Proteogenomics connects somatic mutations to signalling in breast cancer

Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. Here we describe quantitative mass-spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers, of which 77 provided high-quality data. Integrated analyses provided insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. Interrogation of the 5q trans-effects against the Library of Integrated Network-based Cellular Signatures, connected loss of CETN3 and SKP1 to elevated expression of epidermal growth factor receptor (EGFR), and SKP1 loss also to increased SRC tyrosine kinase. Global proteomic data confirmed a stromal-enriched group of proteins in addition to basal and luminal clusters, and pathway analysis of the phosphoproteome identified a G-protein-coupled receptor cluster that was not readily identified at the mRNA level. In addition to ERBB2, other amplicon-associated highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We demonstrate that proteogenomic analysis of breast cancer elucidates the functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets. Quantitative mass-spectrometry-based proteomic and phosphoproteomic analyses of genomically annotated human breast cancer samples elucidates functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies potential therapeutic targets. This large-scale collaborative study describes quantitative-mass spectrometry-based proteomic and phosphoproteomic analyses of 105 breast cancer samples from The Cancer Genome Atlas (TCGA), representing the four principal mRNA-defined breast cancer intrinsic subtypes. The result is a high-quality proteomic resource for human breast cancer investigation, achieved using technologies and analytical approaches that illuminate the connections between genome and proteome. The data narrow candidate nominations for driver genes within large deletions and amplified regions, and identify potential therapeutic targets.