Cancer Research Translational Cancer Medicine 2008: Cancer Clinical Trials and Personalized Medicine  Joint Metastasis Research Society-AACR Conference on Metastasis
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[Cancer Research 66, 1792-1798, February 1, 2006]
© 2006 American Association for Cancer Research


Immunology

Application of Bayesian Modeling of Autologous Antibody Responses against Ovarian Tumor-Associated Antigens to Cancer Detection

Al Erkanli1, Douglas D. Taylor3, Deyrick Dean4, Faria Eksir4, Daniel Egger2, James Geyer4, Brad H. Nelson5, Brad Stone6, Herbert A. Fritsche7 and Richard B.S. Roden8

1 Department of Biostatistics and Bioinformatics, Duke University Medical Center; 2 Eno River Capital, Durham, North Carolina; 3 Departments of Obstetrics and Gynecology and Radiation Oncology, University of Louisville School of Medicine, Louisville, Kentucky; 4 Amplistar, Inc., Winston-Salem, North Carolina; 5 Deeley Research Centre, British Columbia Cancer Agency, Victoria, British Columbia, Canada; 6 Benaroya Research Institute, Seattle, Washington; 7 University of Texas M.D. Anderson Cancer Center, Houston, Texas; and 8 Department of Pathology, Johns Hopkins School of Medicine, Baltimore, Maryland

Requests for reprints: Richard B.S. Roden, Department of Pathology, Johns Hopkins School of Medicine, Ross Research Building, Room 512, 720 Rutland Avenue, Baltimore, MD 21205. Phone: 410-502-5161; Fax: 443-287-4295; E-mail: roden{at}jhmi.edu.

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. (Cancer Res 2006; 66(3): 1792-8)




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HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Cell Growth & Differentiation
Copyright © 2006 by the American Association for Cancer Research.