Background/Purpose:

Diagnosis and monitoring of patients with SLE usually requires careful evaluation by a rheumatologist. However, difficulties in accurately quantifing disease and treatment response can make patient care subjective and inconsistent. A quantitative and reproducible test that facilitates SLE differential diagnosis and disease stratification may help decrease time to definitive diagnosis and improve treatment outcomes.

The Immunosignature (IS) technology uses a high-density array of 126,000 unique peptides designed to survey an individual’s antibody repertoire. For autoimmune diseases, differential antibody binding profiles have the potential to provide accurate differential diagnosis, disease activity, and progression measures as well as provide more comprehensive autoantigen profiles.

The objective of this study was to use the IS technology to differentiate and characterize the autoantibody profiles in subjects with SLE from healthy controls and subjects with inflammatory and non-inflammatory diseases that share symptoms with SLE patients.

Methods:

A well-annotated cohort of 371 serum samples was prospectively collected and included patients with SLE (n=75), RA (n=95), SS (n=20), OA (n=24), FM (n=22), other disease (OD) (n=76) and healthy controls (HC) (n=59). Subjects with rheumatological diseases were diagnosed based on ACR criteria. There were no significant differences in gender, race or ethnicity across all groups. Antibody (IgG)-peptide binding with significant intensity differences between contrasting groups was identified by Bonferroni adjusted t-test. Support vector machine classifiers were trained using the most distinguishing peptides. Classifier performance was evaluated by a cross-validation routine that included feature selection, model training and testing. Distinguishing peptides were mapped to putative autoantigens using a BLAST routine modified for array amino acid composition.

Results:

The number of peptides significantly different between SLE contrasts and the cross validated classification performance as measured by the area under the curve (AUC) are shown in the Table. Significant peptides associated with SLE were mapped to known and novel putative autoantigens, including a known immunogenic epitope of SS-B.

Conclusion:

Based on these observations, the IS technology can be used to classify subjects with SLE from healthy controls or subjects with diseases that have common symptoms or are similarly charaterized by underlying immunological dysregulation. The ability to broadly survey the antibody repertoire within and between diseases allows for the identification of putative autoantigens and may provide additional approaches to subsetting patients. These results need verification in cohorts from other sites and validation in blinded studies.

ACR Meeting Abstracts - https://acrabstracts.org/abstract/immunosignature-autoantibody-profiles-provide-mechanistic-insight-into-systemic-lupus-erythematosus-and-differentiation-from-symptomatically-overlapping-diseases/