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

类风湿性关节炎(RA)是最常见的炎症性关节炎，影响着全球1%的人口和英国约50万人。它影响各个年龄段，其特征是关节衬里(滑膜)发炎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，导致疾病进展失控，生产力下降和残疾，在英国导致患者痛苦，每年花费约48亿GB。类风湿关节炎是一种临床上非常多样化的疾病，在患者被诊断时，我们无法预测疾病的可能结果，或者为什么在某些患者中治疗有效，而在另一些患者中无效。缺乏预测标记物导致：疾病控制延迟；不必要地接触潜在有毒药物；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/或定义他们详细的功能或分子组成(称为内型)来对患者进行分类。我们从750多名处于疾病三个不同阶段的患者收集了大量独特的样本(滑膜组织和血液)，并使用不同的药物进行治疗：早期疾病患者、晚期疾病患者和目前药物无效的耐药RA患者。这项研究的目的是开发一种使用DNA编码产物RNA对患者进行分类的方法，这将导致蛋白质的产生，而蛋白质是我们身体的组成部分。观察被称为转录组的RNA，可以指出哪些基因是活跃的，而在RA中，有助于识别那些导致炎症和关节损伤的基因。我们已经对我们大量收集的疾病组织中的RNA进行了分析(测序)，在早期疾病患者中，我们已经确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)找出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，以及这些特征是否因治疗而改变。也就是说，它们是否是保守的，这样我们只需要对患者进行一次分类，或者它们是否随着时间的推移而改变并需要重复测试。2)为了评估一种药物的作用方式与患者组织中识别的签名相匹配是否意味着治疗比它们不匹配时更有效，以便我们可以找出哪些治疗方法对个别患者最有效。3)对RNA进行测序以识别签名是复杂、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，可以在医院常规使用。4)如果我们发现重要的特征，我们将看看是否也能在血液样本中识别出这些特征，这些样本比活组织检查更容易获取。5)我们将使用签名数据和临床信息应用数学模型，目的是开发一种过程来改善患者接受的护理。如果我们能识别出更有可能发展为侵袭性疾病的患者，就可以向他们的临床团队强调这一点，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的恶化。此外，能够预测对患者最有效的治疗方法可能会省去多年的反复试验，直到找到正确的治疗方法，这有可能极大地提高类风湿关节炎患者的生活质量，防止残疾，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.