Objectives Clinical phenotyping and predicting treatment responses in Systemic Lupus Erythematosus (SLE) patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice. Methods RT-PCR of multiple genes from the Interferon M1.2, Interferon M5.12, neutrophil (NPh) and plasma cell (PLC) modules followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood onset SLE cohorts (n = 101 and n = 34, respectively) and associated with clinical features. Disease activity was measured using SELENA-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed 1) all-signatures-low, 2) only IFN high (M1.2 and/or M5.12) and 3) high NPh and/or PLC. Results All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort. Conclusions The identified gene signatures are associated with disease activity and suitable tools to stratify SLE patients into groups with similar activated immune pathways that may guide future treatment choices.

Clinical efficacy of intravenous immunoglobulin treatment (IVIg) is related to its pharmacokinetic (PK) profile. Its usual evaluation, by measuring serum total IgG levels, is imprecise, because IVIg cannot be distinguished from endogenous IgG. We developed ELISAs to specifically monitor the PK of IVIg using the polymorphic determinants G1m(a), G1m(x), and G1m(f). The specificity of the IgG1 allotype assays was sufficient to determine IVIg concentrations as low as 0.1 mg/mL in sera from individuals not expressing the respective markers. IVIg was quantified in posttreatment serum from patients with Guillain-Barré syndrome (GBS) by measuring IgG1 allotypes not expressed endogenously. After serotyping, 27/28 GBS patients were found eligible for IVIg monitoring using one or two genetic markers. In 17 cases, IVIg levels could be determined by both anti-G1m(a) and anti-G1m(x) measurement, showing significant correlation. Longitudinal monitoring of IVIg PK in seven GBS patients showed potential differences in clearance of total IgG versus IVIg-derived IgG, highlighting that total IgG measurements may not accurately reflect IVIg PK. To summarize, anti-IgG1 allotype assays can discriminate between endogenous IgG and therapeutic polyclonal IgG. These assays will be an important tool to better understand the variability in IVIg PK and treatment response of all patients treated with IVIg.

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ObjectiveTo combine targeted transcriptomic and proteomic data in an unsupervised hierarchical clustering method to stratify patients with childhood-onset SLE (cSLE) into similar biological phenotypes, and study the immunological cellular landscape that characterises the clusters.MethodsTargeted whole blood gene expression and serum cytokines were determined in patients with cSLE, preselected on disease activity state (at diagnosis, Low Lupus Disease Activity State (LLDAS), flare). Unsupervised hierarchical clustering, agnostic to disease characteristics, was used to identify clusters with distinct biological phenotypes. Disease activity was scored by clinical SELENA-SLEDAI (Safety of Estrogens in Systemic Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index). High-dimensional 40-colour flow cytometry was used to identify immune cell subsets.ResultsThree unique clusters were identified, each characterised by a set of differentially expressed genes and cytokines, and by disease activity state: cluster 1 contained primarily patients in LLDAS, cluster 2 contained mainly treatment-naïve patients at diagnosis and cluster 3 contained a mixed group of patients, namely in LLDAS, at diagnosis and disease flare. The biological phenotypes did not reflect previous organ system involvement and over time, patients could move from one cluster to another. Healthy controls clustered together in cluster 1. Specific immune cell subsets, including CD11c+ B cells, conventional dendritic cells, plasmablasts and early effector CD4+ T cells, differed between the clusters.ConclusionUsing a targeted multiomic approach, we clustered patients into distinct biological phenotypes that are related to disease activity state but not to organ system involvement. This supports a new concept where choice of treatment and tapering strategies are not solely based on clinical phenotype but includes measuring novel biological parameters.