Identification of Clinically Significant Prostate Cancer by Combined PCA3 and AMACR mRNA Detection in Urine Samples

Abstract

Purpose: Preclinical evaluation of PCA3 and AMACR transcript simultaneous detection in urine to diagnose clinical significant prostate cancer (prostate cancer with Gleason score ≥7) in a Russian cohort.

Patients and methods: We analyzed urine samples of patients with a total serum PSA ≥2 ng/mL: 31 men with prostate cancer scheduled for radical prostatectomy, 128 men scheduled for first diagnostic biopsy (prebiopsy cohort). PCA3, AMACR, PSA and GPI transcripts were detected by multiplex reverse transcription quantitative polymerase chain reaction, and the results were used for scores for calculation and statistical analysis.

Results: There was no significant difference between clinically significant and nonsignificant prostate cancer PCA3 scores. However, there was a significant difference in the AMACR score (patients scheduled for radical prostatectomy p=0.0088, prebiopsy cohort p=0.029). We estimated AUCs, optimal cutoffs, sensitivities and specificities for PCa and csPCa detection in the prebiopsy cohort by tPSA, PCA3 score, PCPT Risk Calculator and classification models based on tPSA, PCA3 score and AMACR score. In the clinically significant prostate cancer ROC analysis, the PCA3 score AUC was 0.632 (95%CI: 0.511-0.752), the AMACR score AUC was 0.711 (95%CI: 0.617-0.806) and AUC of classification model based on the PCA3 score, the AMACR score and total PSA was 0.72 (95%CI: 0.58-0.83). In addition, the correlation of the AMACR score with the ratio of total RNA and RNA of prostate cells in urine was shown (tau=0.347, p=6.542e-09). Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use.

Conclusion: The AMACR score is a good predictor of clinically significant prostate cancer. Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use. Evaluation of the AMACR score and classification models based on it for clinically significant prostate cancer detection with larger samples and a follow-up analysis is promising.

Keywords: RNA; alpha-methylacyl-CoA racemase; early diagnosis; neoplasm grading; prostatic neoplasms.

Figure 1

PCA3 and AMACR scores in groups of patients with BPH, non-csPCa and csPCa. Box whiskers correspond to 5th and 95th percentile outliers are not shown. (A) PCA3 score, prebiopsy cohort, (B) AMACR score, prebiopsy cohort, (C) PCA3 score, patients scheduled for RPE, (D) AMACR score, patients scheduled for RPE.

Figure 2

PCa and csPCa detection in the prebiopsy cohort. The number of patients is indicated by n. (A) ROC curves for PCa detection by AMACR score and PCA3 score (whole prebiopsy cohort), (B) ROC curves for csPCa detection by AMACR score and PCA3 score (whole prebiopsy cohort), (C) ROC curves for PCa detection by AMACR score, PCA3 score and PCPTRC, (D) ROC curves for csPCa detection by AMACR score, PCA3 score and PCPTRC, (E) ROC curve for PCa detection by classification model, (F) ROC curve for csPCa detection by classification model.

Figure 3

The AMACR and GPI score relationship. The Pearson correlation coefficient (cor), Kendall correlation coefficient (tau), p-values for them and linear regression equation are shown in the left corner of each plot. (A) The prebiopsy cohort, (B) only BPH group.