Molecular Phenotypes within and across disease boundaries in IPF and COPD

描述（由申请人提供）： 慢性吸烟相关的肺部疾病，如COPD和IPF，影响了相当一部分人群。在20世纪世纪的最后几十年中，肺部临床研究人员致力于基于生理学、成像、组织学和最重要的显著阴性结果来定义和识别最纯粹的慢性肺部疾病类别。这些新的疾病分类创建了一个统一的肺部疾病词汇表，使临床医生和研究人员之间能够更好地沟通，并迅速被广泛接受。虽然这些临床定义和分类非常重要且被广泛接受，但它们并未考虑大量表现为中间表型或一些不太常见特征的患者，并忽略了肺气肿/COPD和IPF的一些表现的复杂性和潜在重叠。在该提案中，我们计划使用肺组织资源联盟（LTRC），一个由NIH赞助的大型收集IPF和COPD患者的充分表征的肺样本的组织，以使用基因表达微阵列、高通量并行PCR（SmartChip）的新平台以及新的计算方法来定义已知的疾病表型并发现新的疾病表型。我们假设，通过将基因表达谱分析和先进的计算方法应用于足够大且充分表征的IPF和肺气肿/COPD样本队列，我们将能够鉴定出高度不同、可重复和超越当前疾病定义的疾病表型特征的疾病相关基因表达模块。 我们将通过执行以下具体目标来解决这一假设： 1.确定全面表征COPD和IPF的基因表达特征。 2.确定疾病谱发散或会聚的疾病微环境及其与已知疾病表型的相关性 3.生成IPF和COPD的疾病相关模块图。 4.使用定制设计的分子表型分析（PulmoSmartChip）验证COPD和IPF样本独立样本集的模块网络预测。 (End摘要）

DESCRIPTION (provided by applicant): Chronic smoking related lung diseases such as COPD and IPF affect a significant portion of the population. In the last decades of the 20th century pulmonary clinical researchers dedicated significant efforts to define and identify the purest classes of chronic lung diseases based on physiology, imaging, histology and most importantly significant negative findings. These new disease classifications created a unified vocabulary of lung diseases that allowed better communication between clinicians and researchers and became rapidly and widely accepted. While critically important and widely accepted, these clinical definitions and classification did not account for a large number of patients that presented with intermediate phenotypes or some less common features and overlooked the complexity and potential overlap of some of the manifestations of emphysema/COPD and IPF. In this proposal we plan to use the lung Tissue Resource Consortium (LTRC), a large NIH sponsored collection of well characterized lung samples from patients with IPF and COPD to define known disease phenotypes and discover new disease phenotypes using gene expression microarrays, a novel platforms for high throughput parallel PCR (SmartChip) as well as novel computational approaches. We hypothesize that by applying gene expression profiling and advanced computational approaches to a large enough and well characterized cohort of samples of IPF and emphysema/COPD we will be able to identify disease relevant gene expression modules that are highly distinct, reproducible and characteristic of disease phenotypes that go beyond current disease definitions. We will address this hypothesis by performing the following specific aims: 1. To determine the gene expression signatures that globally characterize COPD and IPF. 2. To identify disease microenvironments in which disease profiles diverge or converge and their relevance to known disease phenotypes 3. To generate a disease relevant module map of IPF and COPD. 4. To validate module networks predictions on an independent sample set of COPD and IPF samples using the custom designed molecular phenotyping assay (PulmoSmartChip). (End of Abstract)