A major theme in current bioinformatics deals with the computational analysis of biological data produced by modern high-throughput measurement technologies. These technologies enable the examination of a tissue sample from different biological aspects. Each technology produces high-resolution profile composed of hundreds to thousands of features that describe that sample on a certain biological level.
This wealth of high-resolution biological data can be used both separately and jointly to improve our understanding of the biological mechanisms underlying different subtypes of cancer. Unsupervised analysis performed on a collection of cancer profiles can partition the samples into biologically distinct groups exhibiting clinical importance, thus paving the way to personalized medicine by which patients are classified to a specific subtype and treated accordingly.
In this study, we applied clustering analysis on hundreds of breast cancer samples using RNA-Seq and DNA methylation data. The resulting sample clusters were characterized using the available clinical data and were also compared to the previously described subtypes. Gene enrichment analysis performed on subtype differential genes shed light on the biological themes underlying the new sample groups.
Our analysis of the expression data revealed two biologically distinct subgroups of Luminal-A samples, exhibiting differential expression of immune related genes. Analysis of the methylation data identified a cluster of patients with poor survival prognosis. By further characterizing these and other novel subtypes we hope to advance our understanding of cancer heterogeneity and also promote the development of subtype specific diagnosis and treatment.