Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.
基于数据非依赖性采集（DIA）的质谱技术已发展成为一种强大的定量工具，在精准医学等多个领域具有广泛应用前景。结合稳定同位素重组蛋白标准品，该策略能够实现可靠的蛋白质鉴定和精确定量。本研究采用一种全面的靶向蛋白质组学方法，对包含15种不同癌症类型、共计1800份血浆样本的泛癌队列进行了分析。我们成功实现了253种蛋白质的多重绝对定量。该检测方法具有较低的实验内变异，在近两千次进样中定量分析的1013条肽段的变异系数低于20%（CV=17.2%）。通过差异表达分析和机器学习，本研究鉴定出由七种蛋白质靶标组成的潜在生物标志物组合，可用于多发性骨髓瘤患者的诊断。包含补体C1复合物、JCHAIN和CD5L等标志物的组合构建的预测模型，在区分多发性骨髓瘤患者与其他癌症患者时曲线下面积（AUC）达到0.96。所有这些蛋白质均已知与免疫球蛋白存在相互作用。
Absolute Quantification of Pan-Cancer Plasma Proteomes Reveals Unique Signature in Multiple Myeloma
肿瘤(癌症)患者之家