哮喘中气道病理异质性：使用拓扑数据分析可视化疾病微团簇
2018/04/04
摘要
背景：哮喘是一种以气道壁病变为基础的复杂慢性疾病。结构性气道病理和细胞炎症的变化如何导致哮喘以及严重程度的不同尚不清楚。
目的：我们使用拓扑数据分析（TDA）评估病理异质性，旨在可视化疾病簇和微团簇。
方法：对202名成人患者发现群（142名哮喘患者和60名健康受试者）和外部复制人群（59名重症哮喘患者）进行了评估。 在支气管活检样品中检查病理学和基因表达。 通过仅使用病理变量应用TDA来创建病理学驱动的视觉网络。
结果：在发现群中，TDA确定了4个群组/网络，其中包含多个由群组级别统计数据掩盖的微型群集/兴趣区域。 具体而言，TDA1组包括大量的健康受试者，微群集代表将健康受试者连接至轻至中度哮喘患者的拓扑连续体。 另外三个TDA组患有中至重度哮喘（气道平滑肌高，网状基底膜高和重塑低组），并含有许多具有不同病理和临床特征的微团。 在所有病理组中鉴定出互斥的TH2和TH17组织基因表达特征。在重症哮喘亚组的发现和外部复制中通过持续同源性分析仅鉴定了高度相似的“病理数据形状”。
结论：我们使用TDA鉴定并复制了哮喘的新型病理表型。我们的方法适用于其他复杂的慢性疾病。
Airway pathological heterogeneity in asthma: Visualization of disease microclusters using topological data analysis.
Siddiqui S, Shikotra A, Richardson M, Doran E, Choy D, Bell A, Austin CD, Eastham-Anderson J, Hargadon B, Arron JR, Wardlaw A, Brightling CE, Heaney LG, Bradding P.
Abstract
BACKGROUND:Asthma is a complex chronic disease underpinned by pathological changes within the airway wall. How variations in structural airway pathology and cellular inflammation contribute to the expression and severity of asthma are poorly understood.
OBJECTIVE:Therefore we evaluated pathological heterogeneity using topological data analysis (TDA) with the aim of visualizing disease clusters and microclusters.
METHODS:A 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.
RESULTS:In 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 Muscle High, Reticular Basement Membrane High, and Remodeling Low 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.
CONCLUSIONS:We have identified and replicated novel pathological phenotypes of asthma using TDA. Our methodology is applicable to other complex chronic diseases.
背景：哮喘是一种以气道壁病变为基础的复杂慢性疾病。结构性气道病理和细胞炎症的变化如何导致哮喘以及严重程度的不同尚不清楚。
目的：我们使用拓扑数据分析（TDA）评估病理异质性，旨在可视化疾病簇和微团簇。
方法：对202名成人患者发现群（142名哮喘患者和60名健康受试者）和外部复制人群（59名重症哮喘患者）进行了评估。 在支气管活检样品中检查病理学和基因表达。 通过仅使用病理变量应用TDA来创建病理学驱动的视觉网络。
结果：在发现群中，TDA确定了4个群组/网络，其中包含多个由群组级别统计数据掩盖的微型群集/兴趣区域。 具体而言，TDA1组包括大量的健康受试者，微群集代表将健康受试者连接至轻至中度哮喘患者的拓扑连续体。 另外三个TDA组患有中至重度哮喘（气道平滑肌高，网状基底膜高和重塑低组），并含有许多具有不同病理和临床特征的微团。 在所有病理组中鉴定出互斥的TH2和TH17组织基因表达特征。在重症哮喘亚组的发现和外部复制中通过持续同源性分析仅鉴定了高度相似的“病理数据形状”。
结论：我们使用TDA鉴定并复制了哮喘的新型病理表型。我们的方法适用于其他复杂的慢性疾病。
(中日友好医院呼吸与危重症医学科 王瑞茵 摘译 林江涛 审校)
(J Allergy Clin Immunol. 2018 Mar 7. pii: S0091-6749(18)30039-3. doi: 10.1016/j.jaci.2017.12.982. [Epub ahead of print])
(J Allergy Clin Immunol. 2018 Mar 7. pii: S0091-6749(18)30039-3. doi: 10.1016/j.jaci.2017.12.982. [Epub ahead of print])
Airway pathological heterogeneity in asthma: Visualization of disease microclusters using topological data analysis.
Siddiqui S, Shikotra A, Richardson M, Doran E, Choy D, Bell A, Austin CD, Eastham-Anderson J, Hargadon B, Arron JR, Wardlaw A, Brightling CE, Heaney LG, Bradding P.
Abstract
BACKGROUND:Asthma is a complex chronic disease underpinned by pathological changes within the airway wall. How variations in structural airway pathology and cellular inflammation contribute to the expression and severity of asthma are poorly understood.
OBJECTIVE:Therefore we evaluated pathological heterogeneity using topological data analysis (TDA) with the aim of visualizing disease clusters and microclusters.
METHODS:A 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.
RESULTS:In 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 Muscle High, Reticular Basement Membrane High, and Remodeling Low 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.
CONCLUSIONS:We have identified and replicated novel pathological phenotypes of asthma using TDA. Our methodology is applicable to other complex chronic diseases.
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