No abstract

To date, the major target of biologic therapeutics in systemic lupus erythematosus (SLE) has been the B cell, which produces pathogenic autoantibodies. Recently, targeting type I IFN, which is elaborated by plasmacytoid dendritic cells (pDCs) in response to endosomal TLR7 and TLR9 stimulation by SLE immune complexes, has shown promising results. pDCs express high levels of the IL-3Rα chain (CD123), suggesting an alternative potential targeting strategy. We have developed an anti-CD123 monoclonal antibody, CSL362, and show here that it affects key cell types and cytokines that contribute to SLE. CSL362 potently depletes pDCs via antibody-dependent cell-mediated cytotoxicity, markedly reducing TLR7, TLR9, and SLE serum-induced IFN-α production and IFN-α-upregulated gene expression. The antibody also inhibits TLR7- and TLR9-induced plasmablast expansion by reducing IFN-α and IL-6 production. These effects are more pronounced than with IFN-α blockade alone, possibly because pDC depletion reduces production of other IFN subtypes, such as type III, as well as non-IFN proinflammatory cytokines, such as IL-6. In addition, CSL362 depletes basophils and inhibits IL-3 signaling. These effects were confirmed in cells derived from a heterogeneous population of SLE donors, various IFN-dependent autoimmune diseases, and healthy controls. We also demonstrate in vivo activity of CSL362 following its s.c. administration to cynomolgus monkeys. This spectrum of effects provides a preclinical rationale for the therapeutic evaluation of CSL362 in SLE.

Purpose: Multiple myeloma is an incurable disease withheterogeneous clinical behavior.Bortezomib has offered some patients with relapsed and refractory disease an opportunity for prolonged survival. However, there remains apaucityofdatainpatients treatedwithbortezomibthataccurately delineates and identifies such patients.This information is crucial to guide management. Experimental Design: In this study, we aimed to identify the patients most likely to respond to bortezomib salvage therapy. We analyzed the baseline clinical variables and profiled the baseline expression of a broad range of immunohistochemical markers of cell cycle activity, apoptosis, and angiogenesis in a large cohort of multiply relapsed myeloma patients recruited to one of two prospective multicentre trials assessing the efficacy of bortezomib salvage therapy. Results: Using the European Group for Bone Marrow Transplantation criteria, response (complete or partial) to bortezomib salvage therapy was associated with a previous history of complete response to alternative antimyeloma treatment. Patients who expressed cyclin D1 were more likely to achieve a response. In contrast, patients who expressed p16INK4A , cytoplasmic p53, and the highest intensity of Bcl-2 staining had a poor response. Patients who achieved a response to bortezomib and those patients who expressed cyclin D1at baseline showed a significant survival advantage. Patients who expressed FGFR3, a poor prognostic marker, responded equally well and had similar outcomes with bortezomib compared with FGFR3-negative patients. Conclusions: Baseline clinical variables and selective immunohistochemical markers expressed by patients may be used effectively to identify patients that are most likely to achieve a meaningful clinical response to bortezomib salvage therapy.

Objectives Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients’ whole‐blood transcriptomes. Methods We applied machine learning approaches to RNA‐sequencing (RNA‐seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta‐analysis on three recently published whole‐blood RNA‐seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. Results Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease‐related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. Conclusion Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.