Integrating transcriptomic and metabolomic data from people with rheumatoid arthritis to predict clinical response to drug treatment

整合类风湿关节炎患者的转录组学和代谢组学数据来预测药物治疗的临床反应

• 批准号: 2287787
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2287787
• 关键词: Integrating; transcriptomic; metabolomic; data; people

Abstract: Rheumatoid arthritis is a chronic inflammatory disease characterized by immune-mediated pathology. In the order of 400,000 people in the UK suffer from the disease to varying degrees. The underlying pathology is still not sufficiently understood. However, it is clear that different patients display different responses to treatment with either conventional disease-modifying antirheumatic drugs (cDMARDs) e.g. methotrexate or biologic bDMARDs such as anti-TNF monoclonal antibodies and JAK inhibitors (tsDMARDs). The Scottish Early RA Cohort (SERA n=1173), together with a series of clinical trials that have been led by the University of Glasgow in people with early RA e.g. "ORBIT" and "TASER", have generated a unique biobank within which a range of transcriptomic and metabolomics datasets have been derived but as yet not integrated in terms of data analyses. Specifically, we have RNAseq data available from SERA (early untreated RA and undifferentiated arthritis), RNAseq and mass spec metabolomic data available from TASER (early untreated RA) and RNAseq and shortly metabolomics datasets available from ORBIT (RA failing first cDMARD and randomised to receive rituximab or TNF inhibitor(see McInnes et al. 2017). Biomarker approaches using pharmacogenomic approaches or using RNAseq alone have been unhelpful so far in RA in offering reliable drug response prediction. Combined metabolomics and trancriptomics data are likely to provide more inference about the factors that distinguish between responders and non-responders, but combination of these data types remains an open problem. This project therefore seeks to address this problem in order to identify new biomarkers arising from combinatorial scoring systems, that can be used to predict likely response and enable choice of the right treatment for each patient.Aims:1. Analyse and compare transcriptomic datasets from several RA patient cohorts2. Analyse and compare metabolomics datasets form several RA patient cohorts3. Integrate metabolomic and transcriptomic data to seek additional inference on pathways predictive of therapeutic response4. Generate a panel of metabolite/transcript biomarkers to enable robust prospective prediction of therapeutic outcomes in RA treatmentTraining outcomes:The student will receive directed Bioinformatics training to analyse transcriptomic (Supervisor: Watson) and metabolomics (Supervisor: Barrett) datasets using existing and novel software. They will also receive training in machine learning, Bayesian statistics and computer programming (Supervisor: Rogers) to develop novel algorithms that enable new ways to probe combined metabolomics and transcriptomics datasets. This computational work will feed into clinical understanding of RA (Supervisor: McInnes).References:McInnes, I.B., Schett, G. (2017) Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet. 389, 2328-2337. PMID: 28612747Creek, D.J., Jankevics, A., Burgess, K.E., Breitling, R., Barrett, M.P. (2012) IDEOM: an Excel interface for analysis of LC-MS-based metabolomics data. Bioinformatics. 28, 1048-9. PMID: 22308147Robert C, Watson M. Errors in RNA-Seq quantification affect genes of relevance to human disease (2015) Genome Biol. 16:177 PMID: 26335491van der Hooft, J.J., Wandy, J., Barrett, M.P., Burgess, K.E., Rogers, S (2016) Topic modeling for untargeted substructure exploration in metabolomics.. Proc Natl Acad Sci U S A. 113, 13738-13743. PMID: 27856765

【摘要】：类风湿性关节炎是一种以免疫介导病理为特征的慢性炎症性疾病。英国约有40万人不同程度地患有这种疾病。潜在的病理学仍然没有得到充分的了解。然而，很明显，不同的患者对传统疾病缓解抗风湿药物 (cDMARD) 的治疗表现出不同的反应，例如：甲氨蝶呤或生物 bDMARD，例如抗 TNF 单克隆抗体和 JAK 抑制剂 (tsDMARD)。苏格兰早期 RA 队列 (SERA n=1173) 以及格拉斯哥大学在早期 RA 患者中开展的一系列临床试验，例如“ORBIT”和“TASER”已经生成了一个独特的生物库，其中已经导出了一系列转录组学和代谢组学数据集，但尚未在数据分析方面进行整合。具体来说，我们有来自 SERA（早期未经治疗的 RA 和未分化关节炎）的 RNAseq 数据，来自 TASER（早期未经治疗的 RA）的 RNAseq 和质谱代谢组学数据，以及来自 ORBIT 的 RNAseq 和短期代谢组学数据集（RA 首次 cDMARD 失败并随机接受）利妥昔单抗或 TNF 抑制剂（参见 McInnes et al. 2017）使用药物基因组学方法或单独使用 RNAseq 的生物标志物方法具有迄今为止，在 RA 领域，组合代谢组学和转录组学数据对于区分反应者和非反应者的因素没有提供任何帮助，但这些数据类型的组合仍然是一个悬而未决的问题。旨在解决这个问题，以便识别组合评分系统产生的新生物标志物，这些标志物可用于预测可能的反应并为每个患者选择正确的治疗方法。 目的： 1. 分析和比较。来自多个 RA 患者队列的转录组数据集2。分析和比较几个 RA 患者队列的代谢组学数据集3。整合代谢组学和转录组学数据，以寻求对预测治疗反应的途径的额外推论4。生成一组代谢物/转录生物标志物，以便对 RA 治疗中的治疗结果进行稳健的前瞻性预测。 培训结果：学生将接受定向生物信息学培训，以使用现有和新颖的软件分析转录组学（主管：Watson）和代谢组学（主管：Barrett）数据集。他们还将接受机器学习、贝叶斯统计和计算机编程方面的培训（主管：罗杰斯），以开发新颖的算法，从而实现探索代谢组学和转录组学组合数据集的新方法。这项计算工作将有助于对 RA 的临床理解（主管：McInnes）。参考文献：McInnes, I.B., Schett, G. (2017) 类风湿关节炎治疗的病理遗传学见解。柳叶刀。 389、2328-2337。 PMID：28612747Creek, D.J.、Jankevics, A.、Burgess, K.E.、Breiling, R.、Barrett, M.P. (2012) IDEOM：用于分析基于 LC-MS 的代谢组学数据的 Excel 界面。生物信息学。 28、1048-9。 PMID：22308147Robert C，Watson M。RNA-Seq 定量中的错误会影响与人类疾病相关的基因（2015）基因组生物学。 16:177 PMID: 26335491van der Hooft, J.J., Wandy, J., Barrett, M.P., Burgess, K.E., Rogers, S (2016) 代谢组学中非目标亚结构探索的主题建模.. Proc Natl Acad Sci U S A. 113, 13738 -13743。电话号码：27856765