Purpose: Our goal was to examine a panel of 11 biochemical variables, measured in cytosolic extracts of ovarian tissues (normal, benign, and malignant) by quantitative ELISAs for their ability to diagnose, prognose, and predict response to chemotherapy of ovarian cancer patients. Experimental Design: Eleven proteins were measured (9 kallikreins, B7-H4, and CA125) in cytosolic extracts of 259 ovarian tumor tissues, 50 tissues from benign conditions, 35 normal tissues, and 44 tissues from nonovarian tumors that metastasized to the ovary. Odds ratios and hazard ratios and their 95% confidence interval were calculated. Time-dependent receiver operating characteristic curves for censored survival data were used to evaluate the performance of the biomarkers. Resampling was used to validate the performance. Results: Most biomarkers effectively separated cancer from noncancer groups. A composite marker provided an area under the curve of 0.97 (95% confidence interval, 0.95-0.99) for discriminating normal and cancer groups. Univariately, hK5 and hK6 were positively associated with progression. After adjusting for clinical variables in multivariate analysis, both hK10 and hK11 significantly predicted time to progression. Increasing levels of hK13 were associated with chemotherapy response, and the predictive power of hK13 to chemotherapy response was improved by a panel of five biomarkers. Conclusions: The evidence shows that a group of kallikreins and multiparametric combinations with other biomarkers and clinical variables can significantly assist with ovarian cancer classification, prognosis, and response to platinum-based chemotherapy. In particular, we developed a multiparametric strategy for predicting ovarian cancer response to chemotherapy, comprising several biomarkers and clinical features.

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Background: Human tissue kallikrein 7 (gene, KLK7; protein, hK7) is a member of the kallikrein family of secreted serine proteases. Reports indicate that in ovarian cancer, KLK7 is significantly up-regulated at the mRNA level. The aim of this study was to determine whether hK7, measured quantitatively by ELISA in ovarian cancer cytosols, is a prognostic biomarker for ovarian cancer. Methods: We used a newly developed ELISA with 2 monoclonal antibodies to quantify hK7 production in 260 ovarian tumor cytosols and correlated these data with various clinicopathologic variables and patient outcomes [progression-free survival (PFS) and overall survival (OS)] over a median follow-up period of 52 months. Results: Median (range) hK7 concentration in ovarian tumor cytosols was 2.84 (0 -32.8) ng/mg of total protein. Compared with healthy and benign ovarian tissues and nonovarian tumors that metastasized to the ovary, malignant ovarian tumor cytosols highly overproduced hK7 (P <0.001). We used the median value as the cutoff value to categorize tumors as hK7-positive and hK7-negative. Women with hK7-positive tumors most frequently had advanced-stage disease, higher tumor grade (G3), suboptimal debulking, and serous or undifferentiated histotype (P <0.001). Univariate analysis showed that hK7 positivity was associated with significantly

Supplementary Data from A Multiparametric Panel for Ovarian Cancer Diagnosis, Prognosis, and Response to Chemotherapy

<div>Abstract<p><b>Purpose:</b> Our goal was to examine a panel of 11 biochemical variables, measured in cytosolic extracts of ovarian tissues (normal, benign, and malignant) by quantitative ELISAs for their ability to diagnose, prognose, and predict response to chemotherapy of ovarian cancer patients.</p><p><b>Experimental Design:</b> Eleven proteins were measured (9 kallikreins, B7-H4, and CA125) in cytosolic extracts of 259 ovarian tumor tissues, 50 tissues from benign conditions, 35 normal tissues, and 44 tissues from nonovarian tumors that metastasized to the ovary. Odds ratios and hazard ratios and their 95% confidence interval were calculated. Time-dependent receiver operating characteristic curves for censored survival data were used to evaluate the performance of the biomarkers. Resampling was used to validate the performance.</p><p><b>Results:</b> Most biomarkers effectively separated cancer from noncancer groups. A composite marker provided an area under the curve of 0.97 (95% confidence interval, 0.95-0.99) for discriminating normal and cancer groups. Univariately, hK5 and hK6 were positively associated with progression. After adjusting for clinical variables in multivariate analysis, both hK10 and hK11 significantly predicted time to progression. Increasing levels of hK13 were associated with chemotherapy response, and the predictive power of hK13 to chemotherapy response was improved by a panel of five biomarkers.</p><p><b>Conclusions:</b> The evidence shows that a group of kallikreins and multiparametric combinations with other biomarkers and clinical variables can significantly assist with ovarian cancer classification, prognosis, and response to platinum-based chemotherapy. In particular, we developed a multiparametric strategy for predicting ovarian cancer response to chemotherapy, comprising several biomarkers and clinical features.</p></div>