Pos1412 multiparametric comparison of the adult-onset and childhood-onset systemic lupus erythematosus (sle) immunomes reveals multiple derangements

Background Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease. Disease presentation is heterogeneous and characterized by alternating flares and remissions. Recent treatment developments have been made with FDA approval of anifrolumab for adult SLE in 2021 [1] but there are many patients with inadequate therapeutic response. Furthermore, with lack of specific molecular therapeutic targets, current therapy relies mainly on general immunosuppression. This can adversely affect bone health and impede growth of childhood-onset SLE patients. Treatment for paediatric patients is extrapolated from adult studies with the assumption that underlying immunopathogenic mechanisms are similar. However, children are not little adults and a better understanding of underlying disease mechanisms using a holistic and multiparametric approach is needed to improve current theragnostics. Objectives We aim to use a high-dimensional approach to characterise and compare immune signatures of adult-onset and childhood-onset SLE patients, as well as compare their immunological profiles with age-matched healthy controls. In the process, we hope to study the roles of B and T cells in SLE to better understand the adaptive immune response. Methods Peripheral blood mononuclear cells from adult-onset and childhood-onset SLE patients underwent mass cytometry. The adult-onset SLE group consists of 26 SLE (timepoints: 40, median age: 40 years) and 23 age-matched healthy subjects. The childhood-onset SLE group consists of 30 SLE (53, 14 years) and 17 age-matched healthy subjects. Data was analysed and visualized using the Extended Polydimensional Immunome Characterisation (EPIC) machine learning platform [2] . Normalization and FlowSOM clustering were performed with 43 functionally and phenotypically important immune markers. Mann Whitney U test identified significantly different cluster frequencies. Results Unsupervised analysis revealed multiple significant differences (p<0.05) between the adult-onset and childhood-onset SLE immunomes. Of note, an activated CD11c + Tbet + CD21 - B cell subset was significantly enriched in childhood-onset SLE (median: 0.42%, interquartile range 0.16-0.72% of CD45 + PBMCs) versus adult-onset SLE (0.15%; 0.09-0.31%; p<0.001). These age-associated B cells (ABCs) increase in prevalence with age and presence of autoimmune disease. A significant increase in childhood-onset SLE may offer us insights into the differences with adult-onset SLE; the former group tends to have more active disease over time. Transitional B cells (CD24 + CD38 + ) were also significantly increased in childhood-onset SLE (0.91%; 0.41-1.84%) compared to their adult counterparts (0.3%; 0.043-0.65%; p<0.001). In the T cell compartment, an activated CD4 + IL21 + subset was expanded in childhood-onset SLE (0.41%; 0.23-0.68%) compared to adult-onset SLE (0.19%; 0.1-0.38%; p<0.001). IL21 plays a key role in B cell activation and this cell subset is therefore of mechanistic interest. Another CD8 + CD45RA + BAFF + T cell subset was enriched in adult-onset (1.95%; 1.24-2.86%) versus childhood-onset SLE (0.88%; 0.45-1.37%; p<0.001). B-cell activating factor (BAFF) supports autoreactive B cell survival in autoimmune disease and an anti-BAFF drug (belimumab) is FDA-approved for SLE. Despite increasing use of belimumab, not all patients respond equally, so BAFF inhibition alone may not adequately alter disease activity. Conclusion With a multiparametric unbiased approach comparing adult and paediatric SLE patients, we identified cell subsets that may be of immunopathogenic importance. Studying these cell interactions in further detail may identify pathological pathways to facilitate improvements in SLE theragnostics. References [1]Furie R et al . Anifrolumab, an Anti-Interferon-α Receptor Monoclonal Antibody, in Moderate-to-Severe SLE. Arth. & Rheum. 2017; 69 (2): 376-86. [2]Yeo JG et al . The EPIC web-based reference and discovery tool for cytometry data. Nat. Biotech. 2020; 38 (6): 679-84. Acknowledgements This research is supported by the National Research Foundation (NMRC) Singapore under its NMRC Centre Grant Programme (MOH-000988) and is administered by the Ministry of Health, Singapore. Other NMRC grants (CIRG21nov-0031 and CSAINV22jul-0008) are also gratefully acknowledged. Disclosure of Interests None Declared.