Glycoproteomic comparison of clinical triple-negative and luminal breast tumors.

Triple-negative (TN) breast cancer accounts for ∼ 15% of breast cancers and is characterized by a high likelihood of relapse and a lack of targeted therapies. In contrast, luminal-type tumors that express the estrogen and progesterone receptors (ER+/PR+) and lack expression of human epidermal growth factor receptor 2 (Her2-) are treated with targeted hormonal therapy and carry a better prognosis. To identify potential targets for the development of future therapeutics aimed specifically at TN breast cancers, we have used a hydrazide-based glycoproteomic workflow to compare protein expression in clinical tumors from nine TN (Her2-/ER-/PR-) and nine luminal (Her2-/ER+/PR+) patients. Using a label-free LC-MS based approach, we identified and quantified 2264 proteins. Of these, 90 proteins were more highly expressed and 86 proteins were underexpressed in the TN tumors relative to the luminal tumors. The expression level of four of these potential targets was verified in the original set of tumors by Western blot and correlated well with our mass-spectrometry-based quantification. Furthermore, 30% of the proteins differentially expressed between luminal and TN tumors were validated in a larger cohort of 406 TN and 469 luminal tumors through corresponding differences in their mRNA expression in publically available microarray data. A group of 29 of these differentially expressed proteins was shown to correctly classify 88% of TN and luminal tumors using microarray data of their associated mRNA levels. Interestingly, even within a group of TN breast cancer patients, the expression levels of these same mRNAs were able to significantly predict patient survival, suggesting that these proteins play a role in the aggressiveness seen in TN tumors. This study provides a comprehensive list of potential targets for the development of diagnostic and therapeutic agents specifically aimed at treating TN breast cancer and demonstrates the utility of using publicly available microarray data to further prioritize potential targets.