Predicting Prostate Biopsy Outcome Research Articles

MRI-based nomogram for the prediction of prostate cancer diagnosis: A multi-centre validated patient–physician decision tool

Objective: To update and externally validate a magnetic resonance imaging (MRI)-based nomogram for predicting prostate biopsy outcomes with a multi-centre cohort. Materials and methods: Prospective data from five UK-based centres were analysed. All men were biopsy naïve. Those with missing data, no MRI, or prostate-specific antigen (PSA) > 30 ng/mL were excluded. Logistic regression analysis was used to confirm predictors of prostate cancer outcomes including MRI-PIRADS (Prostate Imaging Reporting and Data System) score, PSA density, and age. Clinically significant disease was defined as International Society of Urological Pathology (ISUP) Grade Group ⩾ 2 (Gleason grade ⩾ 7). Biopsy strategy included transrectal and transperineal approaches. Nomograms were produced using logistic regression analysis results. Results: A total of 506 men were included in the analysis with median age 66 (interquartile range (IQR) = 60–69). Median PSA was 6.6 ng/mL (IQR = 4.72–9.26). PIRADS ⩾ 3 was reported in 387 (76.4%). Grade Group ⩾ 2 detection was 227 (44.9%) and 318 (62.8%) for any cancer. Performance of the MRI-based nomogram was an area under curve (AUC) of 0.84 (95% confidence interval (CI) = 0.81–0.88) for Grade Group ⩾ 2% and 0.85 (95% CI = 0.82–0.88) for any prostate cancer. Conclusion: We present external validation of a novel MRI-based nomogram in a multi-centre UK-based cohort, showing good discrimination in identifying men at high risk of having clinically significant disease. These findings support this risk calculator use in the prostate biopsy decision-making process. Level of evidence: 2c

Adaptation of the prostate biopsy collaborative group risk calculator in patients with PSA less than 10ng/ml improves its performance.

The prostate biopsy collaborative group risk calculator (PBCGRC) is a newly developed risk estimator for predicting prostate biopsy outcomes. However, its clinical usefulness is still unknown within the so-called gray area of PSA values. This study aimed to determine whether updating the PBCGRC improves its predictive performance for predicting any-grade and high-grade (HG), defined as biopsy Gleason score ≥ 7, prostate cancer (PCa) in patients with prostate-specific antigen (PSA) less than 10ng/ml. The risk of any-grade and HGPCa was calculated using the PBCG risk calculation formulas updated by recalibration in the large, logistic recalibration and model revision. Predictive performances of the PBCGRC and the updated models were compared using discrimination, calibration, and clinical utility. Within the study sample of 526 patients, PCa was detected in 193 (36.7%), and 78 (14.8%) of them had HGPCa. According to the calibration curves, the PBCGRC overestimated the risk of PCa. Predictive accuracy of the revised model was higher [the area under the receiver-operating characteristic curve (AUCs), 65.4% and 70.2%] than that of the PBCGRC (AUCs, 60.4% and 64.3%) for any-grade and HGPCa. The net benefit was greater for model revision in comparison with the original model. The performance accuracy of PBCGRC for the prediction of any and HGPC in men undergoing prostate biopsy with PSA levels below 10ng/ml is suboptimal. The model revision resulted with significant improvement in model performance. However, external validation of the revised model is necessary before its routine use in clinical practice.

PD52-06 COMPUTATIONAL IMMUNOGENOMIC MODELING OF IMMUNOEVASIVE AGGRESSIVE PROSTATE CANCER

You have accessJournal of UrologyProstate Cancer: Markers II (PD52)1 Apr 2020PD52-06 COMPUTATIONAL IMMUNOGENOMIC MODELING OF IMMUNOEVASIVE AGGRESSIVE PROSTATE CANCER Howard Korman*, Kirk Wojno, Amin Kassis, Ricardo Henao, Jason Hafron, Kenneth Kernen, Geoffrey Erickson, Harry Stylli, Shrikant Mane, Angela Busta, Elyse Marriott, Rima Tinawi-Aljundi, Mathew Putzi, and Philip Kantoff Howard Korman*Howard Korman* More articles by this author , Kirk WojnoKirk Wojno More articles by this author , Amin KassisAmin Kassis More articles by this author , Ricardo HenaoRicardo Henao More articles by this author , Jason HafronJason Hafron More articles by this author , Kenneth KernenKenneth Kernen More articles by this author , Geoffrey EricksonGeoffrey Erickson More articles by this author , Harry StylliHarry Stylli More articles by this author , Shrikant ManeShrikant Mane More articles by this author , Angela BustaAngela Busta More articles by this author , Elyse MarriottElyse Marriott More articles by this author , Rima Tinawi-AljundiRima Tinawi-Aljundi More articles by this author , Mathew PutziMathew Putzi More articles by this author , and Philip KantoffPhilip Kantoff More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000954.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Peripheral blood CD14/CD2 RNA expression ratios have shown associations with prostate biopsy features. The objectives of this study are: 1) To develop a computational immunogenomic model of immunoevasive aggressive prostate cancer and validate this model on an independent data set, 2) determine if dimensionality reduction strategies are effective in minimizing model overfitting, 3) explore pathway associations and determine links with biopsy features, and 4) compare this optimized model with mainstream published models predicting biopsy features. METHODS: Peripheral blood samples from 696 urology patients between 40 and 75 years of age were obtained under IRB approval, prior to their scheduled prostate biopsy. Whole transcriptome RNA sequencing was performed on two purified cell types (CD14 and CD2) and RNA-sequencing expression levels were used to develop predictive models correlating to biopsy results using 50-fold cross validation. An independent validation set of 301 patients was used to confirm the results. Receiver Operating Characteristic (ROC) curves were created to compute p-values comparing the area under the curve (AUC) of individual predictive variables to the overall model, as well as to published mainstream models. RESULTS: In the discovery set the AUC of the ROC was 0.75 for positive versus negative biopsy results (BR), 0.82 for Gleason grade group (GG), 0.84 for number of positive biopsy needle cores (CP) of, 0.81 for maximum involvement of a core by tumor (MI), and 0.80 for the aggregate of the biopsy features (ABF). The degree of overfitting was estimated by the drop of performance seen in the independent validation set and was not statistically significant for any of the output variables. Our Model performs significantly better than mainstream models such as the PCPT and the PBCG risk calculators. Interesting pathway associations between tumor volume estimates and antigen processing, along with antigen peptide processing by MHC class II, are observed. CONCLUSIONS: A whole transcriptome RNA-seq algorithm based on clinical immunogenomic parameters determined by the analysis of peripheral blood samples from pre-biopsy prostate cancer patients predicts prostate biopsy features significantly better than established and widely used algorithms. The performance characteristics of this model have been validated on an independent data set with no significant overfitting or drop in performance noted. This study demonstrates the power of RNA expression patterns of pure populations of peripheral blood immune cells to predict prostate biopsy outcome. Source of Funding: OncoCell MDx © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1091-e1092 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Howard Korman* More articles by this author Kirk Wojno More articles by this author Amin Kassis More articles by this author Ricardo Henao More articles by this author Jason Hafron More articles by this author Kenneth Kernen More articles by this author Geoffrey Erickson More articles by this author Harry Stylli More articles by this author Shrikant Mane More articles by this author Angela Busta More articles by this author Elyse Marriott More articles by this author Rima Tinawi-Aljundi More articles by this author Mathew Putzi More articles by this author Philip Kantoff More articles by this author Expand All Advertisement PDF downloadLoading ...

Detection of Prostate Cancer Antigen 3 and Prostate Cancer Susceptibility Candidate in Non-DRE Urine Improves Diagnosis of Prostate Cancer in Chinese Population.

Although prostate biopsy is the gold standard for the diagnosis of prostate cancer, it also leads to high incidence of negative biopsies and the diagnosis of clinically low-risk prostate cancer and the subsequent overtreatment. It remains an unmet need to discover new biomarkers in order to defer the unnecessary biopsies in clinical practice. In this study, we described a new method, LBXexo score, to measure the urine exosomal PCA3/PRAC expression from non-DRE urine as a noninvasive diagnosis to improve the detection rate in Chinese population with a low serum PSA level. First-voided urine samples were collected to isolate exosomes, and exosomal RNAs of PCA3 and PRAC were measured by quantitative reverse transcription PCR. A significant increase in exoPCA3/PRAC was observed in both any-grade and high-grade prostate cancer groups when compared with the biopsy-negative group. Receiver-operating characteristic curve analyses showed that the LBXexo score significantly improved diagnostic performance in predicting biopsy results, with AUCs of 0.723 (p=0.017) and 0.736 (p=0.038) for any-grade and high-grade (GS ≥ 7) prostate cancer, respectively. For high-grade cancer, LBXexo had the negative and positive predictive values of 100% and 27.59%, respectively, and could potentially avoid unnecessary biopsy. This is the first report in Chinese population that demonstrates the predictive value of the exosomal expression of PCA3 and PRAC derived from non-DRE urine in predicting prostate biopsy outcomes. It could be used in clinical practice to make a better informed biopsy decision and avoid unnecessary biopsies in Chinese population.

9 - The diagnostic value of PSA density in predicting prostate biopsy outcomes

9 - The diagnostic value of PSA density in predicting prostate biopsy outcomes

Prostate Health Index (PHI) improves prostate cancer detection at initial biopsy in Taiwanese men with PSA 4–10 ng/mL

In this study, we aimed to validate the Prostate Health Index (PHI) for the detection of prostate cancer (PCa). We prospectively enrolled patients aged 50–75 years with a serum prostate specific antigen (PSA) level of 4–10 ng/mL undergoing transrectal biopsy of the prostate between April 2016 and May 2017. The primary outcome was the diagnostic performance of various PSA derivatives (total PSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI) to predict PCa. The secondary outcome was comparisons of PSA derivatives between patients with a Gleason score (GS) ≤6 and GS ≥7. PCa was diagnosed in 36 of 154 (23.4%) patients, and 26 (16.9%) had a GS ≥7. The areas under the receiver operating characteristic curves were significantly greater in %p 2PSA and PHI than in PSA (0.76 vs. 0.57, p = 0.015 and 0.77 vs. 0.57, p = 0.004, respectively). Patients with a GS ≥7PCa had marginally higher %p2PSA and PHI than those with a GS of 6 (17.8 vs. 12.73, p = 0.06; 46.58 vs. 31.55, p = 0.05). At a PHI cutoff value of 29.6, the sensitivity and specificity were 77.8% and 67.8% in detecting PCa, respectively. In addition, 57.1% of the patients avoided an unnecessary biopsy, while three patients (1.9%) with GS 7 PCa were missed. In conclusion, the ability of %p2PSA and PHI to predict prostate biopsy outcome was better than that of PSA and %fPSA in the initial biopsy in Taiwanese men with serum PSA between 4 and 10 ng/mL.

Prostate health index significantly reduced unnecessary prostate biopsies in patients with PSA 2-10 ng/mL and PSA >10 ng/mL: Results from a Multicenter Study in China.

The performance of prostate health index (phi) in predicting prostate biopsy outcomes has been well established for patients with prostate-specific antigen (PSA) values between 2 and 10 ng/mL. However, the performance of phi remains unknown in patients with PSA >10 ng/mL, the vast majority in Chinese biopsy patients. We aimed to assess the ability of phi to predict prostate cancer (PCa) and high-grade disease (Gleason Score ≥7) on biopsy in a Chinese population. This is a prospective, observational, multi-center study of consecutive patients who underwent a transrectal ultrasound guided prostate biopsy at four hospitals in Shanghai, China from August 2013 to December 2014. In the cohort of 1538 patients, the detection rate of PCa was 40.2%. phi had a significantly better predictive performance for PCa than total PSA (tPSA). The areas under the receiver operating characteristic curve (AUC) were 0.90 and 0.79 for phi and tPSA, respectively, P < 0.0001. A considerable proportion of patients in the cohort had PSAs >10 ng/mL (N = 838, 54.5%). The detection rates of PCa were 35.9% and 57.7% in patients with tPSA 10.1-20 and 20.1-50 ng/mL, respectively. The AUCs of phi (0.79 and 0.89, for these two groups, respectively) were also significantly higher than tPSA (0.57 and 0.63, respectively), both P < 0.0001. If a phi ≤35 was used as the cutoff, 599/1538 (39%) biopsies could have been avoided at a cost of missing small numbers of PCa patients: 49 (7.93%) PCa patients, including 18 (3.69%) high-grade tumors. Results from this study suggest that phi can be used to predict PCa and high-grade disease in Chinese men with high PSA levels (>10 ng/mL).

Predicting Prostate Biopsy Outcomes: A Preliminary Investigation on Screening with Ultrahigh B-Value Diffusion-Weighted Imaging as an Innovative Diagnostic Biomarker.

BackgroundRoutine screening of prostate specific antigen (PSA) is no longer recommended because of a high rate of over-diagnosis of prostate cancer (PCa).ObjectiveTo evaluate the efficacy of diffusion-weighted magnetic resonance imaging (DW-MRI) for PCa detection, and to explore the clinical utility of ultrahigh b-value DW-MRI in predicting prostate biopsy outcomes.Methodology73 male patients were selected for the study. They underwent 3T MRI using T2WI conventional DW-MRI with b-value 1000 s/mm2, and ultrahigh b-value DW-MRI with b-values of 2000 s/mm2 and 3000 s/mm2. Two radiologists evaluated individual prostate gland images on a 5-point rating scale using PI-RADS, for the purpose of region-specific comparisons among modalities. Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and likelihood ratios (LR) were investigated for each MRI modality. The area under the receiver operating characteristic (ROC) curve (AUC) was also calculated.ResultsResults showed the improved diagnostic value of ultrahigh b-value DWI-MRI for detection of PCa when compared to other b values and conventional MRI protocols. Sensitivity values for 3000 s/mm2 in both peripheral zone (PZ) and transition zone (TZ) were significantly higher than those observed with conventional DW-MRI—Specificity values for 3000 s/mm2 in the TZ were significantly higher than other b-value images, whereas specificity values using 3000 s/mm2 in the PZ were not significantly higher than 2000 s/mm2 images. PPV and NPV between 3000 s/mm2 and the other three modalities were significantly higher for both PZ and TZ images. The PLRs and NLRs of b-value 3000 s/mm2 DW-MRI in the PZ and TZ were also recorded. ROC analysis showed greater AUCs for the b value 3000 s/mm2 DWI than for the other three modalities.ConclusionsDW-MRI with a b-value of 3000 s/mm2 was found to be the most accurate and reliable MRI modality for PCa tumor detection and localization, particularly for TZ lesion discrimination. It may be stated that the b-value of 3000 s/mm2 is a novel, improved diagnostic biomarker with greater predictive accuracy for PCa prior to biopsy.

Performance of the Prostate Health Index in predicting prostate biopsy outcomes among men with a negative digital rectal examination and transrectal ultrasonography.

The [-2]proPSA (p2PSA) and its derivatives, the p2PSA-to-free PSA ratio (%p2PSA), and the Prostate Health Index (PHI) have greatly improved discrimination between men with and without prostate cancer (PCa) in prostate biopsies. However, little is known about their performance in cases where a digital rectal examination (DRE) and transrectal ultrasonography (TRUS) are negative. A prospective cohort of 261 consecutive patients in China with negative DRE and TRUS were recruited and underwent prostate biopsies. A serum sample had collected before the biopsy was used to measure various PSA derivatives, including total prostate-specific antigen (tPSA), free PSA, and p2PSA. For each patient, the free-to-total PSA ratio (%fPSA), PSA density (PSAD), p2PSA-to-free PSA ratio (%p2PSA), and PHI were calculated. Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC) and the biopsy rate at 91% sensitivity. The AUC scores within the entire cohort with respect to age, tPSA, %fPSA, PSAD, p2PSA, %p2PSA, and PHI were 0.598, 0.751, 0.646, 0.789, 0.814, 0.808, and 0.853, respectively. PHI was the best predictor of prostate biopsy results, especially in patients with a tPSA of 10.1–20 ng ml−1. Compared with other markers, at a sensitivity of 91%, PHI was the most useful for determining which men did not need to undergo biopsy, thereby avoiding unnecessary procedures. The use of PHI could improve the accuracy of PCa detection by predicting prostate biopsy outcomes among men with a negative DRE and TRUS in China.

Urinary PCA3 to predict prostate cancer in a cohort of 1015 patients

Summary Aim To evaluate the performance of urinary PCA3 test to predict prostate biopsy outcome in a large French cohort. Patients and methods A urine sample was prospectively obtained from 1015 patients undergoing prostate biopsies to determine the PCA3 score. The predictive value of PCA3 was explored using receiver operating characteristic curve analysis (ROC), multivariable logistic regression analysis and decision curve analysis. Results The median PCA3 score was significantly higher in patients with positive biopsies. The PCA3 score AUC was 0.76 (0.73–0.79), significantly higher than that of PSA (0.55; 0.51–0.58). At the cutoff of 35, sensitivity was 68%, specificity 71%, positive and negative predictive values 67% and 71%, and accuracy 69%. Using multivariate analysis, PCA3 score appeared as an independent predictor of biopsy outcome and its addition to a base model including usual clinicobiological parameters resulted in a significant increase in predictive accuracy. At the cutoff of 20, about 1/2 of the ultimately unnecessary biopsies would have been avoided while ignoring 7% of cancers with Gleason score ≥ 7. PCA3 score did not correlate with Gleason score but did correlate with tumor volume (proportion of positive cores). Conclusion Urinary PCA3 is a useful test with high diagnostic performances for early prostate cancer diagnosis. Its correlation with cancer aggressiveness seems rather represented by a link to prostate volume than Gleason score. Level of evidence 5.

Test urinaire PCA3 et diagnostic du cancer prostatique : étude à partir de 1015 patients

To evaluate the performance of urinary PCA3 test to predict prostate biopsy outcome in a large French cohort. A urine sample was prospectively obtained in 1015 patients undergoing prostate biopsies to determine the PCA3 score. The predictive value of PCA3 was explored using receiver operating characteristic curve analysis (ROC), multivariable logistic regression analysis and decision curve analysis. The median PCA3 score was significantly higher in patients with positive biopsies. The PCA3 score AUC was 0.76 (0.73-0.79), significantly higher than that of PSA (0.55; 0.51-0.58). At the cut-off of 35, sensitivity was 68 %, specificity 71 %, positive and negative predictive values 67 % and 71 %, and accuracy 69 %. Using multivariate analysis, PCA3 score appeared as an independent predictor of biopsy outcome and its addition to a base model including usual clinico-biological parameters resulted in a significant increase in predictive accuracy. At the cut-off of 20, about 1/2 of the eventual useless biopsies would have been avoided while ignoring 7 % of cancers with Gleason score ≥ 7. PCA3 score did not correlate to Gleason score but correlated to tumor volume (proportion of invaded cores). Urinary PCA3 is a useful test with high diagnostic performance for early prostate cancer diagnosis. Its correlation with cancer aggressiveness seems rather represented by a link to prostate volume than Gleason score.

Clinical utility of a novel urine-based gene fusion TTTY15-USP9Y in predicting prostate biopsy outcome

ObjectiveIn recent years, great effort has been made to explore new biomarkers for early detection of prostate cancer. Our previous study has demonstrated the high prevalence of TTTY15-USP9Y in prostate cancer samples from a Chinese population. Our aim was to evaluate the clinical utility of TTTY15-USP9Y in predicting the prostate biopsy outcome. Materials and methodsWe retrospectively examined the expression of TTTY15-USP9Y in 226 qualified urine sediment samples. Total RNA was extracted from the urine sediment by using TRIzol reagent, and complementary DNA was synthesized using TransPlex Complete Whole Transcriptome Amplification Kit (WTA2). Real-time quantitative polymerase chain reaction was performed to evaluate the expression of TTTY15-USP9Y and the prostate cancer–specific antigen (PSA) level. The TTTY15-USP9Y score was calculated as 2Ct(PSA)-Ct(TTTY15-USP9Y)× 1,000. ResultsThe TTTY15-USP9Y score was statistically significantly higher in men with positive biopsy outcome than in men with negative biopsy outcome (P<0.001). The area under the curve was 0.828 for the TTTY15-USP9Y score in the entire patient cohort. The TTTY15-USP9Y score׳s cutoff of 90.28 provided the optimal balance between sensitivity (84.0%) and specificity (77.5%). The combination of PSA level and the TTTY15-USP9Y score significantly improved the diagnostic performance of PSA level (P = 0.001). The TTTY15-USP9Y score alone was superior to PSA level, percent free PSA, and PSA density (serum PSA/prostate volume) in the subgroup of clinical interest (PSA level: 4–10ng/ml, gray zone). Univariable and multivariable logistic analyses indicated that TTTY15-USP9Y score, PSA level, age, and prostate volume were independent predictors of PCa. Adding the TTTY15-USP9Y score in the clinical base model (PSA level, age, and prostate volume) could bring a higher net benefit and reduce more unnecessary biopsies in the defined range of interest (10%–40% threshold probability). ConclusionIn conclusion, our study explored the potential utility of measuring the TTTY15-USP9Y score in post–digital rectal examination urine samples to predict biopsy outcome and provided the basis for the utility of this novel gene fusion in multicenter and large cohort studies.

Configuration and validation of a novel prostate disease nomogram predicting prostate biopsy outcome: A prospective study correlating clinical indicators among Filipino adult males with elevated PSA level

ObjectiveTo configure and validate a novel prostate disease nomogram providing prostate biopsy outcome probabilities from a prospective study correlating clinical indicators and diagnostic parameters among Filipino adult male with elevated serum total prostate specific antigen (PSA) level. MethodsAll men with an elevated serum total PSA underwent initial prostate biopsy at our institution from January 2011 to August 2014 were included. Clinical indicators, diagnostic parameters, which include PSA level and PSA-derivatives, were collected as predictive factors for biopsy outcome. Multiple logistic-regression analysis involving a backward elimination selection procedure was used to select independent predictors. A nomogram was developed to calculate the probability of the biopsy outcomes. External validation of the nomogram was performed using separate data set from another center for determination of sensitivity and specificity. A receiver-operating characteristic (ROC) curve was used to assess the accuracy in predicting differential biopsy outcome. ResultsTotal of 552 patients was included. One hundred and ninety-one (34.6%) patients had benign prostatic hyperplasia, and 165 (29.9%) had chronic prostatitis. The remaining 196 (35.5%) patients had prostate adenocarcinoma. The significant independent variables used to predict biopsy outcome were age, family history of prostate cancer, prior antibiotic intake, PSA level, PSA-density, PSA-velocity, echogenic findings on ultrasound, and DRE status. The areas under the receiver-operating characteristic curve for prostate cancer using PSA alone and the nomogram were 0.688 and 0.804, respectively. ConclusionThe nomogram configured based on routinely available clinical parameters, provides high predictive accuracy with good performance characteristics in predicting the prostate biopsy outcome such as presence of prostate cancer, high Gleason prostate cancer, benign prostatic hyperplasia, and chronic prostatitis.

Percent free prostate-specific antigen is effective to predict prostate biopsy outcome in Chinese men with prostate-specific antigen between 10.1 and 20.0 ng ml(-1).

Percent free prostatic-specific antigen (%fPSA) has been introduced as a tool to avoid unnecessary biopsies in patients with a serum PSA level of 4.0–10.0 ng ml−1, however, it remains controversial whether %fPSA is effective in PSA range of 10.1–20.0 ng ml−1 in both Chinese and Western population. In this study, the diagnostic performance of %fPSA and serum PSA in predicting prostate cancer (PCa) and high-grade PCa (HGPCa) was analyzed in a multi-center biopsy cohort of 5915 consecutive Chinese patients who underwent prostate biopsy in 22 hospitals across China from January 1, 2010 to December 31, 2013. The indication for biopsy was PSA>4.0 ng ml−1 or/and suspicious digital rectal examination. Total and free serum PSA determinations were performed by three types of electrochemiluminescence immunoassays with recalibration to the World Health Organization standards. The diagnostics accuracy of PSA, %fPSA and %fPSA in combination with PSA (%fPSA + PSA) was determined by the area under the receivers operating characteristic curve (AUC). %fPSA was more effective than PSA in men aged ≥60 years old. The AUC was 0.584 and 0.635 in men aged ≥60 years old with a PSA of 4.0–10.0 ng ml−1 and 10.1–20.0 ng ml−1, respectively. The AUC of %fPSA was superior to that of PSA in predicting HGPCa in patients ≥60 years old in these two PSA range. Our results indicated that %fPSA is both statistically effective and clinical applicable to predict prostate biopsy outcome in Chinese patients aged ≥60 years old with a PSA of 4.0–10.0 ng ml−1 and 10.1–20.0 ng ml−1.

Le test urinaire PCA3 : quelles perspectives ?

Since its introduction 10years ago, the urinary PCA3 test was evaluated in several studies that allowed demonstration of its diagnostic value when predicting prostate biopsy outcome. Its good specificity, notably when compared to seric PSA, offers to the urologist the opportunity to better evaluate whether prostate biopsies are warranted, especially in patients with at least one set of previous negative biopsies. Several points remain to be determined such as its true influence on prostate decision making in clinical practice and the resulting medico-economic benefit. The place of the urinary PCA3 test among other developing prostate cancer biomarkers, notably PHI index and urinary TMPRSS2:ERG fusions, has also to be defined.

Long non-coding RNA metastasis associated in lung adenocarcinoma transcript 1 derived miniRNA as a novel plasma-based biomarker for diagnosing prostate cancer

Examining plasma RNA is an emerging non-invasive diagnosis technique. However, whether tumour-derived long non-coding RNAs (lncRNAs) in plasma can be used as a novel approach to detect human prostate cancer (PCa) has not yet been established. The study was divided into three parts: (1) the characteristics of PCa-related lncRNA fragments were systematically studied in the plasma or serum of 25 patients; (2) the source of the circulating lncRNA fragments was explored in vitro and in vivo; and (3) the diagnostic performance of metastasis associated in lung adenocarcinoma transcript 1 (MALAT-1) derived (MD) miniRNA was validated in an independent cohort of 192 patients. The expression levels of lncRNAs were measured by quantitative real time polymerase chain reaction (qRT-PCR). The MD-miniRNA copies were calculated using a standard curve in an area under the ROC curve (AUC)-receiver operating characteristic (ROC) analysis. Genome-wide profiling revealed that MALAT-1 and prostate cancer gene 3 (PCA3) are overexpressed in PCa tissues. Plasma lncRNAs probably exist in the form of fragments in a stable form. MD-miniRNA enters cell culture medium at measurable levels, and MD-miniRNA derived from human PCa xenografts actually enters the circulation in vivo and can be measured to distinguish xenografted mice from controls. In addition, plasma MD-miniRNA levels are significantly elevated in PCa patients compared to non-PCa patients (p<0.001). At a cut-off of 867.8 MD-miniRNA copies per microlitre of plasma, the sensitivity is 58.6%, 58.6% and 43.5% and the specificity is 84.8%, 84.8% and 81.6% for discriminating PCa from non-PCa, positive biopsy from negative biopsy and positive biopsy from negative biopsy, respectively. We conclude that MD-miniRNA can be used as a novel plasma-based biomarker for PCa detection and can improve diagnostic accuracy by predicting prostate biopsy outcomes. Further large-scale studies are needed to confirm our findings.

Genome-wide Association Study Identifies Genetic Determinants of Urine PCA3 Levels in Men

Prostate cancer gene 3 (PCA3) is a non-coding gene specifically overexpressed in prostate cancer (PCa) that has great potential as a clinical biomarker for predicting prostate biopsy outcome. However, genetic determinants of PCA3 expression level remain unknown. To investigate the association between genetic variants and PCA3 mRNA level, a genome-wide association study was conducted in 1371 men of European descent in the REduction by DUtasteride of prostate Cancer Events trial. First-voided urine specimens containing prostate cells were obtained after digital rectal examination. The PROGENSA PCA3 assay was used to determine PCA3 score in the urinary samples. A linear regression model was used to detect the associations between (single nucleotide polymorphisms) SNPs and PCA3 score under an additive genetic model, adjusting for age and population stratification. Two SNPs, rs10993994 in β-microseminoprotein at 10q11.23 and rs10424878 in kallikrein-related peptidase 2 at 19q13.33, were associated with PCA3 score at genome-wide significance level (P = 1.22 x 10-9 and 1.06 x 10-8, respectively). Men carrying the rs10993994 “T” allele or rs10424878 “A” allele had higher PCA3 score compared with men carrying rs10993994 “C” allele or rs10424878 “G” allele (β = 1.25 and 1.24, respectively). This is the first comprehensive search for genetic determinants of PCA3 score. The novel loci identified may provide insight into the molecular mechanisms of PCA3 expression as a potential marker of PCa.

Development and Internal Validation of a Prostate Health Index Based Nomogram for Predicting Prostate Cancer at Extended Biopsy

We developed and validated a Prostate Health Index (Beckman Coulter, Brea, California) based nomogram to predict prostate cancer at extended prostate biopsy. The study population consisted of 729 patients who were scheduled for prostate biopsy following suspicious digital rectal examination and/or increased prostate specific antigen. Total and free prostate specific antigen, percent free-to-total prostate specific antigen, [-2]proPSA and the prostate health index [([-2]proPSA/free prostate specific antigen) × √total prostate specific antigen)] were determined. Logistic regression models were fitted to test prostate cancer predictors. Predictive accuracy estimates of biopsy outcome predictions were quantified. Regression coefficients were used to create a decision making tool to predict prostate cancer. A calibration plot was used to evaluate the extent of overestimating or underestimating the observed prostate cancer rate. Decision curve analysis provided an estimate of the net benefit obtained using the prostate health index based nomogram. Overall 280 of 729 patients (38.4%) were diagnosed with prostate cancer at extended prostate biopsy. On accuracy analyses prostate health index emerged as the most informative predictor of prostate cancer (AUC 0.70) compared to established predictors, such as total prostate specific antigen (0.51) and percent free-to-total prostate specific antigen (0.62). Including the prostate health index in a multivariable logistic regression model based on patient age, prostate volume, digital rectal examination and biopsy history significantly increased predictive accuracy by 7% from 0.73 to 0.80 (p <0.001). Nomogram calibration was good. Decision curve analysis showed that using the prostate health index based nomogram resulted in the highest net benefit. The prostate health index based nomogram can assist clinicians in the decision to perform biopsy by providing an accurate estimation of an individual risk of prostate cancer.

Potential Impact of Adding Genetic Markers to Clinical Parameters in Predicting Prostate Biopsy Outcomes in Men Following an Initial Negative Biopsy: Findings from the REDUCE Trial

BackgroundSeveral germline single nucleotide polymorphisms (SNPs) have been consistently associated with prostate cancer (PCa) risk. ObjectiveTo determine whether there is an improvement in PCa risk prediction by adding these SNPs to existing predictors of PCa. Design, setting, and participantsSubjects included men in the placebo arm of the randomized Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial in whom germline DNA was available. All men had an initial negative prostate biopsy and underwent study-mandated biopsies at 2 yr and 4 yr. Predictive performance of baseline clinical parameters and/or a genetic score based on 33 established PCa risk-associated SNPs was evaluated. Outcome measurements and statistical analysisArea under the receiver operating characteristic curves (AUC) were used to compare different models with different predictors. Net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess changes in risk prediction by adding genetic markers. Results and limitationsAmong 1654 men, genetic score was a significant predictor of positive biopsy, even after adjusting for known clinical variables and family history (p=3.41×10−8). The AUC for the genetic score exceeded that of any other PCa predictor at 0.59. Adding the genetic score to the best clinical model improved the AUC from 0.62 to 0.66 (p<0.001), reclassified PCa risk in 33% of men (NRI: 0.10; p=0.002), resulted in higher net benefit from DCA, and decreased the number of biopsies needed to detect the same number of PCa instances. The benefit of adding the genetic score was greatest among men at intermediate risk (25th percentile to 75th percentile). Similar results were found for high-grade (Gleason score ≥7) PCa. A major limitation of this study was its focus on white patients only. ConclusionsAdding genetic markers to current clinical parameters may improve PCa risk prediction. The improvement is modest but may be helpful for better determining the need for repeat prostate biopsy. The clinical impact of these results requires further study.

Predicting prostate biopsy outcome: prostate health index (phi) and prostate cancer antigen 3 (PCA3) are useful biomarkers

Indication for prostate biopsy is presently mainly based on prostate-specific antigen (PSA) serum levels and digital-rectal examination (DRE). In view of the unsatisfactory accuracy of these two diagnostic exams, research has focused on novel markers to improve pre-biopsy prostate cancer detection, such as phi and PCA3.The purpose of this prospective study was to assess the diagnostic accuracy of phi and PCA3 for prostate cancer using biopsy as gold standard.Phi index (Beckman coulter immunoassay), PCA3 score (Progensa PCA3 assay) and other established biomarkers (tPSA, fPSA and %fPSA) were assessed before a 18-core prostate biopsy in a group of 251 subjects at their first biopsy.Values of %p2PSA and phi were significantly higher in patients with PCa compared with PCa-negative group (p<0.001) and also compared with high grade prostatic intraepithelial neoplasia (HGPIN) (p<0.001). PCA3 score values were significantly higher in PCa compared with PCa-negative subjects (p<0.001) and in HGPIN vs PCa-negative patients (p<0.001). ROC curve analysis showed that %p2PSA, phi and PCA3 are predictive of malignancy.In conclusion, %p2PSA, phi and PCA3 may predict a diagnosis of PCa in men undergoing their first prostate biopsy. PCA3 score is more useful in discriminating between HGPIN and non-cancer.