An immunome perturbation is present in juvenile idiopathic arthritis patients who are in remission and will relapse upon anti-TNFα withdrawal

Abstract

ObjectivesBiologics treatment with anti-TNFα is efficacious in juvenile idiopathic arthritic (JIA) patients. Despite displaying clinical inactivity during treatment, many patients will flare upon cessation of therapy. The inability to definitively discriminate patients who will relapse or continue to remain in remission after therapy withdrawal is currently a major unmet medical need. CD4 T cells have been implicated in active disease, yet how they contribute to disease persistence despite treatment is unknown.MethodsWe interrogated the circulatory reservoir of CD4+ immune subsets at the single cell resolution with mass cytometry (CyToF) of JIA patients (n=20) who displayed continuous clinical inactivity for at least 6 months with anti-TNFα, and were subsequently withdrawn from therapy for 8 months, and scored as relapse or remission. These patients were examined prior to therapy withdrawal for putative subsets that could discriminate relapse from remission. We verified on a separate JIA cohort (n=16), the continued dysregulation of these circulatory subsets 8 months into therapy withdrawal. The immunological transcriptomic signature of CD4 memory in relapse/remission patients was examined with Nanostring.ResultsAn inflammatory memory subset of CD3+CD4+CD45RA−TNFα+ T cells deficient in immune checkpoints (PD1−CD152−) was present in relapse patients prior to therapy withdrawal. Transcriptomic profiling reveals divergence between relapse and remission patients in disease centric pathways involving (a) TCR activation, (b) apoptosis, (c) TNFα, (d) NF-kB and (e) MAPK signalling.ConclusionsA unique discriminatory immunomic and transcriptomic signature is associated with relapse patients and may explain how relapse occurs.

CyNET- a network analysis framework for high dimensionality, system level analyses of the functional Immunome

“…Newer visualization methods (1-3) allowed a holistic understanding of the immune system. These developments are particularly relevant for clinical and translational research, and have sparked new approaches for data-driven theragnostics tools for precision medicine (4)(5)(6) and clinical development (7,8) However, approaches that consider the relationship among the components of the immune response at system levels are still lacking. In real in-vivo conditions, various immune cell subsets interact and influence each other's functions.…”