Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC–MS/MS) analyses were completed, generating six 2D LC–MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC–MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.

中文翻译：

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乳腺癌异种移植组织的长期定量蛋白质组学分析的质量评估

临床蛋白质组学需要对人类标本进行大规模分析，以取得统计学意义。我们评估了一种基于iTRAQ（等压标记的相对定量和绝对定量）的定量蛋白质组学策略的长期可重复性，该方法使用一个通道作为不同iTRAQ组中所有样品的参考。总共完成了148个液相色谱串联质谱分析（LC-MS / MS），生成了代表人和基础亚型的人鼠乳腺癌异种移植组织的6个二维LC-MS / MS数据集。此类大规模研究要求实施可靠的指标，以评估技术和生物变异性对定性和定量数据的贡献。因此，我们根据每个肽谱匹配的质量得出量化置信度得分，以从每个分析中删除量化离群值。将置信度分数过滤和统计分析相结合后，可从7个月的LC-MS / MS数据集中获得可重复的蛋白质鉴定和定量结果。这项研究为大型临床蛋白质组学项目的研究设计提供了关于长期稳定性和技术考虑因素的首次质量评估。