A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact

Germline mutations in prostate cancer: a systematic review of the evidence for personalized medicine

BACKGROUND

The goal of precision medicine in prostate cancer (PCa) is to individualize the treatment according to the patient's germline mutation status. PCa has a very high rate of genetic predisposition compared with other cancers in men, with an estimated rate of cancers ascribable to hereditary factors of 5-15%.

METHODS

A systematic search (PubMed, Web of Science, and ClinicalTrials.gov) of English literature from 2000 to 2022, using the keywords "prostate cancer", "germline mutations", "family history", and "inheritance" was conducted, according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

RESULTS

The search identified 980 publications. Of these, 200 papers were removed before screening (duplicates, non-English literature, and publication year before 2000) and 245 records were excluded after title/abstract screening. Finally, 50 articles were included in the final analysis. We analyze the latest evidence on the genetic basis of PCa predisposition and clinical implications for more personalized screening protocols and therapeutic management of this high-prevalent cancer.

DISCUSSION

Emerging data show that germline mutations in homologous recombination genes (BRCA1/2, ATM, CHECK2), in mismatch repair genes (MLH1, MLH2, MSH6), and other additional genes are associated with the development and aggressiveness of PCa. Germline testing and genetic counseling have increasingly important implications in cancer screening and therapeutic decisions making for patients affected by PCa. Patients with localized PCa and some gene mutations are more likely to develop aggressive cancer, so active treatment may be preferable to active surveillance for these patients. Moreover, in patients with metastatic PCa, these gene alterations may be useful biomarkers for predicting response to specific therapy such as PARP inhibitors, recently approved for the treatment of metastatic castration-resistant PCa. The evidence supports recent guidelines and recommendations considering germline genetic testing for patients with a positive family history of PCa or men with high risk or metastatic disease.

Bayesian linear mixed model with multiple random effects for family-based genetic studies

Motivation: Family-based study design is one of the popular designs used in genetic research, and the whole-genome sequencing data obtained from family-based studies offer many unique features for risk prediction studies. They can not only provide a more comprehensive view of many complex diseases, but also utilize information in the design to further improve the prediction accuracy. While promising, existing analytical methods often ignore the information embedded in the study design and overlook the predictive effects of rare variants, leading to a prediction model with sub-optimal performance. Results: We proposed a Bayesian linear mixed model for the prediction analysis of sequencing data obtained from family-based studies. Our method can not only capture predictive effects from both common and rare variants, but also easily accommodate various disease model assumptions. It uses information embedded in the study design to form surrogates, where the predictive effects from unmeasured/unknown genetic and environmental risk factors can be modelled. Through extensive simulation studies and the analysis of sequencing data obtained from the Michigan State University Twin Registry study, we have demonstrated that the proposed method outperforms commonly adopted techniques. Availability: R package is available at https://github.com/yhai943/FBLMM.

Prediction of future risk of any and higher-grade prostate cancer based on the PLCO and SELECT trials

BACKGROUND

A model was built that characterized effects of individual factors on five-year prostate cancer (PCa) risk in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial (PLCO) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT). This model was validated in a third San Antonio Biomarkers of Risk (SABOR) screening cohort.

METHODS

A prediction model for 1- to 5-year risk of developing PCa and Gleason > 7 PCa (HG PCa) was built on PLCO and SELECT using the Cox proportional hazards model adjusting for patient baseline characteristics. Random forests and neural networks were compared to Cox proportional hazard survival models, using the trial datasets for model building and the SABOR cohort for model evaluation. The most accurate prediction model is included in an online calculator.

RESULTS

The respective rates of PCa were 8.9%, 7.2%, and 11.1% in PLCO (n = 31,495), SELECT (n = 35,507), and SABOR (n = 1790) over median follow-up of 11.7, 8.1 and 9.0 years. The Cox model showed higher prostate-specific antigen (PSA), BMI and age, and African American race to be associated with PCa and HGPCa. Five-year risk predictions from the combined SELECT and PLCO model effectively discriminated risk in the SABOR cohort with C-index 0.76 (95% CI [0.72, 0.79]) for PCa, and 0.74 (95% CI [0.65,0.83]) for HGPCa.

CONCLUSIONS

A 1- to 5-year PCa risk prediction model developed from PLCO and SELECT was validated with SABOR and implemented online. This model can individualize and inform shared screening decisions.

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.

Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease

Prostate cancer (PrCa) is a heterogeneous disease, which presents in individual patients across a diverse phenotypic spectrum ranging from indolent to fatal forms. No robust biomarkers are currently available to enable routine screening for PrCa or to distinguish clinically significant forms, therefore late stage identification of advanced disease and overdiagnosis plus overtreatment of insignificant disease both remain areas of concern in healthcare provision. PrCa has a substantial heritable component, and technological advances since the completion of the Human Genome Project have facilitated improved identification of inherited genetic factors influencing susceptibility to development of the disease within families and populations. These genetic markers hold promise to enable improved understanding of the biological mechanisms underpinning PrCa development, facilitate genetically informed PrCa screening programmes and guide appropriate treatment provision. However, insight remains largely lacking regarding many aspects of their manifestation; especially in relation to genes associated with aggressive phenotypes, risk factors in non-European populations and appropriate approaches to enable accurate stratification of higher and lower risk individuals. This review discusses the methodology used in the elucidation of genetic loci, genes and individual causal variants responsible for modulating PrCa susceptibility; the current state of understanding of the allelic spectrum contributing to PrCa risk; and prospective future translational applications of these discoveries in the developing eras of genomics and personalised medicine.

Hereditary Predisposition to Prostate Cancer: From Genetics to Clinical Implications

Prostate cancer (PrCa) ranks among the top five cancers for both incidence and mortality worldwide. A significant proportion of PrCa susceptibility has been attributed to inherited predisposition, with 10-20% of cases expected to occur in a hereditary/familial context. Advances in DNA sequencing technologies have uncovered several moderate- to high-penetrance PrCa susceptibility genes, most of which have previously been related to known hereditary cancer syndromes, namely the hereditary breast and ovarian cancer (BRCA1, BRCA2, ATM, CHEK2, and PALB2) and Lynch syndrome (MLH1, MSH2, MSH6, and PMS2) genes. Additional candidate genes have also been suggested, but further evidence is needed to include them in routine genetic testing. Recommendations based on clinical features, family history, and ethnicity have been established for more cost-efficient genetic testing of patients and families who may be at an increased risk of developing PrCa. The identification of alterations in PrCa predisposing genes may help to inform screening strategies, as well as treatment options, in the metastatic setting. This review provides an overview of the genetic basis underlying hereditary predisposition to PrCa, the current genetic screening recommendations, and the implications for clinical management of the disease.

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Prostate Cancer Germline Variations and Implications for Screening and Treatment

Prostate cancer (PCa) is a highly heritable disease, and rapid evolution of sequencing technologies has enabled marked progression of our understanding of its genetic inheritance. A complex polygenic model that involves common low-penetrance susceptibility alleles causing individually small but cumulatively significant risk and rarer genetic variants causing greater risk represent the current most accepted model. Through genome-wide association studies, more than 100 single-nucleotide polymorphisms (SNPs) associated with PCa risk have been identified. Consistent reports have identified germline mutations in the genes BRCA1, BRCA2, MMR, HOXB13, CHEK2, and NBS1 as conferring moderate risks, with some leading to a more aggressive disease behavior. Considering this knowledge, several research strategies have been developed to determine whether targeted prostate screening using genetic information can overcome the limitations of population-based prostate-specific antigen (PSA) screening. Germline DNA-repair mutations are more frequent in men with metastatic disease than previously thought, and these patients have a more favorable response to therapy with poly(adenosine diphosphate [ADP]-ribose) polymerase (PARP) inhibitors. Genomic information is a practical tool that has the potential to enable the concept of precision medicine to become a reality in all steps of PCa patient care.

A Review of Prostate Cancer Genome-Wide Association Studies (GWAS)

Prostate cancer is the most common cancer in men in Europe and the United States. The genetic heritability of prostate cancer is contributed to by both rarely occurring genetic variants with higher penetrance and moderate to commonly occurring variants conferring lower risks. The number of identified variants belonging to the latter category has increased dramatically in the last 10 years with the development of the genome-wide association study (GWAS) and the collaboration of international consortia that have led to the sharing of large-scale genotyping data. Over 40 prostate cancer GWAS have been reported, with approximately 170 common variants now identified. Clinical utility of these variants could include strategies for population-based risk stratification to target prostate cancer screening to men with an increased genetic risk of disease development, while for those who develop prostate cancer, identifying genetic variants could allow treatment to be tailored based on a genetic profile in the early disease setting. Functional studies of identified variants are needed to fully understand underlying mechanisms of disease and identify novel targets for treatment. This review will outline the GWAS carried out in prostate cancer and the common variants identified so far, and how these may be utilized clinically in the screening for and management of prostate cancer. Cancer Epidemiol Biomarkers Prev; 27(8); 845-57. ©2018 AACR.

MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: a pooled analysis from the M-SKIP project

PURPOSE

Melanoma represents an important public health problem, due to its high case-fatality rate. Identification of individuals at high risk would be of major interest to improve early diagnosis and ultimately survival. The aim of this study was to evaluate whether MC1R variants predicted melanoma risk independently of at-risk phenotypic characteristics.

MATERIALS AND METHODS

Data were collected within an international collaboration - the M-SKIP project. The present pooled analysis included data on 3,830 single, primary, sporadic, cutaneous melanoma cases and 2,619 controls from seven previously published case-control studies. All the studies had information on MC1R gene variants by sequencing analysis and on hair color, skin phototype, and freckles, ie, the phenotypic characteristics used to define the red hair phenotype.

RESULTS

The presence of any MC1R variant was associated with melanoma risk independently of phenotypic characteristics (OR 1.60; 95% CI 1.36-1.88). Inclusion of MC1R variants in a risk prediction model increased melanoma predictive accuracy (area under the receiver-operating characteristic curve) by 0.7% over a base clinical model (P=0.002), and 24% of participants were better assessed (net reclassification index 95% CI 20%-30%). Subgroup analysis suggested a possibly stronger role of MC1R in melanoma prediction for participants without the red hair phenotype (net reclassification index: 28%) compared to paler skinned participants (15%).

CONCLUSION

The authors suggest that measuring the MC1R genotype might result in a benefit for melanoma prediction. The results could be a valid starting point to guide the development of scientific protocols assessing melanoma risk prediction tools incorporating the MC1R genotype.

Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test

We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%-86%), followed by 76% (95% CI: 74%-79%) for a FIT alone, and 73% (95% CI: 71%-76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

Men with a susceptibility to prostate cancer and the role of genetic based screening

Prostate cancer is the second most common malignancy affecting men worldwide, and the commonest affecting men of African descent. Significant diagnostic and therapeutic advances have been made in the past decade. Improvements in the accuracy of prostate cancer diagnosis include the uptake of multi-parametric MRI and a shift towards targeted biopsy. We also now have more life-prolonging systemic and hormonal therapies for men with advanced disease at our disposal than ever before. However, the development of robust screening tools and targeted screening programs has not followed at the same pace. Evidence to support population-based screening remains unclear, with the use of PSA as a screening test limiting our ability to discriminate between clinically significant and insignificant disease. Prostate cancer has a large heritable component. Given that most men without risk factors have a low lifetime risk of developing lethal prostate cancer, much work is being done to further our knowledge of how we can best screen men in higher risk categories, such as those with a family history (FH) of the disease or those of African ancestry. These men have been reported to carry upwards of a two-fold increased risk of developing the disease at an earlier age, with evidence suggesting poorer survival outcomes. In men with a FH of prostate cancer, this is felt to be due to rare, high-penetrance mutations and the presence of multiple, common low penetrance alleles, with men carrying specific germline mutations in the BRCA and other DNA repair genes at particularly high risk. To date, large scale genome-wide association studies (GWAS) have led to the discovery of approximately 170 single nucleotide polymorphisms (SNPs) associated with prostate cancer risk, allowing over 30% of prostate cancer risk to be explained. Genomic tests, utilising somatic (prostate biopsy) tissue can also predict the risk of unfavourable pathology, biochemical recurrence and the likelihood of metastatic disease using gene expression. Targeted screening studies are currently under way in men with DNA repair mutations, men with a FH and those of Afro-Caribbean ethnicity which will greater inform our understanding of disease incidence and behaviour in these men, treatment outcomes and developing the most appropriate screening regime for such men. Incorporating a patient's genetic mutation status into risk algorithms allows us an opportunity to develop targeted screening programs for men in whom early cancer detection and treatment will positively influence survival, and in the process offer male family members of affected men the chance to be counselled and screened accordingly.

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.

Prediction of Breast and Prostate Cancer Risks in Male BRCA1 and BRCA2 Mutation Carriers Using Polygenic Risk Scores

Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated-for the first time to our knowledge-associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/ 2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10-6). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10-9). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management.

Using information of relatives in genomic prediction to apply effective stratified medicine

Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to individuals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors ("discovery sample") to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size (Ne), i.e. when individuals are closely related. For example, with the sample size of discovery set N = 3000, heritability h2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.

Incorporating truncating variants in PALB2, CHEK2, and ATM into the BOADICEA breast cancer risk model

PURPOSE

The proliferation of gene panel testing precipitates the need for a breast cancer (BC) risk model that incorporates the effects of mutations in several genes and family history (FH). We extended the BOADICEA model to incorporate the effects of truncating variants in PALB2, CHEK2, and ATM.

METHODS

The BC incidence was modeled via the explicit effects of truncating variants in BRCA1/2, PALB2, CHEK2, and ATM and other unobserved genetic effects using segregation analysis methods.

RESULTS

The predicted average BC risk by age 80 for an ATM mutation carrier is 28%, 30% for CHEK2, 50% for PALB2, and 74% for BRCA1 and BRCA2. However, the BC risks are predicted to increase with FH burden. In families with mutations, predicted risks for mutation-negative members depend on both FH and the specific mutation. The reduction in BC risk after negative predictive testing is greatest when a BRCA1 mutation is identified in the family, but for women whose relatives carry a CHEK2 or ATM mutation, the risks decrease slightly.

CONCLUSIONS

The model may be a valuable tool for counseling women who have undergone gene panel testing for providing consistent risks and harmonizing their clinical management. A Web application can be used to obtain BC risks in clinical practice (http://ccge.medschl.cam.ac.uk/boadicea/).Genet Med 18 12, 1190-1198.

Randomized trial finds that prostate cancer genetic risk score feedback targets prostate-specific antigen screening among at-risk men

BACKGROUND

Prostate-specific antigen (PSA) screening may reduce death due to prostate cancer but leads to the overdiagnosis of many cases of indolent cancer. Targeted use of PSA screening may reduce overdiagnosis. Multimarker genomic testing shows promise for risk assessment and could be used to target PSA screening.

METHODS

To test whether counseling based on the family history (FH) and counseling based on a genetic risk score (GRS) plus FH would differentially affect subsequent PSA screening at 3 months (primary outcome), a randomized trial of FH versus GRS plus FH was conducted with 700 whites aged 40 to 49 years without prior PSA screening. Secondary outcomes included anxiety, recall, physician discussion at 3 months, and PSA screening at 3 years. Pictographs versus numeric presentations of genetic risk were also evaluated.

RESULTS

At 3 months, no significant differences were observed in the rates of PSA screening between the FH arm (2.1%) and the GRS-FH arm (4.5% with GRS-FH vs. 2.1% with FH: χ2 = 3.13, P = .077); however, PSA screening rates at 3 months significantly increased with given risk in the GRS-FH arm (P = .013). Similar results were observed for discussions with physicians at 3 months and PSA screening at 3 years. Average anxiety levels decreased after the individual cancer risk was provided (P = .0007), with no differences between groups. Visual presentation by pictographs did not significantly alter comprehension or anxiety.

CONCLUSIONS

This is likely the first randomized trial of multimarker genomic testing to report genomic targeting of cancer screening. This study found little evidence of concern about excess anxiety or overuse/underuse of PSA screening when multimarker genetic risks were provided to patients. Cancer 2016;122:3564-3575. © 2016 American Cancer Society.

Association between 17q25.3-rs6465657 polymorphism and prostate cancer susceptibility: a meta-analysis based on 19 studies

Background

Genome-wide association studies have identified rs6465657 polymorphism at chromosome 17q25.3 as a new prostate cancer (PCa) susceptibility locus in people of European descent. However, subsequent replication studies have yielded inconsistent results among different ethnicities. In this study, a comprehensive meta-analysis was conducted to systematically evaluate the relationship between rs6465657 polymorphism and PCa risk.

Methods

All the articles involved were identified from PubMed, EMBASE, Web of Science, EBSCO databases, and Google Scholar before December 2015. The odds ratios (ORs) with corresponding 95% confidence internals (95% CIs) were pooled under the allele model. Fourteen eligible articles with 19 studies were finally included.

Results

In the overall population, the 17q25.3-rs6465657C allele was found to be significantly associated with increased risk of PCa compared to the T allele (OR =1.097; 95% CI: 1.061–1.134; P<0.001). In the subgroup analysis stratified by ethnicity, significantly increased risk was found in the Caucasian population (OR =1.120; 95% CI: 1.078–1.162; P<0.001), while the difference of OR did not reach the statistical significance in the Asian or African-American population. The analyses of sensitivity indicated the robust stability of the results, and the Begg’s and Egger’s test indicated that no publication bias existed.

Conclusion

The current meta-analysis suggested that the 17q25.3-rs6465657 polymorphism could be associated with PCa susceptibility, especially in the Caucasians, while this association might be different in other ethnicities.

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.

Germline genetic profiling in prostate cancer: latest developments and potential clinical applications

Familial and twin studies have demonstrated a significant inherited component to prostate cancer predisposition. Genome wide association studies have shown that there are 100 single nucleotide polymorphisms which have been associated with the development of prostate cancer. This review aims to discuss the scientific methods used to identify these susceptibility loci. It will also examine the current clinical utility of these loci, which include the development of risk models as well as predicting treatment efficacy and toxicity. In order to refine the clinical utility of the susceptibility loci, international consortia have been developed to combine statistical power as well as skills and knowledge to further develop models that could be used to predict risk and treatment outcomes.

Cancer Prevention: Obstacles, Challenges and the Road Ahead

Approaches to reduce the global burden of cancer include two major strategies: screening and early detection and active preventive intervention. The latter is the topic of this Commentary and spans a broad range of activities. The genetic heterogeneity and complexity of advanced cancers strongly support the rationale for early interruption of the carcinogenic process and an enhanced focus on prevention as a priority strategy to reduce the burden of cancer; however, the focus of cancer prevention management should be on individuals at high risk and on primary localized disease in which screening and detection should also play a vital role. The timing and dose of (chemo-)preventive intervention also affects response. The intervention may be ineffective if the target population is very high risk or already presenting with preneoplastic lesions with cellular changes that cannot be reversed. The field needs to move beyond general concepts of carcinogenesis to targeted organ site prevention approaches in patients at high risk, as is currently being done for breast and colorectal cancers. Establishing the benefit of new cancer preventive interventions will take years and possibly decades, depending on the outcome being evaluated. We also propose that comparative effectiveness research designs and the value of information obtained from large-scale prevention studies are necessary in order for preventive interventions to become a routine part of cancer management.

Biomarkers for prostate cancer: present challenges and future opportunities

Prostate cancer (PCa) has variable biological potential with multiple treatment options. A more personalized approach, therefore, is needed to better define men at higher risk of developing PCa, discriminate indolent from aggressive disease and improve risk stratification after treatment by predicting the likelihood of progression. This may improve clinical decision-making regarding management, improve selection for active surveillance protocols and minimize morbidity from treatment. Discovery of new biomarkers associated with prostate carcinogenesis present an opportunity to provide patients with novel genetic signatures to better understand their risk of developing PCa and help forecast their clinical course. In this review, we examine the current literature evaluating biomarkers in PCa. We also address current limitations and present several ideas for future studies.

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.

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects

BACKGROUND

Although BRCA1 and BRCA2 mutations account for only ∼27% of the familial aggregation of ovarian cancer (OvC), no OvC risk prediction model currently exists that considers the effects of BRCA1, BRCA2 and other familial factors. Therefore, a currently unresolved problem in clinical genetics is how to counsel women with family history of OvC but no identifiable BRCA1/2 mutations.

METHODS

We used data from 1548 patients with OvC and their relatives from a population-based study, with known BRCA1/2 mutation status, to investigate OvC genetic susceptibility models, using segregation analysis methods.

RESULTS

The most parsimonious model included the effects of BRCA1/2 mutations, and the residual familial aggregation was accounted for by a polygenic component (SD 1.43, 95% CI 1.10 to 1.86), reflecting the multiplicative effects of a large number of genes with small contributions to the familial risk. We estimated that 1 in 630 individuals carries a BRCA1 mutation and 1 in 195 carries a BRCA2 mutation. We extended this model to incorporate the explicit effects of 17 common alleles that are associated with OvC risk. Based on our models, assuming all of the susceptibility genes could be identified we estimate that the half of the female population at highest genetic risk will account for 92% of all OvCs.

CONCLUSIONS

The resulting model can be used to obtain the risk of developing OvC on the basis of BRCA1/2, explicit family history and common alleles. This is the first model that accounts for all OvC familial aggregation and would be useful in the OvC genetic counselling process.

Risk Analysis of Prostate Cancer in PRACTICAL, a Multinational Consortium, Using 25 Known Prostate Cancer Susceptibility Loci

BACKGROUND

Genome-wide association studies have identified multiple genetic variants associated with prostate cancer risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of prostate cancer.

METHODS

We genotyped 25 prostate cancer susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical odds ratios (OR) for prostate cancer associated with different risk strata defined by PRS and derived age-specific absolute risks of developing prostate cancer by PRS stratum and family history.

RESULTS

The prostate cancer risk for men in the top 1% of the PRS distribution was 30.6 (95% CI, 16.4-57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI, 3.2-5.5) fold compared with the median risk. The absolute risk of prostate cancer by age of 85 years was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation = 0.09).

CONCLUSIONS

Risk profiling can identify men at substantially increased or reduced risk of prostate cancer. The effect size, measured by OR per unit PRS, was higher in men at younger ages and in men with family history of prostate cancer. Incorporating additional newly identified loci into a PRS should improve the predictive value of risk profiles.

IMPACT

We demonstrate that the risk profiling based on SNPs can identify men at substantially increased or reduced risk that could have useful implications for targeted prevention and screening programs.

Prediction of breast cancer risk based on profiling with common genetic variants

BACKGROUND

Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking.

METHODS

We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates.

RESULTS

There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer.

CONCLUSIONS

The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

Prostate cancer risk prediction based on complete prostate cancer family history

BACKGROUND

Prostate cancer (PC) relative risks (RRs) are typically estimated based on status of close relatives or presence of any affected relatives. This study provides RR estimates using extensive and specific PC family history.

METHODS

A retrospective population-based study was undertaken to estimate RRs for PC based on complete family history of PC. A total of 635,443 males, all with ancestral genealogy data, were analyzed. RRs for PC were determined based upon PC rates estimated from males with no PC family history (without PC in first, second, or third degree relatives). RRs were determined for a variety of constellations, for example, number of first through third degree relatives; named (grandfather, father, uncle, cousins, brothers); maternal, paternal relationships, and age of onset.

RESULTS

In the 635,443 males analyzed, 18,105 had PC. First-degree RRs ranged from 2.46 (=1 first-degree relative affected, CI = 2.39-2.53) to 7.65 (=4 first-degree relatives affected, CI = 6.28-9.23). Second-degree RRs for probands with 0 affected first-degree relatives ranged from 1.51 (≥1 second-degree relative affected, CI = 1.47-1.56) to 3.09 (≥5 second-degree relatives affected, CI = 2.32-4.03). Third-degree RRs with 0 affected first- and 0 affected second-degree relatives ranged from 1.15 (≥1 affected third-degree relative, CI = 1.12-1.19) to 1.50 (≥5 affected third-degree relatives, CI = 1.35-1.66). RRs based on age at diagnosis were higher for earlier age at diagnoses; for example, RR = 5.54 for ≥1 first-degree relative diagnosed before age 50 years (CI = 1.12-1.19) and RR = 1.78 for >1 second-degree relative diagnosed before age 50 years, CI = 1.33, 2.33. RRs for equivalent maternal versus paternal family history were not significantly different.

CONCLUSIONS

A more complete PC family history using close and distant relatives and age at diagnosis results in a wider range of estimates of individual RR that are potentially more accurate than RRs estimated from summary family history. The presence of PC in second- and even third-degree relatives contributes significantly to risk. Maternal family history is just as significant as paternal family history. PC RRs based on a proband's complete constellation of affected relatives will allow patients and care providers to make more informed screening, monitoring, and treatment decisions.

A review of cancer risk prediction models with genetic variants

Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers and assess their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for cancer risk prediction.

Development of a melanoma risk prediction model incorporating MC1R genotype and indoor tanning exposure: impact of mole phenotype on model performance

Background

Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model.

Methods

Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)).

Results

Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups.

Conclusions

These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals.

Germline variation in cancer-susceptibility genes in a healthy, ancestrally diverse cohort: implications for individual genome sequencing

Technological advances coupled with decreasing costs are bringing whole genome and whole exome sequencing closer to routine clinical use. One of the hurdles to clinical implementation is the high number of variants of unknown significance. For cancer-susceptibility genes, the difficulty in interpreting the clinical relevance of the genomic variants is compounded by the fact that most of what is known about these variants comes from the study of highly selected populations, such as cancer patients or individuals with a family history of cancer. The genetic variation in known cancer-susceptibility genes in the general population has not been well characterized to date. To address this gap, we profiled the nonsynonymous genomic variation in 158 genes causally implicated in carcinogenesis using high-quality whole genome sequences from an ancestrally diverse cohort of 681 healthy individuals. We found that all individuals carry multiple variants that may impact cancer susceptibility, with an average of 68 variants per individual. Of the 2,688 allelic variants identified within the cohort, most are very rare, with 75% found in only 1 or 2 individuals in our population. Allele frequencies vary between ancestral groups, and there are 21 variants for which the minor allele in one population is the major allele in another. Detailed analysis of a selected subset of 5 clinically important cancer genes, BRCA1, BRCA2, KRAS, TP53, and PTEN, highlights differences between germline variants and reported somatic mutations. The dataset can serve a resource of genetic variation in cancer-susceptibility genes in 6 ancestry groups, an important foundation for the interpretation of cancer risk from personal genome sequences.

Frequent germline deleterious mutations in DNA repair genes in familial prostate cancer cases are associated with advanced disease

BACKGROUND

Prostate cancer (PrCa) is one of the most common diseases to affect men worldwide and among the leading causes of cancer-related death. The purpose of this study was to use second-generation sequencing technology to assess the frequency of deleterious mutations in 22 tumour suppressor genes in familial PrCa and estimate the relative risk of PrCa if these genes are mutated.

METHODS

Germline DNA samples from 191 men with 3 or more cases of PrCa in their family were sequenced for 22 tumour suppressor genes using Agilent target enrichment and Illumina technology. Analysis for genetic variation was carried out by using a pipeline consisting of BWA, Genome Analysis Toolkit (GATK) and ANNOVAR. Clinical features were correlated with mutation status using standard statistical tests. Modified segregation analysis was used to determine the relative risk of PrCa conferred by the putative loss-of-function (LoF) mutations identified.

RESULTS

We discovered 14 putative LoF mutations in 191 samples (7.3%) and these mutations were more frequently associated with nodal involvement, metastasis or T4 tumour stage (P=0.00164). Segregation analysis of probands with European ancestry estimated that LoF mutations in any of the studied genes confer a relative risk of PrCa of 1.94 (95% CI: 1.56-2.42).

CONCLUSIONS

These findings show that LoF mutations in DNA repair pathway genes predispose to familial PrCa and advanced disease and therefore warrants further investigation. The clinical utility of these findings will become increasingly important as targeted screening and therapies become more widespread.

Association analysis of 9,560 prostate cancer cases from the International Consortium of Prostate Cancer Genetics confirms the role of reported prostate cancer associated SNPs for familial disease

Previous GWAS studies have reported significant associations between various common SNPs and prostate cancer risk using cases unselected for family history. How these variants influence risk in familial prostate cancer is not well studied. Here, we analyzed 25 previously reported SNPs across 14 loci from prior prostate cancer GWAS. The International Consortium for Prostate Cancer Genetics (ICPCG) previously validated some of these using a family-based association method (FBAT). However, this approach suffered reduced power due to the conditional statistics implemented in FBAT. Here, we use a case-control design with an empirical analysis strategy to analyze the ICPCG resource for association between these 25 SNPs and familial prostate cancer risk. Fourteen sites contributed 12,506 samples (9,560 prostate cancer cases, 3,368 with aggressive disease, and 2,946 controls from 2,283 pedigrees). We performed association analysis with Genie software which accounts for relationships. We analyzed all familial prostate cancer cases and the subset of aggressive cases. For the familial prostate cancer phenotype, 20 of the 25 SNPs were at least nominally associated with prostate cancer and 16 remained significant after multiple testing correction (p ≤ 1E (-3)) occurring on chromosomal bands 6q25, 7p15, 8q24, 10q11, 11q13, 17q12, 17q24, and Xp11. For aggressive disease, 16 of the SNPs had at least nominal evidence and 8 were statistically significant including 2p15. The results indicate that the majority of common, low-risk alleles identified in GWAS studies for all prostate cancer also contribute risk for familial prostate cancer, and that some may contribute risk to aggressive disease.

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.

Diffusion-weighted MRI for detecting prostate tumour in men at increased genetic risk

BACKGROUND

Diffusion-weighted (DW)-MRI is invaluable in detecting prostate cancer. We determined its sensitivity and specificity and established interobserver agreement for detecting tumour in men with a family history of prostate cancer stratified by genetic risk.

METHODS

51 men with a family history of prostate cancer underwent T2-W + DW-endorectal MRI at 3.0 T. Presence of tumour was noted at right and left apex, mid and basal prostate sextants by 2 independent observers, 1 experienced and the other inexperienced in endorectal MRI. Sensitivity and specificity against a 10-core sampling technique (lateral and medial cores at each level considered together) in men with >2× population risk based on 71 SNP analysis versus those with lower genetic risk scores was established. Interobserver agreement was determined at a subject level.

RESULTS

Biopsies indicated cancer in 28 sextants in 13/51 men; 32 of 51 men had twice the population risk (>0.25) based on 71 SNP profiling. Sensitivity/specificity per-subject for patients was 90.0%/86.4% (high-risk) vs. 66.7%/100% (low-risk, observer 1) and 60.0%/86.3% (high-risk) vs. 33.3%/93.8% (low-risk, observer 2) with moderate overall inter-observer agreement (kappa = 0.42). Regional sensitivities/specificities for high-risk vs. low-risk for observer 1 apex 72.2%/100% [33.3%/100%], mid 100%/93.1% [100%/97.3%], base 16.7%/98.3% [0%/100%] and for observer 2 apex 36.4%/98.1% [0%/100%], mid 28.6%/96.5% [100%/100%], base 20%/100% [0%/97.3%] were poorer as they failed to detect multiple lesions.

CONCLUSION

Endorectal T2W + DW-MRI at 3.0 T yields high sensitivity and specificity for tumour detection by an experienced observer in screening men with a family history of prostate cancer and increased genetic risk.

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

BACKGROUND

Additional prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) continue to be identified. It is unclear whether addition of newly identified SNPs improves the discriminative performance of biopsy outcomes over previously established SNPs.

METHODS

A total of 667 consecutive patients that underwent prostate biopsy for detection of PCa at Huashan Hospital and Changhai Hospital, Shanghai, China were recruited. Genetic scores were calculated for each patient using various combinations of 29 PCa risk-associated SNPs. Performance of these genetic scores for discriminating prostate biopsy outcomes were compared using the area under a receiver operating characteristic curve (AUC).

RESULTS

The discriminative performance of genetic score derived from a panel of all 29 SNPs (24 previous and 5 new) was similar to that derived from the 24 previously established SNPs, the AUC of which were 0.60 and 0.61, respectively (P = 0.72). When SNPs with the strongest effect on PCa risk (ranked based on contribution to the total genetic variance from an external study) were sequentially added to the models for calculating genetic score, the AUC gradually increased and peaked at 0.62 with the top 13 strongest SNPs. Under the 13-SNP model, the PCa detection rate was 21.52%, 36.74%, and 51.98%, respectively for men with low (<0.5), intermediate (0.5-1.5), and high (>1.5) genetic score, P-trend = 9.91 × 10(-6).

CONCLUSION

Genetic score based on PCa risk-associated SNPs implicated to date is a significant predictor of biopsy outcome. Additional small-effect PCa risk-associated SNPs to be discovered in the future are unlikely to further improve predictive performance.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

The presence of prostate cancer at biopsy is predicted by a number of genetic variants

Several single nucleotide polymorphisms (SNPs) have been associated with an elevated risk of prostate cancer risk. It is not established if they are useful in predicting the presence of prostate cancer at biopsy or if they can be used to define a low-risk group of men. In this study, 4,548 men underwent a prostate biopsy because of an elevated prostate specific antigen (PSA; ≥4 ng/mL) or an abnormal digital rectal examination (DRE). All men were genotyped for 11 selected SNPs. The effect of each SNP, alone and in combination, on prostate cancer prevalence was studied. Of 4,548 men: 1,834 (40.3%) were found to have cancer. A positive association with prostate cancer was seen for 5 of 11 SNPs studied (rs1800629, rs1859962, rs1447295, rs4430796, rs11228565). The cancer detection rate rose with the number of SNP risk alleles from 29% for men with no variant to 63% for men who carried seven or more risk alleles (OR = 4.2; p = 0.002). The SNP data did not improve the predictive power of clinical factors (age, PSA and DRE) for detecting prostate cancer (AUC: 0.726 vs. 0.735; p = 0.4). We were unable to define a group of men with a sufficiently low prevalence of prostate cancer that a biopsy might have been avoided. In conclusion, our data do not support the routine use of SNP polymorphisms as an adjunct test to be used on the context of prostate biopsy for Polish men with an abnormal screening test.

Risk assessment to guide prostate cancer screening decisions: a cost-effectiveness analysis

OBJECTIVES

To apply the most recent evidence from randomised trials of prostate-specific antigen (PSA) screening and explore the potential value of risk assessments to guide the use of PSA screening in practice.

DESIGN

A decision model that incorporated a Markov process was developed in 2012 to estimate the net benefit and cost of PSA screening versus no screening as a function of baseline risk.

MAIN OUTCOME MEASURES

Quality-adjusted life-2013s (QALYs) and costs.

RESULTS

The harms of screening outweighed the benefits under a number of plausible scenarios. Conclusions were sensitive to the estimated quality-of-life impacts of prostate cancer treatment as well as the incidence of cancers not detected by screening tests (poorer prognosis) and those that were detected by screening tests (better prognosis). The base-case incremental cost-effectiveness ratio of PSA screening was $168,611 per QALY for men with average risk, $73,452 per QALY for men with two times the average risk, and $22,938 [corrected] per QALY for men with five times the average risk.

CONCLUSIONS

PSA screening was not found to be cost-effective for men at an average-to-high risk of prostate cancer, but may be cost-effective for men at very high risk. Inexpensive approaches for identifying men at very high risk are needed, as is further research on the size of clinical benefit of early detection in this population. The potential for the costs of risk assessment to be offset by reduced costs of PSA screening also warrants investigation.

A personalised approach to prostate cancer screening based on genotyping of risk founder alleles

Background:

To evaluate whether genotyping for 18 prostate cancer founder variants is helpful in identifying high-risk individuals and for determining optimal screening regimens.

Methods:

A serum PSA level was measured and a digital rectal examination (DRE) was performed on 2907 unaffected men aged 40–90. Three hundred and twenty-three men with an elevated PSA (⩾4 ng ml⁻¹) or an abnormal DRE underwent a prostate biopsy. All men were genotyped for three founder alleles in BRCA1 (5382insC, 4153delA and C61G), for four alleles in CHEK2 (1100delC, IVS2+1G>A, del5395 and I157T), for one allele in NBS1 (657del5), for one allele in HOXB13 (G84E), and for nine low-risk single-nucleotide polymorphisms (SNPs).

Results:

On the basis of an elevated PSA or an abnormal DRE, prostate cancer was diagnosed in 135 of 2907 men (4.6%). In men with a CHEK2 missense mutation I157T, the cancer detection rate among men with an elevated PSA or an abnormal DRE was much higher (10.2%, P=0.0008). The cancer detection rate rose with the number of SNP risk genotypes observed from 1.2% for men with no variant to 8.6% for men who carried six or more variants (P=0.04). No single variant was helpful on its own in predicting the presence of prostate cancer, however, the combination of all rare mutations and SNPs improved predictive power (area under the curve=0.59; P=0.03).

Conclusion:

These results suggest that testing for germline CHEK2 mutations improves the ability to predict the presence of prostate cancer in screened men, however, the clinical utility of incorporating DNA variants in the screening process is marginal.

Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array

Prostate cancer is the most frequently diagnosed cancer in males in developed countries. To identify common prostate cancer susceptibility alleles, we genotyped 211,155 SNPs on a custom Illumina array (iCOGS) in blood DNA from 25,074 prostate cancer cases and 24,272 controls from the international PRACTICAL Consortium. Twenty-three new prostate cancer susceptibility loci were identified at genome-wide significance (P < 5 × 10(-8)). More than 70 prostate cancer susceptibility loci, explaining ∼30% of the familial risk for this disease, have now been identified. On the basis of combined risks conferred by the new and previously known risk loci, the top 1% of the risk distribution has a 4.7-fold higher risk than the average of the population being profiled. These results will facilitate population risk stratification for clinical studies.

Personalized medicine and access to health care: potential for inequitable access?

Personalized medicine promises that an individual's genetic information will be increasingly used to prioritize access to health care. Use of genetic information to inform medical decision making, however, raises questions as to whether such use could be inequitable. Using breast cancer genetic risk prediction models as an example, on the surface clinical use of genetic information is consistent with the tools provided by evidence-based medicine, representing a means to equitably distribute limited health-care resources. However, at present, given limitations inherent to the tools themselves, and the mechanisms surrounding their implementation, it becomes clear that reliance on an individual's genetic information as part of medical decision making could serve as a vehicle through which disparities are perpetuated under public and private health-care delivery models. The potential for inequities arising from using genetic information to determine access to health care has been rarely discussed. Yet, it raises legal and ethical questions distinct from those raised surrounding genetic discrimination in employment or access to private insurance. Given the increasing role personalized medicine is forecast to play in the provision of health care, addressing a broader view of what constitutes genetic discrimination, one that occurs along a continuum and includes inequitable access, will be needed during the implementation of new applications based on individual genetic profiles. Only by anticipating and addressing the potential for inequitable access to health care occurring from using genetic information will we move closer to realizing the goal of personalized medicine: to improve the health of individuals.

Evaluating genetic risk for prostate cancer among Japanese and Latinos

BACKGROUND

There have been few genome-wide association studies (GWAS) of prostate cancer among diverse populations. To search for novel prostate cancer risk variants, we conducted GWAS of prostate cancer in Japanese and Latinos. In addition, we tested prostate cancer risk variants and developed genetic risk models of prostate cancer for Japanese and Latinos.

METHODS

Our first-stage GWAS of prostate cancer included Japanese (cases/controls = 1,033/1,042) and Latino (cases/controls = 1,043/1,057) from the Multiethnic Cohort (MEC). Significant associations from stage I (P < 1.0 × 10(-4)) were examined in silico in GWAS of prostate cancer (stage II) in Japanese (cases/controls = 1,583/3,386) and Europeans (cases/controls = 1,854/1,894).

RESULTS

No novel stage I single-nucleotide polymorphism (SNP) outside of known risk regions reached genome-wide significance. For Japanese, in stage I, the most notable putative novel association was seen with 10 SNPs (P ≤ 8.0 × 10(-6)) at chromosome 2q33; however, this was not replicated in stage II. For Latinos, the most significant association was observed with rs17023900 at the known 3p12 risk locus (stage I: OR = 1.45; P = 7.01 × 10(-5) and stage II: OR = 1.58; P = 3.05 × 10(-7)). The majority of the established risk variants for prostate cancer, 79% and 88%, were positively associated with prostate cancer in Japanese and Latinos (stage I), respectively. The cumulative effects of these variants significantly influence prostate cancer risk (OR per allele = 1.10; P = 2.71 × 10(-25) and OR = 1.07; P = 1.02 × 10(-16) for Japanese and Latinos, respectively).

CONCLUSION AND IMPACT

Our GWAS of prostate cancer did not identify novel genome-wide significant variants. However, our findings show that established risk variants for prostate cancer significantly contribute to risk among Japanese and Latinos.

Germline BRCA1 mutations increase prostate cancer risk

BACKGROUND

Prostate cancer (PrCa) is one of the most common cancers affecting men but its aetiology is poorly understood. Family history of PrCa, particularly at a young age, is a strong risk factor. There have been previous reports of increased PrCa risk in male BRCA1 mutation carriers in female breast cancer families, but there is a controversy as to whether this risk is substantiated. We sought to evaluate the role of germline BRCA1 mutations in PrCa predisposition by performing a candidate gene study in a large UK population sample set.

METHODS

We screened 913 cases aged 36–86 years for germline BRCA1 mutation, with the study enriched for cases with an early age of onset. We analysed the entire coding region of the BRCA1 gene using Sanger sequencing. Multiplex ligation-dependent probe amplification was also used to assess the frequency of large rearrangements in 460 cases.

RESULTS

We identified 4 deleterious mutations and 45 unclassified variants (UV). The frequency of deleterious BRCA1 mutation in this study is 0.45%; three of the mutation carriers were affected at age 65 years and one developed PrCa at 69 years. Using previously estimated population carrier frequencies, deleterious BRCA1 mutations confer a relative risk of PrCa of ~3.75-fold, (95% confidence interval 1.02–9.6) translating to a 8.6% cumulative risk by age 65.

CONCLUSION

This study shows evidence for an increased risk of PrCa in men who harbour germline mutations in BRCA1. This could have a significant impact on possible screening strategies and targeted treatments.

Inherited susceptibility for aggressive prostate cancer

Whether or not there is inherited basis for prostate cancer aggressiveness is not clear, but advances in DNA analysis should provide an answer to this question in the very near future.

Risk-based prostate cancer screening

CONTEXT

Widespread mass screening of prostate cancer (PCa) is not recommended because the balance between benefits and harms is still not well established. The achieved mortality reduction comes with considerable harm such as unnecessary biopsies, overdiagnoses, and overtreatment. Therefore, patient stratification with regard to PCa risk and aggressiveness is necessary to identify those men who are at risk and may actually benefit from early detection.

OBJECTIVE

This review critically examines the current evidence regarding risk-based PCa screening.

EVIDENCE ACQUISITION

A search of the literature was performed using the Medline database. Further studies were selected based on manual searches of reference lists and review articles.

EVIDENCE SYNTHESIS

Prostate-specific antigen (PSA) has been shown to be the single most significant predictive factor for identifying men at increased risk of developing PCa. Especially in men with no additional risk factors, PSA alone provides an appropriate marker up to 30 yr into the future. After assessment of an early PSA test, the screening frequency may be determined based on individualized risk. A limited list of additional factors such as age, comorbidity, prostate volume, family history, ethnicity, and previous biopsy status have been identified to modify risk and are important for consideration in routine practice. In men with a known PSA, risk calculators may hold the promise of identifying those who are at increased risk of having PCa and are therefore candidates for biopsy.

CONCLUSIONS

PSA testing may serve as the foundation for a more risk-based assessment. However, the decision to undergo early PSA testing should be a shared one between the patient and his physician based on information balancing its advantages and disadvantages.

Biomarkers in prostate cancer epidemiology

Understanding the etiology of a disease such as prostate cancer may help in identifying populations at high risk, timely intervention of the disease, and proper treatment. Biomarkers, along with exposure history and clinical data, are useful tools to achieve these goals. Individual risk and population incidence of prostate cancer result from the intervention of genetic susceptibility and exposure. Biochemical, epigenetic, genetic, and imaging biomarkers are used to identify people at high risk for developing prostate cancer. In cancer epidemiology, epigenetic biomarkers offer advantages over other types of biomarkers because they are expressed against a person's genetic background and environmental exposure, and because abnormal events occur early in cancer development, which includes several epigenetic alterations in cancer cells. This article describes different biomarkers that have potential use in studying the epidemiology of prostate cancer. We also discuss the characteristics of an ideal biomarker for prostate cancer, and technologies utilized for biomarker assays. Among epigenetic biomarkers, most reports indicate GSTP1 hypermethylation as the diagnostic marker for prostate cancer; however, NKX2-5, CLSTN1, SPOCK2, SLC16A12, DPYS, and NSE1 also have been reported to be regulated by methylation mechanisms in prostate cancer. Current challenges in utilization of biomarkers in prostate cancer diagnosis and epidemiologic studies and potential solutions also are discussed.