Abstract

Proteogenomics approaches have enabled the generation of extensive information levels when compared to single omics technology studies, although burdened by massive experimental efforts. Here, we developed four improvements of a data independent acquisition mass spectrometry proteogenomics workflow to reveal distinct molecular phenotypes related to breast cancer appearance. We confirm mutational processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Our analyses also validated previously established enrichments of estrogen receptor-dependent molecular features relating to transcription factor expression, and provided evidence for molecular differences related to the presence of mammographic appearances in spiculated tumors. In addition, several transcript-protein pairs displayed radically different abundance correlations depending on the overall clinical and pathological properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant sample groups, and that these protein networks influence both cancer biology as well as the abundance of potential biomarkers and drug targets.