While the term "asthma" has long been known to describe heterogeneous groupings of patients, only recently have data evolved which enable a molecular understanding of the clinical differences. The evolution of transcriptomics (and other 'omics platforms) and improved statistical analyses in combination with large clinical cohorts opened the door for molecular characterization of pathobiologic processes associated with a range of asthma patients. When linked with data from animal models and clinical trials of targeted biologic therapies emerging distinctions arose between patients with and without elevations in Type-2 immune and inflammatory pathways, leading to the confirmation of a broad categorization of Type-2 Hi asthma. Differences in the ratios, sources and location of Type-2 cytokines and their relation to additional immune pathway activation, appear to distinguish several different (sub) molecular phenotypes, and perhaps endotypes of Type-2 HI asthma, which respond differently to broad and targeted anti-inflammatory therapies. Asthma in the absence of Type-2 inflammation is much less well defined, without clear biomarkers, but is generally linked with poor responses to corticosteroids. Integration of "big data" from large cohorts, over time, using machine learning approaches, combined with validation and iterative learning in animal (and human) model systems is needed to identify the biomarkers and tightly defined molecular phenotypes/endotypes required to fulfill the promise of precision medicine.

中文翻译：

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我们是否正在实现哮喘内型和精准医学的承诺？


虽然“哮喘”一词长期以来一直被认为是描述不同类型的患者，但直到最近才出现数据，使人们能够从分子角度理解临床差异。转录组学（和其他组学平台）的发展以及与大型临床队列相结合的统计分析的改进为与一系列哮喘患者相关的病理生物学过程的分子表征打开了大门。当与动物模型和靶向生物治疗临床试验的数据联系起来时，2 型免疫和炎症途径升高和未升高的患者之间出现了新的区别，从而确认了 2 型 Hi 哮喘的广泛分类。 2 型细胞因子的比例、来源和位置的差异及其与额外免疫途径激活的关系，似乎可以区分几种不同的（亚）分子表型，也许还有 2 型 HI 哮喘的内型，它们对广泛和靶向的反应不同抗炎治疗。没有 2 型炎症的哮喘的定义要差得多，没有明确的生物标志物，但通常与皮质类固醇反应不佳有关。随着时间的推移，需要使用机器学习方法整合来自大群体的“大数据”，并结合动物（和人类）模型系统中的验证和迭代学习，以识别实现承诺所需的生物标志物和严格定义的分子表型/内型的精准医学。