Studying exceptional treatment non-responders and genetics to predict treatment response in rheumatoid arthritis

项目摘要/摘要 护理类风湿关节炎(RA)患者的一个主要挑战是确定最佳治疗方案。 几种有效的生物疾病修饰抗风湿药物(BDMARD)可用于RA，反映 治疗方面的进步和类风湿性关节炎的异质性；部分患者有反应，而另一些患者则没有。之前 研究集中在对肿瘤坏死因子抑制剂(TNFi)反应良好的患者，这是最常见的 BDMARD，在找到可用于临床护理的预测因子方面取得的成功有限。这项建议旨在 通过采取不同的方向来弥补知识上的差距。这项研究的目的是集中于 特殊的bDMARD无应答者，定义和描述曾服用≥3类药物的患者 BDMARDS用于RA。我们将测试临床电子健康记录数据(EHR)或 基因组数据可以从TNFi应答者中识别特殊的无应答者。在目标1中，我们利用来自 确定与异常无应答相关的临床因素的~16K名RA患者的EHR队列 使用传统的流行病学方法。此外，我们还将使用机器学习和 主题建模，使我们能够评估更广泛的功能的预测性。举例 功能包括帐单代码、处方和使用Natural从文本注释中提取的医疗概念 语言处理。在目标2中，我们将测试类风湿性关节炎遗传风险因素是否存在于 目的1，以及其他炎症性关节炎，如中轴性脊柱关节病，可以预测特殊的非 对bDMARD治疗的反应。作为目标2的一部分，我们还将纳入任何可预测的临床因素 通过传统或主题建模方法在目标1中确定。最重要的假设是 与对TNFi有反应的患者相比，特殊的无应答者可能不那么类似于RA，具有较少的RA基因 来自叙事笔记的风险等位基因和经典的类风湿关节炎特征。这一定义提供了一种新的方式来细分 表型类风湿性关节炎，重点是那些对治疗反应不佳的患者。这项研究具有重要意义，因为 筛查不仅在临床上有帮助，而且还可以识别患者，为未来新药研究提供靶点 目标。这种方法是创新的，因为它考虑了患者现在拥有更多数据的当代数据 “机会”导致3类bDMARD失效，而在过去，bDMARD的数量有限。 这些数据将使用传统的流行病学模型和较新的方法进行检查 可以集成更广泛的数据类型的建模。最后，这项提案旨在预期 患者前来就诊的时间，基因数据将作为他们医疗记录的一部分。

PROJECT SUMMARY/ABSTRACT A major challenge in caring for patients with rheumatoid arthritis (RA) is determining the optimal therapy. Several effective biologic disease modifying anti-rheumatic drugs (bDMARDs) are available for RA, reflecting both advances in therapy, and the heterogeneity of RA; subsets of patients respond while others do not. Prior studies focused on patients with a good response to tumor necrosis factor inhibitor (TNFi), the most common bDMARD, with limited success in finding predictors that can be used in clinical care. This proposal seeks to address that gap in knowledge by taking a different direction. The objective of this study is to focus on exceptional bDMARD non-responders, defining and characterizing patients who have been on ≥3 classes of bDMARDs for RA. We will test whether data available in clinical electronic health record data (EHR) or genomic data can identify exceptional non-responders from TNFi responders. In Aim 1, we leverage data from an EHR cohort of ~16K RA patients to determine clinical factors associated with exceptional non-response using traditional epidemiologic approaches. As well, we will apply approaches using machine learning and topic modeling that will enable us to evaluate the predictiveness of a broader range of features. Examples of features include billing codes, prescriptions, and medical concepts extracted from text notes using natural language processing. In Aim 2, we will test whether RA genetic risk factors available in a subset of patients in Aim 1, and those of other inflammatory arthritides, e.g. axial spondyloarthropathy, can predict exceptional non- response to bDMARD therapy. As part of aim 2, we will also incorporate any predictive clinical factors identified in Aim 1 through the traditional or topic modeling approach. The overarching hypothesis is that the exceptional non-responders may be less “RA-like” than patients who respond to TNFi, with fewer RA genetic risk alleles and classic RA features from the narrative notes. This definition provides a new way to sub- phenotype RA, focusing on those that will have a poor response to therapy. This study is significant because a screen will be helpful not only in the clinic but can also identify patients to target for future studies of novel drug targets. This approach is innovative because it considers contemporary data where patients now have more “opportunity” to fail 3 classes of bDMARDs, where in the past there were only a limited number available. These data will be examined both using traditional epidemiologic models and newer approaches such as topic modeling that can integrate a broader range of data types. Finally, this proposal is designed to anticipate a time when patients will come for their visit with genetic data as part of their medical record.