Anti-MS4A4A monoclonal antibody, clone 5I3

The JMAAV study was an open-labeled prospective clinical trial, which proposed severity-based treatment protocols for patients with microscopic polyangiitis (MPA). The results suggest that the proposed protocols are useful (remission rate: 89.4%), but are also indicative of relapse or patient demise regardless of the treatment (recurrence rate: 19.0%; mortality rate: 10.6%). The aim of this study is to develop the method to predict response to the treatment in patients with MPA. In the present study, transcriptome analysis was performed using peripheral blood from patients enrolled in the JMAAV study before and 1-week after the beginning of treatment. The gene expression profile before treatment was not directly related to the response to the treatment. However, when the samples from 9 patients with good response (persistent remission for 18 months) were examined, the expression of 88 genes was significantly altered by the treatment. Thirty statistically reliable genes were selected, and then the alteration of expression by the treatment was examined among 22 patients, including 17 with good response, which was defined as persistent remission for 18 months and 5 with poor response, which was defined as relapse after remission or no remission. Discrimination analysis between the alteration of expression of the 30 genes by the treatment and the response identified a combination of 16 genes as the most valuable gene set to predict the response to the treatment. This preliminary study identified IRF7, IFIT1, IFIT5, OASL, CLC, GBP-1, PSMB9, HERC5, CCR1, CD36, MS4A4A, BIRC4BP, PLSCR1, DEFA1/DEFA3, DEFA4, and COL9A2 as the important genes that can predict the response to the treatment in patients with MPA at an early point during the therapy.

Objective. To define a pharmacodynamic biomarker based on gene expression in skin that would provide a biologic measure of the extent of disease in patients with diffuse cutaneous systemic sclerosis (dcSSc) and could be used to monitor skin disease longitudinally. Methods. Skin biopsy specimens obtained from a cohort of patients with dcSSc (including longitudinal specimens) were analyzed by microarray. Expression of genes correlating with the modified Rodnan skin thickness score (MRSS) were examined for change over time using a NanoString platform, and a generalized estimating equation (GEE) was used to define and validate longitudinally measured pharmacodynamic biomarkers composed of multiple genes. Results. Microarray analysis of genes parsed to include only those correlating with the MRSS revealed prominent clusters of profibrotic/transforming growth factor beta-regulated, interferon-regulated/proteasome, macrophage, and vascular marker genes. Using genes changing longitudinally with the MRSS, we defined 2 multigene pharmacodynamic biomarkers. The first was defined mathematically by applying a GEE to longitudinal samples. This modeling method selected cross-sectional THBS1 and longitudinal THBS1 and MS4A4A. The second model was based on a weighted selection of genes, including additional genes that changed statistically significantly over time: CTGF, CD163, CCL2, and WIF1. In an independent validation data set, biomarker levels calculated using both models correlated highly with the MRSS. Conclusion. Skin gene expression can be used effectively to monitor changes in SSc skin disease over time. We implemented 2 relatively simple models on a NanoString platform permitting highly reproducible assays that can be applied directly to samples from patients or collected as part of clinical trials.