Esponders (NR) maximizing the Youden’s J index (sensitivity+specificity-1). We conducted a time-dependent receiver operating characteristics (ROC) analysis for an overall survival of 5 years to define optimal cutoff values for high clinical utility by the area under the curve (AUC). The optimal cutoff values for the final predictors were determined by maximizing the Youden’s J index on the ROC curves. These cutoff values were used to stratify patients in the external validation cohorts as if they had been predicted for their therapeutic outcomes prior to treatment. Patients with higher predictor scores than each drug’s cutoff value were considered to be predicted responders to the drug.PLOS ONE | www.plosone.orgSurvival Improvement by Personalized ChemotherapyFigure 1. Integrated co-expression extrapolation (COXEN) gene expression model (predictor) development and validation procedures. doi:10.1371/journal.pone.0086532.gindependent EOC patient sets. Both univariate and multivariate Cox regression survival analyses showed that paclitaxel predictor scores were MK-571 (sodium salt)MedChemExpress L-660711 sodium salt significantly associated with overall survival (OS) andprogression-free survival (PFS) times for EOC patients in the TCGA-448 cohort (Table 3). Notably, no clinical variables (including debulking status) were significantly associated with long-Table 2. Logistic regression analysis for the paclitaxel prediction of primary order Chaetocin chemotherapy response.Univariatea Validation cohort TCGA-448(n = 351) Variables Predictor Score Surgical outcomes(sub vs optimal) Stage (IV vs II II) Age UVA-51(n = 51) Predictor Score Surgical outcomes(sub vs optimal) Stage (IV vs III) Age Odds ratio (95 CI) 3.574 (1.567, 8.328) 0.313 (0.184,0.531) 0.85 (0.46, 1.622) 1.002 (0.982,1.024) 6.328 (0.884,54.155) 0.202 (0.053,0.677) 0.513 (0.629, 3.375) 0.957 (0.901, 1.013) P-value 0.003*** ,0.001*** 0.611 0.823 0.075* 0.013** 0.487 0.14 Multivariateb Odds ratio (95 CI) 3.591 (1.494, 8.85) 0.327 (0.187,0.568) 0.812 (0.413, 1.639) 1.003 (0.979, 1.027) 9.521 (0.1, 125.726) 0.183 (0.04, 0.71) 2.303(0.222,24.469) 0.948 (0.88, 1.013) P-value 0.005*** ,0.001*** 0.551 0.796 0.063* 0.019** 0.464 0.a An univariate logistic regression analysis was performed for each of the predictor and clinical variables to predict patient clinical response to paclitaxel; statistical significance was reported with overall model significance p-value. b A multivariate logistic regression analysis was performed with predictor and all clinical variables in the same model; the statistical significance of each variable was derived from the fitted model. doi:10.1371/journal.pone.0086532.tPLOS ONE | www.plosone.orgSurvival Improvement by Personalized Chemotherapyterm survival. We were not able to obtain reliable statistical results in this Cox regression survival analysis for the UVA-51 cohort due to its relatively small sample size. Cyclophosphamide and topotecan are largely used for treating recurrent and progressive EOC patients, so only patient OS information after treatment was available for these drugs. We therefore performed both univariate and multivariate Cox regression analyses using the backward variable elimination process to examine whether the two drugs’ predictor scores and other clinical variables were predictive of OS times. Cyclophosphamide predictor scores were found to be significantly associated with overall survival (HR = 0.127; 95 CI: 0.021?.745, p = 0.022), while clinical variables such as surgical outcome, tumor stage, and age.Esponders (NR) maximizing the Youden’s J index (sensitivity+specificity-1). We conducted a time-dependent receiver operating characteristics (ROC) analysis for an overall survival of 5 years to define optimal cutoff values for high clinical utility by the area under the curve (AUC). The optimal cutoff values for the final predictors were determined by maximizing the Youden’s J index on the ROC curves. These cutoff values were used to stratify patients in the external validation cohorts as if they had been predicted for their therapeutic outcomes prior to treatment. Patients with higher predictor scores than each drug’s cutoff value were considered to be predicted responders to the drug.PLOS ONE | www.plosone.orgSurvival Improvement by Personalized ChemotherapyFigure 1. Integrated co-expression extrapolation (COXEN) gene expression model (predictor) development and validation procedures. doi:10.1371/journal.pone.0086532.gindependent EOC patient sets. Both univariate and multivariate Cox regression survival analyses showed that paclitaxel predictor scores were significantly associated with overall survival (OS) andprogression-free survival (PFS) times for EOC patients in the TCGA-448 cohort (Table 3). Notably, no clinical variables (including debulking status) were significantly associated with long-Table 2. Logistic regression analysis for the paclitaxel prediction of primary chemotherapy response.Univariatea Validation cohort TCGA-448(n = 351) Variables Predictor Score Surgical outcomes(sub vs optimal) Stage (IV vs II II) Age UVA-51(n = 51) Predictor Score Surgical outcomes(sub vs optimal) Stage (IV vs III) Age Odds ratio (95 CI) 3.574 (1.567, 8.328) 0.313 (0.184,0.531) 0.85 (0.46, 1.622) 1.002 (0.982,1.024) 6.328 (0.884,54.155) 0.202 (0.053,0.677) 0.513 (0.629, 3.375) 0.957 (0.901, 1.013) P-value 0.003*** ,0.001*** 0.611 0.823 0.075* 0.013** 0.487 0.14 Multivariateb Odds ratio (95 CI) 3.591 (1.494, 8.85) 0.327 (0.187,0.568) 0.812 (0.413, 1.639) 1.003 (0.979, 1.027) 9.521 (0.1, 125.726) 0.183 (0.04, 0.71) 2.303(0.222,24.469) 0.948 (0.88, 1.013) P-value 0.005*** ,0.001*** 0.551 0.796 0.063* 0.019** 0.464 0.a An univariate logistic regression analysis was performed for each of the predictor and clinical variables to predict patient clinical response to paclitaxel; statistical significance was reported with overall model significance p-value. b A multivariate logistic regression analysis was performed with predictor and all clinical variables in the same model; the statistical significance of each variable was derived from the fitted model. doi:10.1371/journal.pone.0086532.tPLOS ONE | www.plosone.orgSurvival Improvement by Personalized Chemotherapyterm survival. We were not able to obtain reliable statistical results in this Cox regression survival analysis for the UVA-51 cohort due to its relatively small sample size. Cyclophosphamide and topotecan are largely used for treating recurrent and progressive EOC patients, so only patient OS information after treatment was available for these drugs. We therefore performed both univariate and multivariate Cox regression analyses using the backward variable elimination process to examine whether the two drugs’ predictor scores and other clinical variables were predictive of OS times. Cyclophosphamide predictor scores were found to be significantly associated with overall survival (HR = 0.127; 95 CI: 0.021?.745, p = 0.022), while clinical variables such as surgical outcome, tumor stage, and age.
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