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Successful Prediction of Prostate Cancer Recurrence by Gene Profiling in Combination with Clinical Data

A 5-year Follow-up Study¹

Dipartimento di Medicina Sperimentale, Plesso Biotecnologico Integrato, Università di Parma, Via Volturno 39-43100 Parma [S. B., A. C.]; Dipartimento di Scienze Biomediche, Università di Modena e Reggio Emilia, Via G. Campi 287-41100 Modena [M. S., D. D., S. A., P. D., A. C.]; and Ospedale Estense-S.Agostino, Divisione Urologia, Via S. Agostino 18-41100 Modena [M. B.], Italy

	ABSTRACT

Top ABSTRACT Introduction Materials and Methods Results and Discussion REFERENCES

 
We show here that gene expression profiling, performed with conventional techniques and focused on a selected group of genes, when used in combination with standard clinical information, provides reliable prognostic prediction of prostate cancer (CaP). We showed previously that changes in the expression of metabolically related genes are involved in CaP progression. We then proceeded to search further for correlations between patients’ gene profiling and recurrence with a 5-year follow-up study conducted on the same cohort of patients in which the molecular data were obtained. CaP prognosis was first assessed on the basis of gene expression profiling alone; then the result was compared with the prediction obtained using clinical and pathological information (Gleason score, Tumor-Node-Metastasis staging, prostate volume, or prostate-specific antigen levels at the time of diagnosis). The best result was obtained with a selected combination of gene profiling and clinical/pathological parameters, which resulted in prediction of recurrence in 95.7% of patients.

	Introduction

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CaP³ incidence is steadily increasing in Western countries. When CaP spreads to local, as well as distant sites, it often becomes androgen independent and refractory to hormonal therapy, finally resulting in recurrence. Because of unfavorable prognosis of extra-prostatic disease, detection of CaP at curable stage is desirable, but the screening methods actually available present limitations. Thus, new markers to guide CaP management are urgently needed. Gene expression analysis is a powerful tool that potentially could increase our knowledge about biology of CaP. The purpose of this study was to show that gene expression profiling, even if performed with conventional techniques, when focused on a selected group of genes provides reliable prognostic prediction of CaP that can be further enhanced when used in combination with standard clinical information. We showed previously (1) that molecular characterization of CaP progression is indeed possible by assessing the expression level of specific sets of metabolically related genes, such as ODC, OAZ, adenosylmethionine decarboxylase, SSAT, histone H3, Gas1, GAPDH, and CLU (also known as sulfated glycoprotein 2, TRPM-2, and ApoJ, a gene involved in prostate gland involution and remodeling; Refs. 2 and 3 ), showing that changes in the expression of all of the genes studied are involved in CaP progression. We then proceeded to search further for possible correlations between patients’ gene profiling and recurrence of CaP with a 5-year follow-up study conducted on the same cohort of patients in which the molecular data were obtained.

	Materials and Methods

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Patient Treatments and Tissue Specimens.
Patients were subjected to adjuvant androgen suppression therapy before surgery, and tissue samples were obtained as described previously (1) . This investigation was performed after approval by the local Human Investigation Committee and after obtaining informed consent from the patients involved in the study.

Northern Hybridization Analysis.
Northern blot analyses were performed in the neoplastic and benign regions (control) of the gland of the same patient as described previously (1) . Only specimens with no sign of benign prostatic hyperplasia or tumor invasion were used as controls. Only specimens with >50% of cancer were used as CaPs. The expression data used in the analysis here presented were the same obtained in the previous study (1) .

Follow-Up and Patient Classification.
After a mean clinical follow-up of 60 months (range: 50–70 months), patients were classified as a function of recurrence: (a) those with no clinical signs of recurrence and PSA < 0.2 ng/ml were ascribed to group 1 (n = 13; mean age = 68.4 ± 4.9 years); and (b) patients with biochemical failure (PSA > 0.2 ng/ml) and/or clinical evidences of relapse and/or metastases were ascribed to group 2 (n = 10; mean age = 71.1 ± 4.7 years).

Statistical Analysis.
Discriminant analysis was used for CaP prognosis. This procedure requires a normal distribution of the data. To this end, we standardized the data relative to the parameters not having this characteristic by conversion to z-norm values according to the following formula:

where Xi is either R (the ratio between benign and malign tissue expression level in the case of the genes studied) or the clinical parameters with regard to the patient studied, is the mean value of the data, and dS is the SD. Z values were then converted by conventional statistic method to z-norm to obtain a normal distribution (data showing normal distribution have mean = 0; dS = 1). Table 1 shows the z-norm values for all parameters used. In Table 1 , 0 and 1 values of TNM column represent T0 and T1 stages, whereas 0 and 1 values of FAM column represent "no familial incidence" or "familial incidence," respectively. Standard data were analyzed using S-PLUS 2000 Professional software (release 3). The statistical software package used is capable to automatically discriminate patients into two groups on the basis of z-norm values by taking into account several parameters simultaneously (Table 1 columns). Then, the software individually compares the group assigned to each patient by this classification with the group assignment presented in the last column of Table 1 (clinical or biochemical recurrence determined after 5-years of follow-up). Thus, each patient is individually classified a priori as "recurrent" or "not recurrent" on the basis of gene profiling/clinical parameter collected at the time of surgery, and then this prediction is compared automatically by the system to the real situation that was clinically determined 5 years later.

	Results and Discussion

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The first set of genes studied were those coding for the regulatory enzymes of polyamine metabolism that, in the prostate gland, are positively controlled by androgens (4 , 5) . Polyamines are necessary for cell growth (6) , and increases in their levels are generally associated with cell proliferation and transformation (7) . Fine regulation of polyamine intracellular concentrations within the physiological range can be achieved only by means of a strict control of polyamine metabolism through complex molecular mechanisms involving the expression of all of the genes involved in polyamine metabolism regulation, namely ODC, OAZ, adenosylmethionine decarboxylase, and SSAT. Thus, we simultaneously analyzed the expression levels of all these genes in CaP, showing that the level of expression of all of them was significantly higher in the neoplastic tissue with respect to the benign counterpart (1) .

The second set of genes studied was related to cell proliferation state, and comprised histone H3, a marker of cell proliferation, Gas1, a marker of cell quiescence, and CLU. CLU is a gene whose protein product undergoes post-translational modifications (8) and is dramatically up-regulated in the rat ventral prostate undergoing tissue regression on surgical castration or pharmacological androgen ablation (9 , 10) . It is expressed in atrophic prostate epithelial cells (5 , 11) , and its mRNA undergoes linear accumulation with aging (11 , 12) . It is up-regulated when normal human fibroblasts are synchronized in the quiescence state and down-regulated when proliferation is induced (13 , 14) . CLUoverexpression caused cell cycle arrest in SV40-immortalized human prostate epithelial cells (15) . In CaP specimens, we showed that the H3 gene was up-regulated, whereas Gas1 and CLU were down-regulated, accounting for an increase in the number of cells in S phase and a decrease of quiescent cells in the tumor. This may be related to higher growth rate and invasiveness of CaP with respect to benign tissue (1) . All these gene expression data were obtained at the time of surgery and recorded as gene profiling. To this aim, we calculated the ratio R (R = mRNA level in the cancer specimen:mRNA level in the benign counterpart of the same gland) for each patient with regard to each gene included in the study. This approach allowed us to take into account individual differences in basal expression of the selected genes in control specimens versus tumor counterparts. All of the data were then standardized and converted into Z values as described in "Materials and Methods." The following parameters were also registered at the time of prostatectomy for each patient: (a) age; (b) PSA value; (c) VOL; (d) FAM (at least one case of CaP in father or brother); and (e) clinical stage (TNM). As mentioned above, Gleason score was assessed directly in the tissue specimens collected at the time of radical prostatectomy. Table 1 shows the complete list of parameters used for classification versus prognosis (patients with no signs of recurrence, group 1; patients with recurrence, group 2; PSA, PSA level at the time of diagnosis).

The data analysis was conducted by means of discriminant analysis in different steps: (a) classification of prostatic tumors and comparison with recurrence considering each single parameter at a time (Table 1 , prediction); (b) clinical and pathological data (6 parameters) were grouped together and compared with recurrence; (c) gene profiling data (eight genes) were compared with recurrence; and (d) clinical/pathological plus gene profiling, for a total of 14 parameters (6 plus 8) were used for prognosis. The prediction results obtained in steps two to four are shown in Table 2 .

We then proceeded further to narrow the number of parameters to be used in the analysis, to find out the minimal profile required for maintaining the best prediction. Table 3 shows that ODC, histone H3, and Gas-1 expression profile in combination with patient age at the time of surgery, Gleason score, and VOL at the time of surgery was the best combination for CaP recurrence prediction.

It is accepted that more precise means for the classifications of CaP malignancy are required. This study is an example of the application of a new taxonomic criterion that, by providing perspectives about CaP progression that can hardly be obtained by the traditional systems alone, enhanced the available prognostic tools. The data collection performed at the time of radical prostatectomy allowed the prediction of CaP progression largely in advance with respect to biochemical failure or clinical evidences of recurrence. A possible explanation for such a result could be that the tumor group 2 (recurrence positive) was exhibiting an aggressive behavior in individual patients already at the time of surgery, leading to early microdissemination of CaP cells and the settling of micrometastasis in the body, which were not detectable with conventional techniques at that time. This leads to the hypothesis that, despite the fact that CaP is often a slow-growing tumor, dissemination of micrometastasis and aggressive behavior could be an early event in cancer progression. Thus, discrimination of those cases of CaP that requires urgent and most effective therapy treatment, from those for which no immediate treatment and watchful waiting is the appropriate option, must be achieved rapidly. The new integrated molecular approach here described has been proven highly effective for CaP classification even when CaP is still localized to the prostate and should be used to guide its clinical management.

	ACKNOWLEDGMENTS

 
We thank Prof. A. Tampieri, University of Modena, for his help in data analysis.

	FOOTNOTES

 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

¹ Supported in part by Associazione Italiana Ricerca sul Cancro 2002, Investigator Grant title "Clusterin and polyamine regulatory genes as new molecular markers for prostate cancer prognosis and therapy."

² To whom requests for reprints should be addressed, at Dipartimento di Medicina Sperimentale, Plesso Biotecnologico Integrato, Università di Parma, Via Volturno 39-43100 Parma, Italy. E-mail: saverio.bettuzzi{at}unipr.it

³ The abbreviations used are: CaP, prostate cancer; CLU, clusterin; FAM, familiarity; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; Gas1, growth arrest-specific gene 1; OAZ, ornithine decarboxylase antizyme; ODC, ornithine decarboxylase; PSA, prostate-specific antigen; SSAT, spermidine/spermine N¹-acetyltransferase; TNM, Tumor-Node-Metastasis; VOL, prostate volume.

Received 1/20/03. Accepted 5/ 8/03.

	REFERENCES

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