Respiratory Medicine Unit, Dept of Medicine and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
Integrative Molecular Phenotyping Laboratory, Division of Physiological Chemistry II, Dept of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
Eur Respir J. 2018 May 10;51(5). doi: 10.1183/13993003.01930-2017. Print 2018 May.
Chronic obstructive pulmonary disease (COPD) is an umbrella diagnosis caused by a multitude of underlying mechanisms, and molecular sub-phenotyping is needed to develop molecular diagnostic/prognostic tools and efficacious treatments.The objective of these studies was to investigate whether multi-omics integration improves the accuracy of molecular classification of COPD in small cohorts.Nine omics data blocks (comprising mRNA, micro RNA, proteomes and metabolomes) collected from several anatomical locations from 52 female subjects were integrated by similarity network fusion (SNF). Multi-omics integration significantly improved the accuracy of group classification of COPD patients from healthy never-smokers and from smokers with normal spirometry, reducing required group sizes from n=30 to n=6 at 95% power. Seven different combinations of four to seven omics platforms achieved >95% accuracy.For the first time, a quantitative relationship between multi-omics data integration and accuracy of data-driven classification power has been demonstrated across nine omics data blocks. Integrating five to seven omics data blocks enabled 100% correct classification of COPD diagnosis with groups as small as n=6 individuals, despite strong confounding effects of current smoking. These results can serve as guidelines for the design of future systems-based multi-omics investigations, with indications that integrating five to six data blocks from several molecular levels and anatomical locations suffices to facilitate unsupervised molecular classification in small cohorts.
慢性阻塞性肺疾病（COPD）是一种由多种潜在机制引起的伞状诊断，需要分子亚表型来开发分子诊断/预后工具和有效的治疗方法。这些研究的目的是探讨多组学整合是否能提高 COPD 分子分类在小队列中的准确性。从 52 名女性受试者的多个解剖部位收集了 9 个组学数据块（包括 mRNA、microRNA、蛋白质组和代谢组），通过相似网络融合（SNF）进行整合。多组学整合显著提高了 COPD 患者与健康从不吸烟者和肺功能正常的吸烟者的群体分类准确性，将所需的群体大小从 n=30 减少到 n=6（95%的功效）。七种不同的四到七个组学平台的组合达到了>95%的准确性。这是首次在九个组学数据块中证明了多组学数据整合与基于数据的分类能力的准确性之间存在定量关系。整合五到七个组学数据块，即使在当前吸烟的强烈混杂影响下，也能使 COPD 诊断的分类达到 100%的正确率，群体大小为 n=6 人。这些结果可以作为未来基于系统的多组学研究的设计指南，表明整合来自几个分子水平和解剖部位的五到六个数据块足以促进小队列的无监督分子分类。
