InSIGHT: Integrating Statistical methods, Immunology and Genomics for Healthcare Translation

点击翻译按钮获取中文摘要

Diseases caused by a malfunction of the immune system are common and can be severe. They include multiple sclerosis (MS) and inflammatory bowel disease (IBD: Crohn's and ulcerative colitis), among many others. They are incurable, often occur in young adulthood, and effect up to 10% of the population at some time in their lives. We are now able to analyse samples from patients with these diseases, and produce datasets of unprecedented detail. Our challenge is to use this information to increase our understanding of disease, and to deliver tests to help guide patient treatment and improve outcome. Our previous MRC programme grant had begun this process through analysis of patient data collected since 2004, which led to a test which predicts long-term outcome in IBD - this is now available to patients to inform them of their likely disease behaviour and to help guide treatment. This grant also funded the development of new, enlarged patient cohorts encompassing different immune diseases: MS, IBD (both Crohn's and UC), SLE, ANCA-associated vasculitis and idiopathic pulmonary fibrosis. We now propose to exploit this newly created resource by developing "integrative analysis" methods to make sense of the data we have collected. These use AI (artificial intelligence, or machine learning) to analyse across different sorts of data (including things such as immune cell numbers, blood protein levels and disease activity scores) and different diseases over time, drawing strength from the complexity of the data collected. In addition to the diseases listed above, for comparison we have also included a cohort of patients with severe COVID-19, all of whom have severe inflammation and over 10% of whom also have autoimmunity (evidence of the immune system directly targeting the patient). This promises to lead to new insights into what drives disease, and importantly also to the development of further tests to help predict disease outcome and help guide patient treatment. This work will produce publicly available data and new analysis pipelines to support future research by the scientific community, will train young researchers in cutting edge methods, but should also tests of practical value to patients in the clinic - hoping to achieve "personalised medicine" by allowing patient management to be tailored to individual patient needs.