Multi-dimensional omics approach to stratification of patients with low back pain
Coordinator: Massimo ALLEGRI
Project Number: 602736
EC contribution: € 5,998,886.00
Project website: under construction
Pain-OMICS is a multidisciplinary consortium of leading clinical, academic and SME researchers in pain and different omics technologies. Genome-wide association studies identified a number of loci associated with pain, but the level of knowledge about underlying mechanisms of different pain syndromes as well as individual variation in the disease course remains inadequate.
Pain-OMICS will capitalise on its existing high quality clinical, genetic, biochemical and pharmacological data and biological samples on over 5000 well characterised patients with low-back pain (LBP) and controls available to our EU and US clinical partners. We will exploit novel technological approaches made available through the expertise and global leading position of our analytical partners.
These comprise cutting edge genomic, epigenomic, glycomic, and activomic approaches which reflect signal transduction and membrane dynamics. We believe that the inclusion of these complementary analyses will elucidate pathways through which acute LBP fails to resolve and becomes chronic LBP.
In addition, these approaches will reveal pathways and biomarkers of chronic pain through which individual differences affects symptoms and response to therapy. Participation of leading clinics on both sides of the Atlantic will enable replication of all finding in at least three independent large cohorts, as well as in prospective study and a large twin cohort.
A complex systems biology approach will be used to integrate, interrogate and understand this multidimensional dataset in order to achieve the aims of identifying novel diagnostic and prognostic biomarkers as well as new targets for therapeutic intervention. The track record of achievement of our partners coupled to participation of research-intensive SMEs is a strong indication that the ambitious work programme will be achieved and provides a framework for rapid translation of research discoveries into solutions for the benefit of large numbers of patients.