AB1372 Towards reforming the taxonomy of human disease: the precisesads cross sectional study

Objectives The PRECISESADS project aims at using OMICs, and bioinformatics to identify new classifications for systemic autoimmune diseases (SADs) known to share common pathophysiological mechanisms in view of personalised treatments. Multi OMICs parameters collected in addition to routine clinical data in a cross-sectional study involving patients suffering from systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjogren’s syndrome (Sjs), rheumatoid arthritis (RA), primary antiphospholipid syndrome (PAPs), mixed connective tissue disease (MCTD), undifferentiated connective tissue disease (UCTD) and healthy controls (HC) will be analysed to identify clinically relevant clusters. Methods A European multi centre, non-randomised, cross-sectional clinical study was conducted in 18 sites and 9 countries. Collection of OMIC data including genetic, epigenomic, transcriptomic (from peripheral blood and from isolated cells), flow cytometry, metabolomics and proteomic in plasma and urine, exosome analysis and classical serology (antibodies and autoantibodies) was organised. Novel and innovative methodologies including fine flow cytometry were conducted. Quality procedures were established to ensure standardisation of samples collection, processing, transportation and storage. Techniques were validated to ensure reproducibility of analyses. Unsupervised bioinformatics and biostatistics approaches will be applied. Results Recruitment started in December 2014 and ended in October 2017. A total of 2656 participants were recruited: 377 RA, 470 SLE, 402 SSc, 385 SjS, 99 MTCD, 106 PAPs, 166 UCTD patients and 651 HCs. Median age was between 46 and 59 years and was consistent with each disease onset peak. 97% of the population was Caucasian. Most of the patients were treated with standard of care therapies and less than 10% were on biologics. OMICs and bioinformatics analyses are on-going. Conclusions We have established one of the largest collaborative multi-OMICs studies from patients with SADs. The most important challenge is now the integration of all these novel data to support hypothesis-free, machine learning-led analytical protocols. It is expected that the integration of data from affected patients, in comparison with well-matched controls, will provide new biomarker-led descriptions of clusters of potentially etiologically distinct disease entities. Acknowledgements This work has received support from the EU/EFPIA/Innovative Medicines Initiative Joint Undertaking PRECISESADS grant n° 1 15 565 Disclosure of Interest None declared