This Article Full Text Full Text (PDF) Supplementary Data Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Denkert, C. Articles by Fiehn, O. Search for Related Content PubMed PubMed Citation Articles by Denkert, C. Articles by Fiehn, O.

Mass Spectrometry–Based Metabolic Profiling Reveals Different Metabolite Patterns in Invasive Ovarian Carcinomas and Ovarian Borderline Tumors

¹ Institute of Pathology and ² Department of Gynecology and Obstetrics, Charité University Hospital; ³ provitro GmbH, Berlin, Germany; ⁴ University of California Davis, Genome Center, Davis, California; and ⁵ Leco GmbH, Moenchengladbach, Germany

Requests for reprints: Carsten Denkert, Institute of Pathology, Charité Hospital, Campus Mitte, Schumannstr. 20/21, D-10117 Berlin, Germany. Phone: 49-30-450-536047; Fax: 49-30-450-536900; E-mail: carsten.denkert{at}charite.de.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. We have used a metabolite profiling approach to test the hypothesis that quantitative signatures of primary metabolites can be used to characterize molecular changes in ovarian tumor tissues. Sixty-six invasive ovarian carcinomas and nine borderline tumors of the ovary were analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF MS) using a novel contamination-free injector system. After automated mass spectral deconvolution, 291 metabolites were detected, of which 114 (39.1%) were annotated as known compounds. By t test statistics with P < 0.01, 51 metabolites were significantly different between borderline tumors and carcinomas, with a false discovery rate of 7.8%, estimated with repeated permutation analysis. Principal component analysis (PCA) revealed four principal components that were significantly different between both groups, with the highest significance found for the second component (P = 0.00000009). PCA as well as additional supervised predictive models allowed a separation of 88% of the borderline tumors from the carcinomas. Our study shows for the first time that large-scale metabolic profiling using GC-TOF MS is suitable for analysis of fresh frozen human tumor samples, and that there is a consistent and significant change in primary metabolism of ovarian tumors, which can be detected using multivariate statistical approaches. We conclude that metabolomics is a promising high-throughput, automated approach in addition to functional genomics and proteomics for analyses of molecular changes in malignant tumors. (Cancer Res 2006; 66(22): 10795-804)