Proteomic maps of breast cancer subtypes

Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.