Autoantibody signatures can be used to identify disease subtypes. This can lead to a better understanding of the underlying pathogenic mechanisms, informing drug discovery and patient treatment decisions.

Predictive autoantibody biomarker signatures can be developed to identify drug responders and non-responders, and biologically relevant sub-cohorts. These insights enable intra-disease stratification, driving more efficient and economical study designs and clinical trial patient enrichment.

Having the ability to interrogate many autoantibody targets in a single assay can help accelerate the identification and validation of signatures with high predictive value.

Accelerating discovery of autoantibody signatures

Sengenics can help, with solutions to support discovery of biomarker signatures. Our high-density protein microarray assays enable highly specific and reproducible detection of disease-relevant autoantibodies directly from patient serum.

Related Study

Patient Stratification in Systemic Lupus Erythematosus (SLE)

Heatmap of unsupervised hierarchical clustering of 79 validated autoantibodies in SLE patients from discovery (n = 186) and validation  (n = 91) cohorts. Autoantibodies cluster into four distinct groups. These clusters plausibly represent distinct molecular sub-types of SLE, with different disease trajectories and different responses to treatment.

Source: M.J. Lewis, et al., Autoantibodies targeting TLR and SMAD pathways define new subgroups in systemic lupus erythematosus, Journal of Autoimmunity (2018).

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