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.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/b-cell-profile-as-a-biomarker-of-disease-segmentation-and-flare-prognosis-in-sle/