PDF(Pubmed)
Abstract：
Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification, and quantification, making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In in a previous review in 2017, we described technological and conceptual limitations that had held back success. We proposed a \'rectangular strategy\' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification, and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. Shorter gradients, new scan modes, and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multiprotein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.
摘要：
准确的生物标志物是精准医学的关键和必要的前提,然而，现有的往往是无特异性的，新的进入诊所的速度非常慢。基于质谱（MS）的蛋白质组学以其非靶向性质而著称，鉴定和定量的特异性使其成为生物标志物发现和常规测量的理想技术。与亲和粘合剂技术相比，它具有独特的属性，如OLINK邻近延伸测定和SOMAscan。在之前的一篇综述中，我们描述了阻碍成功的技术和概念限制(Geyer等人。,2017)。我们提出了一种“矩形策略”，通过最小化队列特异性效应来更好地分离真实的生物标志物。今天,这与基于MS的蛋白质组学技术的进步相融合，例如增加的样品吞吐量，识别和量化的深度。因此,生物标志物发现研究变得更加成功，生产能承受独立验证的生物标志物候选物，在某些情况下,已经超过了最先进的临床检测。我们总结了过去几年的发展，包括大型和独立队列的好处，这是临床接受所必需的。机器学习或深度学习也需要它们。较短的渐变，新的扫描模式和多路复用将大幅增加吞吐量，交叉研究整合，和量化，包括绝对水平的代理。我们已经发现,多蛋白质组固有地比当前的单分析物测试更稳健,并且更好地捕获人类表型的复杂性。临床中的常规MS测量正迅速成为可行的选择。体液中的全套蛋白质（全局蛋白质组）是最重要的参考和最佳的过程控制。此外,它越来越拥有所有可以从有针对性的分析中获得的信息，尽管后者可能是进入常规使用的最直接的方法。许多挑战依然存在,尤其是监管和道德性质，但是基于MS的临床应用前景从未如此光明。
