Correlation of primary tumor prostate-specific membrane antigen expression with disease recurrence in prostate cancer

Abstract

Purpose: The restricted expression of the surface glycoprotein prostrate-specific membrane antigen (PSMA) to normal prostate tissue, primary and metastatic prostate cancer (PCa), and the neovasculature of various nonprostatic epithelial malignancies has enabled targeting strategies for PCa treatment using anti-PSMA antibodies.

Experimental design: Using prostatectomy specimens, immunohistochemical staining for PSMA (7E11 antibody) was performed on formalin-fixed paraffin-embedded sections of 136 cases of PCa. Cytoplasmic immunoreactivity was scored for intensity and distribution, and results were correlated with tumor grade, pathological stage, DNA ploidy status (Feulgen spectroscopy), and disease recurrence. PSMA mRNA expression in selected primary tumors and metastatic lesions was also detected using in situ hybridization and autoradiography.

Results: Generally, PCa cells expressed relatively increased levels of PSMA as compared with benign elements. Among the PCa cases, increased (high) PSMA expression correlated with tumor grade (P = 0.030), pathological stage (P = 0.029), aneuploidy (P = 0.010), and biochemical recurrence (P = 0.001). The mean serum prostate-specific antigen level of 18.28 ng/ml at the time of diagnosis for the PSMA-overexpressing tumors was significantly greater than the mean serum prostate-specific antigen of 9.10 ng/ml for the non-PMSA-overexpressing group (P = 0.006). On multivariate analysis, pathological stage (P = 0.018) and PSMA expression (P = 0.002) were independent predictors of biochemical recurrence. PSMA protein overexpression in high-grade primary PCa tumors and metastatic lesions also correlated with increased PSMA mRNA expression levels using in situ hybridization and autoradiography.

Conclusions: This study demonstrates for the first time that overexpression of PSMA in primary PCa correlates with other adverse traditional prognostic factors and independently predicts disease outcome.