Search

Conventional clinical variables cannot accurately differentiate indolent from aggressive prostate cancer in patients on active surveillance. We investigated promising circulating miRNA biomarkers to predict the reclassification of active surveillance cases.We collected serum samples from 2 independent active surveillance cohorts of 196 and 133 patients for the training and validation, respectively, of candidate miRNAs. All patients were treatment naïve and diagnosed with Gleason score 6 prostate cancer. Samples were collected prior to potential reclassification. We analyzed 9 circulating miRNAs previously shown to be associated with prostate cancer progression. Logistic regression and ROC analyses were performed to assess the predictive ability of miRNAs and clinical variables.A 3-miR (miRNA-223, miRNA-24 and miRNA-375) score was significant to predict patient reclassification (training OR 2.72, 95% CI 1.50-4.94 and validation OR 3.70, 95% CI 1.29-10.6). It was independent of clinical characteristics in multivariable models. The ROC AUC was maximized when combining the 3-miR score and prostate specific antigen, indicating additive predictive value. The 3-miR score plus the prostate specific antigen panel cutoff achieved 89% to 90% negative predictive value and 66% to 81% specificity.The 3-miR score combined with prostate specific antigen represents a noninvasive biomarker panel with high negative predictive value. It may be used to identify patients on active surveillance who have truly indolent prostate cancer.

Patients with prostate cancer on active surveillance are monitored by repeat prostate specific antigen measurements, digital rectal examinations and prostate biopsies. A subset of patients on active surveillance will later reclassify with disease progression, prompting definitive treatment. To minimize the risk of under treating such patients on active surveillance minimally invasive tests are urgently needed incorporating biomarkers to identify patients who will reclassify.We assessed post-digital rectal examination urine samples of patients on active surveillance for select DNA methylation biomarkers that were previously investigated in radical prostatectomy specimens and shown to correlate with an increasing risk of prostate cancer. Post-digital rectal examination urine samples were prospectively collected from 153 men on active surveillance who were diagnosed with Gleason score 6 disease. Urinary sediment DNA was analyzed for 8 DNA methylation biomarkers by multiplex MethyLight assay. Correlative analyses were performed on gene methylation and clinicopathological variables to test the ability to predict patient risk reclassification.Using backward logistic regression a 4-gene methylation classifier panel (APC, CRIP3, GSTP1 and HOXD8) was identified. The classifier panel was able to predict patient reclassification (OR 2.559, 95% CI 1.257-5.212). We observed this panel to be an independent and superior predictor compared to current clinical predictors such as prostate specific antigen at diagnosis or the percent of tumor positive cores in the initial biopsy.We report that a urine based classifier panel of 4 methylation biomarkers predicts disease progression in patients on active surveillance. Once validated in independent active surveillance cohorts, these promising biomarkers may help establish a less invasive method to monitor patients on active surveillance programs.

INTRODUCTION AND OBJECTIVES:Aberrant DNA methylation is very common in bladder cancer (BC) pathogenesis and progression. These epigenetic alterations are detectable in voided urine and might be use...

RESULTS: Our series of patients included 39 BCG-successful and 20 BCG-failure patients. The expression levels of OCT3/4 in the BCG non-responders were significantly higher than those of the BCG responders. In vitro study UM-UC3 showed a strong resistance to BCG treatment than T24. The expression levels of CSLCs-related genes (OCT3/4, Nanog, and SOX2) and EMT-related genes (Snail) increased after BCG treatment in UM-UC3 whereas T24 showed the contrary results. In sphere formation assay UM-UC3 showed a higher colonization ability than T24 after BCG treatment. CONCLUSIONS: Higher expression levels of CSLCsand EMT-related genes reflected BCG failure. The population of CSLCs in the primary CIS may be one of the predictors for BCG failure.

INTRODUCTION AND OBJECTIVES: The majority of men with prostate cancer (PCa) are diagnosed with low-risk disease given effective PSA screening. To avoid over-treatment, men with low-risk PCa are encouraged to be followed on an Active Surveillance (AS) protocol in which repeated blood tests, digital rectal examinations (DRE) and prostate biopsies are utilized to monitor disease progression. Aberrant DNA methylation is a common feature in a variety of tumours. Tumour-specific gene methylation would be an ideal biomarker since only minute amounts of DNA are required which could be extracted from post-DRE urine samples, thus being a much less invasive method for PCa screening as compared to frequent biopsies. Our group previously identified and validated a panel of DNA methylation biomarkers that predicted the presence of high risk disease and/or post treatment biochemical recurrence in radical prostatectomy samples. In the current study, we investigated if this same panel of biomarkers can be detected in post-DRE urine samples of PCa patients participating in an AS program, and whether methylation of these biomarkers could have prognostic value in predicting development of high-risk PCa. METHODS: We collected post DRE urine samples from PCa patients with lowand intermediate-risk PCa patients followed in an AS program. DNA extracted from urine cell pellets was analyzed for selected methylation biomarkers by multiplex MethyLight, a highly sensitive, methylation specific qPCR assay. Patients were stratified using Chi-square analysis by the percent of methylated reference (PMR). Correlative studies were performed on methylation data and clinical variables such as Gleason score and clinical stage. RESULTS: DNA extracted from DRE urine cell pellets of 142 patients was analyzed for the presence of 9 DNA methylation biomarkers. DNA methylation in one or more biomarkers was seen in 73% of patients. Methylation of 3 independent biomarkers was able to distinguish between low(GS6) and intermediate-risk (GS7) PCa (p

INTRODUCTION AND OBJECTIVES: Epigenetic changes, including promoter CpG island hypermethylation, occur frequently in bladder cancer (BC) and may be exploited as a means for BC detection and distinction between high-grade (HG) and low-grade (LG) disease. METHODS: To determine epigenetic differences between LG and HG BC we performed genome-wide methylation analysis with Agilent Human CpG Island Microarrays on a panel of fresh frozen BC tissue samples. Using Linear Models for Microarray Data (LIMMA) and local-pooled-error (LPE) approaches and unsupervised hierarchical clustering we identified 990 probes comprising a 32-gene panel that completely distinguished LG from HG BC based on methylation status. We selected five representative differentially methylated genes (DMGs) from our 32-gene panel for methylation validation by real-time PCRbased MethyLight in a series of 40 LG cases ageand sex-matched to 40 HG cases. RESULTS: Among the DMGs tested, including EOMES, GP5, PAX6, TCF4, and ZSCAN12, we identified that EOMES, GP5, and ZSCAN12 methylation significantly differs between normal, LG, and HG disease. GP5 and ZSCAN12, two novel methylated genes in BC, are significantly hypermethylated in HG versus LG BC (p1⁄40.006 and 0.028, respectively). Pathway enrichment analysis and functional annotation determined the most frequently methylated pathways in HG BC were enriched for anterior/posterior pattern specification, embryonic skeletal system development, and neuron fate commitment. The molecular functions of the most enriched genes were involved in DNA binding and transcription factor activity. CONCLUSIONS: These results indicate the ability to distinguish normal tissue from cancer, as well as LG from HG tumours, and reveal important pathways dysregulated in HG BC. Ultimately, the creation of a methylation panel able to distinguish between disease phenotypes will improve disease management and patient outcomes.