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

由免疫系统故障引起的疾病很常见，而且可能很严重。它们包括多发性硬化症（MS）和炎症性肠病（IBD：克罗恩病和溃疡性结肠炎）等等。它们是无法治愈的，通常发生在年轻的成年期，并且在他们生命中的某个时期影响到高达10%的人口。我们现在能够分析这些疾病患者的样本，并产生前所未有的详细数据集。我们面临的挑战是利用这些信息来增加我们对疾病的了解，并提供测试来帮助指导患者治疗和改善结果。我们之前的MRC项目拨款通过分析自2004年以来收集的患者数据开始了这一过程，这导致了一项预测IBD长期结果的测试-现在患者可以使用该测试来告知他们可能的疾病行为并帮助指导治疗。这笔拨款还资助了新的、扩大的患者队列的开发，包括不同的免疫疾病：MS、IBD（克罗恩病和UC）、SLE、anca相关血管炎和特发性肺纤维化。我们现在建议通过开发“综合分析”方法来理解我们收集的数据，从而利用这一新创建的资源。它们使用AI（人工智能或机器学习）来分析不同类型的数据（包括免疫细胞数量、血液蛋白水平和疾病活动评分等数据）和不同时期的不同疾病，从所收集数据的复杂性中汲取力量。除了上面列出的疾病外，为了进行比较，我们还纳入了一组严重的COVID-19患者，他们都有严重的炎症，其中超过10%的人也有自身免疫（免疫系统直接针对患者的证据）。这有望让我们对疾病的成因有新的认识，而且重要的是，这也有助于开发进一步的测试，以帮助预测疾病的结果，并帮助指导患者的治疗。这项工作将产生公开可用的数据和新的分析管道，以支持科学界未来的研究，将训练年轻的研究人员使用尖端的方法，但也应该在临床中测试对患者的实用价值——希望通过允许根据患者的个人需求定制患者管理来实现“个性化医疗”。

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.