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 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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Tyanova S,Albrechtsen R,Kronqvist P,Cox J,Mann M,Geiger Tdoi
10.1038/ncomms10259subject
Has Abstractpub_date
2016-01-04 00:00:00pages
10259issn
2041-1723pii
ncomms10259journal_volume
7pub_type
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