Detection of Prostate Cancer-Associated Transcripts in Urinary Extracellular Vesicles
The relatively high rate of negative prostate biopsy demonstrates the need for additional non-invasive biomarkers to improve the diagnosis and classification of prostate cancers prior to biopsy. We have previously established that post-DRE extracellular vesicles (EVs) are enriched for prostate-specific RNAs, making them an ideal target for prostate cancer biomarker discovery. In our current study, we aimed to identify genes with increased expression in the post-DRE urine EVs of patients with aggressive prostate cancer.
We developed and refined a method for the targeted analysis of over 260 different RNA transcripts in post-DRE urine EVs. Genes were selected for inclusion in our assay based on their prior inclusion on tissue biomarker panels of aggressive prostate cancer, or from analysis of TCGA datasets to identify genes with both significant over-expression in aggressive prostate cancer and minimal expression in normal genitourinary tissues.
We prepared RNA from the post-DRE urine EVs of 29 men and used our targeted RNA sequencing assay to measure expression of over 260 different transcripts. We detected 92 transcripts (34%) in all 29 specimens, with only 19 transcripts (7%) being undetectable in any specimen. Comparison of patients with no evidence of disease (n = 15) and patients with Gleason Score ≥7 prostate cancer (n = 14) identified 25 genes with significantly higher expression in patients with aggressive prostate cancer, including known prostate cancer-associated genes such as PCA3, AMACR, and SPINK1, while KLK3 showed no significant difference in expression (Figure 1).
Post-DRE urine EVs contain transcripts that are indicative of prostate cancer status and represent a novel source for biomarker discovery and development. Further investigation of patient specimens with this method offers the potential to determine combinations of transcripts that could be used to non-invasively detect and monitor prostate cancer status.
Funding: NCI Early Detection Research Network U01 CA113913