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Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Background Sepsis is a life-threatening organ dysfunction. A fast diagnosis is crucial for patient management. Proteins that are synthesized during the inflammatory response can be used as biomarkers, helping in a rapid clinical assessment or an early diagnosis of infection. The aim of this study was to identify biomarkers of inflammation for the diagnosis and prognosis of infection in patients with suspected sepsis. Methods In total 406 episodes were included in a prospective cohort study. Plasma was collected from all patients with suspected sepsis, for whom blood cultures were drawn, in the emergency department (ED), the department of infectious diseases, or the haemodialysis unit on the first day of a new episode. Samples were analysed using a 92-plex proteomic panel based on a proximity extension assay with oligonucleotide-labelled antibody probe pairs (OLink, Uppsala, Sweden). Supervised and unsupervised differential expression analyses and pathway enrichment analyses were performed to search for inflammatory proteins that were different between patients with viral or bacterial sepsis and between patients with worse or less severe outcome. Results Supervised differential expression analysis revealed 21 proteins that were significantly lower in circulation of patients with viral infections compared to patients with bacterial infections. More strongly, higher expression levels were observed for 38 proteins in patients with high SOFA scores (> 4), and for 21 proteins in patients with worse outcome. These proteins are mostly involved in pathways known to be activated early in the inflammatory response. Unsupervised, hierarchical clustering confirmed that inflammatory response was more strongly related to disease severity than to aetiology. Conclusion Several differentially expressed inflammatory proteins were identified that could be used as biomarkers for sepsis. These proteins are mostly related to disease severity. Within the setting of an emergency department, they could be used for outcome prediction, patient monitoring, and directing diagnostics. Trail registration number: clinicaltrial.gov identifier NCT03841162. This study is part of the FAPIC project and has received funding from the European Union’s Horizon 2020 research and innovation program under GA 634137. The human biological material used in this publication was stored in and released from the University Biobank Limburg (UBiLim) [16]. This study is part of the Limburg Clinical Research Center (LCRC) UHasselt-ZOL-Jessa, supported by the foundation Limburg Sterk Merk (LSM), Hasselt University, Ziekenhuis Oost-Limburg and Jessa Hospital. This research has been presented as an e-poster during the European Conference of Clinical Microbiology and Infectious Diseases (ECCMID), July 2021.

The Population Reference Interval (PRI) refers to the range of outcomes that are expected in a healthy population for a clinical or a diagnostic measurement. It is widely used in daily clinical practice and is essential for assisting clinical decision-making in diagnostics and treatment. In this manuscript, we start from the observation that each healthy individual has its own range for a given variable, depending on personal biological traits. This Individual Reference Interval (IRI) can be calculated and be utilised in clinical practice, in combination with the PRI for improved decision making. Nonparametric estimation of IRIs would require quite long time series. To circumvent this problem, we propose methods based on quantile models in combination with penalised parameter estimation methods that allow for information-sharing among the subjects. Our approach considers the calculation of an IRI as a prediction problem rather than an estimation problem. We perform a simulation study designed to benchmark the methods under different assumptions. From the simulation study we conclude that the new methods are robust and provide empirical coverages close to the nominal level. Finally, we evaluate the methods on real-life data consisting of eleven clinical tests and metabolomics measurements from the VITO IAM Frontier study.