哮喘中的气道病理异质性:应用拓扑数据分析可视化疾病微团簇

2018/06/04

   摘要
   背景:哮喘是一类基于气道壁病理性改变的复杂的慢性疾病。气道病理学变化和细胞炎症如何影响哮喘的发生及其严重程度尚不清楚。
   目的:因此,我们使用拓扑数据分析(TDA)评估病理异质性,旨在将疾病簇和微团簇可视化。
   方法:对202名成人患者(142名哮喘患者和60名健康受试者)和外部复制群体(59名重度哮喘患者)进行评估。在支气管活检样品中检查病理学改变和基因表达。通过单独的病理变量来应用TDA创建病理学介导的可视化网络。
   结果:在现有的队列中,TDA确定了4个群组/网络,其中包含多个被群组级别统计数据掩盖的感兴趣的微型群集/区域。具体而言,其中1个TDA组包括了大量的健康受试者,其微团簇代表了连接健康受试者与轻中度哮喘患者的拓扑连续体。另外三个TDA组包含了中重度哮喘患者(包括高气道平滑肌组, 高网状基底膜组,低重塑组),并涵盖了多个具有不同病理学和临床特征的微团簇。在所有病理组中均鉴定出互斥的TH2和TH17组织基因的表达特征。应用于重症哮喘亚组的发现和外部复制通过对持续同源性的分析仅识别出高度相似的“病理数据形状”。
   结论:我们使用TDA鉴定并复制了新的哮喘病理表型。我们的方法也适用于其他复杂的慢性疾病。

 
(中国医科大学附属第一医院呼吸与危重症医学科 李文扬 摘译 杨冬 审校)
(Salman Siddiqui,et al. JACI 2018 Mar 3 Abstract)
 
 
 
Airway pathological heterogeneity in asthma:Visualization of disease microclusters using topological data analysis
 
Salman Siddiqui,et al. JACI 2018 Mar 3 Abstract
 
Abstract
BACKGROUNDAsthmais a complex chronic disease underpinned by pathological changes within the airway wall. How variations instructural airway pathology and cellular inflammation contribute to the expression and severity of asthma are poorly understood.
OBJECTIVETherefore we evaluated pathological heterogeneity using topological data analysis (TDA) with the aim of visualizing disease clusters and microclusters.
METHODSA discovery population of 202 adult patients (142 asthmatic patients and 60 healthy subjects) and an external replication population (59 patients with severe asthma) were evaluated. Pathology and gene expression were examined in bronchial biopsy samples. TDA was applied by using pathological variables alone to create pathology-driven visual networks.
RESULTIn the discovery cohort TDA identified 4 groups/networks with multiple microclusters/regions of interest that were masked by group-level statistics. Specifically, TDA group 1 consisted of a high proportion of healthy subjects, with a microcluster representing a topological continuum connecting healthy subjects to patients with mild-to-moderate asthma.Three additional TDA groups with moderate-to-severe asthma(Airway Smooth MuscleHigh, Reticular BasementMembraneHigh, and RemodelingLow groups) were identified and contained numerous microclusters with varying pathological and clinical features. Mutually exclusive TH2 and TH17 tissue gene expression signatures were identified in all pathological groups. Discovery and external replication applied to the severe asthma subgroup identified only highly similar ‘‘pathological data shapes’’ through analyses of persistent homology.
CONCLUSIONSWe have identified and replicated novel pathological phenotypes of asthma using TDA. Our methodology is applicable to other complex chronic diseases.


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