Translational
Abstract Submission
Marta Aguilar Zamora
Research Fellow
Hospital Universitario Dr. Peset, Valencia. Fundación Valenciana de Reumatologia
Hui Lu
Research Assistant
University of Bath
Danynag Li
Research Assistant
University of Bath
Zoe Betteridge, PhD
Research Fellow
University of Bath
Katie Dutton
Research Fellow
University of Leeds
Md Yuzaiful Md Yusof, MRCP, PhD
NIHR Clinical Lecturer
University of Leeds
Antonios Psarras, PhD
Post Doctoral Researcher
University of Leeds
Ian Bruce, PhD
Professor of Rheumatology
University of Manchester
Neil McHugh
Professor of Rheumatology
University of Bath
Edward Vital, MRCP, PhD
Associate Professor and Honorary Consultant
University of Leeds
University of Leeds
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).
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.