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.

抽象的全球有超过 3 亿人患有哮喘，估计在治疗上花费了 819 亿美元。 2013 年美国哮喘的诊断和治疗。未受控制的哮喘与成倍增加相关直接费用；据估计，20%的哮喘患者承担了80%的经济费用。对于严重哮喘，有多种 FDA 批准的新生物疗法，但它们仍然非常昂贵。并且有很大比例的无反应者可能无法可靠地区分。应答者和非应答者中，约 40% 的预期应答者病情持续恶化，约 40% 那些预计不会做出反应的人将变得没有症状。在这项提案中，我们将使用基因组小说方法。使用治疗诱导的表型来评估和预测各种哮喘严重程度和范围内的反应我们使用临床指标、“组学”方法探索了这种综合表征。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，进一步定义疾病异质性，并揭示与中度至重度疾病治疗相关的新生物学机制为了解决这一假设，我们围绕以下组合制定了三个具体目标。一个特征明确的人内诱发表型临床队列，包括同时患有 2 型（即 2 型）的受试者。那些根据当前生物标志物预期有反应的人）和非 2 型中度至重度哮喘，抗 IL5 (benralizumab) 和抗 IL4/IL13 (dupilumab)，具有深度基因组分析，包括单细胞和批量每种生物干预措施中痰液和血液中的 RNA 测序的首要目标是。识别并随后验证预测每种治疗反应的药物基因组转录本，从而我们的第二个目标是利用生物制剂产生临床相关的生物标志物。生物干预作为特定途径的免疫调节剂，作为“人类击倒模型”来引发单细胞水平对生物疗法的潜在机制反应。通过影响临床哮喘的基因组信号特征为队列提供新的见解亚型并通过分子内型的鉴定，将其与传统的临床进行比较亚型并评估其对生物制剂的反应，包括传统的方法。统计模型以及新颖的系统医学和网络生物学方法。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融合成功完成这项研究将有助于了解哮喘对生物制剂的严重反应。达到前所未有的水平，提供可直接临床应用的新型治疗生物标志物，并详细说明以前未知的严重哮喘背后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.

较低的肌生长抑制素和较高的 MUC1 水平与哮喘中美泊利单抗和奥马珠单抗的更好反应相关：蛋白质-蛋白质相互作用分析。

DOI：
发表时间：
2023-12-06
期刊：
影响因子：
5.8
作者：
Akenroye, Ayobami;Nopsopon, Tanawin;Cho, Laura;Moll, Matthew;Weiss, Scott T
通讯作者：
Weiss, Scott T

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：
发表时间：
2023-03
期刊：
影响因子：
0
作者：
Landeo;Defante, Andrew;Cernelc;Akong, Kathryn;Rao, Aparna;Lesser, Daniel;Duong, Thu Elizabeth;Cheng, Eulalia R Y;Ryu, Julie;Tantisira, Kelan
通讯作者：
Tantisira, Kelan

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

使用机器学习来提高我们对儿童 COVID-19 感染的了解。