Prostate Cancer Risk-associated Single Nucleotide Polymorphisms Research Articles

Mechanistic insights into genetic susceptibility to prostate cancer.

Prostate cancer (PCa) is the second most common cancer in men and is a highly heritable disease that affects millions of individuals worldwide. Genome-wide association studies have to date discovered nearly 270 genetic loci harboring hundreds of single nucleotide polymorphisms (SNPs) that are associated with PCa susceptibility. In contrast, the functional characterization of the mechanisms underlying PCa risk association is still growing. Given that PCa risk-associated SNPs are highly enriched in noncoding cis-regulatory genomic regions, accumulating evidence suggests a widespread modulation of transcription factor chromatin binding and allelic enhancer activity by these noncoding SNPs, thereby dysregulating gene expression. Emerging studies have shown that a proportion of noncoding variants can modulate the formation of transcription factor complexes at enhancers and CTCF-mediated 3D genome architecture. Interestingly, DNA methylation-regulated CTCF binding could orchestrate a long-range chromatin interaction between PCa risk enhancer and causative genes. Additionally, one-causal-variant-two-risk genes or multiple-risk-variant-multiple-genes are prevalent in some PCa risk-associated loci. In this review, we will discuss the current understanding of the general principles of SNP-mediated gene regulation, experimental advances, and functional evidence supporting the mechanistic roles of several PCa genetic loci with potential clinical impact on disease prevention and treatment.

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

Few studies have evaluated the association between a single-nucleotide polymorphism-based genetic risk score (GRS) and patient age at prostate cancer (PCa) diagnosis. 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. 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. 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). Prostate cancer diagnosis-free survival among men of different risk groups. 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. 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.

Impact of Prostate-specific Antigen on a Baseline Prostate Cancer Risk Assessment Including Genetic Risk

To determine to what extent prostate cancer (PCa) risk prediction is improved by adding prostate-specific antigen (PSA) to a baseline model including genetic risk. Peripheral blood deoxyribonucleic acid was obtained from Caucasian men undergoing prostate biopsy at the University of Toronto (September 1, 2008 to January 31, 2010). Thirty-three PCa risk-associated single nucleotide polymorphisms were genotyped to generate the prostate cancer genetic score 33 (PGS-33). Primary outcome is PCa on study prostate biopsy. Logistic regression, area under the receiver-operating characteristic curves (AUC), and net reclassification improvement were used to compare models. Among 670 patients, 323 (48.2%) were diagnosed with PCa. The PGS-33 was highly associated with biopsy-detectable PCa (odds ratio, 1.66; P = 5.86E-05; AUC, 0.59) compared with PSA (odds ratio, 1.33; P = .01; AUC, 0.55). PSA did not improve risk prediction when added to a baseline model (age, family history, digital rectal examination, and PGS-33) for overall risk (AUC, 0.66 vs 0.66; P = .86) or Gleason score ≥7 PCa (AUC, 0.71 vs 0.73; P = .15). Net reclassification improvement analyses demonstrated no appropriate reclassifications with the addition of PSA to the baseline model for overall PCa but did show some benefit for reclassification of men thought to be at higher baseline risk in the high-grade PCa analysis. In a baseline model of PCa risk including the PGS-33, PSA does not add to risk prediction for overall PCa for men presenting for "for-cause" biopsy. These findings suggest that PSA screening may be minimized in men at low baseline risk.

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.

Variants at IRX4 as prostate cancer expression quantitative trait loci

Genome-wide association studies (GWAS) have identified numerous prostate cancer-associated risk loci. Some variants at these loci may be regulatory and influence expression of nearby genes. Such loci are known as cis-expression quantitative trait loci (cis-eQTL). As cis-eQTLs are highly tissue-specific, we asked if GWAS-identified prostate cancer risk loci are cis-eQTLs in human prostate tumor tissues. We investigated 50 prostate cancer samples for their genotype at 59 prostate cancer risk-associated single-nucleotide polymorphisms (SNPs) and performed cis-eQTL analysis of transcripts from paired primary tumors within two megabase windows. We tested 586 transcript-genotype associations, of which 27 were significant (false discovery rate ≤10%). An equivalent eQTL analysis of the same prostate cancer risk loci in lymphoblastoid cell lines did not result in any significant associations. The top-ranked cis-eQTL involved the IRX4 (Iroquois homeobox protein 4) transcript and rs12653946, tagged by rs10866528 in our study (P=4.91 × 10(-5)). Replication studies, linkage disequilibrium, and imputation analyses highlight population specificity at this locus. We independently validated IRX4 as a potential prostate cancer risk gene through cis-eQTL analysis of prostate cancer risk variants. Cis-eQTL analysis in relevant tissues, even with a small sample size, can be a powerful method to expedite functional follow-up of GWAS.

Prediction of prostate cancer from prostate biopsy in Chinese men using a genetic score derived from 24 prostate cancer risk-associated SNPs

Twenty-four prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) in Chinese men have been cataloged. We evaluated whether these SNPs can independently predict outcomes of prostate biopsy, and improve the predictive performance of existing clinical variables. Three hundred eight consecutive patients that underwent prostate biopsy for detection of PCa at Huashan Hospital, Shanghai, China between April 2011 and August 2012 were recruited. Clinical variables such as serum prostate-specific antigen (PSA) levels and peripheral blood samples were collected prior to a 10-core biopsy. A genetic score based on these 24 PCa associated SNPs was calculated for each individual. Among 308 patients underwent prostate biopsy, 141 (45.8%) were diagnosed with PCa. Genetic score was significantly higher in patients with PCa (median = 1.30) than without (median = 0.89), P = 3.81 × 10(-6). The difference remained significant after adjusting for age and total PSA, P = 0.007. The PCa detection rate increased with increasing genetic score; 26.3%, 43.2%, and 60.0% for men with lower (<0.5), average (0.5-1.5), and higher (>1.5) genetic score, respectively, P(-trend) = 0.0003. For patients with moderately elevated PSA levels (1.6-20 ng/ml), the PCa detection rate was 31.2% overall and was 16.7%, 31.2%, and 40.9% for men with lower (<0.5), average (0.5-1.5), and higher (>1.5) genetic score, respectively, P(-trend) = 0.03. For patients with PSA ≥ 20 ng/ml, however, the PCa detection rates were high (>69%) regardless of genetic score. A genetic score based on PCa risk-associated SNPs is an independent predictor of prostate biopsy outcomes in Chinese men and may be helpful to determine the need for prostate biopsy among patients within a "gray zone" of PCa risk.

Potential clinical utilities of prostate cancer risk-associated SNPs

Abstract Prostate cancer (PCa) is the most common solid organ malignancy affecting American men and the second leading cause of cancer related death. Recently, more than 30 PCa risk-associated single nucleotide polymorphisms (SNPs) have been discovered from genome-wide association studies (GWAS). These risk-associated SNPs have been consistently replicated in multiple case-control study populations of European descent. Although each of these SNPs is only moderately associated with PCa risk, a genetic score based on a combination of risk-associated SNPs can be used to identify men at high risk for PCa. However, these results were obtained from case-control studies which have several significant limitations. The validity of these genetic associations, the relative performance of genetic markers for PCa risk prediction compared to existing clinical variables such as PSA, and more importantly, whether genetic markers add value to existing clinical variables, are key questions that need to be addressed in well-controlled prospective studies prior to their consideration for clinical use. In this presentation, I will present the findings of PCa risk prediction using multiple risk-associated SNPs. I will also discuss our effort to assess clinical validity and clinical utility of these genetic markers in the Reduction by DUtasteride of Prostate Cancer Events (REDUCE) study, a large randomized clinical trial. All men in the REDUCE trial had a negative biopsy at baseline and were followed-up for four years, with scheduled not-for-cause (i.e. regardless of PSA levels and other clinical indications) prostate biopsies at years 2 and 4. Therefore, this study design minimizes the potential impact of PSA detection bias on associations between PCa risk and SNPs. In addition, because it is a clinical trial, a number of clinical variables, such as free/total PSA ratio and prostate volume were measured at baseline using a standardized protocol. Therefore, it allows us to compare performance of genetic markers and existing clinical variables in predicting positive prostate biopsy in the same study. Finally, the study design allows us to assess whether genomic-targeted chemoprevention, based on estimated risk from genetic markers, can improve efficacy of chemoprevention. This study represents an important next and critical step towards an assessment of the potential clinical utility of genetic markers.