The utility of the [-2]pro-prostate-specific antigen level as a prognostic marker in patients with castration-resistant prostate cancer treated with enzalutamide.

BACKGROUND: The prostate health index (phi) derived using [-2]pro-prostate-specific antigen (p2PSA), a precursor of PSA, has been shown to predict cancer in the gray zone. However, the utility of p2PSA in predicting outcomes for castration-resistant prostate cancer (CRPC) patients remains unknown. Therefore, in this study, we aimed to evaluate the usefulness of p2PSA in predicting the efficacy and prognosis of enzalutamide treatment in CRPC patients. METHODS: We conducted a prospective study of CRPC patients treated with enzalutamide at our institution, measuring p2PSA levels in 98 pre-treatment serum samples. All patients were divided into two groups based on the median values of each parameter. The PSA progression-free survival (PSA-PFS) and overall survival (OS) were compared using the Kaplan-Meier method. This study was approved by the Institutional Review Board of Gunma University Hospital (IRB No. 2021-092, 1983). RESULTS: The median PSA level before enzalutamide treatment was 25.59 ng/mL, the median p2PSA level was 208.75 pg/mL, and the median phi was 187.95. PSA, p2PSA, and phi were not all predictors of PSA-PFS. However, the OS was significantly better in the low-value groups (log-rank p-values of PSA, p2PSA, and phi were 0.024, 0.034, and 0.018, respectively). In the docetaxel (DOC)-naive group (n = 58), PSA was not a predictor of OS, but p2PSA and phi were significantly associated with better OS in the low group. This relationship was not observed in the DOC-treated group. CONCLUSIONS: Our study elucidates the usefulness of p2PSA in predicting outcomes for CRPC patients treated with enzalutamide. It suggests that p2PSA and phi may be prognostic markers after enzalutamide administration in CRPC patients.

Genetic polymorphisms at 19q13.33 are associated with [-2]proPSA (p2PSA) levels and provide additional predictive value to prostate health index for prostate cancer.

BACKGROUND: Prostate health index (phi), a derivative of [-2]proPSA (p2PSA), has shown better accuracy than prostate-specific antigen (PSA) in prostate cancer (PCa) detection. The present study was to investigate whether previously identified PSA-associated single nucleotide polymorphisms (SNPs) influence p2PSA or phi levels and lead to potential clinical utility. METHODS: We conducted an observational prospective study with 2268 consecutive patients who underwent prostate biopsy in three tertiary medical centers from August 2013 to March 2019. Genotyping data of the 46 candidate genes with a ± 100 kb window were tested for association with p2PSA and phi levels using linear regression. Multivariable logistic regression models were performed and internally validated using repeated tenfold cross-validation. We further calculated personalized phi cutoff values based on the significant genotypes. Discriminative performance was assessed using decision curve analysis and net reclassification improvement (NRI) index. RESULTS: We detected 11 significant variants at 19q13.33 which were p2PSA-associated independent of PCa. The most significant SNP, rs198978 in KLK2 (Pcombined = 5.73 × 10-9 ), was also associated with phi values (Pcombined = 3.20 × 10-6 ). Compared to the two commonly used phi cutoffs of 27.0 and 36.0, the personalized phi cutoffs had a significant NRI for PCa ranged from 5.23% to 9.70% among men carrying variant types (all p < .01). CONCLUSION: Rs198978, is independently associated with p2PSA values, and can improve the diagnostic ability of phi for PCa using personalized cutoff values.

[Epigenetic markers of early diagnostics of prostate cancer.]

The last 25 years, the prostate specific antigen (PSA) of the serum is used to diagnose prostate cancer. The experience of applying the PSA test showed its inconsistency as a diagnostic marker due to low cancerspecificity. Along with this, the next wave of biomarkers of prostate cancer appeared, which may supplement or, in due course, replace PSA due to higher sensitivity and cancerspecificity. This expanding panel of biomarkers was supplemented, basically, with new genomic technologies, which allowed to look impartially at cancer biology. Such efforts gave several notable success stories, quickly moving biomarkers from the laboratory table to clinical practice. The bulk of biomarker research focuses on early diagnosis of the disease, rather than on predictions that will allow for the prevention of the disease. This article examines the current state of biomarker studies of prostate cancer, including the revolutionary significance of the PSA test and its impact on early detection of prostate cancer, recent advances in biomarker detection, and a further developmental vector that improves the clinical management of this disease.

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.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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).

Improving multivariable prostate cancer risk assessment using the Prostate Health Index.

OBJECTIVES: To analyse the clinical utility of a prediction model incorporating both clinical information and a novel biomarker, p2PSA, in order to inform the decision for prostate biopsy in an Irish cohort of men referred for prostate cancer assessment. PATIENTS AND METHODS: Serum isolated from 250 men from three tertiary referral centres with pre-biopsy blood draws was analysed for total prostate-specific antigen (PSA), free PSA (fPSA) and p2PSA. From this, the Prostate Health Index (PHI) score was calculated (PHI = (p2PSA/fPSA)*√tPSA). The men's clinical information was used to derive their risk according to the Prostate Cancer Prevention Trial (PCPT) risk model. Two clinical prediction models were created via multivariable regression consisting of age, family history, abnormality on digital rectal examination, previous negative biopsy and either PSA or PHI score, respectively. Calibration plots, receiver-operating characteristic (ROC) curves and decision curves were generated to assess the performance of the three models. RESULTS: The PSA model and PHI model were both well calibrated in this cohort, with the PHI model showing the best correlation between predicted probabilities and actual outcome. The areas under the ROC curve for the PHI model, PSA model and PCPT model were 0.77, 0.71 and 0.69, respectively, for the prediction of prostate cancer (PCa) and 0.79, 0.72 and 0.72, respectively, for the prediction of high grade PCa. Decision-curve analysis showed a superior net benefit of the PHI model over both the PSA model and the PCPT risk model in the diagnosis of PCa and high grade PCa over the entire range of risk probabilities. CONCLUSION: A logical and standardized approach to the use of clinical risk factors can allow more accurate risk stratification of men under investigation for PCa. The measurement of p2PSA and the integration of this biomarker into a clinical prediction model can further increase the accuracy of risk stratification, helping to better inform the decision for prostate biopsy in a referral population.

The percentage of prostate-specific antigen (PSA) isoform [-2]proPSA and the Prostate Health Index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men aged ≤65 years.

OBJECTIVES: To prospectively test the diagnostic accuracy of the percentage of prostate specific antigen (PSA) isoform [-2]proPSA (%p2PSA) and the Prostate Health Index (PHI), and to determine their role for discrimination between significant and insignificant prostate cancer at initial and repeat prostate biopsy in men aged ≤65 years. PATIENTS AND METHODS: The diagnostic performance of %p2PSA and PHI were evaluated in a multicentre study. In all, 769 men aged ≤65 years scheduled for initial or repeat prostate biopsy were recruited in four sites based on a total PSA (t-PSA) level of 1.6-8.0 ng/mL World Health Organization (WHO) calibrated (2-10 ng/mL Hybritech-calibrated). Serum samples were measured for the concentration of t-PSA, free PSA (f-PSA) and p2PSA with Beckman Coulter immunoassays on Access-2 or DxI800 instruments. PHI was calculated as (p2PSA/f-PSA × âˆšt-PSA). Uni- and multivariable logistic regression models and an artificial neural network (ANN) were complemented by decision curve analysis (DCA). RESULTS: In univariate analysis %p2PSA and PHI were the best predictors of prostate cancer detection in all patients (area under the curve [AUC] 0.72 and 0.73, respectively), at initial (AUC 0.67 and 0.69) and repeat biopsy (AUC 0.74 and 0.74). t-PSA and %f-PSA performed less accurately for all patients (AUC 0.54 and 0.62). For detection of significant prostate cancer (based on Prostate Cancer Research International Active Surveillance [PRIAS] criteria) the %p2PSA and PHI equally demonstrated best performance (AUC 0.70 and 0.73) compared with t-PSA and %f-PSA (AUC 0.54 and 0.59). In multivariate analysis PHI we added to a base model of age, prostate volume, digital rectal examination, t-PSA and %f-PSA. PHI was strongest in predicting prostate cancer in all patients, at initial and repeat biopsy and for significant prostate cancer (AUC 0.73, 0.68, 0.78 and 0.72, respectively). In DCA for all patients the ANN showed the broadest threshold probability and best net benefit. PHI as single parameter and the base model + PHI were equivalent with threshold probability and net benefit nearing those of the ANN. For significant cancers the ANN was the strongest parameter in DCA. CONCLUSION: The present multicentre study showed that %p2PSA and PHI have a superior diagnostic performance for detecting prostate cancer in the PSA range of 1.6-8.0 ng/mL compared with t-PSA and %f-PSA at initial and repeat biopsy and for predicting significant prostate cancer in men aged ≤65 years. They are equally superior for counselling patients before biopsy.

Clinical performance of serum isoform [-2]proPSA (p2PSA), and its derivatives %p2PSA and the Prostate Health Index, in men aged <60 years: results from a multicentric European study.

OBJECTIVES: To test the hypothesis that [-2]proPSA (p2PSA) and its derivatives are more accurate than total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA) and fPSA as percentage of tPSA (%fPSA) in detecting prostate cancer (PCa) in men aged <60 years. PATIENTS AND METHODS: The analysis consisted of a nested case-control study from the PRO- PSA Multicentric European Study (PROMEtheuS) project. The primary outcomes were measures of sensibility, specificity and accuracy of serum p2PSA, p2PSA as percentage of fPSA (%p2PSA) and Beckman Coulter prostate health index (PHI) in men aged <60 years who had undergone a prostate biopsy. The potential reduction in the number of unnecessary biopsies and the characteristics of the potentially missed PCa cases were reported as secondary outcomes. Multivariate logistic regression models were complemented by predictive accuracy and decision-curve analyses. RESULTS: Of the 1036 patients enrolled in the PROMEtheus project, 238 (22.9%) were aged < 60 years. PCa was found in 67 subjects (28.1%); p2PSA, %p2PSA and PHI values were significantly higher (P < 0.001) among these subjects, while no differences were found in tPSA, fPSA and %fPSA values. On univariate analysis, %p2PSA (area under the curve [AUC]: 0.704) and PHI (AUC: 0.7) were the most accurate predictors, and these significantly outperformed tPSA (AUC: 0.549), fPSA (AUC: 0.511) and %fPSA (AUC: 0.557) in the prediction of PCa at biopsy (P ≤ 0.001). In multivariate logistic regression models, %p2PSA and PHI achieved independent predictor status and significantly increased the accuracy of multivariate models by 6.3 and 7.6%, respectively (P ≤ 0.05). CONCLUSION: PHI and %p2PSA are more accurate than the reference standard tests in predicting PCa in young men.

Performance of serum prostate-specific antigen isoform [-2]proPSA (p2PSA) and the prostate health index (PHI) in a Chinese hospital-based biopsy population.

BACKGROUND: The use of serum [-2]proPSA (p2PSA) and its derivative, the prostate health index (PHI), in detecting prostate cancer (PCa) have been consistently shown to have better performance than total prostate-specific antigen (tPSA) in discriminating biopsy outcomes in western countries. However, little is known about their performance in Chinese men. Our objective is to test the performance of p2PSA and PHI and their added value to tPSA in discriminating biopsy outcomes in Chinese men. METHODS: Consecutive patients who underwent prostate biopsy in three tertiary hospitals in Shanghai, China during 2012-2013 were recruited. Serum tPSA, free PSA (fPSA), and p2PSA were measured centrally using Beckman Coulter's DxI 800 Immunoassay System. The primary outcome is PCa and the secondary outcome is high-grade PCa (Gleason Score of 4 + 3 or worse). Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC), detection rate and Decision Curve Analysis (DCA). RESULTS: Among 636 patients who underwent prostate biopsy, PHI was a significant predictor of biopsy outcomes, independent of other clinical variables. The AUC in discriminating PCa from non-PCa was consistently higher for PHI than tPSA in the entire cohort (0.88 vs. 0.81) as well as in patients with tPSA at 2-10 ng/ml (0.73 vs. 0.53), at 10.1-20 ng/ml (0.81 vs. 0.58), and at tPSA >20 ng/ml (0.90 vs. 0.80). The differences were statistically significant in all comparisons, P < 0.01. To detect 90% of all PCa in the cohort, 362 and 457 patients would need to be biopsied based on PHI and tPSA cutoff, respectively, a 21% reduction for PHI. Similar results were found for discriminating high-grade PCa. CONCLUSIONS: PHI provides added value over tPSA in discriminating PCa and high-grade PCa in patients who underwent prostate biopsy in China.

Head-to-head comparison of prostate health index and urinary PCA3 for predicting cancer at initial or repeat biopsy.

PURPOSE: We performed a head-to-head comparison of the PHI (Prostate Health Index) and PCA3. MATERIALS AND METHODS: We evaluated PHI and PCA3 performance in 211 patients undergoing initial (116) or repeat (95) prostate biopsy. Multivariable logistic regression analysis was done using the AUC to test the accuracy of PHI and PCA3 for predicting prostate cancer in the overall population and in each setting. Decision curve analysis was used to compare the clinical benefit of different models. RESULTS: Overall, the AUC of the PHI (0.70) was significantly higher than the AUC of PCA3 (0.59), total prostate specific antigen (0.56) and free-to-total prostate specific antigen (0.60) (p = 0.043, 0.002 and 0.037, respectively). PHI was more accurate than PCA3 for predicting prostate cancer in the initial setting (AUC 0.69 vs 0.57) and in the repeat setting (AUC 0.72 vs 0.63), although no statistically significant difference was observed. Including PCA3 in the base multivariable model (prostate specific antigen plus free-to-total prostate specific antigen plus prostate volume) did not increase predictive accuracy in either setting (AUC 0.79 vs 0.80 and 0.75 vs 0.76, respectively). Conversely, including PHI in the base multivariable model improved predictive accuracy by 5% (AUC 0.79 to 0.84) and 6% (AUC 0.75 to 0.81) in the initial and repeat prostate biopsy settings, respectively. On decision curve analysis the highest net benefit was observed when PHI was added to the base multivariable model. CONCLUSIONS: PHI and PCA3 provide a significant increase in sensitivity and specificity compared to all other examined markers and they may help guide biopsy decisions. PCA3 does not increase the accuracy of predicting prostate cancer when PHI is assessed.

Clinical performance of serum prostate-specific antigen isoform [-2]proPSA (p2PSA) and its derivatives, %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer: results from a multicentre European study, the PROMEtheuS project.

OBJECTIVES: To test the sensitivity, specificity and accuracy of serum prostate-specific antigen isoform [-2]proPSA (p2PSA), %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer (PCa) undergoing prostate biopsy for suspected PCa. To evaluate the potential reduction in unnecessary biopsies and the characteristics of potentially missed cases of PCa that would result from using serum p2PSA, %p2PSA and PHI. PATIENTS AND METHODS: The analysis consisted of a nested case-control study from the PRO-PSA Multicentric European Study, the PROMEtheuS project. All patients had a first-degree relative (father, brother, son) with PCa. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. RESULTS: Of the 1026 patients included in the PROMEtheuS cohort, 158 (15.4%) had a first-degree relative with PCa. p2PSA, %p2PSA and PHI values were significantly higher (P < 0.001), and free/total PSA (%fPSA) values significantly lower (P < 0.001) in the 71 patients with PCa (44.9%) than in patients without PCa. Univariable accuracy analysis showed %p2PSA (area under the receiver-operating characteristic curve [AUC]: 0.733) and PHI (AUC: 0.733) to be the most accurate predictors of PCa at biopsy, significantly outperforming total PSA ([tPSA] AUC: 0.549), free PSA ([fPSA] AUC: 0.489) and %fPSA (AUC: 0.600) (P ≤ 0.001). For %p2PSA a threshold of 1.66 was found to have the best balance between sensitivity and specificity (70.4 and 70.1%; 95% confidence interval [CI]: 58.4-80.7 and 59.4-79.5 respectively). A PHI threshold of 40 was found to have the best balance between sensitivity and specificity (64.8 and 71.3%, respectively; 95% CI 52.5-75.8 and 60.6-80.5). At 90% sensitivity, the thresholds for %p2PSA and PHI were 1.20 and 25.5, with a specificity of 37.9 and 25.5%, respectively. At a %p2PSA threshold of 1.20, a total of 39 (24.8%) biopsies could have been avoided, but two cancers with a Gleason score (GS) of 7 would have been missed. At a PHI threshold of 25.5 a total of 27 (17.2%) biopsies could have been avoided and two (3.8%) cancers with a GS of 7 would have been missed. In multivariable logistic regression models, %p2PSA and PHI achieved independent predictor status and significantly increased the accuracy of multivariable models including PSA and prostate volume by 8.7 and 10%, respectively (P ≤ 0.001). p2PSA, %p2PSA and PHI were directly correlated with Gleason score (ρ: 0.247, P = 0.038; ρ: 0.366, P = 0.002; ρ: 0.464, P < 0.001, respectively). CONCLUSIONS: %p2PSA and PHI are more accurate than tPSA, fPSA and %fPSA in predicting PCa in men with a family history of PCa. Consideration of %p2PSA and PHI results in the avoidance of several unnecessary biopsies. p2PSA, %p2PSA and PHI correlate with cancer aggressiveness.

Evaluation of prostate health index in predicting bone metastasis of prostate cancer before bone scanning.

PURPOSE: In patients undergoing bone scanning, the positive rate of bone metastasis (BM) of prostate cancer (PCa) is quite low. The main purpose of this study was to explore the application of %p2PSA and prostate health index (phi) in predicting BM of PCa before bone scanning to reduce unnecessary bone scanning. METHODS: A total of 279 PCa patients were enrolled in our study. The area under the ROC curve was used to evaluate the prediction accuracy of the variables. Binary logistic regression analysis was performed to establish a prediction model. A multivariate regression model was established to evaluate the predictive value of the variables. The nomogram model was established by R software. The patients were stratified into an intermediate-risk subgroup (T2b-T2c, Gleason score = 6-7) and a high-risk subgroup (cT3-4, Gleason score = 8-10). In the overall cohort and subgroups, McNemar's test was used for comparison of different predictive variables. RESULTS: Of the 279 patients included in the study, 43 patients were identified as having BM by bone scanning. Univariate logistic regression analysis showed that age (p = 0.043), tPSA (p = 0.001), Ki-67 (p = 0.003), Gleason score (p = 0.001), clinical T stage (p < 0.001) and phi (p < 0.001) were significantly different in BM patients. In multivariate regression analysis, the model with phi showed significant diagnostic ability for predicting BM (AUC = 0.854). In the subgroup analysis, phi was significantly superior to tPSA in terms of the positive predictive value at sensitivities of 84.62% and 61.54% in the overall cohort (p < 0.001) and intermediate-risk subgroup (p < 0.001), respectively. Moreover, %p2PSA showed no significant advantage over tPSA (p > 0.05). CONCLUSION: The level of phi was significantly related to the positive rate of BM in initially diagnosed PCa. In PCa patients with clinical stage T2b-T2c and Gleason score = 6-7, phi can be used as a surrogate indicator of tPSA for screening BM.

A Germline Variant at 8q24 Contributes to the Serum p2PSA Level in a Chinese Prostate Biopsy Cohort.

INTRODUCTION: The clinical performance of [-2]proPSA (p2PSA) and its derivatives in predicting the presence and aggressiveness of prostate cancer (PCa) has been well evaluated in prostate biopsy patients. However, no study has been performed to evaluate the common genetic determinants that affect serum level of p2PSA. MATERIALS AND METHODS: Here, we performed a two-stage genome-wide association study (GWAS) on the p2PSA level in Chinese men who underwent a transperineal ultrasound-guided prostate biopsy at Huashan Hospital, Shanghai Cancer Center, and Ruijin Hospital in Shanghai, China. Germline variants significantly associated with the p2PSA level in the first stage (n = 886) were replicated in the second stage (n = 1,128). Multivariate linear regression was used to assess the independent contribution of confirmed single nucleotide polymorphisms (SNPs) and known covariates, such as age, to the level of p2PSA. RESULTS: A novel non-synonymous SNP, rs72725879, in region 8q24.21 of the PRNCR1 gene was significantly associated with the serum level of p2PSA in this two-stage GWAS (p = 2.28 × 10-9). Participants with homozygous "T" alleles at rs72725879 had higher p2PSA levels compared to allele "C" carriers. This variant was also nominally associated with PCa risk (p-combined = 3.44 × 10-18). The association with serum level of p2PSA was still significant after adjusting for PCa risk and age (p = 0.017). CONCLUSIONS: Our study shows that the genetic variants in the 8q24.21 region are associated with the serum level of p2PSA in a large-scale Chinese population. By taking inherited variations between individuals into account, the findings of these genetic variants may help improve the performance of p2PSA in predicting prostate cancer.

Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population.

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.

Establishment of reference intervals for serum [-2]proPSA (p2PSA), %p2PSA and prostate health index in healthy men.

BACKGROUND: Prostate cancer (PCa) is the second most common cancer among men worldwide. Recent research has identified [-2]proPSA (p2PSA), %p2PSA and prostate health index (phi) as new biomarkers for the early diagnosis and grading of PCa. However, few studies have used these parameters in a healthy population. In this study, we aimed to establish reference intervals (RIs) for p2PSA, %p2PSA and phi in healthy men based on age stratification. METHODS: Between April 2016 and March 2018, healthy subjects were recruited. Healthy men were then stratified into four age groups: <40 years, 40-49 years, 50-59 years and ≥60 years. Total PSA (tPSA), free PSA (fPSA), %fPSA, p2PSA, %p2PSA and phi were measured and RIs were established for p2PSA, %p2PSA and phi. RESULTS: In total, 732 healthy men were used for analysis. The RIs of phi were 9.77-48.44 for <40 years of age, 9.85-65.28 for 40-49 years of age, 9.98-39.72 for 50-59 years of age and 8.16-40.76 for ≥60 years of age. The reference values at the age of 40-49 years were generally higher than those at ≥60 years of age. CONCLUSIONS: Age-specific RIs for p2PSA, %p2PSA and phi were established in this study. This first set of established RIs will be invaluable for physicians to make precise medical decisions and carry out appropriate medical interventions.

Predictive value of isoform -2 proprostate-specific antigen and its derived indexes for prostate cancer patients with PSA 4-20 ng/mL / 现代泌尿外科杂志

【Objective】 To evaluate the predictive value of isoform [-2] proprostate-specific antigen, p2 PSA (p2PSA) and its derived indexes for prostate cancer in a Chinese cohort with PSA 4-20 ng/mL. 【Methods】 A total of 139 males scheduled for biopsy were enrolled in the prospective study from Nov.2021 to Jun.2022. The total PSA (tPSA), free PSA (fPSA), fPSA/tPSA (f/t) and p2PSA were collected, and the percentage of p2PSA(%p2PSA) and prostate health index(PHI) were calculated. The predictive value of p2PSA and its derived indexes were compared with traditional indexes with receiver operating characteristic (ROC) curve and Logistic analysis. 【Results】 Prostate cancer was found in 54 cases (38.8%). There were significant statistical differences in tPSA(10.68 vs.8.14, P=0.021), f/t(0.13 vs.0.16, P=0.006), p2PSA(30.25 vs.19.81, P<0.001), %p2PSA(21.52 vs.13.15, P<0.001) and PHI(64.3vs.38.2, P<0.001) between prostate cancer patients and non-prostate cancer patients. The area under the ROC curve (AUC) of tPSA, fPSA, %fPSA, p2PSA, %p2PSA and PHI were 0.63, 0.51, 0.63, 0.71, 0.73, and 0.80, respectively. The inclusion of %p2PSA and PHI significantly increased the prediction efficiency of the basic prediction model (AUCbase+PHI=0.81, AUCbase+%p2PSA=0.78, AUCbase=0.67). With 35 as the recommended cut-off value of PHI, the incidence of meaningless puncture was reduced by 25.8%(36/139). 【Conclusion】 The application of p2PSA and its derived indexes have good predictive value for patients with PSA 4-20 ng/mL. The combined detection of %p2PSA and PHI can significantly increase the detection efficiency of prostate cancer and reduce the incidence of meaningless prostate puncture by 25.8%.

Measurement of serum isoform [-2]proPSA derivatives shows superior accuracy to magnetic resonance imaging in the diagnosis of prostate cancer in patients with a total prostate-specific antigen level of 2-10 ng/ml.

OBJECTIVE: More accurate diagnostic procedures for prostate cancer are needed to avoid unnecessary biopsy due to the low specificity of prostate-specific antigen (PSA). Recent studies showed that the percentage of serum isoform [-2]proPSA (p2PSA) to free PSA (%p2PSA), the Prostate Health Index (PHI) and magnetic resonance imaging (MRI) were more accurate than PSA. The aim of this study was to test the accuracy of %p2PSA, PHI and MRI in discriminating patients with and without prostate cancer. MATERIALS AND METHODS: The subjects were 50 consecutive men with a PSA level of 2.0-10.0 ng/ml, who underwent prostate biopsy from October 2012 to July 2014. These patients underwent multiparametric MRI before biopsy, and their serum samples were measured for PSA, free PSA and p2PSA. The sensitivity, specificity and accuracy of PHI, %p2PSA and MRI were compared with PSA in the diagnosis of biopsy-confirmed prostate cancer. RESULTS: In a univariate analysis, %p2PSA [area under the curve (AUC): 0.811] and PHI (AUC 0.795) were more accurate than MRI (AUC: 0.583) and PSA (AUC: 0.554) for prostate cancer detection. At 60% sensitivity, the specificity of PHI (76.5%) was higher than that of MRI (52.9%). For significant cancer detection, %p2PSA (AUC: 0.745), PHI (AUC: 0.791) and MRI (AUC: 0.739) were marginally more accurate than PSA (AUC: 0.696). At 85% sensitivity, the specificity of MRI (62.1%) was higher than that of PHI (34.5%). CONCLUSION: PHI and %p2PSA can be used for screening the general population and MRI can be used for detection of significant cancer in patients suspected, from screening tests, of having prostate cancer.

Preoperative Prostate-specific Antigen Isoform p2PSA and Its Derivatives, %p2PSA and Prostate Health Index, Predict Pathologic Outcomes in Patients Undergoing Radical Prostatectomy for Prostate Cancer: Results from a Multicentric European Prospective Study.

BACKGROUND: Currently available predictive models fail to assist clinical decision making in prostate cancer (PCa) patients who are potential candidates for radical prostatectomy (RP). New biomarkers would be welcome. OBJECTIVE: To test the hypothesis that prostate-specific antigen (PSA) isoform p2PSA and its derivatives, percentage of p2PSA to free PSA (%p2PSA) and the Prostate Health Index (PHI), predict PCa characteristics at final pathology. DESIGN, SETTING, AND PARTICIPANTS: An observational prospective multicentre European study was performed in 489 consecutive PCa patients treated with RP. Total PSA (tPSA), free PSA (fPSA), and p2PSA levels were determined. The %fPSA [(fPSA / tPSA) × 100], %p2PSA [(p2PSA pg/ml) / (fPSA ng/ml × 1000) × 100], and PHI [(p2PSA / fPSA) × âˆštPSA] were calculated. INTERVENTION: Open or robot-assisted RP. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Logistic regression models were fitted to test the predictors of pT3 stage and/or pathologic Gleason score (GS) ≥7 and to determine their predictive accuracy. The base multivariable model included tPSA, digital rectal examination, biopsy GS, and percentage of positive biopsy cores. Decision curve analysis provided an estimate of the net benefit obtained using p2PSA, %p2PSA, or PHI. RESULTS AND LIMITATIONS: Overall, 344 patients (70%) were affected by pT3 disease or pathologic GS ≥7; pT3 disease and pathologic GS ≥7 were present in 126 patients (26%). At univariable analysis, p2PSA, %p2PSA, and PHI were significant predictors of pT3 disease and/or pathologic GS ≥7 (all p ≤ 0.001). The inclusion of PHI significantly increased the accuracy of the base multivariable model by 2.3% (p=0.003) and 2.4% (p=0.01) for the prediction of pT3 disease and/or pathologic GS ≥7, respectively. However, at decision curve analysis, models including PHI did not show evidence of a greater clinical net benefit. CONCLUSIONS: Both %p2PSA and PHI are significant predictors of unfavourable PCa characteristics at final pathology; however, %p2PSA and PHI did not provide a greater net benefit for clinical decision making. PATIENT SUMMARY: Prostate-specific antigen (PSA) isoform p2PSA and its derivatives, percentage of p2PSA to free PSA and the Prostate Health Index, are associated with adverse characteristics of prostate cancer; however, these biomarkers provided only a slight net benefit for clinical decision making.

Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population / 亚洲男科学杂志(英文版)

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.

Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population / 亚洲男科学杂志(英文版)

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.