PM.20.020 – Defining the immunological landscape to develop personalized medicine strategies across disease & age
The striking patient-to-patient heterogeneity of chronic immune-mediated inflammatory disorders (IMIDs) such as Crohn’s disease, ulcerative colitis, rheumatoid arthritis, and atopic dermatitis, obstructs wide application of single disease and population-based study outcomes. As a result, the much-required improvement in clinical care for patients with complex needs comes to a stand-still, leaving us with the question of how to allocate the right therapeutic to the right patient. The exponential increase of novel biologicals entering the market, makes the development of cost-effective personalized medicine strategies more urgent than ever. What is missing, is a definitive immunological landscape of inflammatory diseases and their treatable traits. This complex task demands a novel scale of disease-overarching thinking by linking disciplines as well as multiple data-layers. Here, we propose to address this challenge by clinical and biological assessment of immunological heterogeneity in clinically well-defined IMID patient cohorts, within a multidisciplinary nation-wide consortium with computational, clinical, and translational immunology experts and patients’ representatives. With innovative computational analyses we will be able to bridge samples and data from various patient and control cohorts and across ages. This unique strategy allows to combine multiple data layers to capture a more complete picture of the disease spectra. Key objective of our consortium is to deliver a strategy that enables integration of data across various experimental profiling technologies with computational frameworks that reduce complex biological fingerprints into easy-to-scale tools for patient stratification, reclassification of disease, and drug (re)purposing. We will implement the most relevant biomarkers into a diagnostic pipeline, exploiting our recently established tools for minimally invasive at-home monitoring in dried blood spots. All data will be made accessible using a FAIR data approach. Together, this program enables prompt, cost-effective, dynamic anticipation in the management of inflammatory conditions, as well as the development of prevention strategies by at-home monitoring of at-risk groups.
at-home diagnostics, biomarkers, disease-overarching, heterogeneity, inflammatory disease, machine learning, multi-omics
|Organisation||UMC Utrecht (UMCU)|
|Name||Dr. F. (Femke) van Wijk|