Genetic predictors of response to anti-TNF therapy in rheumatoid arthritis

描述（由申请人提供）： 类风湿性关节炎（RA）患者的长期预后高度依赖于病程早期对炎症的积极药物控制。尽管在疾病发作后不久选择最佳药物很重要，但没有临床或生物标志物预测药物治疗反应。遗传生物标志物对于阻断炎性细胞因子TNF-α（TNF）的药物特别有用，因为这些药物是一线生物疾病缓解抗风湿药物DMARD，但仅在约30%的患者中诱导缓解。在本申请中，我们的中心假设是，中等效应大小的常见遗传变异预测对抗TNF治疗的反应。为了验证这一假设，我们建议扩展我们建立的多中心合作和可用的GWAS数据，以开发（i）进行GWAS的新统计方法（估计由常见的单核苷酸多态性（SNP）解释的方差），（ii）用于定义EMR中治疗反应的新信息学方法（这将使我们能够收集更多的样本GWAS），和（iii）一个新的框架，直接在人类免疫细胞中测试机制。目的1：分析约1,200例RA患者的GWAS数据，以寻找预测抗TNF治疗反应的常见变异。目标二：在Partners HealthCare、范德比尔特和西北大学使用电子病历（EMR）来定义治疗反应，并对另外约1,200名接受抗TNF治疗的RA患者进行GWAS。目的3：测试预测人类免疫细胞对抗TNF治疗的治疗应答的等位基因的作用机制。

DESCRIPTION (provided by applicant): Long-term outcome in patients with rheumatoid arthritis (RA) is highly dependent upon aggressive pharmacological control of inflammation early in the disease course. Despite the importance of selecting the optimal medication soon after disease onset, there is no clinical or biomarker predictor of drug treatment response. A genetic biomarker would be particularly useful for drugs that block the inflammatory cytokine TNF-alpha (TNF), as these drugs are first-line biological disease modifying anti-rheumatic drugs DMARDs, yet induce remission in only ~30% of patients. In this application, our central hypothesis is that common genetic variants of modest effect size predict response to anti-TNF therapy. To test this hypothesis, we propose to expand upon our established multi-center collaboration and available GWAS data to develop (i) new statistical methods for conducting GWAS (estimating variance explained by common single nucleotide polymorphisms, SNPs), (ii) new informatics methods for defining treatment response in the EMR (which will allow us to collect many more samples for GWAS), and (iii) a novel framework for testing mechanism directly in human immune cells. Aim 1: Analyze GWAS data on ~1,200 RA patients to search for common variants that predict response to anti-TNF therapy. Aim 2: Use electronic medical records (EMR) at Partners HealthCare, Vanderbilt and Northwestern to define treatment response, and conduct a GWAS on ~1,200 additional RA patients treated with anti-TNF therapy. Aim 3: Test mechanism of action of alleles that predict treatment response to anti-TNF therapy in human immune cells.