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Comparative Study
. 2010 May 1;28(13):2159-66.
doi: 10.1200/JCO.2008.19.2484. Epub 2010 Apr 5.

Development of a multimarker assay for early detection of ovarian cancer

Affiliations
Comparative Study

Development of a multimarker assay for early detection of ovarian cancer

Zoya Yurkovetsky et al. J Clin Oncol. .

Abstract

Purpose: Early detection of ovarian cancer has great promise to improve clinical outcome.

Patients and methods: Ninety-six serum biomarkers were analyzed in sera from healthy women and from patients with ovarian cancer, benign pelvic tumors, and breast, colorectal, and lung cancers, using multiplex xMAP bead-based immunoassays. A Metropolis algorithm with Monte Carlo simulation (MMC) was used for analysis of the data.

Results: A training set, including sera from 139 patients with early-stage ovarian cancer, 149 patients with late-stage ovarian cancer, and 1,102 healthy women, was analyzed with MMC algorithm and cross validation to identify an optimal biomarker panel discriminating early-stage cancer from healthy controls. The four-biomarker panel providing the highest diagnostic power of 86% sensitivity (SN) for early-stage and 93% SN for late-stage ovarian cancer at 98% specificity (SP) was comprised of CA-125, HE4, CEA, and VCAM-1. This model was applied to an independent blinded validation set consisting of sera from 44 patients with early-stage ovarian cancer, 124 patients with late-stage ovarian cancer, and 929 healthy women, providing unbiased estimates of 86% SN for stage I and II and 95% SN for stage III and IV disease at 98% SP. This panel was selective for ovarian cancer showing SN of 33% for benign pelvic disease, SN of 6% for breast cancer, SN of 0% for colorectal cancer, and SN of 36% for lung cancer.

Conclusion: A panel of CA-125, HE4, CEA, and VCAM-1, after additional validation, could serve as an initial stage in a screening strategy for epithelial ovarian cancer.

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Conflict of interest statement

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Figures

Fig 1.
Fig 1.
Flowchart of experimental design. MMC, Metropolis algorithm with Monte Carlo simulation; SP, specificity.
Fig 2.
Fig 2.
Cumulative receiver operating characteristic (ROC) curves in the training set and distribution of scores in training and validation sets using the four-biomarker panel with Metropolis algorithm with Monte Carlo simulation (MMC): (A) healthy controls versus stage I to IIB ovarian cancer; (B) healthy controls versus stages IIC to IV ovarian cancer. Solid line indicates four-biomarker panel; dashed line indicates CA125. (C) Distributions of scores created by MMC algorithm. P value over a group denotes statistical significance of differences between each group member and appropriate healthy control. Breast, colon, and lung cancers are compared with early-stage ovarian cancer in the validation set.

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