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An expression signature at diagnosis to estimate prostate cancer patients' overall survival.

Peng Z, Skoog L, Hellborg H, Jonstam G, Wingmo IL, Hjälm-Eriksson M, Harmenberg U, Cedermark GC, Andersson K, Ahrlund-Richter L, Pramana S, Pawitan Y, Nistér M, Nilsson S, Li C - Prostate Cancer Prostatic Dis. (2014)

Bottom Line: The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91-11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79-6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype.The expression signature can potentially be used to estimate overall survival time.When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.

ABSTRACT

Background: This study aimed to identify biomarkers for estimating the overall and prostate cancer (PCa)-specific survival in PCa patients at diagnosis.

Methods: To explore the importance of embryonic stem cell (ESC) gene signatures, we identified 641 ESC gene predictors (ESCGPs) using published microarray data sets. ESCGPs were selected in a stepwise manner, and were combined with reported genes. Selected genes were analyzed by multiplex quantitative polymerase chain reaction using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 PCa patients diagnosed between 1986 and 2001. Of these patients, there was overall and PCa-specific survival data available for 97.9%, and 77.9% were primarily treated by hormone therapy only. Univariate and multivariate Cox proportional hazard ratios and Kaplan-Meier plots were used for the survival analysis, and a k-nearest neighbor (kNN) algorithm for estimating overall survival.

Results: An expression signature of VGLL3, IGFBP3 and F3 was shown sufficient to categorize the patients into high-, intermediate- and low-risk subtypes. The median overall survival times of the subtypes were 3.23, 4.00 and 9.85 years, respectively. The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91-11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79-6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype. The kNN models that included the gene expression signature outperformed the one designed on clinical parameters alone.

Conclusions: The expression signature can potentially be used to estimate overall survival time. When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

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Related in: MedlinePlus

Receiver operating characteristic (ROC) curves for 5-year survival prediction. Prediction of survival time was modeled using a parametric model based on the assumption of the Weibull distribution. ROC curves at 5-year survival prediction show the sensitivity and the specificity of survival prediction. Overall (upper panel), PCa-specific (middle panel) and non-PCa-specific survival (bottom panel) predictions at 5 years were determined by the clinical parameters alone (black lines), and by both clinical parameters and the tumor subtypes classified by embryonic stem cell gene predictor (ESCGP) signature (red lines). The area under the curve (AUC) values of overall, PCa-specific and non-PCa-specific survival predictions were all increased by adding ESCGP signature. Positive predictive value (PPV) and negative predictive value (NPV) both increased.
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fig4: Receiver operating characteristic (ROC) curves for 5-year survival prediction. Prediction of survival time was modeled using a parametric model based on the assumption of the Weibull distribution. ROC curves at 5-year survival prediction show the sensitivity and the specificity of survival prediction. Overall (upper panel), PCa-specific (middle panel) and non-PCa-specific survival (bottom panel) predictions at 5 years were determined by the clinical parameters alone (black lines), and by both clinical parameters and the tumor subtypes classified by embryonic stem cell gene predictor (ESCGP) signature (red lines). The area under the curve (AUC) values of overall, PCa-specific and non-PCa-specific survival predictions were all increased by adding ESCGP signature. Positive predictive value (PPV) and negative predictive value (NPV) both increased.

Mentions: To assess the predictive performance of the selected ESCGP genes, different kNN classification algorithms were developed using the training set to estimate the overall survival.33 When evaluated on the test set (Table 5), the performance of the kNN model using only clinical parameters was similar to the random model, whereas all kNN models including the selected ESCGP genes were significantly (P<0.04) better than the random model. Another illustration of predictive performance was obtained using a parametric model. This model was used to estimate whether the use of tumor subtype classification,34 according to the expression signature of VGLL3, IGFBP3 and F3, could improve the prediction of survival beyond that estimated using the available clinical parameters (Figure 4). Compared with the prediction model that used only the clinical parameters, the addition of the tumor subtype classification improved sensitivity and specificity of the overall survival prediction from 0.775 to 0.800, and from 0.660 to 0.766, respectively (at 5 years; Figure 4). Receiver operating characteristic curves at 5-year survival were estimated to show the sensitivity and the specificity of survival prediction. The area under the receiver operating characteristic curve value was increased from 0.755 to 0.815 in overall survival prediction, from 0.726 to 0.793 in PCa-specific survival prediction and from 0.730 to 0.793 in non-PCa-specific survival prediction, respectively (Figure 4).


An expression signature at diagnosis to estimate prostate cancer patients' overall survival.

Peng Z, Skoog L, Hellborg H, Jonstam G, Wingmo IL, Hjälm-Eriksson M, Harmenberg U, Cedermark GC, Andersson K, Ahrlund-Richter L, Pramana S, Pawitan Y, Nistér M, Nilsson S, Li C - Prostate Cancer Prostatic Dis. (2014)

Receiver operating characteristic (ROC) curves for 5-year survival prediction. Prediction of survival time was modeled using a parametric model based on the assumption of the Weibull distribution. ROC curves at 5-year survival prediction show the sensitivity and the specificity of survival prediction. Overall (upper panel), PCa-specific (middle panel) and non-PCa-specific survival (bottom panel) predictions at 5 years were determined by the clinical parameters alone (black lines), and by both clinical parameters and the tumor subtypes classified by embryonic stem cell gene predictor (ESCGP) signature (red lines). The area under the curve (AUC) values of overall, PCa-specific and non-PCa-specific survival predictions were all increased by adding ESCGP signature. Positive predictive value (PPV) and negative predictive value (NPV) both increased.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921673&req=5

fig4: Receiver operating characteristic (ROC) curves for 5-year survival prediction. Prediction of survival time was modeled using a parametric model based on the assumption of the Weibull distribution. ROC curves at 5-year survival prediction show the sensitivity and the specificity of survival prediction. Overall (upper panel), PCa-specific (middle panel) and non-PCa-specific survival (bottom panel) predictions at 5 years were determined by the clinical parameters alone (black lines), and by both clinical parameters and the tumor subtypes classified by embryonic stem cell gene predictor (ESCGP) signature (red lines). The area under the curve (AUC) values of overall, PCa-specific and non-PCa-specific survival predictions were all increased by adding ESCGP signature. Positive predictive value (PPV) and negative predictive value (NPV) both increased.
Mentions: To assess the predictive performance of the selected ESCGP genes, different kNN classification algorithms were developed using the training set to estimate the overall survival.33 When evaluated on the test set (Table 5), the performance of the kNN model using only clinical parameters was similar to the random model, whereas all kNN models including the selected ESCGP genes were significantly (P<0.04) better than the random model. Another illustration of predictive performance was obtained using a parametric model. This model was used to estimate whether the use of tumor subtype classification,34 according to the expression signature of VGLL3, IGFBP3 and F3, could improve the prediction of survival beyond that estimated using the available clinical parameters (Figure 4). Compared with the prediction model that used only the clinical parameters, the addition of the tumor subtype classification improved sensitivity and specificity of the overall survival prediction from 0.775 to 0.800, and from 0.660 to 0.766, respectively (at 5 years; Figure 4). Receiver operating characteristic curves at 5-year survival were estimated to show the sensitivity and the specificity of survival prediction. The area under the receiver operating characteristic curve value was increased from 0.755 to 0.815 in overall survival prediction, from 0.726 to 0.793 in PCa-specific survival prediction and from 0.730 to 0.793 in non-PCa-specific survival prediction, respectively (Figure 4).

Bottom Line: The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91-11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79-6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype.The expression signature can potentially be used to estimate overall survival time.When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.

ABSTRACT

Background: This study aimed to identify biomarkers for estimating the overall and prostate cancer (PCa)-specific survival in PCa patients at diagnosis.

Methods: To explore the importance of embryonic stem cell (ESC) gene signatures, we identified 641 ESC gene predictors (ESCGPs) using published microarray data sets. ESCGPs were selected in a stepwise manner, and were combined with reported genes. Selected genes were analyzed by multiplex quantitative polymerase chain reaction using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 PCa patients diagnosed between 1986 and 2001. Of these patients, there was overall and PCa-specific survival data available for 97.9%, and 77.9% were primarily treated by hormone therapy only. Univariate and multivariate Cox proportional hazard ratios and Kaplan-Meier plots were used for the survival analysis, and a k-nearest neighbor (kNN) algorithm for estimating overall survival.

Results: An expression signature of VGLL3, IGFBP3 and F3 was shown sufficient to categorize the patients into high-, intermediate- and low-risk subtypes. The median overall survival times of the subtypes were 3.23, 4.00 and 9.85 years, respectively. The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91-11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79-6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype. The kNN models that included the gene expression signature outperformed the one designed on clinical parameters alone.

Conclusions: The expression signature can potentially be used to estimate overall survival time. When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

Show MeSH
Related in: MedlinePlus