Multiple manifestations of genetic and non-genetic factors in multiple sclerosis disentangled with a multi-omics approach to accelerate personalised medicine
The complex interactions between genetic and non-genetic factors produce heterogeneities in patients as reflected in the diversity of pathophysiology, clinical manifestations, response to therapies, disease development and progression. Yet, the full potential of personalized medicine entails biomarker-guided delivery of efficient therapies in stratified patient populations.
 
MultipleMS will therefore develop, validate, and exploit methods for patient stratification in Multiple Sclerosis, a chronic inflammatory disease and a leading causes of non-traumatic disability in young adults, with an estimated cost of €37 000 per patient per year over a duration of 30 years. Here we benefit from several large clinical cohorts with multiple data types, including genetic and lifestyle information. This in combination with publically available multi-omics maps enables us to identify biomarkers of the clinical course and the response to existing therapies in a real-world setting, and to gain in-depth knowledge of distinct pathogenic pathways setting the stage for development of new interventions.

To create strategic global synergies, MultipleMS includes 21 partners and covers not only the necessary clinical, biological, and computational expertise, but also includes six industry partners ensuring dissemination and exploitation of the methods and clinical decision support system. Moreover, the pharmaceutical industry partners provide expertise to ensure optimal selection and validation of clinically relevant biomarkers and new targets. Our conceptual personalized approach can readily be adapted to other immune-mediated diseases with a complex gene-lifestyle background and broad clinical spectrum with heterogeneity in treatment response.
 
MultipleMS therefore goes significantly beyond current state-of-the-art thereby broadly affecting European policies, healthcare systems, innovation in translating big data and basic research into evidence-based personalized clinical applications.