Abstract

Ten genes that code for cell adhesion molecules have been identified by gene array analysis that are up-regulated in ovarian carcinoma tissue samples and are relatively specific to ovarian carcinoma. The objective of this project is to identify one or more cell adhesion molecule in which: (a) altered gene expression corresponds to altered protein expression, (b) protein expression is associated with patient information, and (c) the biological relevance of the molecules can be defined with respect to ovarian carcinoma. These molecules may potentially serve as early detection biomarkers or for detection of recurrence of ovarian carcinoma. In addition, these molecules may prove useful in the design of novel therapeutic regimens for ovarian carcinoma.
In Aim #1, the ten cell adhesion proteins will be localized by immunohistochemistry in tissue samples of ovarian carcinoma vs. other tissue types. Their molecular weight in tissue samples will be verified by Western immunoblotting or radioimmunoprecipitation.
In Aim #2, blood and ascites samples from patients with ovarian carcinoma will be screened for the presence of the proteins by Western immunoblotting or radioimmunoprecipitation. The level of each protein in the patients' circulation will be quantitated by ELISA or radioimmunoassay. Statistical analysis will be used to identify the association between tissue distribution, protein expression, and circulating levels of the proteins, and tissue histology and patient outcome/information.
In Aim #3, the levels of expression of the proteins on the surface and in the spent media of fresh ovarian carcinoma patient samples and established cell lines will be quantitated. The role of these cell adhesion proteins in ovarian carcinoma cell adhesion, invasion, and aggregate formation will be elucidated by use of blocking monoclonal antibodies and RNA interference. The association between levels of expression/secretion of the cell adhesion proteins with biological functional activities and patient information will be determined.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA106878-03
Application #
7216694
Study Section
Special Emphasis Panel (ZRG1-CBSS (01))
Program Officer
Kim, Kelly Y
Project Start
2005-05-01
Project End
2010-04-30
Budget Start
2007-07-09
Budget End
2008-04-30
Support Year
3
Fiscal Year
2007
Total Cost
$271,860
Indirect Cost

  Institution

Name
University of Minnesota Twin Cities
Department
Pathology
Type
Schools of Medicine
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455

  Publications

Rizzardi, Anthony E; Johnson, Arthur T; Vogel, Rachel Isaksson et al. (2012) Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagn Pathol 7:42
Nikas, Jason B; Boylan, Kristin L M; Skubitz, Amy P N et al. (2011) Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer. Cancer Inform 10:233-47
Boylan, Kristin L M; Misemer, Benjamin; De Rycke, Melissa S et al. (2011) Claudin 4 Is differentially expressed between ovarian cancer subtypes and plays a role in spheroid formation. Int J Mol Sci 12:1334-58
Derycke, Melissa S; Pambuccian, Stefan E; Gilks, C Blake et al. (2010) Nectin 4 overexpression in ovarian cancer tissues and serum: potential role as a serum biomarker. Am J Clin Pathol 134:835-45
Andersen, John D; Boylan, Kristin Lm; Jemmerson, Ronald et al. (2010) Leucine-rich alpha-2-glycoprotein-1 is upregulated in sera and tumors of ovarian cancer patients. J Ovarian Res 3:21
Andersen, John D; Boylan, Kristin L M; Xue, Feifei S et al. (2010) Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in-gel electrophoresis. Electrophoresis 31:599-610
Boylan, Kristin Lm; Andersen, John D; Anderson, Lorraine B et al. (2010) Quantitative proteomic analysis by iTRAQ(R) for the identification of candidate biomarkers in ovarian cancer serum. Proteome Sci 8:31
DeRycke, Melissa S; Andersen, John D; Harrington, Katherine M et al. (2009) S100A1 expression in ovarian and endometrial endometrioid carcinomas is a prognostic indicator of relapse-free survival. Am J Clin Pathol 132:846-56
Tchagang, Alain B; Tewfik, Ahmed H; DeRycke, Melissa S et al. (2008) Early detection of ovarian cancer using group biomarkers. Mol Cancer Ther 7:27-37
Skubitz, Keith M; Pambuccian, Stefan; Manivel, J Carlos et al. (2008) Identification of heterogeneity among soft tissue sarcomas by gene expression profiles from different tumors. J Transl Med 6:23

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