Genetic variants and family history predict prostate cancer similar to prostate-specific antigen

How well do polygenic risk scores identify men at high risk for prostate cancer? Systematic review and meta-analysis

OBJECTIVES

Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men.

PATIENTS AND METHODS

Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC. Heterogeneity is expressed as I² scores. Publication bias was evaluated using funnel plots and Egger tests.

RESULTS

The ability of PRS to identify men with PCa was modest (pooled AUC 0.63, 95% CI 0.62-0.64) with moderate consistency (I² 64%). Combining PRS with clinical variables increased the pooled AUC to 0.74 (0.68-0.81). Meta-regression showed only negligible increase in AUC for adding incremental SNPs. Despite moderate heterogeneity, publication bias was not evident.

CONCLUSION

Typically, PRS accuracy is comparable to PSA or family history with a pooled AUC value 0.63 indicating mediocre performance for PRS alone.

Appraisal and development of evidence-based clinical decision support to enable perioperative pharmacogenomic application

Variable responses to medications complicates perioperative care. As a potential solution, we evaluated and synthesized pharmacogenomic evidence that may inform anesthesia and pain prescribing to identify clinically actionable drug/gene pairs. Clinical decision-support (CDS) summaries were developed and were evaluated using Appraisal of Guidelines for Research and Evaluation (AGREE) II. We found that 93/180 (51%) of commonly-used perioperative medications had some published pharmacogenomic information, with 18 having actionable evidence: celecoxib/diclofenac/flurbiprofen/ibuprofen/piroxicam/CYP2C9, codeine/oxycodone/tramadol CYP2D6, desflurane/enflurane/halothane/isoflurane/sevoflurane/succinylcholine/RYR1/CACNA1S, diazepam/CYP2C19, phenytoin/CYP2C9, succinylcholine/mivacurium/BCHE, and morphine/OPRM1. Novel CDS summaries were developed for these 18 medications. AGREE II mean ± standard deviation scores were high for Scope and Purpose (95.0 ± 2.8), Rigor of Development (93.2 ± 2.8), Clarity of Presentation (87.3 ± 3.0), and Applicability (86.5 ± 3.7) (maximum score = 100). Overall mean guideline quality score was 6.7 ± 0.2 (maximum score = 7). All summaries were recommended for clinical implementation. A critical mass of pharmacogenomic evidence exists for select medications commonly used in the perioperative setting, warranting prospective examination for clinical utility.

Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer

Background

DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer (PCa). However, it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores (PRSs). Here, we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation, and other genomic information using an integrative method.

Methods

Using data from the PRACTICAL consortium, we derived multiple sets of genetic scores, including those based on available single‐nucleotide polymorphisms through widely used methods of pruning and thresholding, LDpred, LDpred‐funt, AnnoPred, and EBPRS, as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy. In the tuning step, using the UK Biobank data (1458 prevalent cases and 1467 controls), we selected PRSs with the best performance. Using an independent set of data from the UK Biobank, we developed an integrative PRS combining information from individual scores. Furthermore, in the testing step, we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls.

Results

Our constructed PRS had improved performance (C statistics: 76.1%) over PRSs constructed by individual benchmark methods (from 69.6% to 74.7%). Furthermore, our new PRS had much higher risk assessment power than family history. The overall net reclassification improvement was 69.0% by adding PRS to the baseline model compared with 12.5% by adding family history.

Conclusions

We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa. Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

Single nucleotide polymorphisms (SNPs) in prostate cancer: its implications in diagnostics and therapeutics

Prostate cancer is one of the most frequently diagnosed malignancies in developed countries and approximately 248,530 new cases of prostate cancer are likely to be diagnosed in the United States in 2021. During the late 1990s and 2000s, the prostate cancer-related death rate has decreased by 4% per year on average because of advancements in prostate-specific antigen (PSA) testing. However, the non-specificity of PSA to distinguish between benign and malignant forms of cancer is a major concern in the management of prostate cancer. Despite other risk factors in the pathogenesis of prostate cancer, recent advancement in molecular genetics suggests that genetic heredity plays a crucial role in prostate carcinogenesis. Approximately, 60% of heritability and more than 100 well-recognized single-nucleotide-polymorphisms (SNPs) have been found to be associated with prostate cancer and constitute a major risk factor in the development of prostate cancer. Recent findings revealed that a low to moderate effect on the progression of prostate cancer of individual SNPs was observed compared to a strong progressive effect when SNPs were in combination. Here, in this review, we made an attempt to critically analyze the role of SNPs and associated genes in the development of prostate cancer and their implications in diagnostics and therapeutics. A better understanding of the role of SNPs in prostate cancer susceptibility may improve risk prediction, enhance fine-mapping, and furnish new insights into the underlying pathophysiology of prostate cancer.

The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer

INTRODUCTION

Neighborhood socioeconomic (nSES) factors have been implicated in prostate cancer (PCa) disparities. In line with the Precision Medicine Initiative that suggests clinical and socioenvironmental factors can impact PCa outcomes, we determined whether nSES variables are associated with time to PCa diagnosis and could inform PCa clinical risk assessment.

MATERIALS AND METHODS

The study sample included 358 high risk men (PCa family history and/or Black race), aged 35-69 years, enrolled in an early detection program. Patient variables were linked to 78 nSES variables (employment, income, etc.) from previous literature via geocoding. Patient-level models, including baseline age, prostate specific antigen (PSA), digital rectal exam, as well as combined models (patient plus nSES variables) by race/PCa family history subgroups were built after variable reduction methods using Cox regression and LASSO machine-learning. Model fit of patient and combined models (AIC) were compared; p-values<0.05 were significant. Model-based high/low nSES exposure scores were calculated and the 5-year predicted probability of PCa was plotted against PSA by high/low neighborhood score to preliminarily assess clinical relevance.

RESULTS

In combined models, nSES variables were significantly associated with time to PCa diagnosis. Workers mode of transportation and low income were significant in White men with a PCa family history. Homeownership (%owner-occupied houses with >3 bedrooms) and unemployment were significant in Black men with and without a PCa family history, respectively. The 5-year predicted probability of PCa was higher in men with a high neighborhood score (weighted combination of significant nSES variables) compared to a low score (e.g., Baseline PSA level of 4ng/mL for men with PCa family history: White-26.7% vs 7.7%; Black-56.2% vs 29.7%).

DISCUSSION

Utilizing neighborhood data during patient risk assessment may be useful for high risk men affected by disparities. However, future studies with larger samples and validation/replication steps are needed.

The PROFILE Feasibility Study: Targeted Screening of Men With a Family History of Prostate Cancer

BACKGROUND

A better assessment of individualized prostate cancer (PrCa) risk is needed to improve screening. The use of the prostate-specific antigen (PSA) level for screening in the general population has limitations and is not currently advocated. Approximately 100 common single nucleotide polymorphisms (SNPs) have been identified that are associated with the risk of developing PrCa. The PROFILE pilot study explored the feasibility of using SNP profiling in men with a family history (FH) of PrCa to investigate the probability of detecting PrCa at prostate biopsy (PB). The primary aim of this pilot study was to determine the safety and feasibility of PrCa screening using transrectal ultrasound-guided PB with or without diffusion-weighted magnetic resonance imaging (DW-MRI) in men with a FH. A secondary aim was to evaluate the potential use of SNP profiling as a screening tool in this population.

PATIENTS AND METHODS

A total of 100 men aged 40-69 years with a FH of PrCa underwent PB, regardless of their baseline PSA level. Polygenic risk scores (PRSs) were calculated for each participant using 71 common PrCa susceptibility alleles. We treated the disease outcome at PB as the outcome variable and evaluated its associations with the PRS, PSA level, and DW-MRI findings using univariate logistic regression.

RESULTS

Of the 100 men, 25 were diagnosed with PrCa, of whom 12 (48%) had clinically significant disease. Four adverse events occurred and no deaths. The PSA level and age at study entry were associated with PrCa at PB (p = .00037 and p = .00004, respectively).

CONCLUSION

The results of the present pilot study have demonstrated that PB is a feasible and safe method of PrCa screening in men with a FH, with a high proportion of PrCa identified requiring radical treatment. It is feasible to collect data on PrCa-risk SNPs to evaluate their combined effect as a potential screening tool. A larger prospective study powered to detect statistical associations is in progress.

IMPLICATIONS FOR PRACTICE

Prostate biopsy is a feasible and safe approach to prostate cancer screening in men with a family history and detects a high proportion of prostate cancer that needs radical treatment. Calculating a polygenic risk score using prostate cancer risk single nucleotide polymorphisms could be a potential future screening tool for prostate cancer.

Multigene panels in prostate cancer risk assessment: a systematic review

PURPOSE

Single-nucleotide polymorphism (SNP) panel tests have been proposed for use in the detection of, and prediction of risk for, prostate cancer and as prognostic indicator in affected men. A systematic review was undertaken to address three research questions to evaluate the analytic validity, clinical validity, clinical utility, and prognostic validity of SNP-based panels.

METHODS

Data sources comprised MEDLINE, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, and EMBASE; these were searched from inception to April 2013. The gray-literature searches included contact with manufacturers. Eligible studies included English-language studies evaluating commercially available SNP panels. Study selection and risk of bias assessment were undertaken by two independent reviewers.

RESULTS

Twenty-one studies met eligibility criteria. All focused on clinical validity and evaluated 18 individual panels with 2 to 35 SNPs. All had poor discriminative ability (overall area under receiver-operator characteristic curves, 58-74%; incremental gain resulting from inclusion of SNP data, 2.5-11%) for predicting risk of prostate cancer and/or distinguishing between aggressive and asymptomatic/latent disease. The risk of bias of the studies, as assessed by the Newcastle Ottawa Scale (NOS) and Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tools, was moderate.

CONCLUSION

The evidence on currently available SNP panels is insufficient to assess analytic validity, and at best the panels assessed would add a small and clinically unimportant improvement to factors such as age and family history in risk stratification (clinical validity). No evidence on the clinical utility of current panels is available.Genet Med 18 6, 535-544.

The clinic-pathological characteristics of prostate cancer in an Irish subpopulation with a serum PSA less than 4.0ng/ml

Background:

Prostate specific antigen (PSA) has been used as a biomarker for prostate cancer for the last 20 years. Traditionally, a serum PSA <4 ng/ml has been used as a general cut-off between normal and abnormal readings. There is evidence to demonstrate that men with a normal serum PSA can develop prostate cancer. The aim of this study was to investigate the clinico-pathological features of prostate cancer in a non-screened Irish cohort with serum PSA <4 ng/ml.

Methods:

A retrospective analysis was performed of all patients who underwent radical retropubic prostatectomy (RRP) in a tertiary referral unit over a 10-year period (2000–2010). Clinico-pathological characteristics were collated including those from trans-rectal ultrasound-guided (TRUS) prostate biopsies and radical prostatectomy specimens.

Results:

Between 2000 and 2010, 651 men underwent an RRP, with 43 (6.6%) having a serum PSA <4 ng/ml. The median PSA was 3.2 ng/ml (range 0.8–4.0). Nineteen (44.2%) had palpable disease on direct rectal examination (DRE). Following prostatectomy, 28 (65.12%) had Gleason 6 disease, 14 (32.56%) had Gleason 7 disease and one (2.32%) had Gleason 8 disease. Five (11.63%) patients were upgraded from TRUS biopsy to final histopathology. Six (13.95%) patients had pathological evidence of extracapsular extension on final pathology. Three (6.98%) patients experienced biochemical recurrence and received salvage radiation therapy after a median time of 24 months. The median follow-up was 106 months (range 36–158). Twenty (46.51%) patients had a first-degree family history of prostate cancer.

Conclusions:

A PSA cut-off of 4 ng/ml has commonly been used in the detection of prostate cancer. Our study emphasizes that this cut-off is inappropriate and that no specific level of PSA can be used. Management decisions need to be individualized based on index of suspicion with concomitant counselling and rectal examination.

Prostate cancer in young men: an important clinical entity

Prostate cancer is considered a disease of older men (aged >65 years), but today over 10% of new diagnoses in the USA occur in young men aged ≤55 years. Early-onset prostate cancer, that is prostate cancer diagnosed at age ≤55 years, differs from prostate cancer diagnosed at an older age in several ways. Firstly, among men with high-grade and advanced-stage prostate cancer, those diagnosed at a young age have a higher cause-specific mortality than men diagnosed at an older age, except those over age 80 years. This finding suggests that important biological differences exist between early-onset prostate cancer and late-onset disease. Secondly, early-onset prostate cancer has a strong genetic component, which indicates that young men with prostate cancer could benefit from evaluation of genetic risk. Furthermore, although the majority of men with early-onset prostate cancer are diagnosed with low-risk disease, the extended life expectancy of these patients exposes them to long-term effects of treatment-related morbidities and to long-term risk of disease progression leading to death from prostate cancer. For these reasons, patients with early-onset prostate cancer pose unique challenges, as well as opportunities, for both research and clinical communities. Current data suggest that early-onset prostate cancer is a distinct phenotype-from both an aetiological and clinical perspective-that deserves further attention.

Does genotyping of risk-associated single nucleotide polymorphisms improve patient selection for prostate biopsy when combined with a prostate cancer risk calculator?

BACKGROUND

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with higher risk of prostate cancer (PCa). This study aimed to evaluate whether published SNPs improve the performance of a clinical risk-calculator in predicting prostate biopsy result.

METHODS

Three hundred forty-six patients with a previous prostate biopsy (191 positive, 155 negative) were enrolled. After literature search, nine SNPs were selected for their statistically significant association with increased PCa risk. Allelic odds ratios were computed and a new logistic regression model was built integrating the clinical risk score (i.e., prior biopsy results, PSA level, prostate volume, transrectal ultrasound, and digital rectal examination) and a multilocus genetic risk score (MGRS). Areas under the receiver operating characteristic (ROC) curves (AUC) of the clinical score alone versus the integrated clinic-genetic model were compared. The added value of the MGRS was assessed using the Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) statistics.

RESULTS

Predictive performance of the integrated clinico-genetic model (AUC = 0.781) was slightly higher than predictive performance of the clinical score alone (AUC = 0.770). The prediction of PCa was significantly improved with an IDI of 0.015 (P-value = 0.035) and a continuous NRI of 0.403 (P-value < 0.001).

CONCLUSIONS

The predictive performance of the clinical model was only slightly improved by adding MGRS questioning the real clinical added value with regards to the cost of genetic testing and performance of current inexpensive clinical risk-calculators.

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.

Imaging and Markers as Novel Diagnostic Tools in Detecting Insignificant Prostate Cancer: A Critical Overview

Recent therapeutic advances for managing low-risk prostate cancer include the active surveillance and focal treatment. However, locating a tumor and detecting its volume by adequate sampling is still problematic. Development of predictive biomarkers guiding individual therapeutic choices remains an ongoing challenge. At the same time, prostate cancer magnetic resonance imaging is gaining increasing importance for prostate diagnostics. The high morphological resolution of T2-weighted imaging and functional MRI methods may increase the specificity and sensitivity of diagnostics. Also, recent studies founded an ability of novel biomarkers to identify clinically insignificant prostate cancer, risk of progression, and association with poor differentiation and, therefore, with clinical significance. Probably, the above mentioned methods would improve tumor characterization in terms of its volume, aggressiveness, and focality. In this review, we attempted to evaluate the applications of novel imaging techniques and biomarkers in assessing the significance of the prostate cancer.

Prostate cancer genomics by high-throughput technologies: genome-wide association study and sequencing analysis

Prostate cancer (PC) is the most common malignancy in males. It is evident that genetic factors at both germline and somatic levels play critical roles in prostate carcinogenesis. Recently, genome-wide association studies (GWAS) by high-throughput genotyping technology have identified more than 70 germline variants of various genes or chromosome loci that are significantly associated with PC susceptibility. They include multiple 8q24 loci, prostate-specific genes, and metabolism-related genes. Somatic alterations in PC genomes have been explored by high-throughput sequencing technologies such as whole-genome sequencing and RNA sequencing, which have identified a variety of androgen-responsive events and fusion transcripts represented by E26 transformation-specific (ETS) gene fusions. Recent innovations in high-throughput genomic technologies have enabled us to analyze PC genomics more comprehensively, more precisely, and on a larger scale in multiple ethnic groups to increase our understanding of PC genomics and biology in germline and somatic studies, which can ultimately lead to personalized medicine for PC diagnosis, prevention, and therapy. However, these data indicate that the PC genome is more complex and heterogeneous than we expected from GWAS and sequencing analyses.

Genetic and molecular differences in prostate carcinogenesis between African American and Caucasian American men

Prostate cancer is the most common non-skin cancer and the second leading cause of cancer-related death for men in the United States. Prostate cancer incidence and associated mortality are highest in African American men in comparison to other races. The observed differences in incidence and disease aggressiveness at presentation support a potential role for different pathways of prostate carcinogenesis between African American and Caucasian men. This review focuses on some of the recent molecular biology discoveries, which have been investigated in prostate carcinogenesis and their likely contribution to the known discrepancies across race and ethnicity. Key discussion points include the androgen receptor gene structure and function, genome-wide association studies and epigenetics. The new observations of the ethnic differences of the ERG oncogene, the most common prostate cancer gene, are providing new insights into ERG based stratification of prostate cancers in the context of ethnically diverse patient populations. This rapidly advancing knowledge has the likely potential to benefit clinical practice. Current and future work will improve the ability to sub-type prostate cancers by molecular alterations and lead to targeted therapy against this common malignancy.

Alcohol consumption and PSA-detected prostate cancer risk--a case-control nested in the ProtecT study

Alcohol is an established carcinogen but not an established risk factor for prostate cancer, despite some recent prospective studies suggesting increased risk among heavy drinkers. The aim of this study was to investigate the role of alcohol on prostate-specific antigen (PSA) levels and prostate cancer risk. Two thousand four hundred PSA detected prostate cancer cases and 12,700 controls matched on age and general practice were identified through a case-control study nested in the PSA-testing phase of a large UK-based randomized controlled trial for prostate cancer treatment (ProtecT). Linear and multinomial logistic regression models were used to estimate ratios of geometric means (RGMs) of PSA and relative risk ratios (RRRs) of prostate cancer by stage and grade, with 95% confidence intervals (CIs), associated with weekly alcohol intake and drinking patterns. We found evidence of lower PSA (RGM 0.98, 95% CI: 0.98-0.99) and decreased risk of low Gleason-grade (RRR 0.96; 95%CI 0.93-0.99) but increased risk of high-grade prostate cancer (RRR 1.04; 95%CI 0.99-1.08; p(difference) =0.004) per 10 units/week increase in alcohol consumption, not explained by current BMI, blood pressure, comorbidities, or reverse causation. This is the first large population-based study to find evidence of lower PSA levels for increasing alcohol consumption, with potential public health implications for the detection of prostate cancer. Our results also support a modestly higher risk of high-grade disease for heavy drinkers, but require independent replication to establish the nature of the association of alcohol with low-grade disease, preferably in cohorts with a heterogeneous case-mix.

Developing genetic epidemiological models to predict risk for nasopharyngeal carcinoma in high-risk population of China

To date, the only established model for assessing risk for nasopharyngeal carcinoma (NPC) relies on the sero-status of the Epstein-Barr virus (EBV). By contrast, the risk assessment models proposed here include environmental risk factors, family history of NPC, and information on genetic variants. The models were developed using epidemiological and genetic data from a large case-control study, which included 1,387 subjects with NPC and 1,459 controls of Cantonese origin. The predictive accuracy of the models were then assessed by calculating the area under the receiver-operating characteristic curves (AUC). To compare the discriminatory improvement of models with and without genetic information, we estimated the net reclassification improvement (NRI) and integrated discrimination index (IDI). Well-established environmental risk factors for NPC include consumption of salted fish and preserved vegetables and cigarette smoking (in pack years). The environmental model alone shows modest discriminatory ability (AUC = 0.68; 95% CI: 0.66, 0.70), which is only slightly increased by the addition of data on family history of NPC (AUC = 0.70; 95% CI: 0.68, 0.72). With the addition of data on genetic variants, however, our model’s discriminatory ability rises to 0.74 (95% CI: 0.72, 0.76). The improvements in NRI and IDI also suggest the potential usefulness of considering genetic variants when screening for NPC in endemic areas. If these findings are confirmed in larger cohort and population-based case-control studies, use of the new models to analyse data from NPC-endemic areas could well lead to earlier detection of NPC.

Reproducibility, performance, and clinical utility of a genetic risk prediction model for prostate cancer in Japanese

Prostate specific antigen (PSA) is widely used as a diagnostic biomarker for prostate cancer (PC). However, due to its low predictive performance, many patients without PC suffer from the harms of unnecessary prostate needle biopsies. The present study aims to evaluate the reproducibility and performance of a genetic risk prediction model in Japanese and estimate its utility as a diagnostic biomarker in a clinical scenario. We created a logistic regression model incorporating 16 SNPs that were significantly associated with PC in a genome-wide association study of Japanese population using 689 cases and 749 male controls. The model was validated by two independent sets of Japanese samples comprising 3,294 cases and 6,281 male controls. The areas under curve (AUC) of the model were 0.679, 0.655, and 0.661 for the samples used to create the model and those used for validation. The AUCs were not significantly altered in samples with PSA 1-10 ng/ml. 24.2% and 9.7% of the patients had odds ratio <0.5 (low risk) or >2 (high risk) in the model. Assuming the overall positive rate of prostate needle biopsies to be 20%, the positive biopsy rates were 10.7% and 42.4% for the low and high genetic risk groups respectively. Our genetic risk prediction model for PC was highly reproducible, and its predictive performance was not influenced by PSA. The model could have a potential to affect clinical decision when it is applied to patients with gray-zone PSA, which should be confirmed in future clinical studies.

The genetics of cancer risk

One hundred years ago, decades before the discovery of the structure of DNA, debate raged regarding how human traits were passed from one generation to the next. Phenotypes, including risk of disease, had long been recognized as having a familial component. Yet it was difficult to reconcile genetic segregation as described by Mendel with observations exhaustively documented by Karl Pearson and others regarding the normal distribution of human characteristics. In 1918, R. A. Fisher published his landmark article, "The Correlation Between Relatives on the Supposition of Mendelian Inheritance," bridging this divide and demonstrating that multiple alleles, all individually obeying Mendel's laws, account for the phenotypic variation observed in nature.Since that time, geneticists have sought to identify the link between genotype and phenotype. Trait-associated alleles vary in their frequency and degree of penetrance. Some minor alleles may approach a frequency of 50% in the human population, whereas others are present within only a few individuals. The spectrum for penetrance is similarly wide. These characteristics jointly determine the segregation pattern of a given trait, which, in turn, determine the method used to map the trait. Until recently, identification of rare, highly penetrant alleles was most practical. Revolutionary studies in genomics reported over the past decade have made interrogation of most of the spectrum of genetic variation feasible.The following article reviews recent discoveries in the genetic basis of inherited cancer risk and how these discoveries inform cancer biology and patient management. Although this article focuses on prostate cancer, the principles are generic for any cancer and, indeed, for any trait.

Resisting the screening imperative: patienthood, populations and politics in prostate cancer detection technologies for the UK

The introduction of mass screening programmes in the UK has been controversial. It is instructive to examine medical conditions for which screening has been actively considered but not introduced, such as prostate cancer. Incidence of the disease has escalated during the last 20 years, partly due to the upsurge in use of PSA (prostate-specific antigen) detection technology. The controversy is moving into a new phase, associated with the development of new molecular genetic biomarkers and tests derived from genome-association studies. The paper outlines the most recent scientific and technological developments for the three types of detection technology - PSA, genetic, and genomic. Applying concepts of risk, technology governance and technology expectations, it is shown that central public health governance actors continue to resist the tidal wave of new technologies through a variety of increasingly diverse governance modes. In the case of PSA, a governance trend moving beyond 'responsibilisation' to citizen rights is shown, whereas in the case of genetic tests and genomic risk profiling, state public health agencies are shown to be engaging in a form of technology expectation management, as it responds to a new marketplace of private commercial testing and mediatised science-based visions of future healthcare.

Prostate cancer genomics, biology, and risk assessment through genome-wide association studies

Prostate cancer (PC) is the most common malignancy observed in men. It is evident that genetic factors play some important roles in PC etiology. Recently, genome-wide association studies in diverse ethnic groups have identified more than 40 germline variants of various genes or chromosomal loci that are significantly associated with PC susceptibility, including multiple 8q24 loci, prostate-specific genes, metabolic and hormone-related genes, and many regions where no coding gene is annotated. However, there are only a few variants or genes for which biological significance or functions have been elucidated so far. The greatest challenge related to genome-wide association studies loci in prostate genomics is to understand the functional consequences of these PC-associated loci and their involvement in PC biology and carcinogenesis. There have been attempts to determine PC risk estimations by combining multiple PC-associated variants for clinical tests, and these can identify a very minor population with high risk of PC. However, they cannot distinguish risk of aggressive PC from that of non-aggressive PC. Further identification of PC-susceptibility loci in larger genome-wide association studies cohorts and biological insights gained from such functional analyses have the potential to translate into clinical benefits, including the development of reliable biomarkers, risk estimation, and effective strategies for screening and prevention of PC.

An epidemiological reappraisal of the familial aggregation of prostate cancer: a meta-analysis

Studies on familial aggregation of cancer may suggest an overall contribution of inherited genes or a shared environment in the development of malignant disease. We performed a meta-analysis on familial clustering of prostate cancer. Out of 74 studies reporting data on familial aggregation of prostate cancer in unselected populations retrieved by a Pubmed search and browsing references, 33 independent studies meeting the inclusion criteria were used in the analysis performed with the random effects model. The pooled rate ratio (RR) for first-degree family history, i.e. affected father or brother, is 2.48 (95% confidence interval: 2.25-2.74). The incidence rate for men who have a brother who got prostate cancer increases 3.14 times (CI:2.37-4.15), and for those with affected father 2.35 times (CI:2.02-2.72). The pooled estimate of RR for two or more affected first-degree family members relative to no history in father and in brother is 4.39 (CI:2.61-7.39). First-degree family history appears to increase the incidence rate of prostate cancer more in men under 65 (RR:2.87, CI:2.21-3.74), than in men aged 65 and older (RR:1.92, CI:1.49-2.47), p for interaction = 0.002. The attributable fraction among those having an affected first-degree relative equals to 59.7% (CI:55.6-63.5%) for men at all ages, 65.2% (CI:57.7-71.4%) for men younger than 65 and 47.9% (CI:37.1-56.8%) for men aged 65 or older. For those with a family history in 2 or more first-degree family members 77.2% (CI:65.4-85.0%) of prostate cancer incidence can be attributed to the familial clustering. Our combined estimates show strong familial clustering and a significant effect-modification by age meaning that familial aggregation was associated with earlier disease onset (before age 65).

Genetics of prostate cancer risk

For decades, physicians and researchers have recognized that family history is a significant risk factor for prostate cancer. The identification of the genes responsible for inherited risk, however, proved difficult. With the sequencing of the human genome and the completion of the initial phases of the International HapMap Project, the tools are available to scan the entire genome and find genetic markers for disease. Since 2006, more than 30 inherited variants strongly associated with prostate cancer have been reported. As the inherited component of the disease is revealed, efforts are ongoing to translate genetic findings into the clinic.

Combining 33 genetic variants with prostate-specific antigen for prediction of prostate cancer: longitudinal study

The aim of this study was to investigate if a genetic risk score including 33 common genetic variants improves prediction of prostate cancer when added to measures of prostate-specific antigen (PSA). We conducted a case-control study nested within the Northern Sweden Health and Disease Cohort (NSHDC), a prospective cohort in northern Sweden. A total of 520 cases and 988 controls matched for age, and date of blood draw were identified by linkage between the regional cancer register and the NSHDC. Receiver operating characteristic curves with area under curve (AUC) estimates were used as measures of prostate cancer prediction. The AUC for the genetic risk score was 64.3% [95% confidence interval (CI) = 61.4-67.2], and the AUC for total PSA and the ratio of free to total PSA was 86.2% (95% CI = 84.4-88.1). A model including the genetic risk score, total PSA and the ratio of free to total PSA increased the AUC to 87.2% (95% CI = 85.4-89.0, p difference = 0.002). The addition of a genetic risk score to PSA resulted in a marginal improvement in prostate cancer prediction that would not seem useful for clinical risk assessment.

Polymorphisms at the Microseminoprotein-beta locus associated with physiologic variation in beta-microseminoprotein and prostate-specific antigen levels

BACKGROUND

rs10993994, a single nucleotide polymorphism (SNP) at the genetic locus encoding beta-microseminoprotein (beta-MSP), is associated with both prostate cancer risk and levels of blood prostate-specific antigen (PSA), a biomarker used in prostate cancer screening. Therefore, we wished to determine the association between SNPs at MSMB, the gene encoding beta-MSP, and the levels of prostate-produced biomarkers beta-MSP, PSA, and human kallikrein 2 (hK2) in blood and semen.

METHODS

Blood and semen from 304 healthy young Swedish men (ages 18-21) were assayed for beta-MSP, PSA, and hK2. SNPs around MSMB were genotyped from matched DNA and analyzed for quantitative association with biomarker levels. Empirical P values were multiple test-corrected and the independence of each SNP's effect was determined.

RESULTS

rs10993994 was significantly associated with the blood and semen levels of beta-MSP (both P < 1.0 x 10(-7)) and PSA (P = 0.00014 and P = 0.0019), and semen levels of hK2 (P = 0.00027). Additional copies of the prostate cancer risk allele resulted in lower beta-MSP but higher PSA levels, and singly explained 23% and 5% of the variation seen in semen beta-MSP and PSA, respectively. Additional SNPs at MSMB are associated with beta-MSP and PSA independently of rs10993994.

CONCLUSIONS

SNPs at MSMB correlate with physiologic variation in beta-MSP and PSA levels in the blood and semen of healthy young Swedish men. In particular, rs10993994 has a strong effect on beta-MSP levels.

IMPACT

Our results suggest a mechanism by which rs10993994 might predispose to prostate cancer and raise the possibility that genetic variation might need to be considered in interpreting the levels of these biomarkers.

Analysis of the 10q11 cancer risk locus implicates MSMB and NCOA4 in human prostate tumorigenesis

Genome-wide association studies (GWAS) have established a variant, rs10993994, on chromosome 10q11 as being associated with prostate cancer risk. Since the variant is located outside of a protein-coding region, the target genes driving tumorigenesis are not readily apparent. Two genes nearest to this variant, MSMB and NCOA4, are strong candidates for mediating the effects of rs109939934. In a cohort of 180 individuals, we demonstrate that the rs10993994 risk allele is associated with decreased expression of two MSMB isoforms in histologically normal and malignant prostate tissue. In addition, the risk allele is associated with increased expression of five NCOA4 isoforms in histologically normal prostate tissue only. No consistent association with either gene is observed in breast or colon tissue. In conjunction with these findings, suppression of MSMB expression or NCOA4 overexpression promotes anchorage-independent growth of prostate epithelial cells, but not growth of breast epithelial cells. These data suggest that germline variation at chromosome 10q11 contributes to prostate cancer risk by influencing expression of at least two genes. More broadly, the findings demonstrate that disease risk alleles may influence multiple genes, and associations between genotype and expression may only be observed in the context of specific tissue and disease states.

Replication of prostate cancer risk loci on 8q24, 11q13, 17q12, 19q33, and Xp11 in African Americans

BACKGROUND

Prostate cancer (Pca) is a common malignancy that disproportionately affects African American men (AA). Recently there have been several genome-wide association studies (GWAS) implicating new prostate cancer risk loci along chromosomes 2, 3, 6, 7, 8, 10, 11, 12, 17, 19, and X in populations of European ancestry. Given the higher incidence and mortality for AAs, and differences in allele frequencies and haplotype structures between African and European descent populations, it is important to assess the impact of these candidate risk loci in AAs.

METHODS

Here we evaluated 20 single nucleotide polymorphisms (SNPs) associated with prostate cancer risk in recent GWAS studies, in AA prostate cancer cases and controls.

RESULTS

We replicated five of the SNPs in our AA population, rs10896449 on 11q13.2 (P = 0.009), rs2735839 on 19q33.33 region, (P = 0.04), rs443076 on chromosome 17q12 (P = 0.008), rs5945572 on Xp11.22 (P = 0.05), as well as the rare variant specific to west African ancestry, bd11934905 in region 2 of 8q24 (P = 1 x 10(-4)).

CONCLUSIONS

While we were able to replicate a few of the previous GWAS SNPs, we were not able to confirm the vast majority of these associations in our AA population. This finding further supports the need to perform GWAS and additional fine mapping in AAs to locate additional susceptibility loci.

Genetic variants in association studies--review of strengths and weaknesses in study design and current knowledge of impact on cancer risk

Sequencing of the human genome has recently been completed and mapping of the complete genomic variation is ongoing. During the last decade there has been a huge expansion of studies of genetic variants, both with respect to association studies of disease risk and for studies of genetic factors of prognosis and treatments response, i.e., pharmacogenomics. The use of genetics to predict a patient's risk of disease or treatment response is one step toward an improved personalised prevention and screening modality for the prevention of cancer and treatment selection. The technology and statistical methods for completing whole genome tagging of variants and genome wide association studies has developed rapidly over the last decade. After identifying the genetic loci with the strongest, statistical associations with disease risk, future studies will need to further characterise the genotype-phenotype relationship to provide a biological basis for prevention and treatment decisions according to genetic profile. This review discusses some of the general issues and problems of study design; we also discuss challenges in conducting valid association studies in rare cancers such as paediatric brain tumours, where there is support for genetic susceptibility but difficulties in assembling large sample sizes. The clinical interpretation and implementation of genetic association studies with respect to disease risk and treatment is not yet well defined and remains an important area of future research.

Two independent prostate cancer risk-associated Loci at 11q13

Single nucleotide polymorphisms (SNP) at 11q13 were recently implicated in prostate cancer risk by two genome-wide association studies and were consistently replicated in multiple study populations. To explore prostate cancer association in the regions flanking these SNPs, we genotyped 31 tagging SNPs in a approximately 110 kb region at 11q13 in a Swedish case-control study (Cancer of the Prostate in Sweden), including 2,899 cases and 1,722 controls. We found evidence of prostate cancer association for the previously implicated SNPs including rs10896449, which we termed locus 1. In addition, multiple SNPs on the centromeric side of the region, including rs12418451, were also significantly associated with prostate cancer risk (termed locus 2). The two groups of SNPs were separated by a recombination hotspot. We then evaluated these two representative SNPs in an additional approximately 4,000 cases and approximately 3,000 controls from three study populations and confirmed both loci at 11q13. In the combined allelic test of all four populations, P = 4.0 x 10(-11) for rs10896449 at locus 1 and P = 1.2 x 10(-6) for rs12418451 at locus 2, and both remained significant after adjusting for the other locus and study population. The prostate cancer association at these two 11q13 loci was unlikely confounded by prostate-specific antigen (PSA) detection bias because neither SNP was associated with PSA levels in controls. Unlike locus 1, in which no known gene is located, several putative mRNAs are in close proximity to locus 2. Additional confirmation studies at locus 2 and functional studies for both loci are needed to advance our knowledge on the etiology of prostate cancer.

Fine mapping association study and functional analysis implicate a SNP in MSMB at 10q11 as a causal variant for prostate cancer risk

A single nucleotide polymorphism (SNP) at 10q11 (rs10993994) in the 5' region of the MSMB gene was recently implicated in prostate cancer risk in two genome-wide association studies. To identify possible causal variants in the region, we genotyped 16 tagging SNPs and imputed 29 additional SNPs in approximately 65 kb genomic region at 10q11 in a Swedish population-based case-control study (CAncer of the Prostate in Sweden), including 2899 cases and 1722 controls. We found evidence for two independent loci, separated by a recombination hotspot, associated with prostate cancer risk. Among multiple significant SNPs at locus 1, the initial SNP rs10993994 was most significant. Importantly, using an MSMB promoter reporter assay, we showed that the risk allele of this SNP had only 13% of the promoter activity of the wild-type allele in a prostate cancer model, LNCaP cells. Curiously, the second, novel locus (locus 2) was within NCOA4 (also known as ARA70), which is known to enhance androgen receptor transcriptional activity in prostate cancer cells. However, its association was only weakly confirmed in one of the three additional study populations. The observations that rs10993994 is the strongest associated variant in the region and its risk allele has a major effect on the transcriptional activity of MSMB, a gene with previously described prostate cancer suppressor function, together suggest the T allele of rs10993994 as a potential causal variant at 10q11 that confers increased risk of prostate cancer.