Advancement of clinical proteomics for systems medicine

Disease initiation and progression is a highly dynamic process. To pinpoint alterations resulting in deregulation, systems medicine approaches combining high-quality data generation with the development of mechanistic mathematical models are required. As biological functionality is executed by proteins and their abundance critically determine cell context specific information processing, a quantitative proteomics approach suitable for the generation of quantitative data for mathematical modelling and sufficiently robust for clinical translation is required. A critical basis for the development of a robust clinically-applicable pipeline is the collection of patient samples according to standard operating procedures (SOPs) (Wessels et al., Transl Lung Cancer Research 2020; Gegner et al., Front Mol Biosci 2022). Key steps of our clinical proteomics pipeline have been the optimization of the lysis of cells, extracellular matrix, tissue and blood plasma, semi-automated sample processing and the inclusion of quality standards. To ensure standardized data processing and thereby secure reproducibility of results our division developed the bioinformatic data analysis tool MSPypeline (Heming et al., Bioinform Adv 2022). With these developments in place, we could already demonstrate that the generated data substantially improves calibration of our mechanism-based dynamic pathway models that we are developing for TGFβ, HGF, EGF, IL-6, TNFα, IFNα and Epo-signal transduction.