It is widely acknowledged that we need better biomarkers for management of patients with systemic lupus erythematosus (SLE). While many have been proposed, few new markers have yet made it into clinical practice due to lack of robust validation studies. Historically, antibody titres, complement proteins, immunoglobulin titres and acute phase markers are widely used in clinical practice, although the evidence base and utility of these is also limited. The need for better biomarkers was highlighted in the recent EULAR guidelines for the management of SLE and for treating to target and it is worth considering these guidelines for questions that biomarkers should answer, and appropriate endpoints for clinical validation.1 In the EULAR guidelines for management of SLE a research agenda emphasised the need to predict susceptibility to develop SLE, involvement of particular organ systems over others, and response to specific therapeutic agents over others.1 Several of the 2014 EULAR treat to target guidelines suggest the need for biomarkers too.2 For example: prevention of flares is an objective that would be easier to meet if these could be predicted. Glucocorticoid tapering or withdrawal is recommended, but this may be difficult if we cannot predict which patients would flare. Finally, these guidelines state that treatment should not be escalated based on solely on persistent serological activity, highlighting the weakness of routinely used biomarkers. In clinical validation studies, like outcome measures, biomarkers must be shown to demonstrate truth (e.g. they measure what they say they measure), discrimination (e.g. classifying patients correctly and predicting prognosis), and feasibility (e.g. use of standard samples types, transportation and reliable assays in clinically accredited laboratories). Additionally for biomarkers, there may be issues of pre-analytic validation. Some of the most promising biomarkers in the field of SLE measure type I interferon (IFN) activity. type I IFN (i.e. IFN alpha, beta, kappa, epsilon and omega) are known to be important in lupus based on genetic susceptibility data. They are difficult to measure directly in serum due to binding to the abundant IFNAR receptor, and non-circulating sources. Instead, most assays measure cellular responses. The best validated of these measure expression of a set of genes known to respond to Type I IFN – an ‘interferon signature’. Interferons are a complex system with many different ligands and responder cells. Recent data have shown that IFN stimulated genes cluster into subgroups with different clinical significance, rather than a single ‘interferon signature’. This may improve their clinical utility. Gene expression assays for interferon have helped to stratify therapies that target interferon, and other therapeutic targets. These assays also predict clinical flares, glucocorticoid use. More recently, it has been shown that interferon scores can predict onset of SLE.3 In this latter work, the separation of interferon-stimulated genes into subgroups was crucial. The measurement of IFN-I status using whole blood IFN stimulated gene (ISG) expression has two key weaknesses in interpreting pathogenic processes. First, changes in expression may reflect expansion or contraction of certain circulating leukocyte populations that differ in their level of ISG expression.4 This characteristically occurs in inflammatory diseases. In the case of SLE, lymphopenia is almost universally seen.4 So any difference in whole blood gene expression may not necessarily indicate a change in production or exposure to IFN-I. Second, analysing whole blood ISG expression does not allow detection of key pathogenic processes among the noise of other, less relevant, effects of IFN-I on biology. For example, B cells are a key mediator in SLE. In these respects, flow cytometric biomarkers, such as memory B cell tetherin, may be advantageous, as they indicate the response to interferon in a particular cell type. Another important area of biomarkers that also uses flow cytometry is monitoring of B cell numbers after rituximab therapy. It was initially thought that rituximab induced complete B cell depletion, which left the explanation for poor clinical responses unclear, and left no biomarker to guide retreatment decisions. These assumptions were reversed by assays optimised to reliably measure plasmablasts in a routine clinical context as well as other B cell subsets in lower numbers. Plasmablasts have low expression of CD20 and are not directly killed by rituximab. They have a short half-life in the circulation, so their continued presence in the absence of other B cell subsets after rituximab may indicate ongoing B cell activity in other tissues. Such ‘highly sensitive flow cytometry’ studies demonstrated first that B lineage cell depletion was often incomplete in non-responders, which has ultimately led to trials of more intensive B cell depletion therapies. Further, plasmablast repopulation has been shown to be a predictor of impending relapse after rituximab in several studies. Other biomarkers with evidence of clinical validation include cell-bound complement, which may offer advantages of soluble complement product assays, other gene expression signatures, such as plasmablast and neutrophil signatures, and serum proteins, some of which may reflect interferon status. The challenge in future years will be to harmonise measurement of these biologic parameters and implement into clinical practice. Learning objectives Describe the need for better biomarkers in SLE Explain how better understanding of IFN and SLE disease expression will improve patient outcomes Discuss the potential challenges of measuring biologic parameters in clinical practice References Fanouriakis A, Kostopoulou M, Alunno A, et al. 2019 update of the EULAR recommendations for the management of systemic lupus erythematosus. Ann Rheum Dis 2019;78(6):736–45. van Vollenhoven RF, Mosca M, Bertsias G, et al. Treat-to-target in systemic lupus erythematosus: recommendations from an international task force. Ann Rheum Dis 2014;73(6):958–67. Md Yusof MY, Psarras A, El-Sherbiny YM, et al. Prediction of autoimmune connective tissue disease in an at-risk cohort: prognostic value of a novel two-score system for interferon status. Ann Rheum Dis 2018;77(10):1432–39. El-Sherbiny YM, Md Yusof MY, Psarras A, et al. B cell tetherin: a flow-cytometric cell-specific assay for response to Type-I interferon predicts clinical features and flares in SLE. bioRxiv 2019:554352.