Quantitative proteomic analysis by iTRAQ® for the identification of candidate biomarkers in ovarian cancer serum

Kristin L.M. Boylan, John D. Andersen, Lorraine B. Anderson, Lee Ann Higgins, Amy P.N. Skubitz

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Background: Ovarian cancer is the most lethal gynecologic malignancy, with the majority of cases diagnosed at an advanced stage when treatments are less successful. Novel serum protein markers are needed to detect ovarian cancer in its earliest stage; when detected early, survival rates are over 90%. The identification of new serum biomarkers is hindered by the presence of a small number of highly abundant proteins that comprise approximately 95% of serum total protein. In this study, we used pooled serum depleted of the most highly abundant proteins to reduce the dynamic range of proteins, and thereby enhance the identification of serum biomarkers using the quantitative proteomic method iTRAQ®.Results: Medium and low abundance proteins from 6 serum pools of 10 patients each from women with serous ovarian carcinoma, and 6 non-cancer control pools were labeled with isobaric tags using iTRAQ® to determine the relative abundance of serum proteins identified by MS. A total of 220 unique proteins were identified and fourteen proteins were elevated in ovarian cancer compared to control serum pools, including several novel candidate ovarian cancer biomarkers: extracellular matrix protein-1, leucine-rich alpha-2 glycoprotein-1, lipopolysaccharide binding protein-1, and proteoglycan-4. Western immunoblotting validated the relative increases in serum protein levels for several of the proteins identified.Conclusions: This study provides the first analysis of immunodepleted serum in combination with iTRAQ® to measure relative protein expression in ovarian cancer patients for the pursuit of serum biomarkers. Several candidate biomarkers were identified which warrant further development.

Original languageEnglish (US)
Article number31
JournalProteome Science
Volume8
DOIs
StatePublished - Jun 14 2010

Bibliographical note

Funding Information:
We would like to thank the Gynecologic Oncology Group (GOG) Tissue Bank for the serum samples. We also thank Thomas McGowan of the University of Minnesota Center for Mass Spectrometry and Proteomics, and Dr. Yanji Xu of the University of Minnesota Supercomputing Institute for computational support, and Dr. Timothy Griffin (University of Minnesota) for critical reading of the manuscript. This work was supported by grants from the Minnesota Ovarian Cancer Alliance, the National Institutes of Health/National Cancer Institute (R01CA106878), Cancurables, and the Minnesota Medical Foundation. The MS and DIGE analyses were performed at the Center for Mass Spectrometry and Proteomics at the University of Minnesota, which is supported in part by grants from the National Science Foundation (9871237, 0215759 and CHE0078192) and the National Institutes of Health (RRR15808).

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