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

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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.