The overall goal of this project is to obtain novel information on the molecular mechanisms of tumor metastasis, in order to contribute to understanding of the pathogenesis of this process and to obtain data that would be clinically useful. The proposed approach is designed to progress from identifying and verifying metastasis-associated proteins and transcripts, to genetic manipulation of specific genes in matched metastatic and non-metastatic clonal cell lines, and to lead into clinical correlation studies using archived human tumor tissues. In order to facilitate research investigations into the molecular mechanisms of breast tumor metastasis, we have derived a series of monoclonal breast tumor cell lines in which candidate metastasis-associated genes can be screened and functionally tested. Monoclonal tumor cell lines of opposite metastatic propensity were derived from the polyclonal breast carcinoma cell lines MDA-MB-435 and MDA-MB-231. Within this proposal, we will conduct a detailed analysis of the protein profile of the series of breast tumor cells using a newly developed 2D liquid separation/mass-mapping proteomic approach. This method uses isoelectric focusing and non-porous silica reversed-phase high-performance liquid chromatography to create an image of pI vs Mr analogous to 2D gel electrophoresis. Proteins will be identified based upon MALDI-TOF-MS peptide mapping and intact Mr, and quantitative comparisons will be performed between cell line lysate samples. Proteins identified and validated as being differentially expressed with regard to metastatic phenotype will be selected for genetic manipulation and resulting cell lines will be tested for behavior using in vitro and in vivo assays. Finally, the expression of candidate proteins/genes will be studied in archived normal and diseased human breast tissue samples. Correlation with clinicopathologic information will identify those candidate breast antigens with the most promising diagnostic and prognostic marker utility.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA108597-02
Application #
6946344
Study Section
Special Emphasis Panel (ZRG1-CBSS (01))
Program Officer
Lively, Tracy (LUGO)
Project Start
2004-09-10
Project End
2009-06-30
Budget Start
2005-09-12
Budget End
2006-06-30
Support Year
2
Fiscal Year
2005
Total Cost
$265,829
Indirect Cost
Name
University of Florida
Department
Pathology
Type
Schools of Medicine
DUNS #
969663814
City
Gainesville
State
FL
Country
United States
Zip Code
32611
Sun, Yijun; Yao, Jin; Nowak, Norma J et al. (2014) Cancer progression modeling using static sample data. Genome Biol 15:440
Giacoia, Evan Gomes; Miyake, Makito; Lawton, Adrienne et al. (2014) PAI-1 leads to G1-phase cell-cycle progression through cyclin D3/cdk4/6 upregulation. Mol Cancer Res 12:322-34
Popescu, Nicholas C; Goodison, Steve (2014) Deleted in liver cancer-1 (DLC1): an emerging metastasis suppressor gene. Mol Diagn Ther 18:293-302
Karhemo, Piia-Riitta; Ravela, Suvi; Laakso, Marko et al. (2012) An optimized isolation of biotinylated cell surface proteins reveals novel players in cancer metastasis. J Proteomics 77:87-100
Zhang, Kejing; Sefah, Kwame; Tang, Lili et al. (2012) A novel aptamer developed for breast cancer cell internalization. ChemMedChem 7:79-84
Lee, Chia-Yao; Marzan, David; Lin, Grace et al. (2011) ?2 Integrin-Dependent Suppression of Pancreatic Adenocarcinoma Cell Invasion Involves Ectodomain Regulation of Kallikrein-Related Peptidase-5. J Oncol 2011:365651
Yang, Na; Feng, Shun; Shedden, Kerby et al. (2011) Urinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification. Clin Cancer Res 17:3349-59
Sun, Yijun; Urquidi, Virginia; Goodison, Steve (2010) Derivation of molecular signatures for breast cancer recurrence prediction using a two-way validation approach. Breast Cancer Res Treat 119:593-9
Sun, Yijun; Todorovic, Sinisa; Goodison, Steve (2010) Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans Pattern Anal Mach Intell 32:1610-26
Xie, Xiaolei; Feng, Shun; Vuong, Huy et al. (2010) A comparative phosphoproteomic analysis of a human tumor metastasis model using a label-free quantitative approach. Electrophoresis 31:1842-52

Showing the most recent 10 out of 26 publications