Comprehensive serum profiling for the discovery of epithelial ovarian cancer biomarkers.

FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933) and CA-125 (AUC=0.907) were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer.

Development and preliminary evaluation of a multivariate index assay for ovarian cancer.

BACKGROUND: Most women with a clinical presentation consistent with ovarian cancer have benign conditions. Therefore methods to distinguish women with ovarian cancer from those with benign conditions would be beneficial. We describe the development and preliminary evaluation of a serum-based multivariate assay for ovarian cancer. This hypothesis-driven study examined whether an informative pattern could be detected in stage I disease that persists through later stages. METHODOLOGY/PRINCIPAL FINDINGS: Sera, collected under uniform protocols from multiple institutions, representing 176 cases and 187 controls from women presenting for surgery were examined using high-throughput, multiplexed immunoassays. All stages and common subtypes of epithelial ovarian cancer, and the most common benign ovarian conditions were represented. A panel of 104 antigens, 44 autoimmune and 56 infectious disease markers were assayed and informative combinations identified. Using a training set of 91 stage I data sets, representing 61 individual samples, and an equivalent number of controls, an 11-analyte profile, composed of CA-125, CA 19-9, EGF-R, C-reactive protein, myoglobin, apolipoprotein A1, apolipoprotein CIII, MIP-1alpha, IL-6, IL-18 and tenascin C was identified and appears informative for all stages and common subtypes of ovarian cancer. Using a testing set of 245 samples, approximately twice the size of the model building set, the classifier had 91.3% sensitivity and 88.5% specificity. While these preliminary results are promising, further refinement and extensive validation of the classifier in a clinical trial is necessary to determine if the test has clinical value. CONCLUSIONS/SIGNIFICANCE: We describe a blood-based assay using 11 analytes that can distinguish women with ovarian cancer from those with benign conditions. Preliminary evaluation of the classifier suggests it has the potential to offer approximately 90% sensitivity and 90% specificity. While promising, the performance needs to be assessed in a blinded clinical validation study.

Multianalyte profiling of serum antigens and autoimmune and infectious disease molecules to identify biomarkers dysregulated in epithelial ovarian cancer.

Ovarian cancer is the deadliest gynecologic cancer in the United States. When detected early, the 5-year survival rate is 92%, although most cases remain undetected until the late stages where 5-year survival rates are 30%. Serum biomarkers may hold promise. Although many markers have been proposed and multivariate diagnostic models were built to fit the data on small, disparate sample sets, there has been no systematic evaluation of these markers on a single, large, well-defined sample set. To address this, we evaluated the dysregulation of 204 molecules in a sample set consisting of serum from 294 patients, collected from multiple collection sites, under a well-defined Gynecologic Oncology Group protocol. The population, weighted with early-stage cancers to assess biomarker value for early detection, contained all stages of ovarian cancer and common benign gynecologic conditions. The panel of serum molecules was assayed using rigorously qualified, high-throughput, multiplexed immunoassays and evaluated for their independent ovarian cancer diagnostic potential. Seventy-seven biomarkers were dysregulated in the ovarian cancer samples, although cancer antigen 125, C-reactive protein, epidermal growth factor receptor, interleukin 10, interleukin 8, connective tissue growth factor, haptoglobin, and tissue inhibitor of metalloproteinase 1 stood out as the most informative. When analyzed by cancer subtype and stage, there were differences in the relative value of biomarkers. In this study, using a large sample cohort, we show that some of the reported ovarian cancer biomarkers are more robust than others, and we identify additional informative candidates. These findings may guide the development of multivariate diagnostic models, which should be tested on additional, prospectively collected samples.