PROJECT SUMMARY/ABSTRACT The goal of this project is to investigate the upper airway molecular and clinical predictors of systemic relapse in antineutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis (AAV). AAV is a multi-organ rheumatic disease with prominent upper airway involvement that often precedes disease in other organs. Relapses are common and difficult to predict, resulting in long-term use of immunosuppressive therapies. There is a critical need to understand the events leading up to relapse which may provide new insights into early processes which incite or amplify autoimmune responses as well as facilitate development of prognostic biomarkers which can inform treatment decision (e.g., escalate or de-escalate systemic immunosuppressive therapy). This proposal is based on preliminary data indicating that, in patients with AAV who are in clinical remission, nasal gene expression and bacterial profiling identifies epithelial and microbial alterations which are detectable 6 months prior to systemic disease relapse, even in patients without sinonasal symptoms at relapse and regardless of immunosuppressive therapy. Similarly, when examining upper airway disease using a patient-reported outcome measure, the SinoNasal Outcome Test-22 (SNOT-22), greater sinonasal symptom burden was associated with a 3-fold higher risk of relapse within 2 years. The central hypothesis is that, among patients with quiescent AAV, there exists a relapse-prone disease state that is detectable using a combination of molecular profiling, clinical, and patient-reported measures. This project will perform a longitudinal cohort study by leveraging an existing upper airway-dedicated biorepository of AAV (banked nasal samples from 847 visits in 191 patients) as well as prospectively recruit 150 new patients at a major vasculitis-focused clinical and research center. Also, longitudinal collection of patient-reported data will be electronically captured in 300 patients through the Vasculitis Patient-Powered Research Network, a national research network with over 2,000 highly engaged patients with AAV. The specific aims are to: (1) define molecular changes in the upper airway that precede relapse of AAV using high-throughput sequencing, and (2) define the patient-reported upper airway symptoms preceding relapse of AAV and the added value of clinical and molecular data to identify the relapse-prone subgroup. We will evaluate the contribution of each data type as well as combine data using machine learning approaches to train and validate a prediction model of relapse within 2 years. Using integrative multi-omic analyses, we will identify biologically distinct subtypes of patients who are in clinical remission. The long-term translational objective is to use the biologic insights gained in this study to create reliable prognostic biomarkers which inform treatment decision-making (e.g., escalate/maintain vs de-escalate immunosuppressive therapy) as well as gain mecha...