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294 Immunophenotypic subgroups of SLE defined by autoantibodies, gene expression and flow cytometric analysis
  1. Marta Aguilar Zamora1,
  2. Hui Lu2,
  3. Danynag Li2,
  4. Zoe Betteridge2,
  5. Katie Dutton3,
  6. Md Yuzaiful Md Yusof3,
  7. Antonios Psarras3,
  8. The MASTERPLANS Consortium,
  9. Ian N Bruce4,
  10. Neil McHugh2 and
  11. Edward Vital3
  1. 1Hospital Universitario Dr. Peset, Valencia. Fundación Valenciana de Reumatologia
  2. 2University of Bath
  3. 3University of Leeds
  4. 4University of Manchester


Background SLE may be stratified according to a range of different immune assessments but the relationships between these are less well defined. MASTERPLANS is an MRC-funded consortium that seeks to identify immunophenotypic subgroups of patients that predict response to therapy. Our objective here was to analyse a clinically well-phenotyped patients using a suite of immune assessments and identify inter-relationships between these features as well as subgroups of patients who may differ in response to therapy.

Methods 143 SLE patients were evaluated for clinical phenotype using BILAG-2004, autoantibodies using radioimmunoprecipitation (IP, University of Bath), two interferon scores (IFN-Score-A and IFN-Score-B), flow cytometry for major circulating immune cell subsets, as well as the surface protein expression of tetherin on each subset, a cell-specific assay for IFN response.

Unsupervised hierarchical clustering was used to define autoantibody subgroups. IFN scores (reflected dCT) were compared between the groups using multivariate models. Other variables were compared using Kruskal-Wallis test with pairwise comparisons.

Results Using IP, 141 patients could be divided into five subgroups: U1RNP/Sm+only (n=23), Ro60 +only (n=8), U1RNP/Sm+Ro60+ (n=6), Ro60 +Ro52+La+(n=11), Ro52+ (n=16) and other ANA (n=77).

Antibody subgroups was strongly associated with IFN-Score-A (F=4.39, p=0.001). Expression was lowest for other ANA, intermediate for single antibody groups, and highest with multiple positive antibodies. Multivariate linear regression, including interaction terms between antibody types, revealed that Ro60 and U1RNP/Sm were the independent predictors of IFN-Score-A level (p=0.051 and 0.009 respectively). There was no association between autoantibody status and IFN-Score-B (F=0.973, p=0.438).

In flow cytometry, the U1RNP/Sm group was notable for significantly lower numbers of CD4-T-cells and memory-B-cells. Memory -B-cells were also lower in antibody-positive groups compared to other ANA. Tetherin expression was increased in antibody positive groups, but to a similar extent on most cell subsets. Memory B cell tetherin was significantly higher in the groups with multiple positive antibodies.

U1RNP/Sm+was associated with renal involvement (p=0.004). Mucocutaneous involvement was greater in the Ro60 +Ro52+La+ group (p=0.037).

Abstract 294 Table 1

Multivariate analysis of antibody status and IFN Score A

Conclusions This cohort revealed relationships between immune features. U1RNP/Sm antibody was notable for defining a group of patients with a cluster of immune abnormalities, including the greatest elevation of IFN activity, greater abnormalities on flow cytometry and clinical renal involvement. This was independent to the IFN-Score-B high status that predicts better clinical response to rituximab (presented elsewhere at this conference). Future work in MASTERPLANS will investigate the significance of these subgroups for response to therapy.

Funding Source(s): Medical Research Council, National Institute for Health Research

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