Radioresistance remains a significant obstacle in the treatment of Prostate Cancer (PCa). To simulate the clinical scenario of irradiation resistance (IRR), we created DU145-IRR PCa cell lines by treatment with 2 Gy daily IR for 59 fractions. DU145-IRR cells acquired an aggressive phenotype as evidenced by increased clonogenic survival, tumorigenic potential and invasiveness. We performed transcriptome profiling to discover dysregulated genes in DU145-IRR cells and identified the long non-coding RNA (lncRNA), Urothelial carcinoma-associated 1 (UCA1). We first investigated the role of UCA1 in radiation response and found that UCA1 abundance was significantly higher in DU145-IRR cells compared to control cells. UCA1 siRNA-knockdown reversed the aggressive phenotype and significantly increased sensitivity to IR. UCA1 depletion inhibited growth, induced cell cycle arrest at the G2/M transition and decreased activation of the pro-survival Akt pathway. We then studied the clinical significance of UCA1 expression in two independent cohorts of PCa patients: MSKCC (130 patients) and CPC-GENE (209 patients). UCA1 over-expression was associated with decreased 5-year disease-free survival in MSKCC patients (HR = 2.9; p = 0.007) and a trend toward lower biochemical recurrence-free survival in CPC-GENE patients (HR = 2.7; p = 0.05). We showed for the first time that UCA1 depletion induces radiosensitivity, decreases proliferative capacity and disrupts cell cycle progression, which may occur through altered Akt signaling and induced cell cycle arrest at the G2/M transition. Our results indicate that UCA1 might have prognostic value in PCa and be a potential therapeutic target.

Background

The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids.

Methods

We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40).

Results

We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort).

Conclusions

These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.