A multiparametric panel for ovarian cancer diagnosis, prognosis, and response to chemotherapy

Yingye Zheng, Dionyssios Katsaros, Shannon J C Shan, Irene Rigault De La Longrais, Mauro Porpiglia, Andreas Scorilas, Nam W. Kim, Robert L. Wolfert, Iris Simon, Lin Li, Ziding Feng, Eleftherios P. Diamandis

Research output: Contribution to journalArticle

Abstract

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 datawere 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 inmultivariate 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.

Original languageEnglish (US)
Pages (from-to)6984-6992
Number of pages9
JournalClinical Cancer Research
Volume13
Issue number23
DOIs
StatePublished - Dec 1 2007
Externally publishedYes

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Ovarian Neoplasms
Biomarkers
Drug Therapy
Kallikreins
Neoplasms
Confidence Intervals
Tissue Extracts
Platinum
ROC Curve
Area Under Curve
Ovary
Research Design
Enzyme-Linked Immunosorbent Assay
Odds Ratio
Survival
Proteins

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

Zheng, Y., Katsaros, D., Shan, S. J. C., De La Longrais, I. R., Porpiglia, M., Scorilas, A., ... Diamandis, E. P. (2007). A multiparametric panel for ovarian cancer diagnosis, prognosis, and response to chemotherapy. Clinical Cancer Research, 13(23), 6984-6992. https://doi.org/10.1158/1078-0432.CCR-07-1409

A multiparametric panel for ovarian cancer diagnosis, prognosis, and response to chemotherapy. / Zheng, Yingye; Katsaros, Dionyssios; Shan, Shannon J C; De La Longrais, Irene Rigault; Porpiglia, Mauro; Scorilas, Andreas; Kim, Nam W.; Wolfert, Robert L.; Simon, Iris; Li, Lin; Feng, Ziding; Diamandis, Eleftherios P.

In: Clinical Cancer Research, Vol. 13, No. 23, 01.12.2007, p. 6984-6992.

Research output: Contribution to journalArticle

Zheng, Y, Katsaros, D, Shan, SJC, De La Longrais, IR, Porpiglia, M, Scorilas, A, Kim, NW, Wolfert, RL, Simon, I, Li, L, Feng, Z & Diamandis, EP 2007, 'A multiparametric panel for ovarian cancer diagnosis, prognosis, and response to chemotherapy', Clinical Cancer Research, vol. 13, no. 23, pp. 6984-6992. https://doi.org/10.1158/1078-0432.CCR-07-1409
Zheng Y, Katsaros D, Shan SJC, De La Longrais IR, Porpiglia M, Scorilas A et al. A multiparametric panel for ovarian cancer diagnosis, prognosis, and response to chemotherapy. Clinical Cancer Research. 2007 Dec 1;13(23):6984-6992. https://doi.org/10.1158/1078-0432.CCR-07-1409
Zheng, Yingye ; Katsaros, Dionyssios ; Shan, Shannon J C ; De La Longrais, Irene Rigault ; Porpiglia, Mauro ; Scorilas, Andreas ; Kim, Nam W. ; Wolfert, Robert L. ; Simon, Iris ; Li, Lin ; Feng, Ziding ; Diamandis, Eleftherios P. / A multiparametric panel for ovarian cancer diagnosis, prognosis, and response to chemotherapy. In: Clinical Cancer Research. 2007 ; Vol. 13, No. 23. pp. 6984-6992.
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AU - Katsaros, Dionyssios

AU - Shan, Shannon J C

AU - De La Longrais, Irene Rigault

AU - Porpiglia, Mauro

AU - Scorilas, Andreas

AU - Kim, Nam W.

AU - Wolfert, Robert L.

AU - Simon, Iris

AU - Li, Lin

AU - Feng, Ziding

AU - Diamandis, Eleftherios P.

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N2 - 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 datawere 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 inmultivariate 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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