Evaluation of glycomic profiling as a diagnostic biomarker for epithelial ovarian cancer

Abstract

Background: Prior studies suggested that glycans were differentially expressed in patients with ovarian cancer and controls. We hypothesized that glycan-based biomarkers might serve as a diagnostic test for ovarian cancer and evaluated the ability of glycans to distinguish ovarian cancer cases from matched controls.

Methods: Serum samples were obtained from the tissue-banking repository of the Gynecologic Oncology Group, and included healthy female controls (n = 100), women diagnosed with low malignant potential (LMP) tumors (n = 52), and epithelial ovarian cancers (EOC) cases (n = 147). Cases and controls were matched on age at enrollment within ±5 years. Serum samples were analyzed by glycomics analysis to detect abundance differences in glycan expression levels. A two-stage procedure was carried out for biomarker discovery and validation. Candidate classifiers of glycans that separated cases from controls were developed using a training set in the discovery phase and the classification performance of the candidate classifiers was assessed using independent test samples that were not used in discovery.

Results: The patterns of glycans showed discriminatory power for distinguishing EOC and LMP cases from controls. Candidate glycan-based biomarkers developed on a training set (sensitivity, 86% and specificity, 95.8% for distinguishing EOC from controls through leave-one-out cross-validation) confirmed their potential use as a detection test using an independent test set (sensitivity, 70% and specificity, 86.5%).

Conclusion: Formal investigations of glycan biomarkers that distinguish cases and controls show great promise for an ovarian cancer diagnostic test. Further validation of a glycan-based test for detection of ovarian cancer is warranted.

Impact: An emerging diagnostic test based on the knowledge gained from understanding the glycobiology should lead to an assay that improves sensitivity and specificity and allows for early detection of ovarian cancer.

Figure 1

Receiver operating characteristic (ROC) curves for the top three glycans singly and CA125 that demonstrate diagnostic ability for separating healthy controls to stages III–IV EOC cases (blue line) of the OC1 training set and healthy controls to LMP tumors (green line), stages I–II EOC cases (red line), and stages III–IV EOC cases (black line) of the OC2 test set.

Figure 2

ROC curves for multiplex classifiers consisting of three, nine, and 11 glycan compositions that demonstrate diagnostic ability for separating healthy controls to stages III–IV EOC cases (blue line) of the OC1 training set and healthy controls to LMP tumors (green line), stages I–II EOC cases (red line), and stages III–IV EOC cases (black line) of the OC2 test set.

Figure 3

Glycans that are differentially expressed can be subdivided into three different groups based on their structural features. Glycans are represented as mannose (green circle), galactose (yellow circle), GlcNAc (blue square), fucose (red triangle), and sialic acid (purple diamond).