Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles

Abstract

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.