的蛋白质表达变化。在治疗前分析患者血浆，在个体患者中发现了 29 种具有显著变化的蛋白质。过氧化物还原酶 II 水平的变化通过 Western blot 得到证实。这种基于 q 值的统计方法结合参考标准样本，可以在进行预测、预后和/或药效学生物标志物发现的临床研究的设计和执行中充满信心地应用。功效分析提供了研究启动前所需的信息。
