Breast cancer tumors are classified within different subtypes based on the expression of three types of cell surface receptor proteins. These proteins connect the extracellular environment with cellular activities via signaling pathways. To elicit cell responses, signaling pathways employ protein phosphorylation as a regulatory mechanism, as adding a phosphate group to a particular protein can switch its activity on or off. However, normal signaling can go awry if mutations in signaling proteins lead the cell to believe its components are always phosphorylated and active. Consequently, the entire signaling pathway functions abnormally, which can lead to uncontrolled cell growth characteristic of cancer. Given its potential role as a molecular mechanism of disease, our research focuses on obtaining a global view of the phosphoproteome, or the cell’s entire set of expressed, phosphorylated proteins, of 10 breast cancer cell lines. To generate lists of phosphoproteins present in these cell lines, we are concurrently extracting and analyzing cell line phosphopeptide samples via mass spectrometry, an analytic technique that produces characteristic spectra for individual phosphopeptides that are then matched to a library of known protein spectra through the use of computer algorithms. In our preliminary results, we have generated large-scale data sets identifying thousands of phosphorylated proteins present in two cell lines and generated side-by-side comparisons of the proteins expressed in each line. By identifying common phosphorylated proteins across all of the cell lines, our goal is to deduce a molecular signature for breast cancer. This signature can be used in future studies to design a universal protein inhibitor drug as an alternative anti-cancer therapy. By identifying the phosphoproteins exclusive to each cell type, we can uniquely characterize specific proteins active in one type of breast cancer which may facilitate development of inhibitor drugs as a form of personalized medicine.