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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

Outline of a stepwise gene selection process. (a) Identification of 641 embryonic stem cell gene predictors (ESCGPs) by bioinformatic analysis. Previously published data sets of whole-genome complementary DNA microarrays derived from five human ESC lines and 115 human normal tissues from various organs were retrieved from the Stanford Microarray Database (SMD). After a data-centering process, a sub-data set with expression profile of 24 361 genes in the ESC lines was isolated from the combined whole data set. A single-class significance analysis of microarray (SAM) was performed and a SAM plot was generated. The 328 genes with the highest expression levels and 313 genes with the lowest expression levels were identified, in total 641 ESCGPs. (b) Identification of 258 ESCGPs in prostate cancer (PCa). PCa ESCGPs were identified by matching the list of the 641 ESCGPs and the list of 5513 genes published by Lapointe et al.9 When clustering the 112 PCa tissue samples and comparing the cluster results when using all 5513 genes and when using only the 258 ESCGPs present in the data set, nearly identical results were obtained. Sample labeling: PL, lymph node metastasis; PN, normal prostate tissue; PT, prostate tumor. Three cases (marked green) were placed in different classification positions and two cases (purple) were consistently misclassified. (c) Selection of important candidate ESCGPs for clinical survival correlation. Of 258 PCa ESCGPs, 34 genes were selected by their high-ranking order in the SAM analysis identifying significant genes for the subtype classification or for the discriminating between tumor and normal samples. Of these 34 ESCGPs, 19 were selected based on their markedly different expression patterns and robust performances in RT-PCR reactions (Supplementary Figure S1). The 19 ESCGPs and the 5 reported genes were included in the optimization of the 4-plex qPCR method using RNAs from PCa cell lines. (d) Identification of the ESCGP signature in Subset 1. After the 4-plex qPCR optimization, the method was used to analyze 36 fresh–frozen fine-needle aspiration (FNA) biopsies taken from PCa patients (Subset 1). RNAs could be extracted in 28 biopsies. A series of cluster analyses using different gene combinations revealed that the ESCGP signature VGLL3, IGFBP3 and F3 classified Subset 1 samples into three subtypes with strong survival correlations. The level of gene expression increases from blue to red, whereas the delta Ct value decreases from blue to red. Gray areas represent missing qPCR data.
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fig1: Outline of a stepwise gene selection process. (a) Identification of 641 embryonic stem cell gene predictors (ESCGPs) by bioinformatic analysis. Previously published data sets of whole-genome complementary DNA microarrays derived from five human ESC lines and 115 human normal tissues from various organs were retrieved from the Stanford Microarray Database (SMD). After a data-centering process, a sub-data set with expression profile of 24 361 genes in the ESC lines was isolated from the combined whole data set. A single-class significance analysis of microarray (SAM) was performed and a SAM plot was generated. The 328 genes with the highest expression levels and 313 genes with the lowest expression levels were identified, in total 641 ESCGPs. (b) Identification of 258 ESCGPs in prostate cancer (PCa). PCa ESCGPs were identified by matching the list of the 641 ESCGPs and the list of 5513 genes published by Lapointe et al.9 When clustering the 112 PCa tissue samples and comparing the cluster results when using all 5513 genes and when using only the 258 ESCGPs present in the data set, nearly identical results were obtained. Sample labeling: PL, lymph node metastasis; PN, normal prostate tissue; PT, prostate tumor. Three cases (marked green) were placed in different classification positions and two cases (purple) were consistently misclassified. (c) Selection of important candidate ESCGPs for clinical survival correlation. Of 258 PCa ESCGPs, 34 genes were selected by their high-ranking order in the SAM analysis identifying significant genes for the subtype classification or for the discriminating between tumor and normal samples. Of these 34 ESCGPs, 19 were selected based on their markedly different expression patterns and robust performances in RT-PCR reactions (Supplementary Figure S1). The 19 ESCGPs and the 5 reported genes were included in the optimization of the 4-plex qPCR method using RNAs from PCa cell lines. (d) Identification of the ESCGP signature in Subset 1. After the 4-plex qPCR optimization, the method was used to analyze 36 fresh–frozen fine-needle aspiration (FNA) biopsies taken from PCa patients (Subset 1). RNAs could be extracted in 28 biopsies. A series of cluster analyses using different gene combinations revealed that the ESCGP signature VGLL3, IGFBP3 and F3 classified Subset 1 samples into three subtypes with strong survival correlations. The level of gene expression increases from blue to red, whereas the delta Ct value decreases from blue to red. Gray areas represent missing qPCR data.

Mentions: Written approval from the local ethics committee was obtained for the molecular analysis of biological samples from PCa patients. This study was conducted in a stepwise manner. The procedure for selecting and verifying genes is outlined in Figure 1 and described in detail as follows.


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)

Outline of a stepwise gene selection process. (a) Identification of 641 embryonic stem cell gene predictors (ESCGPs) by bioinformatic analysis. Previously published data sets of whole-genome complementary DNA microarrays derived from five human ESC lines and 115 human normal tissues from various organs were retrieved from the Stanford Microarray Database (SMD). After a data-centering process, a sub-data set with expression profile of 24 361 genes in the ESC lines was isolated from the combined whole data set. A single-class significance analysis of microarray (SAM) was performed and a SAM plot was generated. The 328 genes with the highest expression levels and 313 genes with the lowest expression levels were identified, in total 641 ESCGPs. (b) Identification of 258 ESCGPs in prostate cancer (PCa). PCa ESCGPs were identified by matching the list of the 641 ESCGPs and the list of 5513 genes published by Lapointe et al.9 When clustering the 112 PCa tissue samples and comparing the cluster results when using all 5513 genes and when using only the 258 ESCGPs present in the data set, nearly identical results were obtained. Sample labeling: PL, lymph node metastasis; PN, normal prostate tissue; PT, prostate tumor. Three cases (marked green) were placed in different classification positions and two cases (purple) were consistently misclassified. (c) Selection of important candidate ESCGPs for clinical survival correlation. Of 258 PCa ESCGPs, 34 genes were selected by their high-ranking order in the SAM analysis identifying significant genes for the subtype classification or for the discriminating between tumor and normal samples. Of these 34 ESCGPs, 19 were selected based on their markedly different expression patterns and robust performances in RT-PCR reactions (Supplementary Figure S1). The 19 ESCGPs and the 5 reported genes were included in the optimization of the 4-plex qPCR method using RNAs from PCa cell lines. (d) Identification of the ESCGP signature in Subset 1. After the 4-plex qPCR optimization, the method was used to analyze 36 fresh–frozen fine-needle aspiration (FNA) biopsies taken from PCa patients (Subset 1). RNAs could be extracted in 28 biopsies. A series of cluster analyses using different gene combinations revealed that the ESCGP signature VGLL3, IGFBP3 and F3 classified Subset 1 samples into three subtypes with strong survival correlations. The level of gene expression increases from blue to red, whereas the delta Ct value decreases from blue to red. Gray areas represent missing qPCR data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Outline of a stepwise gene selection process. (a) Identification of 641 embryonic stem cell gene predictors (ESCGPs) by bioinformatic analysis. Previously published data sets of whole-genome complementary DNA microarrays derived from five human ESC lines and 115 human normal tissues from various organs were retrieved from the Stanford Microarray Database (SMD). After a data-centering process, a sub-data set with expression profile of 24 361 genes in the ESC lines was isolated from the combined whole data set. A single-class significance analysis of microarray (SAM) was performed and a SAM plot was generated. The 328 genes with the highest expression levels and 313 genes with the lowest expression levels were identified, in total 641 ESCGPs. (b) Identification of 258 ESCGPs in prostate cancer (PCa). PCa ESCGPs were identified by matching the list of the 641 ESCGPs and the list of 5513 genes published by Lapointe et al.9 When clustering the 112 PCa tissue samples and comparing the cluster results when using all 5513 genes and when using only the 258 ESCGPs present in the data set, nearly identical results were obtained. Sample labeling: PL, lymph node metastasis; PN, normal prostate tissue; PT, prostate tumor. Three cases (marked green) were placed in different classification positions and two cases (purple) were consistently misclassified. (c) Selection of important candidate ESCGPs for clinical survival correlation. Of 258 PCa ESCGPs, 34 genes were selected by their high-ranking order in the SAM analysis identifying significant genes for the subtype classification or for the discriminating between tumor and normal samples. Of these 34 ESCGPs, 19 were selected based on their markedly different expression patterns and robust performances in RT-PCR reactions (Supplementary Figure S1). The 19 ESCGPs and the 5 reported genes were included in the optimization of the 4-plex qPCR method using RNAs from PCa cell lines. (d) Identification of the ESCGP signature in Subset 1. After the 4-plex qPCR optimization, the method was used to analyze 36 fresh–frozen fine-needle aspiration (FNA) biopsies taken from PCa patients (Subset 1). RNAs could be extracted in 28 biopsies. A series of cluster analyses using different gene combinations revealed that the ESCGP signature VGLL3, IGFBP3 and F3 classified Subset 1 samples into three subtypes with strong survival correlations. The level of gene expression increases from blue to red, whereas the delta Ct value decreases from blue to red. Gray areas represent missing qPCR data.
Mentions: Written approval from the local ethics committee was obtained for the molecular analysis of biological samples from PCa patients. This study was conducted in a stepwise manner. The procedure for selecting and verifying genes is outlined in Figure 1 and described in detail as follows.

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