Proteomic Maps of Breast Cancer Subtypes

Using MS-based proteomics for systems-wide profiling (instead of genomics and transcriptomics), Geiger, T. et al. employed FFPE filter-aided sample preparation (FASP) in combination with a super-SILAC approach to quantify >10,000 proteins (from >157,000 sequence-unique peptides) in clinical breast cancer tumor samples. The functional networks within and between breast cancer subtypes were examined, while novel cancer regulators and substype-specific biological processes were discovered. To support clinical translation, a computational workflow based on support vector machine (SVM)-based classification was developed for protein identification according to breast cancer subtype.

Abstract

Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. 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 signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics.

Tyanova S, Albrechtsen R, Kronqvist P, Cox J, et al.

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