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Abstract Number: 3157

B Cell Profile As a Biomarker of Disease Segmentation and Flare Prognosis in SLE

Chungwen Wei1, Bridget Neary2, Jamie Biear3, Jennifer Barnard3,4, Michelle Petri5, Alex Rosenberg6, Jennifer H. Anolik7 and Ignacio Sanz1, 1Rheumatology, Emory University, Atlanta, GA, 2Emory University, Atlanta, GA, 3Rheumatology, University of Rochester, Rochester, NY, 4Allergy, Immunology and Rheumatology, University of Rochester, Rochester, NY, 5Rheumatology, Johns Hopkins University Hospital, Baltimore, MD, 6University of Rochester, Rochester, NY, 7University of Rochester Medical Center, Rochester, NY

Meeting: 2015 ACR/ARHP Annual Meeting

Date of first publication: September 29, 2015

Keywords: B cells, biomarkers and flow cytometry, SLE

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Session Information

Date: Tuesday, November 10, 2015

Session Title: B cell Biology and Targets in Autoimmune Disease: Novel B cell roles in RA and SLE

Session Type: ACR Concurrent Abstract Session

Session Time: 4:30PM-6:00PM

Background/Purpose: B cell abnormalities in SLE are well-established contributors to disease pathogenesis. Perturbation of B cell homeostasis in SLE is often described separately for each affected B cell subset independent of others. Such univariate approaches fail to reveal how collections of subsets and their relative distribution might contribute to patient segmentation. Hence, we sought a global B cell profiling approach in conjunction with comprehensive clinical parameters to identify distinct B cell signatures in SLE.

Methods: PBMCs were analyzed by flow cytometry from healthy controls (HC) (n=25) and SLE patients (n=135). B cell subsets were identified using the following markers: IgD, CD19, CD27, CD38, CD24, CXCR3, CD21, CD24, CD95, MitoTracker Green, CD10, IgM, CD23. Patients met ACR criteria for the classification of SLE. Disease activity and flares were measured by SELENA-SLEDAI and PGA. All clinical and experimental data were imported into a database where multivariate clustering methods were used to seek natural divisions based on the B cell profiles and relationship to clinical parameters. Flare incidence was followed in a subset of patients with no active disease at baseline (n=52).

Results: High dimensional multiparameter analysis identifies three major clusters of SLE patients based on the B cell profiles. Cluster 1 is characterized by expansions of IgD–CD27+ switched memory (SM), IgD–CD27– double negative (DN) cells and plasmablasts, with a concomitant reduction of IgD+CD27– naïve and transitional (N+T) B cells. Of the expanded SM and DN subsets, cells that exhibit the activated phenotype (CD21–, CD24– or CD95+) are particularly in abundance. A fine subset analysis of the contracted N+T population further reveals that, in contrary to the reduction of resting naïve B cells, there is a pronounced increase in activated naïve B cells (IgD+CD27–MTG+CD24–). This cluster with an activated B cell profile is significantly enriched with patients who present high SLEDAI, multiple autoantibodies and elevated serum IFNa activity, and who are of African descent. In contrast, patients in cluster 3 exhibit a B cell profile that is similar to that of HC and are least likely to present high disease activity. Cluster 2 is exemplified by a greatly expanded N+T subset with moderate expansions of activated SM and DN. To evaluate the application of B cell profiling in predicting future lupus flare, low-SLEDAI, non-flaring patients from each cluster at baseline were followed for flare incidences over a period of two years. Preliminary results show that such patients in clusters 1 and 2 experience shorter time lag to first flare and higher incidence of flares than the counterparts from cluster 3.

Conclusion: A system-wide view of B cell populations provides a means to segment the lupus patients.  Significantly, inactive patients with an activated B cell profile appear to have a higher propensity to develop a flare sooner. Our results provide a proof of concept that, when combined with other informative clinical parameters, B cell profiling offers a systems biology approach to identifying potential biomarkers to estimate risk of disease progression and to initiate early treatment that might halt disease progression or improve long-term outcome.


Disclosure: C. Wei, None; B. Neary, None; J. Biear, None; J. Barnard, None; M. Petri, None; A. Rosenberg, None; J. H. Anolik, None; I. Sanz, None.

To cite this abstract in AMA style:

Wei C, Neary B, Biear J, Barnard J, Petri M, Rosenberg A, Anolik JH, Sanz I. B Cell Profile As a Biomarker of Disease Segmentation and Flare Prognosis in SLE [abstract]. Arthritis Rheumatol. 2015; 67 (suppl 10). https://acrabstracts.org/abstract/b-cell-profile-as-a-biomarker-of-disease-segmentation-and-flare-prognosis-in-sle/. Accessed March 21, 2023.
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