This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The purpose of this study is to investigate breast cancer cell-line proteomes using novel strategies for differential protein analyses coupled with the availability of genome and functional databases. We combined high-throughput proteome (MS/MS) data with 1) transcript expression and 2) protein-protein interaction data to identify networks of proteins differentially regulated in between normal and cancer cell lines. This MS/MS data was also used to develop a collection of accurate mass and time (AMT) tags, which enabled the quantitative comparison of isotopically labeled proteins among these same cells. Using high-throughput LC-MS/MS techniques and iterative searches for eight forms of post-translational modifications (PTMs) we identified a functionally diverse collection of unmodified and modified proteins. 2299 unmodified proteins were identified from 724,566 MS/MS spectra including nine proteins that were preferentially modified in cancer cell lines compared to non-cancerous cells. By mapping this dataset to publicly available protein-protein interaction and mRNA abundance measurements, we isolated several networks of functionally related proteins that appear to preferentially segregate with the cancer phenotype. We pinpointed eight modified proteins and one protein network whose significance in cancer warrants further investigation and validation in breast cancer. A collection of AMT tags for more than 5000 polypeptides in normal and cancerous human epithelial cells was generated using the high mass accuracy of LC-FTICR and the collection of peptides previously identified from prior LC-MS/MS runs. One non-cancer cell line (HMEC) and four cancer cell lines were individually labeled with 18O isotope and compared to a reference mixture of proteins labeled with 16O. Labeled peptide pairs were detected for more than 500 proteins among all five cell lines. Protein abundance measurements correlated poorly the gene expression data obtained for these same cell lines. More than 30 protein were identified in these cancer cell lines that had more than 3-fold difference in expression compared to the non-cancer cell line. Clustering analysis also revealed two functionally related protein groups with similar expression profiles.
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