Prostate Health Index and Prostate Health Index Density as diagnostic tools for improved prostate cancer detection

Background. To evaluate the diagnostic potential of [-2] proPSA (p2PSA), %p2PSA, Prostate Health Index (phi), and phi density (PHID) as independent biomarkers and in composition of multivariable models in predicting high-grade prostatic intraepithelial neoplasia (HGPIN) and overall and clinically significant prostate cancer (PCa). Methods. 210 males scheduled for prostate biopsy with total PSA (tPSA) range 2-10 ng/mL and normal digital rectal examination were enrolled in the prospective study. Blood samples to measure tPSA, free PSA (fPSA), and p2PSA were collected immediately before 12-core prostate biopsy. Clinically significant PCa definition was based on Epstein's criteria or ISUP grade≥2 at biopsy. Results. PCa has been diagnosed in 112 (53.3%) patients. Epstein significant and ISUP grade≥2 PCa have been identified in 81 (72.3%) and 40 (35.7%) patients, respectively. Isolated HGPIN at biopsy have been identified in 24 (11.4%) patients. Higher p2PSA and its derivative mean values were associated with PCa. At 90% sensitivity, PHID with cut-off value of 0.54 have demonstrated the highest sensitivity of 35.7% for overall PCa detection, so PHID and phi with cut-off values of 33.2 and 0.63 have demonstrated the specificity of 34.7% and 34.1% for ISUP grade≥2 PCa detection at biopsy, respectively. In univariate ROC analysis, PHID with AUC of 0.77 and 0.80 was the most accurate predictor of overall and Epstein significant PCa, respectively, so phi with AUC of 0.77 was the most accurate predictor of ISUP grade≥2 PCa at biopsy. In multivariate logistic regression analysis, phi improved diagnostic accuracy of multivariable models by 5% in predicting ISUP grade≥2 PCa. Conclusions. PHID and phi have shown the greatest specificity at 90% sensitivity in predicting overall and clinically significant PCa and would lead to significantly avoid unnecessary biopsies. PHID is the most accurate predictor of overall and Epstein significant PCa, so phi is the most accurate predictor of ISUP grade≥2 PCa. phi significantly improves the diagnostic accuracy of multivariable models in predicting ISUP grade≥2 PCa

Significance of prostate health index and its density to predict aggressive prostate cancer at final pathology

Purpose: Prostate Health Index (PHI) and %p2PSA have demonstrated more accurate overall and aggressive prostate cancer (PC) detection at prostate biopsy level, however a significant number of PC patients undergo upgrading and upstaging following definitive surgery. The purpose of our study was to evaluate the ability of p2PSA and its derivatives to predict clinically significant PC at final pathology. Methods: Blood samples from 51 patients, who underwent radical prostatectomy (RP), were collected pre-operatively and tPSA, fPSA, as well as p2PSA values were estimated. %p2PSA, PHI and PHI density (PHID) were calculated according to the relevant formulas. Clinically significant PC was defined as ISUP (International Society of Urological Pathology) grade group ≥2 at final pathology. Results: Mean value of PHID was significantly higher (1.74 vs. 1.24, p = 0.031) in patients with clinically significant PC at final pathology. At ROC analysis, PHI, PHID and %fPSA were the most accurate predictors of clinically significant disease with AUC of 0.69, 0.70 and 0.76, respectively. PHI has demonstrated the best net benefit in predicting clinically significant PC at RP specimens. Conclusions: PHI and PHID demonstrate high predicting value of clinically significant PC at final RP pathology and may define more precisely the preoperative diagnosis of this disease

Promoter Methylation of PRKCB, ADAMTS12, and NAALAD2 Is Specific to Prostate Cancer and Predicts Biochemical Disease Recurrence

The molecular diversity of prostate cancer (PCa) has been demonstrated by recent genome-wide studies, proposing a significant number of different molecular markers. However, only a few of them have been transferred into clinical practice so far. The present study aimed to identify and validate novel DNA methylation biomarkers for PCa diagnosis and prognosis. Microarray-based methylome data of well-characterized cancerous and noncancerous prostate tissue (NPT) pairs was used for the initial screening. Ten protein-coding genes were selected for validation in a set of 151 PCa, 51 NPT, as well as 17 benign prostatic hyperplasia samples. The Prostate Cancer Dataset (PRAD) of The Cancer Genome Atlas (TCGA) was utilized for independent validation of our findings. Methylation frequencies of ADAMTS12, CCDC181, FILIP1L, NAALAD2, PRKCB, and ZMIZ1 were up to 91% in our study. PCa specific methylation of ADAMTS12, CCDC181, NAALAD2, and PRKCB was demonstrated by qualitative and quantitative means (all p < 0.05). In agreement with PRAD, promoter methylation of these four genes was associated with the transcript down-regulation in the Lithuanian cohort (all p < 0.05). Methylation of ADAMTS12, NAALAD2, and PRKCB was independently predictive for biochemical disease recurrence, while NAALAD2 and PRKCB increased the prognostic power of multivariate models (all p < 0.01). The present study identified methylation of ADAMTS12, NAALAD2, and PRKCB as novel diagnostic and prognostic PCa biomarkers that might guide treatment decisions in clinical practice

Analysis of AR-FL and AR-V1 in Whole Blood of Patients with Castration Resistant Prostate Cancer as a Tool for Predicting Response to Abiraterone Acetate

PURPOSE: Reliable molecular diagnostic tools are still unavailable for making informed treatment decisions and monitoring the response in patients with castration resistant prostate cancer. We evaluated the significance of whole blood circulating androgen receptor transcripts of full length (AR-FL) and splice variants (AR-V1, AR-V3 and AR-V7) as biomarkers of abiraterone acetate treatment resistance in patients with castration resistant prostate cancer. MATERIALS AND METHODS: After retrospective analysis in 112 prostate specimens AR-FL, AR-V1, AR-V3 and AR-V7 were evaluated in 185 serial blood samples, prospectively collected from 102 patients with castration resistant prostate cancer before and during abiraterone acetate therapy via reverse transcription quantitative polymerase chain reaction. RESULTS: AR-FL was present in all samples while AR-V1, AR-V3, AR-V7 and at least 1 of them was detected in 17%, 55%, 65% and 81% of castration resistant prostate cancer blood samples, respectively. The highest amount of AR-V1 was found in blood of patients whose response time was short and medium in comparison to extended. Patients with a higher level of AR-FL and/or AR-V1 had the shortest progression-free survival and overall survival (p <0.0001). CONCLUSIONS: Blood circulating AR-FL or AR-V1 can serve as blood based biomarkers for identification of the primary resistance to abiraterone acetate and the tool to monitor de novo resistance development during abiraterone acetate treatment