Expression Signature to Estimate Survival in Prostate Cancer
Expression Signature to Estimate Survival in Prostate Cancer
This report discusses the ability to estimate the overall and cancer-specific survival using gene expression levels in PCa samples. If such a measure would become available, it would provide an important and orthogonal complement to the currently available data used in the decision process for selecting treatment for individual patients.
Numerous attempts to produce prognostic methods for PCa use surrogate end points like biochemical relapse or even cancer-specific mortality. This is probably due to the fact that data sets for surrogate end points are more easily obtained. However, it has been shown that nearly 50% of the patients die of diseases other than PCa. The identification of biomarkers that correlate with overall survival of PCa is rare. Our results demonstrate that PCa tumor subtypes classified by the gene expression signature of VGLL3, IGFBP3 and F3 at the time of diagnosis are clearly correlated with overall and cancer-specific survival in the evaluated cohort.
The gene expression signature was independent of age, PSA level, World Health Organization tumor grade and clinical stage. Furthermore, as shown through the kNN model and the parametric prediction model, this signature demonstrated clear prognostic value and a potential to further improve the prognostic accuracy of conventional clinical parameters. Following validation on additional cohorts, this ESCGP signature could be particularly beneficial in the clinical management of early-stage PCa. In such cases, the accuracy of conventional clinical parameters for the prediction of cancer-specific and overall survival are limited by a relatively low PSA level, localized disease stage and insufficient tumor material for Gleason scoring. When evaluating such small tumor samples, the ESCGP has a potential to improve the assessment.
Overall survival is the real lifetime determined by the aggressiveness of PCa and patient's other conditions or comorbidities. The ability to estimate overall survival by the ESCGP signature may reflect the biological functions of the three genes. Both F3 and IGFBP3 have been shown associated with metastasis development in prostate and other cancers. They have also been shown important in the development of many non-cancer diseases of the coagulatory, cardiovascular and metabolic systems, diseases that are common causes of death in PCa patients. The positive correlation between prolonged survival and increased expression of F3 was unexpected and may suggest that PCa cells with higher levels of F3 are strongly androgen-dependent and sensitive to castration treatment. The functions of VGLL3 have yet to be studied. VGLL3 shows clear correlation with the age at diagnosis (Supplementary Tables S6 and S7), which is an important patient risk factor that strongly influences the patient overall survival and treatment decision. We suggest that the expression of VGLL3 may reflect the patient's biological age that currently can be estimated only by physician's subjective observation. Therefore, the combination of these three genes could provide a molecular classification sufficient to estimate overall survival.
Several reported gene markers (AMACR, EZH2, c-MAF-a, c-MAF-b and MUC1) selected from previous studies were also validated in our FNA cohort (Supplementary Table S5); however, they were not as strong as the ESCGP signature when estimating overall survival. Owing to the limited RNA quantity present in the FNA samples, the previously reported 'stemness' gene signature could not be compared with our ESCGP signature, although this comparison would be warranted in future studies.
The present study was driven by the stem cell hypothesis, whereby ESC gene expression signatures are thought to be associated with the prognosis of various cancers. Our results demonstrate that the 258 ESCGPs could classify an independent PCa data set in a nearly identical manner as compared with using the complete 5513 genes identified in a previous study (Figure 1b). Furthermore, PCa tumor subtypes classified by the ESCGP signature of VGLL3, IGFBP3 and F3 at the time of diagnosis clearly correlated with overall and PCa-specific survival. These two results support the stem cell hypothesis.
The stepwise procedure implemented in the current study has both advantages and disadvantages. We find it advantageous to use an initially wide concept and incrementally narrow the scope through use of independent historic data sets and new measurements, as illustrated in Figure 1. The drawback is that Subset 1 of the patient database was part of selecting the ESCGP signature, leaving a smaller set of patient material for validation. In our case, the limited availability of FNA samples prevented us from dividing them into one discovery set and one validation set. On the other hand, the availability of a series of high-quality, fresh–frozen FNA samples with nearly complete survival data made it possible to complete this study. Currently, evaluation of Gleason score using transrectal ultrasound-guided prostate biopsy samples has become the major diagnostic procedure for PCa. A direct comparison and correlation between this signature and Gleason score needs to be established using biopsy samples. All in all, before implementing the ESCGP signature in clinical practice, it has to be validated in an independent data set using sample material readily available in pathology laboratories. Such a validation study has been initiated in our laboratory.
In conclusion, the ESCGP signature is a promising biomarker combination suitable for estimating the survival of PCa patients. After validation in an independent large cohort study, it would provide an important and orthogonal complement to the currently clinical parameters routinely used in the process of treatment decision for individual patients, in particular for early-stage PCas.
Discussion
This report discusses the ability to estimate the overall and cancer-specific survival using gene expression levels in PCa samples. If such a measure would become available, it would provide an important and orthogonal complement to the currently available data used in the decision process for selecting treatment for individual patients.
Numerous attempts to produce prognostic methods for PCa use surrogate end points like biochemical relapse or even cancer-specific mortality. This is probably due to the fact that data sets for surrogate end points are more easily obtained. However, it has been shown that nearly 50% of the patients die of diseases other than PCa. The identification of biomarkers that correlate with overall survival of PCa is rare. Our results demonstrate that PCa tumor subtypes classified by the gene expression signature of VGLL3, IGFBP3 and F3 at the time of diagnosis are clearly correlated with overall and cancer-specific survival in the evaluated cohort.
The gene expression signature was independent of age, PSA level, World Health Organization tumor grade and clinical stage. Furthermore, as shown through the kNN model and the parametric prediction model, this signature demonstrated clear prognostic value and a potential to further improve the prognostic accuracy of conventional clinical parameters. Following validation on additional cohorts, this ESCGP signature could be particularly beneficial in the clinical management of early-stage PCa. In such cases, the accuracy of conventional clinical parameters for the prediction of cancer-specific and overall survival are limited by a relatively low PSA level, localized disease stage and insufficient tumor material for Gleason scoring. When evaluating such small tumor samples, the ESCGP has a potential to improve the assessment.
Overall survival is the real lifetime determined by the aggressiveness of PCa and patient's other conditions or comorbidities. The ability to estimate overall survival by the ESCGP signature may reflect the biological functions of the three genes. Both F3 and IGFBP3 have been shown associated with metastasis development in prostate and other cancers. They have also been shown important in the development of many non-cancer diseases of the coagulatory, cardiovascular and metabolic systems, diseases that are common causes of death in PCa patients. The positive correlation between prolonged survival and increased expression of F3 was unexpected and may suggest that PCa cells with higher levels of F3 are strongly androgen-dependent and sensitive to castration treatment. The functions of VGLL3 have yet to be studied. VGLL3 shows clear correlation with the age at diagnosis (Supplementary Tables S6 and S7), which is an important patient risk factor that strongly influences the patient overall survival and treatment decision. We suggest that the expression of VGLL3 may reflect the patient's biological age that currently can be estimated only by physician's subjective observation. Therefore, the combination of these three genes could provide a molecular classification sufficient to estimate overall survival.
Several reported gene markers (AMACR, EZH2, c-MAF-a, c-MAF-b and MUC1) selected from previous studies were also validated in our FNA cohort (Supplementary Table S5); however, they were not as strong as the ESCGP signature when estimating overall survival. Owing to the limited RNA quantity present in the FNA samples, the previously reported 'stemness' gene signature could not be compared with our ESCGP signature, although this comparison would be warranted in future studies.
The present study was driven by the stem cell hypothesis, whereby ESC gene expression signatures are thought to be associated with the prognosis of various cancers. Our results demonstrate that the 258 ESCGPs could classify an independent PCa data set in a nearly identical manner as compared with using the complete 5513 genes identified in a previous study (Figure 1b). Furthermore, PCa tumor subtypes classified by the ESCGP signature of VGLL3, IGFBP3 and F3 at the time of diagnosis clearly correlated with overall and PCa-specific survival. These two results support the stem cell hypothesis.
The stepwise procedure implemented in the current study has both advantages and disadvantages. We find it advantageous to use an initially wide concept and incrementally narrow the scope through use of independent historic data sets and new measurements, as illustrated in Figure 1. The drawback is that Subset 1 of the patient database was part of selecting the ESCGP signature, leaving a smaller set of patient material for validation. In our case, the limited availability of FNA samples prevented us from dividing them into one discovery set and one validation set. On the other hand, the availability of a series of high-quality, fresh–frozen FNA samples with nearly complete survival data made it possible to complete this study. Currently, evaluation of Gleason score using transrectal ultrasound-guided prostate biopsy samples has become the major diagnostic procedure for PCa. A direct comparison and correlation between this signature and Gleason score needs to be established using biopsy samples. All in all, before implementing the ESCGP signature in clinical practice, it has to be validated in an independent data set using sample material readily available in pathology laboratories. Such a validation study has been initiated in our laboratory.
In conclusion, the ESCGP signature is a promising biomarker combination suitable for estimating the survival of PCa patients. After validation in an independent large cohort study, it would provide an important and orthogonal complement to the currently clinical parameters routinely used in the process of treatment decision for individual patients, in particular for early-stage PCas.
