Expression Profiling and the Clinical Course of Systemic Vasculitis

Systemic vasculitis (AASV), caused when the immune system attacks the body, is a severe disease with a high risk of renal failure and premature death. As it is hard to predict how the disease will progress or respond to toxic immune suppressive therapy, such treatment may be used excessively, risking side effects such as infection and cancer. Clinicians, bioinformaticians, and immunologists have teamed up in Cambridge to study patients with vasculitis using modern technology to detect patterns of gene expression in blood cells. These patterns will be integrated with clinical information, providing new ways to measure disease activity and predict response to treatment. New tests thus generated may enable targeting of therapy to people who most need it, improving treatment effectiveness and reducing toxicity.

Autoimmune diseases are major public health problems that affect 8-10% of the adult population. They have variable clinical features and often demonstrate unpredictable responses to therapy. ANCA-associated systemic vasculitis (AASV) is one such autoimmune disease, characterised by vascular inflammation and often severe tissue damage, with an incidence of 40/million/year. The optimal choice and length of treatment regimens is not known and no good markers predict therapeutic response or disease flare. There is therefore an urgent need to develop methods to predict the need for, and duration of, treatment in patients with AASV. Such ?biomarkers? might also provide an early definition of remission allowing truncation of treatment, or herald relapse allowing pre-emptive, briefer or less toxic therapy.

The Cambridge / Hinxton Centre for Translational Research in Autoimmune Disease (CHiC TRIAD) is a group of clinicians, bioinformaticians, and immunologists formed with the aim of integrating both clinical and microarray data in a cross-sectional and prospective fashion using state-of-the-art bioinformatics. Similar approaches have proven to be successful in the field of oncology, where microarray-based diagnostics are already entering clinical practice. We will study AASV before, during and after therapy with both conventional treatment and novel biological therapies. Microarray assays using purified leucocyte subsets will produce expression data which will be integrated with clinical, serological and immunological data using existing and novel informatics tools. This unique resource will allow us to define patterns of gene expression that will predict responses to treatment and that will also uncover novel ways of determining disease activity and of predicting drug toxicity. Tests generated from these findings will improve patient outcome by leading to better targeted therapy, with significant and beneficial effects on drug-related toxicity and treatment efficacy. In the longer term these techniques should be useful in the management of other autoimmune diseases.