Proteomics and peptidomics of breast cancer subtypes

In our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome, including noncoding RNA (ncRNA)–derived micropeptides, of different breast cancer subtypes. In total, 19 tumor and 18 paired nontumor samples were used to obtain the proteome and peptidome. Among these, 5 samples were classified as LABC (plus 5 paired nontumor samples), 4 as LBBC (HER2−) (plus 4 paired nontumor samples), 4 as HER2+ breast cancer (plus 3 paired nontumor samples), and 6 as TNBC (plus 6 paired nontumor samples). We identified that each subtype depends on different protein expression patterns to sustain its malignancy,and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00). We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared with nontumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. In addition, some MPs presented potential as anticancer, antiinflammatory, and antiangiogenic molecules.