Proteomic maps of breast cancer subtypes

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.

Quantifying compartment‐associated variations of protein abundance in proteomics data

“…We analyzed seven mass spectrometry datasets covering the proteomes of different mammalian tissues (Geiger et al , 2013), cell types (Azimifar et al , 2014; Sharma et al , 2015), healthy and diseased states (Wiśniewski et al , 2012; Guo et al , 2015; Tyanova et al , 2016), and cancer development stages (Wisńiewski et al , 2015). In these experiments, the abundance fold change (FC) of thousands of proteins has been calculated using standard differential analysis approaches (see Materials and Methods section).…”

“…Percentage of protein with (i) no GO annotation, (ii) any GO cellular component annotation, (iii) annotation to ten major cellular compartments (nucleus, cytoplasm, mitochondrion, extracellular space, endoplasmic reticulum, Golgi apparatus, cell membrane, nuclear membrane, lysosome, and peroxisome), and (iv) annotation to four major cellular compartments (nucleus, cytoplasm, mitochondrion, and extracellular space) are reported for each dataset.Percentage of proteins annotated to one, two, three, four, five, or more compartments in each dataset, in order (Geiger et al , 2012; Wiśniewski et al , 2012; Azimifar et al , 2014; Guo et al , 2015; Sharma et al , 2015; Wisńiewski et al , 2015; Tyanova et al , 2016). …”

“…Percentage of proteins annotated to one, two, three, four, five, or more compartments in each dataset, in order (Geiger et al , 2012; Wiśniewski et al , 2012; Azimifar et al , 2014; Guo et al , 2015; Sharma et al , 2015; Wisńiewski et al , 2015; Tyanova et al , 2016). …”

“…Scatter plot of average abundance shift of mitochondrial proteins ( x ‐axis) and peroxisomal proteins ( y ‐axis) in seven different datasets (Geiger et al , 2012; Wiśniewski et al , 2012; Azimifar et al , 2014; Guo et al , 2015; Sharma et al , 2015; Wisńiewski et al , 2015; Tyanova et al , 2016); a gray line represents the line fitted using the resulting points (Pearson's R  = 0.97, P  =   3.5 × 10 −4 ). Proteins shared between the two compartments (annotated as both mitochondrial and peroxisomal) were excluded for this analysis.…”

“…Protein intensities from microglia (young and old, each with three replicates), neurons (nine replicates), and astrocyte (three replicates) were collected and log 2 ‐transformed. (iv) Proteome quantification in breast cancer subtypes (Tyanova et al , 2016). Normalized SILAC ratios of proteins from triple negative (11 replicates), Her2‐positive (15 replicates), and ERPR (14 replicates)‐positive breast cancer were collected.…”