Plateau effect of prostate cancer risk-associated SNPs in discriminating prostate biopsy outcomes

The Combined Effect of Polygenic Risk Score and Prostate Health Index in Chinese Men Undergoing Prostate Biopsy

To date, the combined effect of polygenic risk score (PRS) and prostate health index (phi) on PCa diagnosis in men undergoing prostate biopsy has never been investigated. A total of 3166 patients who underwent initial prostate biopsy in three tertiary medical centers from August 2013 to March 2019 were included. PRS was calculated on the basis of the genotype of 102 reported East-Asian-specific risk variants. It was then evaluated in the univariable or multivariable logistic regression models that were internally validated using repeated 10-fold cross-validation. Discriminative performance was assessed by area under the receiver operating curve (AUC) and net reclassification improvement (NRI) index. Compared with men in the first quintile of age and family history adjusted PRS, those in the second, third, fourth, and fifth quintiles were 1.86 (odds ratio, 95% confidence interval (CI): 1.34-2.56), 2.07 (95%CI: 1.50-2.84), 3.26 (95%CI: 2.36-4.48), and 5.06 (95%CI: 3.68-6.97) times as likely to develop PCa (all p < 0.001). Adjustment for other clinical parameters yielded similar results. Among patients with prostate-specific antigen (PSA) at 2-10 ng/mL or 2-20 ng/mL, PRS still had an observable ability to differentiate PCa in the group of prostate health index (phi) at 27-36 (P_trend < 0.05) or >36 (P_trend ≤ 0.001). Notably, men with moderate phi (27-36) but highest PRS (top 20% percentile) would have a comparable risk of PCa (positive rate: 26.7% or 31.3%) than men with high phi (>36) but lowest PRS (bottom 20% percentile positive rate: 27.4% or 34.2%). The combined model of PRS, phi, and other clinical risk factors provided significantly better performance (AUC: 0.904, 95%CI: 0.887-0.921) than models without PRS. Adding PRS to clinical risk models could provide significant net benefit (NRI, from 8.6% to 27.6%), especially in those early onset patients (NRI, from 29.2% to 44.9%). PRS may provide additional predictive value over phi for PCa. The combination of PRS and phi that effectively captured both clinical and genetic PCa risk is clinically practical, even in patients with gray-zone PSA.

Inherited risk assessment and its clinical utility for predicting prostate cancer from diagnostic prostate biopsies

BACKGROUND

Many studies on prostate cancer (PCa) germline variants have been published in the last 15 years. This review critically assesses their clinical validity and explores their utility in prediction of PCa detection rates from prostate biopsy.

METHODS

An integrative review was performed to (1) critically synthesize findings on PCa germline studies from published papers since 2016, including risk-associated single nucleotide polymorphisms (SNPs), polygenic risk score methods such as genetic risk score (GRS), and rare pathogenic mutations (RPMs); (2) exemplify the findings in a large population-based cohort from the UK Biobank (UKB); (3) identify gaps for implementing inherited risk assessment in clinic based on experience from a healthcare system; (4) evaluate available GRS data on their clinical utility in predicting PCa detection rates from prostate biopsies; and (5) describe a prospective germline-based biopsy trial to address existing gaps.

RESULTS

SNP-based GRS and RPMs in four genes (HOXB13, BRCA2, ATM, and CHEK2) were significantly and consistently associated with PCa risk in large well-designed studies. In the UKB, positive family history, RPMs in the four implicated genes, and a high GRS (>1.5) identified 8.12%, 1.61%, and 17.38% of men to be at elevated PCa risk, respectively, with hazard ratios of 1.84, 2.74, and 2.39, respectively. Additionally, the performance of GRS for predicting PCa detection rate on prostate biopsy was consistently supported in several retrospective analyses of transrectal ultrasound (TRUS)-biopsy cohorts. Prospective studies evaluating the performance of all three inherited measures in predicting PCa detection rate from contemporary multiparametric MRI (mpMRI)-based biopsy are lacking. A multicenter germline-based biopsy trial to address these gaps is warranted.

CONCLUSIONS

The complementary performance of three inherited risk measures in PCa risk stratification is consistently supported. Their clinical utility in predicting PCa detection rate, if confirmed in prospective clinical trials, may improve current decision-making for prostate biopsy.

The role of genetic testing in prostate cancer screening, diagnosis, and treatment

PURPOSE OF REVIEW

This review provides an overview of the current role of genetic testing in prostate cancer screening, diagnosis, and treatment.

RECENT FINDINGS

Recent studies have uncovered few but highly penetrant rare pathogenic mutations (RPMs), in genes, such as BRCA2, with strong prostate cancer risk and outcomes associations. Over 260 single nucleotide polymorphisms (SNPs) have also been identified, each associated with small incremental prostate cancer risk and when combined in a polygenic risk score (PRS), they provide strong prostate cancer risk prediction but do not seem to predict outcomes. Tumor tissue sequencing can also help identify actionable somatic mutations in many patients with advanced prostate cancer and inform on their risk of harboring a germline pathogenic mutation.

SUMMARY

RPM testing, PRS testing, and tumor sequencing all have current and/or potential future roles in personalized prostate cancer care.

Prediction of clinically significant prostate cancer using polygenic risk models in Asians

Purpose

To develop and evaluate the performance of a polygenic risk score (PRS) constructed in a Korean male population to predict clinically significant prostate cancer (csPCa).

Materials and Methods

Total 2,702 PCa samples and 7,485 controls were used to discover csPCa susceptible single nucleotide polymorphisms (SNPs). Males with biopsy-proven or post-radical prostatectomy Gleason score 7 or higher were included for analysis. After genotype imputation for quality control, logistic regression models were applied to test association and calculate effect size. Extracted candidate SNPs were further tested to compare predictive performance according to number of SNPs included in the PRS. The best-fit model was validated in an independent cohort of 311 cases and 822 controls.

Results

Of the 83 candidate SNPs with significant PCa association reported in previous literature, rs72725879 located in PRNCR1 showed the highest significance for PCa risk (odds ratio, 0.597; 95% confidence interval [CI], 0.555–0.641; p=4.3×10^-45). Thirty-two SNPs within 26 distinct loci were further selected for PRS construction. Best performance was found with the top 29 SNPs, with AUC found to be 0.700 (95% CI, 0.667–0.734). Males with very-high PRS (above the 95th percentile) had a 4.92-fold increased risk for csPCa.

Conclusions

Ethnic-specific PRS was developed and validated in Korean males to predict csPCa susceptibility using the largest csPCa sample size in Asia. PRS can be a potential biomarker to predict individual risk. Future multi-ethnic trials are required to further validate our results.

Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer

BACKGROUND

Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).

MATERIALS AND METHOD

180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.

RESULTS

166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.

CONCLUSIONS

Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Application of metastatic biopsy based on "When, Who, Why, Where, How (4W1H)" principle in diagnosis and treatment of metastatic castration-resistance prostate cancer

Background

To determine the feasibility of secondary biopsy of metastatic castration-resistance prostate cancer based on the "4W1H-When, Who, Why, Where, How" principle and analyze the factors that affect tumor detection. Its application will further direct the patients for individualized precision therapy.

Methods

A total of 55 patients were collected for secondary biopsy (27 prostate biopsies and 55 metastases biopsies). The parameters of biopsy location, computed tomography attenuation coefficient, lesion size, core number, laboratory tests, and the use of bone protection were evaluated. Histopathological data and the pathogenesis and etiology classification were used to guide precision treatment.

Results

Fifteen/27 patients had a positive prostate biopsy, and 47/55 had positive metastasis biopsy. Bone metastasis biopsy was positive in 21/29 of cases. Also, parenchymal organs and lymph node biopsies were positive. In the prostate rebiopsy, significant differences were observed between total prostate volume (P=0.028), prostate-specific antigen (PSA) density (P=0.047), PSA velocity (P=0.036), and positive biopsy results. In the bone metastasis biopsy, we divided the patients into biopsy-positive and -negative groups. The computed tomography attenuation coefficient, PSA, alkaline phosphatase, and hemoglobin were related to tumor positive detection. However, the lesion size, core number, bone-sparing agents and previous treatments did not affect tumor detection.

Conclusions

In metastatic castration-resistant prostate cancer (mCRPC) patients, the "4W1H" principle was applied in the second biopsy. The biopsy site, image, and laboratory variables affected the positive of tumor tissue. Further pathological analysis of tumor tissue is essential to guide the precision medicine of mCRPC etiological classification.

Polygenic risk score for genetic evaluation of prostate cancer risk in Asian populations: A narrative review

Decreasing costs of genetic testing and interest in disease inheritance has changed the landscape of cancer prediction in prostate cancer (PCa), and guidelines now include genetic testing for high-risk groups. Familial and hereditary PCa comprises approximately 20% and 5% of all PCa, respectively. Multifaceted disorders like PCa are caused by a combinatory effect of rare genes of high penetrance and smaller genetic variants of relatively lower effect size. Polygenic risk score (PRS) is a novel tool utilizing PCa-associated single nucleotide polymorphisms (SNPs) identified from genome-wide association study (GWAS) to generate an additive estimate of an individual's lifetime genetic risk for cancer. However, most PRS are developed based on GWAS collected from mainly European populations and do not address ethnic differences in PCa genetics. This review highlights the attempts to generate a PRS tailored to Asian males including data from Korea, China, and Japan, and discuss the clinical implications for prediction of early onset and aggressive PCa.

Single-Nucleotide Polymorphism-Based Genetic Risk Score and Patient Age at Prostate Cancer Diagnosis

IMPORTANCE

Few studies have evaluated the association between a single-nucleotide polymorphism-based genetic risk score (GRS) and patient age at prostate cancer (PCa) diagnosis.

OBJECTIVES

To test the association between a GRS and patient age at PCa diagnosis and to compare the performance of a GRS with that of family history (FH) in PCa risk stratification.

DESIGN, SETTING, AND PARTICIPANTS

A cohort study of 3225 white men was conducted as a secondary analysis of the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) chemoprevention trial, a 4-year, randomized, double-blind, placebo-controlled multicenter study conducted from March 2003 to April 2009 to evaluate the safety and efficacy of dutasteride in reducing PCa events. Participants were confirmed to be cancer free by prostate biopsy (6-12 cores) within 6 months prior to the study and underwent 10 core biopsies every 2 years per protocol. The dates for performing data analysis were from July 2016 to October 2019.

INTERVENTIONS

A well-established, population-standardized GRS was calculated for each participant based on 110 known PCa risk-associated single-nucleotide polymorphisms, which is a relative risk compared with the general population. Men were classified into 3 GRS risk groups based on predetermined cutoff values: low (<0.50), average (0.50-1.49), and high (≥1.50).

MAIN OUTCOMES AND MEASURES

Prostate cancer diagnosis-free survival among men of different risk groups.

RESULTS

Among 3225 men (median age, 63 years [interquartile range, 58-67 years]) in the study, 683 (21%) were classified as low risk, 1937 (60%) as average risk, and 605 (19%) as high risk based on GRS alone. In comparison, 2789 (86%) were classified as low or average risk and 436 (14%) as high risk based on FH alone. Men in higher GRS risk groups had a PCa diagnosis-free survival rate that was worse than that of those in the lower GRS risk group (χ2 = 53.3; P < .001 for trend) and in participants with a negative FH of PCa (χ2 = 45.5; P < .001 for trend). Combining GRS and FH further stratified overall genetic risk, indicating that 957 men (30%) were at high genetic risk (either high GRS or positive FH), 1667 men (52%) were at average genetic risk (average GRS and negative FH), and 601 men (19%) were at low genetic risk (low GRS and negative FH). The median PCa diagnosis-free survival was 74 years (95% CI, 73-75 years) for men at high genetic risk, 77 years (95% CI, 75 to >80 years) for men at average genetic risk, and more than 80 years (95% CI, >80 to >80 years) for men at low genetic risk. In contrast, the median PCa diagnosis-free survival was 73 years (95% CI, 71-76 years) for men with a positive FH and 77 years (95% CI, 76-79 years) for men with a negative FH.

CONCLUSIONS AND RELEVANCE

This study suggests that a GRS is significantly associated with patient age at PCa diagnosis. Combining FH and GRS may better stratify inherited risk than FH alone for developing personalized PCa screening strategies.

Concept and benchmarks for assessing narrow-sense validity of genetic risk score values

BACKGROUND

While higher genetic risk score (GRS) has been statistically associated with increased disease risk (broad-sense validity), the concept and tools for assessing the validity of reported GRS values from tests (narrow-sense validity) are underdeveloped.

METHODS

We propose two benchmarks for assessing the narrow-sense validity of GRS. The baseline benchmark requires that the mean GRS value in a general population approximates 1.0. The calibration benchmark assesses the agreement between observed risks and estimated risks (GRS values). We assessed benchmark performance for three prostate cancer (PCa) GRS tests, derived from three SNP panels with increasing stringency of selection criteria, in a PCa chemoprevention trial where 714 of 3225 men were diagnosed with PCa during the 4-year follow-up.

RESULTS

GRS from Panels 1, 2, and 3 were all statistically associated with PCa risk; P = 5.58 × 10^-3 , P = 1 × 10^-3 , and P = 1.5 × 10^-13 , respectively (broad-sense validity). For narrow-sense validity, the mean GRS value among men without PCa was 1.33, 1.09, and 0.98 for Panels 1, 2, and 3, respectively (baseline benchmark). For assessing the calibration benchmark, observed risks were calculated for seven groups of men with GRS values <0.3, 0.3-0.79, 0.8-1.19, 1.2-1.49, 1.5-1.99, 2-2.99, and ≥3. The calibration slope (higher is better) was 0.15, 0.12, and 0.60, and the bias score (lower is better) between the observed risks and GRS values was 0.08, 0.08, and 0.02 for Panels 1, 2, and 3, respectively.

CONCLUSION

Performance differed considerably among GRS tests. We recommend that all GRS tests be evaluated using the two benchmarks before clinical implementation for individual risk assessment.

Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts

BACKGROUND

Genetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers.

METHODS

Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts.

RESULTS

Mean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P_-trend  < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers.

CONCLUSION

Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.

Current progress and questions in germline genetics of prostate cancer

Dramatic progress has been made in the area of germline genetics of prostate cancer (PCa) in the past decade. Both common and rare genetic variants with effects on risk ranging from barely detectable to outright practice-changing have been identified. For men with high risk PCa, the application of genetic testing for inherited pathogenic mutations is becoming standard of care. A major question exists about which additional populations of men to test, as men at all risk levels can potentially benefit by knowing their unique genetic profile of germline susceptibility variants. This article will provide a brief overview of some current issues in understanding inherited susceptibility for PCa.

Validation of the novel susceptibility loci for prostate cancer in a Chinese population

The present study evaluated 23 newly identified susceptibility loci for prostate cancer (PCa) in a Chinese population and assessed whether any validated loci were associated with the genetic risk score (GRS) of PCa in a Chinese population. A total of 1,417 patients with PCa and 1,008 controls were recruited in the present study. The association of each single nucleotide polymorphism (SNP) with PCa risk and PCa aggressiveness was analyzed. The predictive ability of two GRSs based on 30 SNPs (GRS30) and the 9 most significant SNPs (GRS9) in the Chinese population were also compared. Among the 19 SNPs evaluated, 1 SNP (rs7153648 at 14q23) was associated with PCa risk [odds ratio (OR)=1.206, P<0.05)] and 1 SNP (rs636291 at 1p23) was associated with PCa aggressiveness (OR=1.123, P<0.05). GRS30 and GRS9 were significantly increased in patients with PCa compared with that among non-PCa controls. The areas under receiver operating characteristic curves of GRS9 and GRS 30 were similar (0.792 for GRS9 vs. 0.7994 for GRS30, P=0.138). To conclude, among the 19 SNPs evaluated, only 1 SNP was associated with PCa risk in the Chinese population. SNPs that were weakly associated with PCa were unlikely to improve the predictive ability of existing GRS in the Chinese population.

An Integrative Genomics Approach for Associating Genome-Wide Association Studies Information With Localized and Metastatic Prostate Cancer Phenotypes

High-throughput genotyping has enabled discovery of genetic variants associated with an increased risk of developing prostate cancer using genome-wide association studies (GWAS). The goal of this study was to associate GWAS information of patients with primary organ–confined and metastatic prostate cancer using gene expression data and to identify molecular networks and biological pathways enriched for genetic susceptibility variants involved in the 2 disease states. The analysis revealed gene signatures for the 2 disease states and a gene signature distinguishing the 2 patient groups. In addition, the analysis revealed molecular networks and biological pathways enriched for genetic susceptibility variants. The discovered pathways include the androgen, apoptosis, and insulinlike growth factor signaling pathways. This analysis established putative functional bridges between GWAS discoveries and the biological pathways involved in primary organ–confined and metastatic prostate cancer.

A comparison of genetic risk score with family history for estimating prostate cancer risk

Prostate cancer (PCa) testing is recommended by most authoritative groups for high-risk men including those with a family history of the disease. However, family history information is often limited by patient knowledge and clinician intake, and thus, many men are incorrectly assigned to different risk groups. Alternate methods to assess PCa risk are required. In this review, we discuss how genetic variants, referred to as PCa-risk single-nucleotide polymorphisms, can be used to calculate a genetic risk score (GRS). GRS assigns a relatively unique value to all men based on the number of PCa-risk SNPs that an individual carries. This GRS value can provide a more precise estimate of a man's PCa risk. This is particularly relevant in situations when an individual is unaware of his family history. In addition, GRS has utility and can provide a more precise estimate of risk even among men with a positive family history. It can even distinguish risk among relatives with the same degree of family relationships. Taken together, this review serves to provide support for the clinical utility of GRS as an independent test to provide supplemental information to family history. As such, GRS can serve as a platform to help guide-shared decision-making processes regarding the timing and frequency of PCa testing and biopsies.

Clinical validity and utility of genetic risk scores in prostate cancer

Current issues related to prostate cancer (PCa) clinical care (e.g., over-screening, over-diagnosis, and over-treatment of nonaggressive PCa) call for risk assessment tools that can be combined with family history (FH) to stratify disease risk among men in the general population. Since 2007, genome-wide association studies (GWASs) have identified more than 100 SNPs associated with PCa susceptibility. In this review, we discuss (1) the validity of these PCa risk-associated SNPs, individually and collectively; (2) the various methods used for measuring the cumulative effect of multiple SNPs, including genetic risk score (GRS); (3) the adequate number of SNPs needed for risk assessment; (4) reclassification of risk based on evolving numbers of SNPs used to calculate genetic risk, (5) risk assessment for men from various racial groups, and (6) the clinical utility of genetic risk assessment. In conclusion, data available to date support the clinical validity of PCa risk-associated SNPs and GRS in risk assessment among men with or without FH. PCa risk-associated SNPs are not intended for diagnostic use; rather, they should be used the same way as FH. Combining GRS and FH can significantly improve the performance of risk assessment. Improved risk assessment may have important clinical utility in targeted PCa testing. However, clinical trials are urgently needed to evaluate this clinical utility as well as the acceptance of GRS by patients and physicians.

Population-standardized genetic risk score: the SNP-based method of choice for inherited risk assessment of prostate cancer

Several different approaches are available to clinicians for determining prostate cancer (PCa) risk. The clinical validity of various PCa risk assessment methods utilizing single nucleotide polymorphisms (SNPs) has been established; however, these SNP-based methods have not been compared. The objective of this study was to compare the three most commonly used SNP-based methods for PCa risk assessment. Participants were men (n = 1654) enrolled in a prospective study of PCa development. Genotypes of 59 PCa risk-associated SNPs were available in this cohort. Three methods of calculating SNP-based genetic risk scores (GRSs) were used for the evaluation of individual disease risk such as risk allele count (GRS-RAC), weighted risk allele count (GRS-wRAC), and population-standardized genetic risk score (GRS-PS). Mean GRSs were calculated, and performances were compared using area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV). All SNP-based methods were found to be independently associated with PCa (all P < 0.05; hence their clinical validity). The mean GRSs in men with or without PCa using GRS-RAC were 55.15 and 53.46, respectively, using GRS-wRAC were 7.42 and 6.97, respectively, and using GRS-PS were 1.12 and 0.84, respectively (all P < 0.05 for differences between patients with or without PCa). All three SNP-based methods performed similarly in discriminating PCa from non-PCa based on AUC and in predicting PCa risk based on PPV (all P > 0.05 for comparisons between the three methods), and all three SNP-based methods had a significantly higher AUC than family history (all P < 0.05). Results from this study suggest that while the three most commonly used SNP-based methods performed similarly in discriminating PCa from non-PCa at the population level, GRS-PS is the method of choice for risk assessment at the individual level because its value (where 1.0 represents average population risk) can be easily interpreted regardless of the number of risk-associated SNPs used in the calculation.

Influence of age on predictiveness of genetic risk score for prostate cancer in a Chinese hospital-based biopsy cohort

Background

We investigated whether age influences the predictiveness of genetic risk score (GRS) for prostate cancer (PCa) in a Chinese hospital-based biopsy cohort.

Methods

We included consecutive patients who underwent prostate biopsies in two tertiary centers between 2012 and 2014. GRS was calculated using 24 PCa-associated genetic variants and its predictiveness was assessed by area under curve (AUC).

Results

Of 1120 men tested, 724 with prostate-specific antigen (PSA) < 20 ng/ml were selected for further analysis. Patients were divided into 3 groups by age cutoffs at 60 and 70 years. GRS significantly predicted PCa for all patients (AUC: 0.561; 95% CI: 0.514–0.609) and was an independent predictor in multivariate analysis for the 60–70 year-olds (AUC: 0.612, 95% CI: 0.541–0.684), but not for patients aged < 60 years or ≥70 years. For PCa with Gleason score ≥7, GRS discriminative ability was 0.582 (95% CI=0.527–0.637) for all patients, and 0.647 (95% CI: 0.541–0.684) for the 60–70 year-old group.

Conclusion

GRS significantly increased clinical prediction of PCa and high-grade disease in Chinese men aged 60–70 years, which implies that men in this age group would benefit most from genetic testing.

Urinary melatonin-sulfate/cortisol ratio and the presence of prostate cancer: A case-control study

The circadian-related hormones, melatonin and cortisol, have oncostatic and immunosuppressive properties. This study examined the relationship between these two biomarkers and the presence of prostate cancer. We measured their major metabolites in urine collected from 120 newly diagnosed prostate cancer patients and 240 age-matched controls from January 2011 to April 2014. Compared with patients with lower urinary melatonin-sulfate or melatonin-sulfate/cortisol (MT/C) ratio levels, those with above-median levels were significantly less likely to have prostate cancer (adjusted OR (aOR) = 0.59, 95% CI = 0.35–0.99; aOR = 0.46, 95% CI: 0.27–0.77) or advanced stage prostate cancer (aOR = 0.49, 95% CI = 0.26–0.89; aOR = 0.33, 95% CI = 0.17–0.62). The combined effect of both low MT/C ratios and PSA levels exceeding 10 ng/ml was an 8.82-fold greater likelihood of prostate cancer and a 32.06-fold greater likelihood of advanced stage prostate cancer, compared to those with both high MT/C ratios and PSA levels less than 10 ng/ml. In conclusion, patients with high melatonin-sulfate levels or a high MT/C ratio were less likely to have prostate cancer or advanced stage prostate. Besides, a finding of a low MT/C ratio combined with a PSA level exceeding 10 ng/ml showed the greatest potential in detecting prostate cancer and advanced stage prostate cancer.

Effect of body mass index on the performance characteristics of PSA-related markers to detect prostate cancer

To examine whether the predictive performance of prostate-specific antigen (PSA) and PSA-related markers for prostate cancer (PCa) is modified by body mass index (BMI). Patients with a PSA 2-10 ng/mL who underwent multicore prostate biopsies were recruited from three tertiary centers. Serum markers measured included total PSA (tPSA), free-to-total PSA (f/tPSA), p2PSA, percentage of p2PSA (%p2PSA), and prostate health index (PHI). The association between serum markers and PCa risk was assessed by logistic regression. Predictive performance for each marker was quantified using the area under the receiver operator curves (AUC). Among 516 men, 18.2% had PCa at biopsy. For all tested markers, their predictive value on PCa risk was lower in obese patients compared to normal weight patients. We found statistically significant interactions between BMI and tPSA (P = 0.0026) and p2PSA (P = 0.038). PHI achieved an AUC of 0.872 in normal weight patients and 0.745 in obese patients, which outperformed the other predictors regardless of BMI category. In conclusion, PHI achieved the best predictive performance for detecting PCa and was not influenced by BMI.

Development and external validation of a prostate health index-based nomogram for predicting prostate cancer

To develop and externally validate a prostate health index (PHI)-based nomogram for predicting the presence of prostate cancer (PCa) at biopsy in Chinese men with prostate-specific antigen 4–10 ng/mL and normal digital rectal examination (DRE). 347 men were recruited from two hospitals between 2012 and 2014 to develop a PHI-based nomogram to predict PCa. To validate these results, we used a separate cohort of 230 men recruited at another center between 2008 and 2013. Receiver operator curves (ROC) were used to assess the ability to predict PCa. A nomogram was derived from the multivariable logistic regression model and its accuracy was assessed by the area under the ROC (AUC). PHI achieved the highest AUC of 0.839 in the development cohort compared to the other predictors (p < 0.001). Including age and prostate volume, a PHI-based nomogram was constructed and rendered an AUC of 0.877 (95% CI 0.813–0.938). The AUC of the nomogram in the validation cohort was 0.786 (95% CI 0.678–0.894). In clinical effectiveness analyses, the PHI-based nomogram reduced unnecessary biopsies from 42.6% to 27% using a 5% threshold risk of PCa to avoid biopsy with no increase in the number of missed cases relative to conventional biopsy decision.

Current early diagnostic biomarkers of prostate cancer

Prostate cancer (PCa) has become to have the highest incidence and the second mortality rate in western countries, affecting men's health to a large extent. Although prostate-specific antigen (PSA) was discovered to help diagnose the cancer in an early stage for decades, its specificity is relative low, resulting in unnecessary biopsy for healthy people and over-treatment for patients. Thus, it is imperative to identify more and more effective biomarkers for early diagnosis of PCa in order to distinguish patients from healthy populations, which helps guide an early treatment to lower disease-related mortality by noninvasive or minimal invasive approaches. This review generally describes the current early diagnostic biomarkers of PCa in addition to PSA and summarizes the advantages and disadvantages of these biomarkers.

The Xu's chart for prostate biopsy: a visual presentation of the added value of biomarkers to prostate-specific antigen for estimating detection rates of prostate cancer

Elevated serum prostate-specific antigen (PSA) level is the primary indication for prostate biopsy for detection of prostate cancer (PCa) in the modern era. The detection rate of PCa from biopsy is typically below 30%, especially among patients with PSA levels at 4–10 ng ml⁻¹. In the past several years, additional biomarkers, such as Prostate Health Index, PCA3 and genetic risk score (GRS) derived from multiple PCa risk-associated single nucleotide polymorphisms (SNPs) have been shown to provide added value to PSA in discriminating prostate biopsy outcomes. However, the adoption rate of these novel biomarkers in clinics is low, largely due to poor understanding of the added value of novel biomarkers. To address this matter, we developed a chart to visually present (i) expected detection rates of PCa from biopsy with respect to PSA levels, and more importantly, (ii) a range of PCa detection rates at the same PSA levels when novel biomarkers are considered. This chart, called the Xu's chart for prostate biopsy, is not a formal risk prediction model; rather, a simple visual tool for urologists to communicate with their patients an initial evaluation of PCa detection rate based on their PSA levels and a possible recommendation for additional biomarkers. A more comprehensive evaluation of PCa risk using existing risk assessment tools such as nomograms can be followed once additional biomarkers are measured. The current version of the chart is only a prototype and should be further developed to include the detection rate of aggressive PCa, and validated in larger studies.

A genetic-based approach to personalized prostate cancer screening and treatment

PURPOSE OF REVIEW

Recent advances in sequencing technologies have allowed for the identification of genetic variants within germline DNA that can explain a significant portion of the genetic underpinnings of prostate cancer. Despite evidence suggesting that these genetic variants can be used for improved risk stratification, they have not yet been routinely incorporated into routine clinical practice. This review highlights their potential utility in prostate cancer screening.

RECENT FINDINGS

There are now almost 100 genetic variants, called single nucleotide polymorphisms (SNPs) that have been recently found to be associated with the risk of developing prostate cancer. In addition, some of these prostate cancer risk SNPs have also been found to influence prostate specific antigen (PSA) expression levels and potentially aggressive disease.

SUMMARY

Incorporation of panels of prostate cancer risk SNPs into clinical practice offers potential to provide improvements in patient selection for prostate cancer screening; PSA interpretation (e.g. by correcting for the presence of SNPs that influence PSA expression levels; decision for biopsy (using prostate cancer risk SNPs); and possibly the decision for treatment. A proposed clinical algorithm incorporating these prostate cancer risk SNPs is discussed.

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.

The role of single nucleotide polymorphisms in predicting prostate cancer risk and therapeutic decision making

Prostate cancer (PCa) is a major health care problem because of its high prevalence, health-related costs, and mortality. Epidemiological studies have suggested an important role of genetics in PCa development. Because of this, an increasing number of single nucleotide polymorphisms (SNPs) had been suggested to be implicated in the development and progression of PCa. While individual SNPs are only moderately associated with PCa risk, in combination, they have a stronger, dose-dependent association, currently explaining 30% of PCa familial risk. This review aims to give a brief overview of studies in which the possible role of genetic variants was investigated in clinical settings. We will highlight the major research questions in the translation of SNP identification into clinical practice.