11), calmodulin (Invitrogen), and p53 (X02469 ref. TAAs were cloned from PCR products into the prokaryotic expression vector pBADgIII (Invitrogen, Carlsbad, CA). Finally, a set of sera of women with early-stage ovarian cancer ( n = 14) was provided by the Gynecologic Oncology Group (GOG Bethesda, MD). This set was obtained from women with breast cancer ( n = 18), colon cancer ( n = 6), lung cancer ( n = 10), and stage III/IV ovarian cancer ( n = 27) and from healthy women ( n = 23). Anderson Cancer Center (MDACC Houston, TX). Sera were also obtained at the University of Texas M. A learning set of 59 sera and a validation set of 37 sera were obtained at the University of Louisville School of Medicine (ULSM Louisville, KY) from women with stage III/IV ovarian cancer and 32 sera from women attending a gynecology clinic for conditions other than ovarian cancer as controls. Blood samples were allowed to clot at room temperature and then centrifuged at 400 × g to remove clot and cells. Sera were collected as part of informed consent protocols approved by the local institutional review boards, and the study was approved by the Johns Hopkins University Institutional Review Board. Our application of multiplex detection of autologous antibodies to ovarian TAA and Bayesian model selection for detection of EOC complements the CA125 test and implicates p53, HOXB7, and NY-CO-8 in the biology of EOC. A unique feature of the Bayesian approach is the easy incorporation of previously described markers in a natural way into existing models, which cannot be achieved by ad hoc procedures, such as recursive partitioning ( 27– 29). Our model-based approach provides probabilistic assessments of uncertainty through Bayesian learning. MCMC variable selection is a model-based approach with a specified statistical model that puts no distributional restriction on the predictors (markers). Herein, we describe the application of multiplex detection of autologous antibodies to a panel of previously described ovarian TAAs and the Bayesian model/variable selection approach using Markov Chain Monte Carlo (MCMC) computations to determine the relevant TAA biomarkers and the most predictive model. However, a statistically rigorous approach to marker selection is required to develop such a clinically applicable diagnostic test by avoiding problems arising from high correlations among potential markers. We hypothesized that detection of antibodies to a panel of known TAAs could discriminate sera from ovarian cancer patients and healthy women and potentially improve on the performance of the CA125 assay. the percentage of ovarian cancer patients with reactivity to individual TAAs is generally low. 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. Serum antibody to p53 and HOXB7 is positively associated with EOC, whereas NY-CO-8-specific antibody shows negative association. 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. The best model generated an AUC of 0.86 for discrimination between sera of EOC patients and healthy patients using antibody specific to p53, NY-CO-8, and HOXB7. The selected model was subjected to area under the receiver-operator curve (AUC) analysis. 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. Binding of serum antibody of women with EOC or healthy controls to candidate TAA-coated microspheres was assayed in parallel. We tested multiplex detection of antibodies to candidate ovarian TAAs and statistical modeling for discrimination of sera of EOC patients and controls. Patients can generate antibodies to tumor-associated antigens (TAAs). Biomarkers for early detection of epithelial ovarian cancer (EOC) are urgently needed.
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