MICA: Delivering a production platform and atlas for next-generation biomarker discovery, validation and assay development in clinical proteomics

基因组革命将医学科学推进到了一个重要的转折点。我们现在可以尝试理解控制人类功能的生命分子构建块之间复杂的相互作用，以及它们如何在疾病下受到干扰和破坏。这些扰动和功能障碍会导致我们细胞和组织中的生物分子信号，通常与潜在遗传密码和其他生理特征的变化有关。由于每个病例可能不同，单一的共同治疗方案可能对所有患者都有效或安全。这导致了分层医学的概念，其中不同的治疗方法与个体患者的不同分子特征相关联。这种方法成功的关键是一个诊断程序，它可以可靠地表征这些分子，理想情况下是蛋白质，用于早期疾病检测和随后基于药物安全性或有效性的分层。通过在世界范围内建立一些大型设施，包括mrc资助的曼彻斯特斯托勒生物标志物发现中心（SBDC），系统地发现这些所谓的“生物标志物”的努力得到了加强。SBDC是一个价值2500万英镑的设施，结合了最新的仪器和技术，用于蛋白质的高通量分析，对数千个样品的候选生物标志物集进行验证，通过临床测试（“分析”）的开发，用于临床中单个生物标志物的常规测量。SBDC使用质谱（MS）来完成这项工作，这是一种获得样品快照的普遍技术，可以测量每种组成化合物的质量和数量，例如用于分析蛋白质-“蛋白质组学”。SBDC和其他最近成立的中心采用了一种名为数据独立获取（DIA）的MS新策略。与以前的方法不同，DIA可以对样品进行全面的数字记录，从而有可能对所有可检测的蛋白质进行鉴定和定量。然而，由于生物变异，有必要分析多个样本以获得对患者群体的可靠了解。SCIEX有限公司基于dia的SWATH-MS方法已经引起了相当大的临床兴趣，因为它可以对数千个样本的潜在生物标志物进行可靠和可重复的监测。SWATH-MS，像所有临床MS方法一样，必须将蛋白质消化成更小的肽进行分析。然而，当选择可重复的肽作为测试的基础时，这给SWATH-MS分析和MS临床检测的发展带来了挑战。我们最近开发了一种新的统计（贝叶斯）建模方法来评估肽的可重复性，以及一种全新的生物标志物发现工作流程，首次对未处理的数据进行统计建模，从而显著提高了性能。该项目的目的是利用我们工作流程的敏感性，为SBDC和其他机构提供一个强大的、生产质量高的生物标志物发现和验证软件平台。此外，由于SBDC每年将分析多达12,000个样本，并以控制样本收集、制备和分析的严格标准操作程序为基础，它还提供了一个独特的机会，可以整理和了解大量患者群体中健康和疾病中蛋白质水平的生物学和实验变化。我们将建立这种变异的“图谱”，根据遗传、生理和其他临床数据进行分层。为了实现这一目标，我们将把“大数据”计算方法和网络基础设施结合起来。该图谱将使生物标志物验证和多肽表征能够在比目前可能的更早的管道中进行分析开发，并实现我们发现平台的灵敏度和特异性的进一步阶梯式改进。

The genomic revolution has advanced medical science to an important tipping point. We can now attempt to understand the complex interactions between the molecular building blocks of life that control human function and how they are perturbed and break down under disease. These perturbations and dysfunctions can lead to tell-tale biomolecular signals in our cells and tissues, often linked to changes in the underlying genetic code and other physiological characteristics. Since each individual case can be different, a single, common treatment option may not be effective or safe for all patients. This has led to the concept of stratified medicine, where different treatments are associated with the different molecule signatures of the individual patients. Critical to the success of such an approach is a diagnostic programme that can reliably characterise these molecules, ideally the proteins, for early disease detection and subsequent stratification based on drug safety or efficacy. The push to systematically discover these so called 'biomarkers' has been enhanced through the establishment of a number of large-scale facilities worldwide, including the MRC-funded Stoller Biomarker Discovery Centre (SBDC) in Manchester. The SBDC is a £25M facility that combines the latest instrumentation and techniques for high-throughput profiling of proteins, validation of candidate biomarker sets on thousands of samples, through to the development of clinical tests ('assays') for the routine measurement of individual biomarkers in clinic. The SBDC uses mass spectrometry (MS) to do this, a pervasive technique for gaining a snapshot of a sample, which measures each constituent compound's mass and quantity e.g. for profiling proteins - 'proteomics'. The SBDC and other recently launched centres employ a new strategy for MS called Data-Independent Acquisition (DIA). DIA produces a comprehensive digital record of the sample, unlike previous approaches potentially enabling the identification and quantification of all detectable proteins. Nevertheless, due to biological variations, it is necessary to analyse multiple samples to get a reliable understanding of patient populations. The DIA-based SWATH-MS approach from SCIEX Ltd. has generated considerable clinical interest as it enables reliable and reproducible monitoring of potential biomarkers over thousands of samples. SWATH-MS, like all clinical MS approaches, must digest proteins to smaller peptides for analysis. However, this leads to challenges for both SWATH-MS analysis and the development of clinical assays with MS, when selecting reproducible peptide(s) to base the test upon. We have recently developed a new statistical (Bayesian) modelling approach to assess peptide reproducibility, and a fundamentally novel workflow for biomarker discovery that for the first time performs statistical modelling on the unprocessed data delivering a significant performance increase. The purpose of this project is to exploit the sensitivity of our workflow to deliver a robust, production quality biomarker discovery and validation software platform for routine use by the SBDC and beyond. Moreover, since the SBDC will analyse up to 12,000 samples per annum and is underpinned by rigorous standard operating procedures controlling sample collection, preparation and analysis, it also provides a unique opportunity to collate and understand the biological and experimental variation in protein levels across vast patient populations, in health and disease. We will build an 'atlas' of this variation, stratified across genetic, physiological and other clinical data. To achieve this, we will combine 'big data' computing approaches and web infrastructure. The atlas will enable biomarker verification and peptide characterisation for assay development much earlier in the pipeline than is currently possible, and realise further step-change improvements in the sensitivity and specificity of our discovery platform.