Using signatures of T-helper cell inflammation to phenotype human asthma

描述（由申请人提供）：哮喘是一种以气道炎症、可逆性气道阻塞和气道高反应性为特征的常见病。越来越多的人认识到哮喘的表型异质性，包括严重程度和对药物的反应。这些差异很可能是由不同的潜在分子表型驱动的，其发现将为靶向治疗的发展提供信息。在先前对哮喘患者进行的研究中，来自人类气道上皮细胞的基因表达差异确定了两大类患者，其中一组患者的基因表达强烈受炎性t辅助细胞（Th2）细胞因子白介素-13 （IL-13）的驱动，这被称为Th2“高”哮喘，另一组患者的IL-13驱动基因的表达不显著高于健康对照组的水平，或“Th2低”哮喘。这项工作表明，与th2低的哮喘患者相比，th2高的受试者对吸入皮质类固醇有更好的反应。基于已发表的数据和我们自己的初步数据，我们假设炎症的Th1和Th17途径定义了人类哮喘的其他分子表型。我们的目标是验证这一假设，将表型与临床特征（包括对皮质类固醇的反应）联系起来，并开发基于Th1、Th2和Th17特征的人类受试者分子表型诊断工具。Th1， Th2和Th17的特征将从基于微阵列的细胞因子刺激上皮细胞系的全基因组表达谱中开发出来。这些特征将用于根据哮喘患者自身气道上皮细胞的基因表达谱将哮喘患者聚类为辅助性T亚群表型。最具信息量的上皮细胞基因将用于开发基于pcr的测试。将通过使用机器学习算法来开发这些分子表型的非侵入性测试，以发现受试者外周血样本的全基因组表达谱中的模式。分子表型的鉴定，以及可靠的无创Th2、Th1或Th17表型分配测试的可用性，将提供预测哪种药物对个体最有效的潜力，同时避免无效治疗产生不必要的副作用，并通过在临床试验期间提供生物标志物，为针对特定表型的治疗提供信息。

DESCRIPTION (provided by applicant): Asthma is a common disease characterized by airway inflammation, reversible airway obstruction, and airway hyperresponsiveness. There is growing recognition of phenotypic heterogeneity in asthma, including in severity and response to medication. It is likely that these differences are driven by distinct underlying molecular phenotypes, the discovery of which would inform the development of targeted therapies. In prior studies using well-characterized patients with asthma, differences in gene expression from human airway epithelial cells identified two major groups of patients, one in which gene expression is strongly driven by the inflammatory the T-helper cell (Th2) cytokine interleukin-13 (IL-13), which has been called Th2 "high" asthma, and another in which IL-13 driven genes are not significantly expressed above that level found in healthy controls, or "Th2 low" asthma. This work demonstrated that compared to Th2-low asthmatics, Th2-high subjects a better response to inhaled corticosteroids. Based on published data and our own preliminary data, we hypothesize that Th1 and Th17 pathways of inflammation define additional molecular phenotypes of human asthma. Our aims are to test this hypothesis, associate phenotypes to clinical characteristics including response to corticosteroids, and develop diagnostic tools for molecular phenotyping of human subjects based on Th1, Th2 and Th17 signatures. Th1, Th2, and Th17 signatures will be developed from microarray- based whole genome expression-profiling of cytokine-stimulated epithelial cell lines. These signatures will be used to cluster research asthma subjects into T helper subset phenotypes based on the gene expression profiles of their own airway epithelial cells obtained. The most informative epithelial cell genes will be used to develop a PCR-based test. A non-invasive test for these molecular phenotypes will be developed through the use of machine learning algorithms to find patterns in the whole genome expression profiles of subjects' peripheral blood samples. The identification of molecular phenotypes, and the availability of reliable tests to assign Th2, Th1, or Th17 phenotypes non-invasively would provide the potential to predict which medication will work best for an individual while avoiding unnecessary side effects form ineffective therapies, and to inform the development of therapies targeted to specific phenotypes by providing biomarkers during clinical trials. PUBLIC HEALTH RELEVANCE: Asthma is a very common lung disease affecting 7% of adults in America, and despite high treatment costs, up to 30% of patients do not respond to medications. Asthma has many forms with varying triggers, long-term outcomes and responses to medications. The goals of this work are to 1) apply technological advances in human genomics to discover distinct classes of asthma, and 2) develop tests based on these classes to predict which individuals will respond to a specific therapy, leading to more efficient care and less side effects from ineffective medicines.