MICA: Development and Validation of a Transcriptomic-Based Model for Classifying and Predicting Treatment Response in Rheumatoid Arthritis (TRACT-RA)

风湿性关节炎（RA）是最常见的炎性关节炎，影响全球1%的人口，英国约有50万人。它影响所有年龄段，其特征是关节衬里（滑膜）的炎症以及软骨和骨骼的破坏。在大约40%的患者中，目前的药物无效，导致疾病进展不受控制，生产力丧失和残疾，导致患者痛苦，在英国每年花费约48亿英镑。RA在临床上是一种非常多样化的疾病，在患者被诊断时，我们无法预测疾病的可能结果，或者为什么治疗对某些病人有效而对另一些病人无效。这种预测性标记物的缺乏导致：疾病控制的延迟;不必要地暴露于潜在毒性药物;大量浪费宝贵的NHS/社会资源。有几种方法可以使用可观察的特征（称为表型）和/或定义其详细的功能或分子组成（称为内型）对患者进行分类。我们收集了大量独特的样本来自750多名患者的滑膜组织和血液，这些患者处于疾病的三个不同阶段，并接受不同药物治疗：患者有早期疾病，患有更晚期疾病的患者和患有“抵抗性RA”的患者这项研究的目的是开发一种方法，使用称为RNA的DNA编码产物对患者进行分类，这导致蛋白质的产生，蛋白质是我们身体的组成部分。观察RNA，即转录组，表明哪些基因是活跃的，并在RA中有助于识别那些导致炎症和关节损伤的基因。我们已经分析了大量样本中疾病组织的RNA（测序），并在早期疾病患者中鉴定了与不同亚型疾病及其进展相关的不同模式（称为特征）。我们的目标是：1）发现我们在早期关节炎中发现的特征是否也存在于不同的疾病阶段，即在已建立的和耐药的RA患者组中，以及这些特征是否通过治疗而改变。也就是说，它们是否是保守的，因此我们只需要对患者进行一次分类，或者它们是否会随着时间的推移而改变，并需要重复测试。2）为了评估药物的工作方式与患者组织中识别的签名相匹配是否意味着治疗比不匹配时更有效，因此我们可以确定哪些治疗对个体患者最有效。3)通过RNA测序来识别特征是复杂、昂贵和耗时的，因此我们将与一家公司（NanoString）合作，该公司已经开发出在其他疾病中进行类似测试的方法，这些方法可以在医院中常规使用。4)如果我们发现了重要的特征，我们将看看我们是否也可以在血液样本中识别这些特征，这比活检更容易。5)我们将使用签名数据和临床信息应用数学建模，旨在开发一个流程，以改善患者接受的护理。如果我们能够识别出更有可能发展为侵袭性疾病的患者，这可以向他们的临床团队强调，以便更密切地跟踪他们，并迅速提供新的治疗方法来阻止疾病恶化。此外，能够预测可能对患者最有效的治疗方法可以节省多年的“试错”，直到找到正确的治疗方法，这有可能大大提高RA患者的生活质量，预防残疾并为NHS和社会节省资金。

Rheumatoid arthritis (RA) is the most common form of inflammatory arthritis affecting 1% of the population worldwide and around 500,000 people in the UK. It affects all ages and is characterised by inflammation of the joint lining (synovium) and destruction of cartilage and bone. In approximately 40% of patients current medications are ineffective leading to uncontrolled disease progression, loss of productivity and disability, causing patients suffering and costing ~£4.8billion/year in the UK.RA is clinically a very diverse disease and at the time patients are diagnosed we can't predict what the likely outcome of the disease will be, or why in some patients treatments are effective whereas in others they are not. This lack of predictive markers leads to: delays in disease control; unnecessary exposure to potentially toxic drugs; large waste of valuable NHS/societal resources.There are several ways of classifying patients using observable characteristics (known as the phenotype) and/or defining their detailed functional or molecular make-up (known as the endotype). We have a large, unique collection of samples (synovial tissue and blood) from over 750 patients at three distinctive stages of the disease and treated with different drugs: patients have early disease, patient with more advanced disease and patients with "resistant RA" in whom current medication are not effective.The aim of this research is to develop a method to classify patients using a coding product of the DNA called RNA and this leads to the production of proteins the building blocks of our bodies. Looking at the RNA, known as the transcriptome, indicates which genes are active and in RA helps to identify those that cause inflammation and joint damage. We have already analysed (sequenced) the RNA in the disease tissue in our large collections and in the patients with early disease we have identified different patterns (referred to as signatures) associated with different sub-types of disease and how it progresses.Our objectives are:1) To find out whether the signatures we have found in early arthritis are also present at different disease stages i.e. in the established and the resistant RA patient groups, and whether these signatures are modified by treatment. Namely, whether they are conserved, so that we would only need to classify a patient once or whether they change over time and repeating testing is required.2) To evaluate whether matching the way a medicine works to the signature identified in the patients tissue means that treatment is more effective than when they are not matched, so that we can work out which treatments work best for individual patients. 3) The sequencing of RNA to identify the signatures is complicated, expensive and time consuming so we will work with a company (NanoString) which has developed methods for doing similar tests in other diseases that can be used routinely in hospitals. 4) If we find important signatures we will look to see whether we can also identify these in blood samples which are easier to take than a biopsy. 5) We will apply mathematical modelling using the signature data and clinical information with the aim of developing a process to improve the care patients receive. If we can identify patients who are more likely to develop aggressive forms of the disease this could be highlighted to their clinical team so that they are followed more closely and quickly offered new treatments to stop the disease deteriorating. Also, being able to predict the treatment likely to work best for a patient may save many years of 'trial and error' until the right treatment is found, this has the potential to greatly improve the quality of life of RA patients, prevent disability and save money to the NHS and society.