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Leveraging Pharmacogenomics in Asthma for Predication, Mechanism and Endotyping

利用药物基因组学在哮喘中进行预测、机制和内分型

基本信息

批准号：
10346875
负责人：
EUGENE ROLAND BLEECKER
金额：
$ 211.53万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2027-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10346875
关键词：
Address Adrenal Cortex Hormones Affect Asthma Biological Biological Assay Biological Markers Biological Products Biological Response Modifier Therapy Biology Blood Blood specimen Cells Clinical Computing Methodologies Data Development Diagnosis Direct Costs Disease FDA approved Gene Expression Profile Genomic approach Genomics Goals Health Care Costs Human IL4 gene IL5 gene Immunomodulators Individual Inflammatory Interleukin-13 Intervention Link Medicine Modeling Molecular Monitor Outcome Pathway interactions Persons Pharmaceutical Preparations Pharmacogenomics Pharmacology Phenotype Prediction of Response to Therapy Prognostic Marker Randomized Clinical Trials Research Sensitivity and Specificity Series Severities Signal Transduction Specific qualifier value Sputum Statistical Models Subgroup Symptoms System Systems Biology Therapeutic Therapeutic Intervention Transcript Translations United States asthmatic base clinical application clinical biomarkers clinical phenotype clinical subtypes clinically relevant cohort cost effective disease heterogeneity disorder prevention economic cost eosinophil evidence base genomic data genomic signature immunoregulation individual response insight knock-down non-smoking novel novel therapeutics precision medicine predicting response predictive marker primary outcome prognostic prognostic model programs responders and non-responders response response biomarker single-cell RNA sequencing therapeutic biomarker transcriptome transcriptome sequencing transcriptomics treatment response

项目摘要

Abstract Asthma affects over 300 million individuals worldwide. An estimated $81.9 billion dollars were spent on the diagnosis and management of asthma in the U.S. in 2013. Uncontrolled asthma is associated with a doubling of direct costs; it has been estimated that 20% of the subjects with asthma contribute 80% of the economic costs of asthma. For severe asthma, multiple new FDA approved biologic therapies exist, but they remain very expensive and there are a significant proportion of nonresponders. Current biomarkers may not distinguish reliably between responders and non-responders; ~40% of those expected to respond continue to have exacerbations and ~40% of those not expected to respond become symptom free. In this proposal, we will use novel genomics approaches to assess and predict responses using therapy-induced phenotypes across a spectrum of asthma severity and endotypes. We hypothesize that comprehensive characterization using clinical metrics, ‘omics’ approaches, and novel systems biology approaches will generate more precise treatment response biomarkers, further define disease heterogeneity, and uncover novel biologic mechanisms as related to the therapy of moderate to severe asthma. To address this hypothesis, we have specified three specific aims, centered around the combination of a well-characterized, within-person evoked phenotype clinical cohort, including subjects with both type 2 (i.e. those expected to respond based on current biomarkers) and non-type 2 moderate to severe asthma, to anti-IL5 (benralizumab) and anti-IL4/IL13 (dupilumab) with deep genomic interrogation, including single cell and bulk RNA sequencing in both sputum and blood across each of the biologic interventions. The first aim will be to identify, and subsequently validate, pharmacogenomic transcripts that predict response to each therapy, thereby yielding clinically relevant biomarkers for response to asthma biologics. Our second aim takes advantage of the biologic interventions as immunomodulators of specific pathways serving as “human knockdown models” to elicit the underlying mechanistic response at the level of the single cell to the biologic therapies. The final aim will provide novel insights into cohort via the characterization of genomic signals that influence clinical asthma subtypes and via the identification of molecular endotypes, which will be compared to the tradtional clinical subtypes and evaluated for their response to the biologics. Analyses for each aim with include both traditional statistical models as well as novel systems medicine and network biology approaches. The strengths of our study include a melding a unique longitudinal clinical evoked phenotype cohort with state of the art genomics analyses. Successful completion of this study will drive understanding of severe asthma response to biologics to an unprecendented level, provide novel therapeutic biomarkers leading to direct clinical application, and detail previously unknown cellular and genomic pathway mechanisms underlying severe asthma.

摘要全球有超过3亿人患有哮喘。估计花费了819亿美元在 2013年美国哮喘的诊断和治疗。不受控制的哮喘与两倍于直接成本；据估计，20%的哮喘患者贡献了80%的经济成本哮喘。对于严重哮喘，FDA批准的多种新的生物疗法已经存在，但它们仍然非常昂贵而且有相当大比例的人对此无动于衷。目前的生物标志物可能不能可靠地区分应答者和无应答者；约40%的预期应答者继续恶化，约40% 那些预计不会有反应的人变得没有症状。在这项提案中，我们将使用新的基因组学方法使用治疗诱导的表型来评估和预测不同严重程度和哮喘患者的反应内型。我们假设使用临床指标的综合特征、“组学”方法和新的系统生物学方法将产生更精确的治疗反应生物标志物，进一步定义疾病的异质性，并发现与治疗中重度相关的新的生物机制哮喘。为了解决这一假设，我们指定了三个具体目标，围绕以下几个方面一个特征良好的、人内诱发的表型临床队列，包括两种类型的受试者(即那些根据目前的生物标志物预计会有反应的人)和非2型中到重度哮喘，抗IL5 (Benralizumab)和抗IL4/IL13(Dupilumab)，包括单细胞和批量基因组询问对每种生物干预措施的痰和血中的RNA进行测序。第一个目标将是识别并随后验证预测对每种疗法的反应的药物基因组转录本，从而产生对哮喘生物制剂反应的临床相关生物标记物。我们的第二个目标是利用生物干预作为特定途径的免疫调节剂作为“人类基因敲除模型”来诱导在单细胞水平上对生物疗法的潜在机械反应。最终目标将是通过表征影响临床哮喘的基因组信号，提供对队列的新见解亚型和通过分子内型鉴定，这将与传统的临床比较亚型并评估它们对生物制品的反应。对每个目标的分析既包括传统的统计模型以及新的系统医学和网络生物学方法。我们的优势在于研究包括将独特的纵向临床诱发表型队列与最先进的基因组学相结合分析。这项研究的成功完成将推动对生物制剂对严重哮喘反应的理解达到前所未有的水平，提供导致直接临床应用的新的治疗性生物标志物，并详细说明先前未知的导致严重哮喘的细胞和基因组途径机制。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Lower myostatin and higher MUC1 levels are associated with better response to mepolizumab and omalizumab in asthma: a protein-protein interaction analyses.

DOI：
10.1186/s12931-023-02620-1
发表时间：
2023-12-06
期刊：
Respiratory research
影响因子：
5.8
作者：
通讯作者：

Biologic therapies for asthma in underserved populations.

DOI：
10.1016/s0140-6736(22)01383-6
发表时间：
2022-08-13
期刊：
Lancet (London, England)
影响因子：
0
作者：
通讯作者：

Using machine learning to improve our understanding of COVID-19 infection in children.

DOI：
10.1371/journal.pone.0281666
发表时间：
2023
期刊：
PloS one
影响因子：
3.7
作者：
通讯作者：

Leveraging Electronic Health Records for Guideline-Based Asthma Documentation.

利用电子健康记录进行基于指南的哮喘记录。

DOI：
10.1016/j.jaip.2022.11.032
发表时间：
2023
期刊：
The journal of allergy and clinical immunology. In practice
影响因子：
0
作者：
Landeo-Gutierrez,Jeremy;Defante,Andrew;Cernelc-Kohan,Matejka;Akong,Kathryn;Rao,Aparna;Lesser,Daniel;Duong,ThuElizabeth;Cheng,EulaliaRY;Ryu,Julie;Tantisira,Kelan
通讯作者：
Tantisira,Kelan