HELICAL project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813545
Authors: PATTARONE Gisela1,2, COQ Matthieu1, BINDER Harald2, ZÖLLER Daniela2, LITTLE Mark A3, FIRAT Hüseyin1.
1 Firalis SAS, Biomarker R&D, Huningue, France 2 Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Germany 3 Trinity Translational Medicine Institute, Trinity College Dublin
Background/ Objectives: Vasculitis is a heterogeneous group of diseases that affect blood vessels of different sizes. Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) had an estimated prevalence of 200–400 cases per million people. Symptoms of active disease and flares are difficult to distinguish from permanent damage produced by previous episodes. The imperfect sensitivity of current diagnostic standards highlights the needs for new biomarkers for monitoring disease activity, prediction of flare occurrence, distinguishing the drug-induced and drug-free remission states.
Objective: Discriminate the healthy population from patients with active vasculitis using BIOPRED targeted mRNA profiling of circulating immune cells. This targeted sequencing tool allows the precise quantification of 2155 mRNA targets associated with auto-, immune and inflammatory pathways with excellent analytical performance thanks to the elimination of the RNA extraction step.
Methods:
Whole blood samples collected in Paxgene® RNA tubes from patients with vasculitis, other diseases and heathy subjects were processed on the HTG EdgeSeq platform using the BIOPRED panel. Clinical, laboratory and BIOPRED data from 118 patients with AAV were compared with data from other cohorts comprising 828 patients suffering from various inflammatory-autoimmune disorders, and healthy volunteers. Differentially expressed mRNAs were screened in three subgroups, and Vasculitis signatures were identified using different multivariate approaches. The area under the receiver operating characteristic (ROC) curves (AUC) and associated confusion matrix were used as a summary of overall marker performance. Clinical covariates included creatinine level, medication exposure anti-MPO/PR3 antibody status.
Results: A population characteristics description was performed considering a timeline constructed based on AAV active and remission status. Different variables were determined based on the input data to make an integrated analysis obtaining the identification of biomarkers for the diagnosis of AAV. An algorithm was developed linking the laboratory values, panel of mRNA biomarkers and clinical data to predict performance of their different combinations to be used as a diagnosis of vasculitis. Finally, functional enrichment of selected mRNAs was conducted to explore the potential biological pathways of the final mRNA set.
Conclusions:
Integrative analysis of patient data with mRNA-based blood profiling using machine learning tools contribute to the characterization of vasculitis patients and improved definition of clinical remission in patients with AAV. Further studies are needed to validate and improve our precision medicine tool as a predictor of vasculitis relapse risk based on the BIOPRED mRNA candidates.