Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification. Display omitted •Proteotyping of 96 breast tumors by SWATH mass spectrometry•Three key proteins for breast tumor classification•Varying degrees of heterogeneity within conventional breast cancer subtypes•Generally modest correlation between protein and transcript levels in tumor tissue Bouchal et al. explore and confirm the suitability of SWATH-MS for proteotyping of human tumor samples at relatively high throughput. Results indicate that proteotype-based classification resolves more variability than is apparent from conventional subtyping and potentially improves current classification.