aWCluster: A Novel integrative Network-based Clustering of Multiomics Breast Cancer Data

ABSTRACT The remarkable growth of multi-platform genomic profiles has led to the multiomics data integration challenge. In this study, we present a novel network-based integration method of multiomics data as well as a clustering technique founded on the Wasserstein (Earth Mover’s) distance from the theory of optimal mass transport. We applied our proposed method of aggregating multiomics and Wasserstein distance clustering (aWCluster) to invasive breast carcinoma from The Cancer Genome Atlas (TCGA) project. The subtypes were characterized by the concordant effect of mRNA expression, DNA copy number alteration, and DNA methylation as well as the interaction network connectivity of the gene products. aWCluster successfully clusters the breast cancer TCGA data into classes with significantly different survival rates. A gene ontology enrichment analysis of significant genes in the low survival subgroup leads to the well-known phenomenon of tumor hypoxia and the transcription factor ETS1 whose expression is induced by hypoxia. In addition, immune subtype analysis in our clustering via aWCluster recovers the inflammatory immune subtype in a group demonstrating improved prognosis. Consequently, we believe aWCluster has the potential to discover novel subtypes and biomarkers by accentuating the genes that have concordant multiomics measurements in their interaction network, which are challenging to find without the network inference or with single omics analysis.