TY - JOUR
T1 - Proteomic maps of breast cancer subtypes
AU - Tyanova, Stefka
AU - Albrechtsen, Reidar
AU - Kronqvist, Pauliina
AU - Cox, Juergen
AU - Mann, Matthias
AU - Geiger, Tamar
N1 - Funding Information:
We want to thank Ulla Wewer for assistance in sample assembly. The work was funded by the Max Planck Society. T.G.’s work was funded by the Israel Cancer Research Fund and by the Israel Center of Research Excellence program (I-CORE, Gene Regulation in Complex Human Disease Center No 41/11).
PY - 2016/1/4
Y1 - 2016/1/4
N2 - 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 analysed 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.
AB - 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 analysed 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.
UR - http://www.scopus.com/inward/record.url?scp=84953251229&partnerID=8YFLogxK
U2 - 10.1038/ncomms10259
DO - 10.1038/ncomms10259
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C2 - 26725330
AN - SCOPUS:84953251229
SN - 2041-1723
VL - 7
JO - Nature Communications
JF - Nature Communications
M1 - 10259
ER -
