Abstract
Triple-negative breast cancer (TNBC) is characterized by an aggressive clinical presentation and a paucity of clinically actionable genomic alterations. Here, we utilized the Cancer Genome Atlas (TCGA) to explore the proteogenomic landscape of TNBC subtypes to see whether genomic alterations can be inferred from proteomic data. We found only 4% of the protein level changes are explained by mutations, while 21% of the protein and 35% of the transcriptomics changes were determined by copy number alterations (CNAs). We found tighter coupling between proteome and genome in some genes that are predicted to be the targets of drug inhibitors, including CDKs, PI3K, tyrosine kinase (TKI), and mTOR. The validation of our proteogenomic workflow using mass spectrometry Clinical Proteomic Tumor Analysis Consortium (MS-CPTAC) data also demonstrated the highest correlation between protein-RNA-CNA. The integrated proteogenomic approach helps to prioritize potentially actionable targets and may enable the acceleration of personalized cancer treatment.