Breast cancer is a heterogeneous disease. To improve our understanding of this disease and better treat its various forms, more information about the molecular variations that occur among its subtypes is needed. Better biomarkers are needed for its detection and for monitoring response to treatment. Additionally, improved methods for determining the chances of disease-free survival vs. disease recurrence are also needed. Although significant progress has been made in defining gene expression in breast cancer, our knowledge of protein expression profiles of various forms of this disease is limited. The long-term goals of the research are (1) to identify glycoprotein biomarker signatures that will be clinically useful for more effective detection and treatment of breast cancer;and (2) to develop a glycoproteomic breast cancer database that can be used to identify new therapeutic targets and improve our understanding of tumor-initiating cells and mechanisms that give rise to breast tumors. The objective of the current application is to identify glycoprotein signatures that can distinguish between luminal and basal breast cancer cell lines, and glycoprotein signatures that can distinguish between normal breast epithelial cells and malignant cell lines. The central hypothesis is that cell lines derived from malignant breast tumors and from normal breast epithelial cells have distinct glycoprotein profiles that can be used to classify them into various breast cancer subtypes and normal breast epithelial cells, respectively. We also hypothesize that a subset of these glycoproteins will serve as signatures that will distinguish among the three malignant breast cancer subtypes, and normal breast epithelial cells. Guided by strong preliminary data, these hypotheses will be tested through the following Specific Aims: 1a) Identify glycoprotein signatures for normal breast epithelial cells, and for luminal and basal breast cancer cell lines;1b) Define the claudin-low type breast cancer cell glycoproteome;2) Use immunofluorescence to validate glycoprotein signatures that classify breast cancer cell lines into basal and luminal subtypes.
Aim 1 will be accomplished using a mass spectrometry-based protocol, coupled with hierarchical cluster analysis, previously demonstrated in the applicant's hands to result in a glycoprotein signature that distinguishes basal from luminal breast cancer cell lines.
Aim 2 uses flow cytometry and antibodies to glycoprotein to validate the mass spectrometry-derived signature. Identifying differences in cell surface and secreted glycoprotein profiles from normal breast epithelia and those from various subtypes of breast cancer will advance diagnosis and treatment of this heterogeneous disease. Successful completion of the proposed studies will result in the identification of a set of antibodies that can be used to classify breast cancer cells into basal or luminal subtypes, and to discriminate malignant from non-malignant cells. The glycoprotein database produced through this work will provide information on potentially important biological differences between normal breast epithelia and breast cancer subtypes, as well as help identify potential therapeutic targets.

Public Health Relevance

The proposed research addresses the need for improved diagnostic tools and treatments for breast cancer by providing new knowledge that can be translated into potentially new biomarkers and therapeutic targets. Thus, the proposed research is relevant to the part of NIH's mission that pertains to developing fundamental knowledge that will help to reduce the burdens of human illness and lengthen life.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Academic Research Enhancement Awards (AREA) (R15)
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Cancer Biomarkers Study Section (CBSS)
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Patriotis, Christos F
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San Francisco State University
Schools of Arts and Sciences
San Francisco
United States
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Yen, Ten-Yang; Bowen, Spencer; Yen, Roger et al. (2017) Glycoproteins in Claudin-Low Breast Cancer Cell Lines Have a Unique Expression Profile. J Proteome Res 16:1391-1400
Timpe, Leslie C; Li, Dian; Yen, Ten-Yang et al. (2015) Mining the Breast Cancer Proteome for Predictors of Drug Sensitivity. J Proteomics Bioinform 8:204-211
Yen, Ten-Yang; Haste, Nicole; Timpe, Leslie C et al. (2014) Using a cell line breast cancer progression system to identify biomarker candidates. J Proteomics 96:173-83
Yen, Ten-Yang; Dutta, Sucharita M; Litsakos-Cheung, Christina et al. (2013) Overcoming challenges and opening new opportunities in glycoproteomics. Biomolecules 3:270-86
Timpe, Leslie C; Yen, Roger; Haste, Nicole V et al. (2013) Systemic alteration of cell-surface and secreted glycoprotein expression in malignant breast cancer cell lines. Glycobiology 23:1240-9
Yen, Ten-Yang; Macher, Bruce A; McDonald, Claudia A et al. (2012) Glycoprotein profiles of human breast cells demonstrate a clear clustering of normal/benign versus malignant cell lines and basal versus luminal cell lines. J Proteome Res 11:656-67