LLDAS is an attainable treat-to-target goal in childhood-onset SLE

Objectives To study whether clinical remission (CR) and Low Lupus Disease Activity State (LLDAS) are achievable goals in childhood-onset SLE. Methods Data on medication use and disease activity were prospectively collected. LLDAS was defined as Safety of Estrogen in Lupus Erythematosus National Assesment-SLE disease Activity Index (SELENA-SLEDAI) ≤4 with zero scores for renal, Central Nervous System (CNS), serositis, vasculitis and constitutional components, no increase in any SLEDAI component since the previous visit, PGA ≤1, and prednisone dose ≤7.5 mg/day. CR on treatment (Tx) was defined as a Physician Global Assessment 50% of time. 52.9% children achieved CR on Tx, and only 21.6% children achieved CR off Tx. Conclusions LLDAS is an attainable treat-to-target goal in contrast to CR on and off Tx. Even more, LLDAS can be reached with limited use of corticosteroids with early introduction of immunosuppressives

Serum Ifnα2 Levels Are Associated with Disease Activity and Outperform Ifn-I Gene Signature in a Longitudinal Childhood-Onset Sle Cohort

Objective: To study the association of serum IFNα2 levels measured by ultrasensitive single-molecule array (Simoa) and interferon type I gene signature (IGS) with disease activity and determine whether these assays can mark disease activity states in a longitudinal cohort of childhood-onset SLE patients. Methods: Serum IFNα2 levels were measured in 338 samples from 48 cSLE patients and 67 healthy controls using IFNα Simoa assay. Five gene IGS was measured by RT-PCR in paired whole blood samples. Disease activity was measured by clinical SELENA-SLEDAI and BILAG-2004. Low disease activity was defined by Low Lupus Disease Activity State (LLDAS) and flares were characterized by SELENA-SLEDAI flare index. Analysis was performed using linear mixed models. Results: A clear positive correlation was present between serum IFNα2 levels and the IGS (r = 0.78, p < 0.0001). Serum IFNα2 levels and IGS showed the same significant negative trend in the first three years after diagnosis. In this timeframe, mean baseline serum IFNα2 levels decreased with 55.1% (Δ 201 fg/mL, p < 0.001) to a mean value of 164 fg/mL, which was below the calculated threshold of 219.4 fg/mL, which discriminated between patients and healthy controls. In the linear mixed model, serum IFNα2 levels were significantly associated with both cSELENA-SLEDAI and BILAG-2004, while the IGS did not show this association. Both IFN-I assays were able to characterize LLDAS and disease flare in ROC analysis. Conclusions: Serum IFNα2 levels measured by Simoa technology are associated with disease activity scores and characterize disease activity states in cSLE

Targeted multiomics in childhood-onset SLE reveal distinct biological phenotypes associated with disease activity: results from an explorative study

Objective To 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.Methods Targeted 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.Results Three 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.Conclusion Using 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

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES: Clinical phenotyping and predicting treatment responses in 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: Real-time PCR of multiple genes from the IFN M1.2, IFN 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 associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (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 were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES: Clinical phenotyping and predicting treatment responses in 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: Real-time PCR of multiple genes from the IFN M1.2, IFN 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 associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (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 were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES: Clinical phenotyping and predicting treatment responses in 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: Real-time PCR of multiple genes from the IFN M1.2, IFN 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 associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (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 were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices