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.
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.
Authors: Serenus Hua; Cynthia C Williams; Lauren M Dimapasoc; Grace S Ro; Sureyya Ozcan; Suzanne Miyamoto; Carlito B Lebrilla; Hyun Joo An; Gary S Leiserowitz Journal: J Chromatogr A Date: 2013-01-11 Impact factor: 4.759
Authors: Scott R Kronewitter; Hyun Joo An; Maria Lorna de Leoz; Carlito B Lebrilla; Suzanne Miyamoto; Gary S Leiserowitz Journal: Proteomics Date: 2009-06 Impact factor: 3.984
Authors: G S Leiserowitz; C Lebrilla; S Miyamoto; H J An; H Duong; C Kirmiz; B Li; H Liu; K S Lam Journal: Int J Gynecol Cancer Date: 2007-07-26 Impact factor: 3.437
Authors: Lisa M McShane; Margaret M Cavenagh; Tracy G Lively; David A Eberhard; William L Bigbee; P Mickey Williams; Jill P Mesirov; Mei-Yin C Polley; Kelly Y Kim; James V Tricoli; Jeremy M G Taylor; Deborah J Shuman; Richard M Simon; James H Doroshow; Barbara A Conley Journal: Nature Date: 2013-10-17 Impact factor: 49.962
Authors: L Renee Ruhaak; Sandra L Taylor; Carol Stroble; Uyen Thao Nguyen; Evan A Parker; Ting Song; Carlito B Lebrilla; William N Rom; Harvey Pass; Kyoungmi Kim; Karen Kelly; Suzanne Miyamoto Journal: J Proteome Res Date: 2015-09-30 Impact factor: 4.466