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Ovarian tumors range from benign to aggressive malignant tumors, including an intermediate class referred to as borderline carcinoma. The prognosis of the disease is strongly dependent on tumor classification, where patients with borderline tumors have much better prognosis than patients with carcinomas. We here describe the use of hierarchical clustering analysis of quantitative protein expression data for classification of this type of tumor. An accurate classification was not achieved using an unselected set of 1,584 protein spots for clustering analysis. Different approaches were used to select spots that were differentially expressed between tumors of different malignant potential and to use these sets of spots for classification. When sets of proteins were selected that differentiated benign and malignant tumors, borderline tumors clustered in the benign group. This is consistent with the biologic properties of these tumors. Our results indicate that hierarchical clustering analysis is a useful approach for analysis of protein profiles and show that this approach can be used for differential diagnosis of ovarian carcinomas and borderline tumors., (Copyright 2002 Wiley-Liss, Inc.)

Large amounts of data on quantitative gene expression are generated by procedures such as 2-DE analysis of proteins or cDNA microarrays. Quantitative molecular variation may potentially be used for the development of methods for the classification of tumors. We used here the statistical concepts of principal components analysis (PCA) and partial least square analysis (PLS) in an attempt to type ovarian tumors. Using a set of 170 polypeptides, 22 tumors were used to establish a model ("learning set") for classification into 3 groups (benign/borderline/malignant). Eighteen tumors were then used to test the model. Six of 8 carcinomas and 3 of 4 borderline tumors were correctly classified. Two of 6 benign lesions were correctly classified, 3 were classified as borderline and 1 as carcinoma. We conclude that it may be possible to classify tumors according to their constitutive protein expression profile using multivariate analysis, thus making classification by artificial intelligence a future possibility., (Copyright 2000 Wiley-Liss, Inc.)

The process of tumor progression leads to the emergence of multiple clones, and to the development of tumor heterogeneity. One approach to the study of the extent of such heterogeneity is to examine the expression of marker proteins in different tumor areas. Two-dimensional gel electrophoresis (2-DE) is a powerful tool for such studies, since the expression of a large number of polypeptide markers can be evaluated. In the present study, tumor cells were prepared from human ovarian tumors and analyzed by 2-DE and PDQUEST. As judged from the analysis of two different areas in each of nine ovarian tumors, the intratumoral variation in protein expression was low. In contrast, large differences were observed when the protein profiles of different tumors were compared. The differences in gene expression between pairs of malignant carcinomas were slightly larger than the differences observed between pairs of benign tumors. We conclude that 2-DE analysis of intratumoral heterogeneity in ovarian cancer tissue indicates a low degree of heterogeneity.

Studies of multiple markers in tumors are required for adequate biological characterization. We have characterized the expression of multiple proteins in human ovarian tumors using the technique of 2-dimensional gel electrophoresis (2-DE/PDQUEST). Tumor cells were prepared from the tissue of 22 ovarian tumors. Large variations were observed between tumors in the expression of various polypeptides, indicating heterogeneity in gene expression. An increase in the spot density of 2 cell-cycle-related proteins, PCNA and OP18/stathmin, was observed in carcinomas. Borderline tumors expressed low levels of these proteins. Significant increases in the levels of nm23, GST-pi, elongation factor 2 and triose phosphate isomerase were recorded in ovarian carcinomas. Furthermore, decreases in the levels of tropomyosin-2 and lamin C were observed in malignant as compared with benign tumors. The pattern of expression of 9 protein markers was examined in individual tumors. All malignant tumors showed simultaneous alterations in the expression of 5 or more of these proteins, whereas no benign tumor showed alterations in the expression of more than 3 polypeptides. Borderline tumors showed alterations in 0 to 6 markers. We conclude that the simultaneous analysis of multiple polypeptides, which can be achieved by 2-DE, is useful for characterization of gene expression and diagnostic studies in ovarian tumors.

The potentiality of DNA analysis to complement morphologic evaluation in classifying serous ovarian tumors as adenoma, borderline malignancy, or invasive adenocarcinoma was investigated in a series of 54 tumors. The DNA analyses were performed on histologic tumor sections. The primary diagnoses were borderline tumor in 24 cases and invasive adenocarcinoma in 30 (World Health Organization classification). When the specimens were reviewed, 17 of the 54 tumors were reclassified, after which the series consisted of 9 adenomas, 24 borderline tumors, and 21 invasive adenocarcinomas. Rising histologic malignancy grade was associated with increasing numbers of cells showing high DNA content. The DNA levels in the adenomas thus were within the diploid range of a normal cell population. They were somewhat higher in the borderline tumors and were highest in the invasive adenocarcinomas. Though no clear-cut intergroup demarcation was discernible, there was a subgroup of adenocarcinomas with greatly elevated DNA levels, indicating high biologic malignancy. The observations suggested that DNA analyses can complement histologic malignancy grading and can be useful for the recognition or highly malignant tumors among invasive adenocarcinomas.

Specimens from 73 serous ovarian cancers were examined with respect to DNA content of the tumor cells. The prognostic value of DNA analysis, as reflected in patient survival, was retrospectively compared with that of conventional histological assessment of cancer. DNA in individual tumor cells was measured in sections from the original paraffin-embedded specimens. High proportions of cells with very high DNA values were identified in tumors from 16 patients, 10 of whom died of the disease during the follow-up period. The histological classification was invasive adenocarcinoma in 47 tumors and borderline cancer in 26. All of the patients who died had invasive adenocarcinoma. Although both DNA analysis and histological evaluation were sensitive predictors of mortality, the specificity of the DNA method was distinctly higher (0.90 versus 0.42). The study suggests that analysis of the DNA content of tumor cells can be a useful supplement to histological assessment of cancer and accordingly can significantly assist in the planning of treatment.