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)

A subfamily of orphan receptors, estrogen receptor-related receptors (ERRs), has been demonstrated to modulate the transcription of some estrogen responsive genes via variant estrogen response elements (EREs). This study was conducted to determine whether human ERRalpha, ERRbeta, and ERRgamma might be involved in the tumorigenesis of ovarian cancer. RT-PCR was performed to analyze the expression of hERRalpha, hERRbeta, hERRbeta-2, and hERRgamma mRNA in five ovarian cancer cell lines as well as 33 samples of ovarian cancer and 12 samples of normal ovary. Serum CA-125 levels were also analyzed in all samples by ELISA. Progression-free survival and overall survival of patients with different expression of ERRs were analyzed by the Kaplan-Meier method. To analyze the subcellular localization of ERRalpha, a green fluorescent protein (GFP)-reporter plasmid of hERRalpha was constructed and transfected into the ovarian cancer cell line OVCAR-3. Expression of hERRalpha-GFP fusion protein was observed in the nucleus of OVCAR-3 ovarian cancer cell lines. We observed increased expression of hERRalpha mRNA (P = 0.020) and hERRgamma mRNA (P = 0.045) in ovarian cancers compared to normal ovaries. In contrast, hERRbeta was only observed in 9.1% of ovarian cancers. We found a positive correlation between the serum CA-125 levels and hERRalpha expression (P = 0.012), but not hERRbeta and hERRgamma expression. Survival analysis showed that the hERRalpha-positive group has a reduced overall survival (P = 0.015), and the ERRgamma-positive group has a longer progression-free survival (P = 0.020). In multivariate analysis, expression of hERRalpha was an independent prognostic factor for poor survival (relative risk, 3.032; 95% CI, 1.27-6.06). Based on our results, ERRs may play an important role in ovarian cancer. hERRalpha may represent a biomarker of poor prognosis, and hERRgamma may be a new therapeutic target in ovarian cancer.

Ovarian carcinoma has the highest mortality rate among gynaecological malignancies. In this project, we investigated the hypothesis that molecular markers are able to predict outcome of ovarian cancer independently of classical clinical predictors, and that these molecular markers can be validated using independent data sets. We applied a semi-supervised method for prediction of patient survival. Microarrays from a cohort of 80 ovarian carcinomas (TOC cohort) were used for the development of a predictive model, which was then evaluated in an entirely independent cohort of 118 carcinomas (Duke cohort). A 300-gene ovarian prognostic index (OPI) was generated and validated in a leave-one-out approach in the TOC cohort (Kaplan-Meier analysis, p = 0.0087). In a second validation step, the prognostic power of the OPI was confirmed in an independent data set (Duke cohort, p = 0.0063). In multivariate analysis, the OPI was independent of the post-operative residual tumour, the main clinico-pathological prognostic parameter with an adjusted hazard ratio of 6.4 (TOC cohort, CI 1.8-23.5, p = 0.0049) and 1.9 (Duke cohort, CI 1.2-3.0, p = 0.0068). We constructed a combined score of molecular data (OPI) and clinical parameters (residual tumour), which was able to define patient groups with highly significant differences in survival. The integrated analysis of gene expression data as well as residual tumour can be used for optimized assessment of the prognosis of platinum-taxol-treated ovarian cancer. As traditional treatment options are limited, this analysis may be able to optimize clinical management and to identify those patients who would be candidates for new therapeutic strategies.

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Purpose: Vascular endothelial growth factor (VEGF), an important regulator of angiogenesis and vascular permeability, is involved in various steps of ovarian carcinogenesis. Gene polymorphisms within the gene encoding VEGF were shown to be independently associated with an adverse outcome in various malignancies. No data are available for ovarian cancer. Experimental Design: In the present multicenter study, we examined three common polymorphisms within the VEGF gene (−634G/C, −1154G/A, and −2578C/A) known to be associated with an increased VEGF production in 563 Caucasian patients with ovarian cancer from Austria and Germany using pyrosequencing. Results were correlated with clinical data. Results: The three investigated polymorphisms did not correlate with any of the investigated clinicopathologic variables. In univariate and multivariate models, no significant correlations between any polymorphism and patients' overall survival were ascertained. Simultaneous carriage of the three homozygous genotypes (i.e., VEGF −634C/C, VEGF −1154G/G, VEGF −2578C/C) known to be associated with increased VEGF expression in an individual patient, however, was independently associated with a shortened overall survival (hazard ratio, 2.1; 95% confidence interval, 1.1-3.9; P = 0.02). Conclusions: We present the first data on VEGF gene polymorphisms in ovarian cancer. Simultaneous carriage of the three investigated homozygous genotypes was shown to be an independent adverse prognosticator of overall survival.