Application of Bayesian modeling of autologous antibody responses against ovarian tumor-associated antigens to cancer detection.

Published

Journal Article

Biomarkers for early detection of epithelial ovarian cancer (EOC) are urgently needed. Patients can generate antibodies to tumor-associated antigens (TAAs). We tested multiplex detection of antibodies to candidate ovarian TAAs and statistical modeling for discrimination of sera of EOC patients and controls. Binding of serum antibody of women with EOC or healthy controls to candidate TAA-coated microspheres was assayed in parallel. A Bayesian model/variable selection approach using Markov Chain Monte Carlo computations was applied to these data, and serum CA125 values, to determine the best predictive model. The selected model was subjected to area under the receiver-operator curve (AUC) analysis. The best model generated an AUC of 0.86 [95% confidence interval (95% CI), 0.78-0.90] for discrimination between sera of EOC patients and healthy patients using antibody specific to p53, NY-CO-8, and HOXB7. Inclusion of CA125 in the model provided an AUC of 0.89 (95% CI, 0.84-0.92) compared with an AUC of 0.83 (95% CI, 0.81-0.85) using CA125 alone. However, using TAA responses alone, the model discriminated between independent sera of women with nonmalignant gynecologic conditions and those with advanced-stage or early-stage EOC with AUCs of 0.71 (95% CI, 0.67-0.76) and 0.70 (95% CI, 0.48-0.75), respectively. Serum antibody to p53 and HOXB7 is positively associated with EOC, whereas NY-CO-8-specific antibody shows negative association. Bayesian modeling of these TAA-specific serum antibody responses exhibits similar discrimination of patients with early-stage and advanced-stage EOC from women with nonmalignant gynecologic conditions and may be complementary to CA125.

Full Text

Duke Authors

Cited Authors

  • Erkanli, A; Taylor, DD; Dean, D; Eksir, F; Egger, D; Geyer, J; Nelson, BH; Stone, B; Fritsche, HA; Roden, RBS

Published Date

  • February 2006

Published In

Volume / Issue

  • 66 / 3

Start / End Page

  • 1792 - 1798

PubMed ID

  • 16452240

Pubmed Central ID

  • 16452240

Electronic International Standard Serial Number (EISSN)

  • 1538-7445

International Standard Serial Number (ISSN)

  • 0008-5472

Digital Object Identifier (DOI)

  • 10.1158/0008-5472.can-05-0669

Language

  • eng