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Synovial Macrophage Transcriptional Signatures for Predicting Therapeutic Efficacy

Synovial Macrophage Transcriptional Signatures for Predicting Therapeutic Efficacy
用于预测治疗效果的滑膜巨噬细胞转录特征
批准号:
10460247
负责人:
Harris R Perlman
金额:
$57.09万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-18 至 2024-07-31

项目摘要

项目成果

Harris R Perlman的其他基金

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中文摘要
翻译
尽管类风湿性关节炎(RA)患者有许多治疗方法,但很少有信息表明 指导为个别患者选择最有效的治疗方法。百分之四十六的 RA患者对常规疾病缓解(根据ACR 50缓解标准定义) 抗风湿药物(cDMARDs)或cDMARDs加抗肿瘤坏死因子(TNF)治疗。 此外,临床试验中20-40%的RA受试者从未表现出哪怕是最小的反应 (ACR 20响应标准)。因此,显然需要开发基于精确度的治疗方法, 新的生物标志物将增强我们预测治疗性RA的能力, 反应和限制无效治疗。在大多数情况下,外周血已被用于 确定预测性生物标志物,但这些研究缺乏足够的精确度, 纳入临床实践。因此,类似于肿瘤学家,他们通过 为了指导治疗,我们的方法是对滑膜进行活检, RA中的器官,以确定反映对特定治疗的敏感性或耐药性的变化。 我们将六家领先的医疗中心聚集在一起,创建了REASON,这是一个联盟, 建立了患者招募、临床数据管理、超声引导 滑膜活检、细胞分选、RNA测序(RNA-seq)和计算分析。我们 数据显示从超声引导的滑膜组织活检中分离的巨噬细胞 对于RNA-seq来说,来自RA患者的基因足够,在患者之间表现出转录差异, 与RA,而且,重要的是,设置框架的分层与RA患者根据 最主要的疾病途径。我们是第一个确定6个转录模块, 超声引导下滑膜巨噬细胞共调控基因的研究 活检,分别与临床疾病状态和cDMARD相关,或 生物治疗(bDMARD)。这项研究确立了REASON在美国的领导地位 用于超声引导滑膜活检,并证明了其可行性和治疗潜力 分离少量滑膜巨噬细胞进行RNA测序, RA治疗的方法和了解病理生物学。虽然我们发表的研究发现, RA患者中与bDMARD或甲氨蝶呤使用相关的转录特征 对于活动性疾病,主要需要鉴定预测对治疗的反应的基因。 我们的总体假设是,滑膜巨噬细胞的功能基因组分析将 鉴定新转录标记,其告知对特定疗法的应答, 个体患者,从而使研究人员和最终的临床医生能够识别药物 最有可能对每个病人都有效。
英文摘要
Despite the many therapies for patients with rheumatoid arthritis (RA), there is little information to guide selection of the most effective treatment for an individual patient. Forty-sixty percent of patients with RA respond (defined by ACR50 response criteria) to conventional disease modifying anti-rheumatic drugs (cDMARDs) or cDMARDs plus anti-tumor necrosis factor (TNF) therapy. Moreover, 20-40% of RA subjects in clinical trials never demonstrate even a minimal response (ACR20 response criteria). Hence, there is a clear need to develop precision-based therapy for patients with RA, whereby novel biomarkers will enhance our ability to predict therapeutic response and limit ineffective therapy. For the most part, peripheral blood has been utilized for identifying predictive biomarkers, but these studies lacked sufficient precision to allow their incorporation into clinical practice. Thus, similar to an oncologist, who identify mutations through sequencing of tumor biopsies to direct therapy, our approach is to biopsy the synovium, the target organ in RA to identify changes that reflect sensitivity or resistance to a particular therapy. We brought together six leading medical centers to create REASON, a consortium with an established framework for patient recruitment, curation of clinical data, ultrasound-guided synovial biopsies, cell sorting, RNA sequencing (RNA-seq), and computational analyses. Our data show that macrophages isolated from ultrasound-guided synovial tissue biopsies obtained from patients with RA are sufficient for RNA-seq, exhibit transcriptional differences across patients with RA, and, importantly, set the framework for the stratification of patients with RA according to the most prominent disease pathway. We are the first to identify 6 transcriptional modules of co-regulated genes from isolated synovial macrophages via ultrasound-guided synovial biopsy, that are individually associated with clinical disease status and cDMARD or biologic therapy (bDMARD). This study established REASON as a leader in the United States for ultrasound-guided synovial biopsies and demonstrates the feasibility and therapeutic potential of isolating low numbers of synovial macrophages for RNA-seq to establish a precision-medicine approach for RA therapy and to understand pathobiology. While our published study identified transcriptional signatures associated with bDMARD or methotrexate usage in RA patients with active disease, there is a central need to identify genes that are predictive of response to therapy. Our overarching hypothesis is that functional genomic analysis of synovial macrophages will identify novel transcriptional signatures that inform on response to particular therapies in individual patients, thereby enabling researchers and, ultimately, clinicians to identify the drug most likely to work for each patient.
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Synovial Macrophage Transcriptional Signatures for Predicting Therapeutic Efficacy
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