Purpose: Cancer of the ovary confers the worst prognosis among women with gynecologic malignancies, underscoring the need to develop new biomarkers for detection of early disease, particularly those that can be readily monitored in the blood.
Experimental design: We developed an algorithm to identify secreted proteins encoded among approximately 22,500 genes on commercial oligonucleotide arrays and applied it to gene expression profiles of 67 stage I to IV serous papillary carcinomas and 9 crudely enriched normal ovarian tissues, to identify putative diagnostic markers. ELISAs were used to validate increased levels of secreted proteins in patient sera encoded by genes with differentially high expression.
Results: We identified 275 genes predicted to encode secreted proteins with increased/decreased expression in ovarian cancers (<0.5- or >2-fold, P < 0.001). The serum levels of four of these proteins (matrix metalloproteinase-7, osteopontin, secretory leukoprotease inhibitor, and kallikrein 10) were significantly elevated in a series of 67 independent patients with serous ovarian carcinomas compared with 67 healthy controls (P < 0.001, Wilcoxon rank sum test). Optimized support vector machine classifiers with as few as two of these markers (osteopontin or kallikrein 10/matrix metalloproteinase-7) in combination with CA-125 yielded sensitivity and specificity values ranging from 96% to 98.7% and 99.7% to 100%, respectively, with the ability to discern early-stage disease from normal, healthy controls.
Conclusions: Our data suggest that this assay combination warrants further investigation as a multi-analyte diagnostic test for serous ovarian adenocarcinoma.