Translating the genomic architecture of breast cancer into clinical applications.

Abstract

The genetic alterations in breast cancer have in recent years been studied through a variety of techniques: analysis of alterations in individual oncogenes and tumor suppressor genes; gene expression profiling of both messenger RNA and microRNA; global analysis of DNA copy number changes; and most recently, whole-genome sequence analysis. Analysis of the association between genetic alterations and gene expression profiles with prognosis and response to specific treatments will lead to improved possibilities for patient-tailored treatment. Russnes et al. now add an additional view on the complex genetic makeup of breast carcinomas by developing algorithms that can be used to subclassify tumors based on their patterns of genome-wide DNA copy number gains and losses, which vary from very simple (only a few gains and losses) to complex. The algorithms provide indices that can be used in conjunction with results from other genetic analyses to subclassify breast cancer, with the aim of defining subgroups of patients that differ with respect to prognosis and response to therapy.