Abstract
Adalimumab (ADA) is a systemic biological treatment option approved for the treatment of noninfectious uveitis (NIU); however, up to 40% of patients do not respond to the drug, either in a primary or secondary manner. Here, we evaluated the proteomic profile of patients with NIU who fail to ADA to identify proteins implicated in intraocular inflammation, as well as potential biomarkers for treatment response and novel therapeutic targets. Cross-sectional observational study of patients with NIU under ADA treatment for six or more months. Tears were collected with microcapillary tubes and protein analyzed by data-independent acquisition/sequential window acquisition of all theoretical mass spectra. Differentially expressed proteins (DEPs) were defined based on the fold change between their expression in nonresponders (NR) and responders (R). Protein network and gene ontology analysis were performed. The χ2 test for trend and receiver operating characteristic (ROC) curves were used to evaluate potential biomarkers of treatment response. Twenty-nine DEPs, 14 upregulated and 15 downregulated, were detected in NR. These proteins were mainly related to enhanced neutrophil effector functions and redox imbalance. ROC analysis identified defensin-1,3 (DEF-1,3), biotinidase, and ATP-binding cassette transporter A1 as potential biomarkers for treatment response. This is the first study on a clinical cohort of patients with noninfectious uveitis that identifies tear proteins related to neutrophil hyperactivation as drivers of the persistent intraocular inflammation observed in NR to ADA and provides evidence that targeting interleukin 6, Janus kinases, or the complement cascade could be potential alternative therapeutic strategies in these patients. Our results indicate the potential of high-throughput proteomics to provide insights into the underlying pathological mechanisms of persistent intraocular inflammation observed in patients who do not adequately respond to anti-TNF treatment and the value of tear proteomics as a tool for personalized medicine.
Published Version
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