Integrated analysis of -omic landscapes in breast cancer subtypes

Abstract

The subtypes of breast cancer exhibit diverse histology, molecular features, therapeutic response, aggressiveness, and patient outcomes. Multi-omics high-throughput technologies, which are widely used in cancer research, generated waste amounts of multimodal omic datasets calling for new approaches of integrated analyses to uncover patterns of transcriptomic, genomic, and epigenetic changes in breast cancer subtypes and connect them to disease clinical characteristics. Here, we applied multi-layer self-organizing map (ml-SOM) algorithms to PAM50-classified TCGA breast cancer samples to disentangle the diversity of the effects of gene expression, methylation, copy number, and somatic single nucleotide variation in the disease subtypes. Furthermore, we studied the association of perturbed gene modules with survival, prognosis, and other clinical characteristics. Our findings highlight the power of multi-omic analyses to offer a better understanding of the molecular diversity of breast cancer subtypes compared to single-omic analyses. Moreover, they highlight the complex subtype-characteristic associations between gene expression and epigenetic/genomic factors and their implications for survival and clinical outcomes.