Pulmonologists and patients alike are currently confined to battling obstructive lung diseases such as Asthma, COPD and post-COVID with unrefined tools that lump together these spectrum disorders under a single umbrella. We have previously shown that integration of a multiple omics datasets can drastically improve the statistical power to detects subgroups in small clinical cohorts in COPD [1]. Integration of 9 omics data blocks collected from multiple molecular levels (mRNA, microRNA, proteomes, and metabolomes) and multiple anatomical locations (airway epithelium, lung resident immune cells, airway exudates, exosomes, and serum) clearly demonstrated that multi-omics integration using Similarity Network Fusion (SNF) drastically improve the accuracy of group classification. Specifically, the mean accuracy of classification improved from 0.27 for single-omics platforms, to 0.90 for 7-tuple omics, reducing the n needed for the desired 95% accuracy from n=30 for single omics datasets, to n=6 for 7-tuple omics datasets. For the best performing network combinations integration of 5 or more platforms provided 100% correct classification, with sub-groups of n=6-7. The fact that multi-omics integration facilitated correct classification of mild-moderate COPD in the presence of a strong confounder such as current smoking in an unsupervised fashion is unprecedented, as smoking causes alterations of up to 50% of the bio- molecules in the lung. However, missing data represents a major limiting factor in such studies. Missingness of specific data modalities for different subjects will result in vastly different numbers omics combinations available for each pair of subject, as well as variations in sample sizes (n) between the respective omics combinations. To address this issue, we have developed an extension of the Consensus clustering algorithm for integration of multi-omics data, ccml ( cran.rstudio.com/web/packages/ccml) [2], to facilitate inclusion of omics data sets with unequal numbers of missing labels in multi-omics predictions in clinical cohorts. Evaluation of the ccml algorithm using the Karolinska COSMIC cohort demonstrate that ccml effectively predicts molecularly distinct subgroups from the integration of 9 omics datasets, allowing for as much as 60% data missingness in individual data modalities when 5 or more data platforms are integrated. Ccml is a downstream tool for multi-omics integration analysis that mitigates the limitations posed by missing data, a prevalent issue in human cohort studies involving multiple data modalities. In this presentation, we will discuss the application of the above described integration tools in our Karolinska COSMIC cohort ( clinicaltrials.gov/ct2/show/NCT02627872) investigating sex differences in COPD, as well as out HemCOV cohrt (clinicaltrials.gov/study/NCT05894616) investigating post-COVID with respiratory manifestations.
Smart Citations