The development of non-invasive tests for the early detection of aggressive prostate tumours is a major unmet clinical need. miRNAs are promising non-invasive biomarkers: they play essential roles in tumourigenesis, are stable under diverse analytical conditions and can be detected in body fluids.
We measured the longitudinal stability of 673 miRNAs by collecting serial urines 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).
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 inter-individual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (AUC of 0.72; 95% CI = 0.69-0.76 in the training cohort, and AUC of 0.74; 95% CI = 0.55-0.92 in the validation cohort).
These data highlight the importance of quantifying intra- and inter-tumoural heterogeneity in biomarker development. This non-invasive biomarker may usefully supplement invasive or expensive radiologic and tissue-based assays.