Prediction of individual genetic risk to prostate cancer using a polygenic score

PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma

BACKGROUND

Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality.

METHODS

Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive.

RESULTS

The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation.

CONCLUSIONS

The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface).

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer

PURPOSE

Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalized predicted PCa risks on the basis of known moderate- to high-risk pathogenic variants, low-risk common genetic variants, and explicit cancer family history, and to externally validate the model in an independent prospective cohort.

MATERIALS AND METHODS

We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the United Kingdom from 1993 to 2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited from 2006 to 2010 to the independent UK Biobank prospective cohort study.

RESULTS

The most parsimonious model included the effects of pathogenic variants in BRCA2, HOXB13, and BRCA1, and a polygenic score on the basis of 268 common low-risk variants. Residual familial risk was modeled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5 years (C-index, 0.790; 95% CI, 0.783 to 0.797) and 10 years (C-index, 0.772; 95% CI, 0.768 to 0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10 years.

CONCLUSION

To our knowledge, this is the first validated risk model offering personalized PCa risks. The model will assist in counseling men concerned about their risk and can facilitate future risk-stratified population screening approaches.

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.

Polygenic risk score in prostate cancer

PURPOSE OF REVIEW

This study was conducted in order to review the outcomes regarding polygenic risk score (PRS) in prediction of prostate cancer (PCa). With the increasing proficiency of genetic analysis, assessment of PRS for prediction of PCa has been performed in numerous studies. Genetic risk prediction models for PCa that include hundreds to thousands of independent risk-associated variants are under development. For estimation of additive effect of multiple variants, the number of risk alleles carried by an individual is summed, and each variant is weighted according to its estimated effect size for generation of a PRS.

RECENT FINDINGS

Currently, regarding the accuracy of PRS alone, PCa detection rate ranged from 0.56 to 0.67. A higher rate of accuracy of 0.866-0.880 was observed for other models combining PRS with established clinical markers. The results of PRS from Asian populations showed a level of accuracy that is somewhat low compared with values from Western populations (0.63-0.67); however, recent results from Asian cohorts were similar to that of Western counterparts. Here, we review current PRS literature and examine the clinical utility of PRS for prediction of PCa.

SUMMARY

Emerging data from several studies regarding PRS in PCa could be the solution to adding predictive value to PCa risk estimation. Although commercial markers are available, development of a large-scale, well validated PRS model should be undertaken in the near future, in order to translate hypothetical scenarios to actual clinical practice.

Recent advances in nanomaterials for prostate cancer detection and diagnosis

Despite the significant progress in the discovery of biomarkers and the exploitation of technologies for prostate cancer (PCa) detection and diagnosis, the initial screening of these PCa-related biomarkers using current...

Genetics of prostate cancer and its utility in treatment and screening

Prostate cancer heritability is attributed to a combination of rare, moderate to highly penetrant genetic variants as well as commonly occurring variants conferring modest risks [single nucleotide polymorphisms (SNPs)]. Some of the former type of variants (e.g., BRCA2 mutations) predispose particularly to aggressive prostate cancer and confer poorer prognoses compared to men who do not carry mutations. Molecularly targeted treatments such as PARP inhibitors have improved outcomes in men carrying somatic and/or germline DNA repair gene mutations. Ongoing clinical trials are exploring other molecular targeted approaches based on prostate cancer somatic alterations. Genome wide association studies have identified >250 loci that associate with prostate cancer risk. Multi-ancestry analyses have identified shared as well as population specific risk SNPs. Prostate cancer risk SNPs can be used to estimate a polygenic risk score (PRS) to determine an individual's genetic risk of prostate cancer. The odds ratio of prostate cancer development in men whose PRS lies in the top 1% of the risk profile ranges from 9 to 11. Ongoing studies are investigating the utility of a prostate cancer PRS to target population screening to those at highest risk. With the advent of personalized medicine and development of DNA sequencing technologies, access to clinical genetic testing is increasing, and oncology guidelines from bodies such as NCCN and ESMO have been updated to provide criteria for germline testing of "at risk" healthy men as well as those with prostate cancer. Both germline and somatic prostate cancer research have significantly evolved in the past decade and will lead to further development of precision medicine approaches to prostate cancer treatment as well as potentially developing precision population screening models.

Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population

Simple Summary

Gastric cancer remains the major cancer in Japan and worldwide. It is expected that practical intervention strategies for prevention, such as personalized approaches based on genetic risk models, will be developed. Here, we developed and validated a risk prediction model for gastric cancer using genetic, biological, and lifestyle-related risk factors. Results showed that the combination of selected GWAS-identified SNP polymorphisms and other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies; however, the contribution of genetic factors to risk prediction was limited. The greatest contributor to risk prediction was ABCD classification (Helicobacter pylori infection-related factor).

Abstract

Background: As part of our efforts to develop practical intervention applications for cancer prevention, we investigated a risk prediction model for gastric cancer based on genetic, biological, and lifestyle-related risk factors. Methods: We conducted two independent age- and sex-matched case–control studies, the first for model derivation (696 cases and 1392 controls) and the second (795 and 795) for external validation. Using the derivation study data, we developed a prediction model by fitting a conditional logistic regression model using the predictors age, ABCD classification defined by H. pylori infection and gastric atrophy, smoking, alcohol consumption, fruit and vegetable intake, and 3 GWAS-identified polymorphisms. Performance was assessed with regard to discrimination (area under the curve (AUC)) and calibration (calibration plots and Hosmer–Lemeshow test). Results: A combination of selected GWAS-identified polymorphisms and the other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies, with AUCs of 0.77 (95% confidence intervals: 0.75–0.79) and 0.78 (0.77–0.81), respectively. The calibration plots of both studies stayed close to the ideal calibration line. In the validation study, the environmental model (nongenetic model) was significantly more discriminative than the inclusive model, with an AUC value of 0.80 (0.77–0.82). Conclusion: The contribution of genetic factors to risk prediction was limited, and the ABCD classification (H. pylori infection-related factor) contributes most to risk prediction of gastric cancer.

Hispidulin inhibits proliferation, migration, and invasion by promoting autophagy via regulation of PPARγ activation in prostate cancer cells and xenograft models

Prostate cancer (PCa) is one of the important factors of cancer deaths especially in the western countries. Hispidulin (4',5,7-trihydroxy-6-methoxyflavone) is a phenolic flavonoid compound proved to possess anticancer properties, but its effects on PCa are left to be released. The aims of this study were to investigate the effects and the relative mechanisms of Hispidulin on PCa development. Hispidulin administration inhibited proliferation, invasion, and migration, while accelerated apoptosis in Du145 and VCaP cells, which was accompanied by PPARγ activation and autophagy enhancement. The beneficial effects of Hispidulin could be diminished by PPARγ inhibition. Besides, Hispidulin administration suppressed PCa tumorigenicity in Xenograft models, indicating the anticancer properties in vivo. Therefore, our work revealed that the anticancer properties of Hispidulin might be conferred by its activation on PPARγ and autophagy.

Polygenic Risk Scores in Prostate Cancer Risk Assessment and Screening

More than 40% of the risk of developing prostate cancer (PCa) is from genetic factors. Genome-wide association studies have led to the discovery of more than 140 variants associated with PCa risk. Polygenic risk scores (PRS) generated using these variants show promise in identifying individuals at much higher (and lower) lifetime risk than the average man. PCa PRS also improve the predictive value of prostate-specific antigen screening, may inform the age for starting PCa screening, and are informative for development of more aggressive tumors. Despite the promise, few clinical trials have evaluated the benefit of PCa PRS for clinical care.

A genetic risk score for glioblastoma multiforme based on copy number variations

Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer. Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations. We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile. The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.

Gastric Cancer Risk Prediction Using an Epidemiological Risk Assessment Model and Polygenic Risk Score

Simple Summary

Risk prediction models incorporate various established risk factors to estimate individual risk specifically in cancer. These models additionally include biological or genetic risk factors to assess cancer risk more accurately. The polygenic risk score (PRS) combines the effects of multiple single-nucleotide polymorphisms (SNPs) that are associated with disease; its discrimination ability was assessed both alone and when used in combination with conventional risk prediction models. As few studies have evaluated the combination of genetic variants to identify high risk population of gastric cancer (GC), and we examined the performance of a GC risk assessment model in combination with SNPs as a PRS in consideration of Helicobacter pylori (H. pylori) infection status. Such a combination improves the identification of a GC-susceptible population among people with H. pylori infection.

Abstract

We investigated the performance of a gastric cancer (GC) risk assessment model in combination with single-nucleotide polymorphisms (SNPs) as a polygenic risk score (PRS) in consideration of Helicobacter pylori (H. pylori) infection status. Six SNPs identified from genome-wide association studies and a marginal association with GC in the study population were included in the PRS. Discrimination of the GC risk assessment model, PRS, and the combination of the two (PRS-GCS) were examined regarding incremental risk and the area under the receiver operating characteristic curve (AUC), with grouping according to H. pylori infection status. The GC risk assessment model score showed an association with GC, irrespective of H. pylori infection. Conversely, the PRS exhibited an association only for those with H. pylori infection. The PRS did not discriminate GC in those without H. pylori infection, whereas the GC risk assessment model showed a modest discrimination. Among individuals with H. pylori infection, discrimination by the GC risk assessment model and the PRS were comparable, with the PRS-GCS combination resulting in an increase in the AUC of 3%. In addition, the PRS-GCS classified more patients and fewer controls at the highest score quintile in those with H. pylori infection. Overall, the PRS-GCS improved the identification of a GC-susceptible population of people with H. pylori infection. In those without H. pylori infection, the GC risk assessment model was better at identifying the high-risk group.

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.

Precision Medicine Approach in Prostate Cancer

Prostate cancer is the most prevalent type of cancer and the second cause of death in men worldwide. Various diagnostic and treatment procedures are available for this type of malignancy, but High-grade or locally advanced prostate cancers showed the potential to develop to lethal phase that can be causing dead. Therefore, new approaches are needed to prolong patients' survival and to improve their quality of life. Precision medicine is a novel emerging field that plays an essential role in identifying new sub-classifications of diseases and in providing guidance in treatment that is based on individual multi-omics data. Multi-omics approaches include the use of genomics, transcriptomics, proteomics, metabolomics, epigenomics and phenomics data to unravel the complexity of a disease-associated biological network, to predict prognostic biomarkers, and to identify new targeted drugs for individual cancer patients. We review the impact of multi-omics data in the framework of systems biology in the era of precision medicine, emphasising the combination of molecular imaging modalities with highthroughput techniques and the new treatments that target metabolic pathways involved in prostate cancer.

Impact of Personalized Genetic Breast Cancer Risk Estimation With Polygenic Risk Scores on Preventive Endocrine Therapy Intention and Uptake

Endocrine therapy is underutilized to reduce breast cancer incidence among women at increased risk. Polygenic risk scores (PRSs) assessing 77 breast cancer genetic susceptibility loci personalizes risk estimates. We examined effect of personalized PRS breast cancer risk prediction on intention to take and endocrine therapy uptake among women at increased risk. Eligible participants had a 10-year breast cancer risk ≥5% by Tyrer-Cuzick model [International Breast Cancer Intervention Study (IBIS)] or ≥3.0 % 5-year Gail Model risk with no breast cancer history or hereditary breast cancer syndrome. Breast cancer risk was estimated, endocrine therapy options were discussed, and endocrine therapy intent was assessed at baseline. After genotyping, PRS-updated breast cancer risk estimates, endocrine therapy options, and intent to take endocrine therapy were reassessed; endocrine therapy uptake was assessed during follow-up. From March 2016 to October 2017, 151 patients were enrolled [median (range) age, 56.1 (36.0-76.4 years)]. Median 10-year and lifetime IBIS risks were 7.9% and 25.3%. Inclusion of PRS increased lifetime IBIS breast cancer risk estimates for 81 patients (53.6%) and reduced risk for 70 (46.4%). Of participants with increased breast cancer risk by PRS, 39 (41.9%) had greater intent to take endocrine therapy; of those with decreased breast cancer risk by PRS, 28 (46.7%) had less intent to take endocrine therapy (P < 0.001). On multivariable regression, increased breast cancer risk by PRS was associated with greater intent to take endocrine therapy (P < 0.001). Endocrine therapy uptake was greater among participants with increased breast cancer risk by PRS (53.4%) than with decreased risk (20.9%; P < 0.001). PRS testing influenced intent to take and endocrine therapy uptake. Assessing PRS effect on endocrine therapy adherence is needed.Prevention Relevance: Counseling women at increased breast cancer risk using polygenic risk score (PRS) risk estimates can significantly impact preventive endocrine therapy uptake. Further development of PRS testing to personalize breast cancer risk assessments and endocrine therapy counselling may serve to potentially reduce the incidence of breast cancer in the future.

Evaluation of Polygenic Risk Scores for Prediction of Prostate Cancer in Korean Men

Aims

The purpose of this study is to evaluate an aggregate influence of prostate cancer (PCa) susceptibility variants on the development of PCa in Korean men by using the polygenic risk score (PRS) approach.

Methods

An analysis of 1,001 cases of PCa and 2,641 controls was performed to: (i) identify potential PCa-related risk loci in Koreans and (ii) validate the cumulative association between these loci and PCa using the PRS. Subgroup analyses based on risk stratification were conducted to better characterize the potential correlation to key PCa-related clinical outcomes (e.g., Gleason score, prostate-specific antigen levels). The results were replicated using 514 cases of PCa and 548 controls from an independent cohort.

Results

Genome-wide association analysis from our discovery cohort revealed 11 candidate single-nucleotide polymorphisms (SNPs) associated with PCa showing statistical significance of p < 5.0 × 10^–5. Seven variants were located at 8q24.21 (rs1016343, rs16901979, and rs13252298 in PRNCR1; rs4242384, rs7837688, and rs1447295 in CASC8; and rs1512268 in NKX3). Two variants located within HNF1B (rs7501939 and rs4430796) had a significant negative association with PCa risk [odds ratio (OR) = 0.717 and 0.747, p = 6.42 × 10^–7 and 3.67 × 10^–6, respectively]. Of the six independent SNPs that remained after linkage disequilibrium (LD) pruning, the top four SNPs best predicted PCa risk with an area under the receiver operating characteristic curve (AUC) of 0.637 (95% CI: 0.582–0.692). Those with top 25% polygenic risk had a 4.2-fold increased risk of developing PCa compared with those with low risk.

Conclusion

Eleven PCa risk variants in Korean men were identified; PRSs of a subset of these variants could help predict PCa susceptibility.

The effect of sample size on polygenic hazard models for prostate cancer

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

Active monitoring, radical prostatectomy and radical radiotherapy in PSA-detected clinically localised prostate cancer: the ProtecT three-arm RCT

BACKGROUND

Prostate cancer is the most common cancer among men in the UK. Prostate-specific antigen testing followed by biopsy leads to overdetection, overtreatment as well as undertreatment of the disease. Evidence of treatment effectiveness has lacked because of the paucity of randomised controlled trials comparing conventional treatments.

OBJECTIVES

To evaluate the effectiveness of conventional treatments for localised prostate cancer (active monitoring, radical prostatectomy and radical radiotherapy) in men aged 50-69 years.

DESIGN

A prospective, multicentre prostate-specific antigen testing programme followed by a randomised trial of treatment, with a comprehensive cohort follow-up.

SETTING

Prostate-specific antigen testing in primary care and treatment in nine urology departments in the UK.

PARTICIPANTS

Between 2001 and 2009, 228,966 men aged 50-69 years received an invitation to attend an appointment for information about the Prostate testing for cancer and Treatment (ProtecT) study and a prostate-specific antigen test; 82,429 men were tested, 2664 were diagnosed with localised prostate cancer, 1643 agreed to randomisation to active monitoring (n = 545), radical prostatectomy (n = 553) or radical radiotherapy (n = 545) and 997 chose a treatment.

INTERVENTIONS

The interventions were active monitoring, radical prostatectomy and radical radiotherapy.

TRIAL PRIMARY OUTCOME MEASURE

Definite or probable disease-specific mortality at the 10-year median follow-up in randomised participants.

SECONDARY OUTCOME MEASURES

Overall mortality, metastases, disease progression, treatment complications, resource utilisation and patient-reported outcomes.

RESULTS

There were no statistically significant differences between the groups for 17 prostate cancer-specific (p = 0.48) and 169 all-cause (p = 0.87) deaths. Eight men died of prostate cancer in the active monitoring group (1.5 per 1000 person-years, 95% confidence interval 0.7 to 3.0); five died of prostate cancer in the radical prostatectomy group (0.9 per 1000 person-years, 95% confidence interval 0.4 to 2.2 per 1000 person years) and four died of prostate cancer in the radical radiotherapy group (0.7 per 1000 person-years, 95% confidence interval 0.3 to 2.0 per 1000 person years). More men developed metastases in the active monitoring group than in the radical prostatectomy and radical radiotherapy groups: active monitoring, n = 33 (6.3 per 1000 person-years, 95% confidence interval 4.5 to 8.8); radical prostatectomy, n = 13 (2.4 per 1000 person-years, 95% confidence interval 1.4 to 4.2 per 1000 person years); and radical radiotherapy, n = 16 (3.0 per 1000 person-years, 95% confidence interval 1.9 to 4.9 per 1000 person-years; p = 0.004). There were higher rates of disease progression in the active monitoring group than in the radical prostatectomy and radical radiotherapy groups: active monitoring (n = 112; 22.9 per 1000 person-years, 95% confidence interval 19.0 to 27.5 per 1000 person years); radical prostatectomy (n = 46; 8.9 per 1000 person-years, 95% confidence interval 6.7 to 11.9 per 1000 person-years); and radical radiotherapy (n = 46; 9.0 per 1000 person-years, 95% confidence interval 6.7 to 12.0 per 1000 person years; p < 0.001). Radical prostatectomy had the greatest impact on sexual function/urinary continence and remained worse than radical radiotherapy and active monitoring. Radical radiotherapy's impact on sexual function was greatest at 6 months, but recovered somewhat in the majority of participants. Sexual and urinary function gradually declined in the active monitoring group. Bowel function was worse with radical radiotherapy at 6 months, but it recovered with the exception of bloody stools. Urinary voiding and nocturia worsened in the radical radiotherapy group at 6 months but recovered. Condition-specific quality-of-life effects mirrored functional changes. No differences in anxiety/depression or generic or cancer-related quality of life were found. At the National Institute for Health and Care Excellence threshold of £20,000 per quality-adjusted life-year, the probabilities that each arm was the most cost-effective option were 58% (radical radiotherapy), 32% (active monitoring) and 10% (radical prostatectomy).

LIMITATIONS

A single prostate-specific antigen test and transrectal ultrasound biopsies were used. There were very few non-white men in the trial. The majority of men had low- and intermediate-risk disease. Longer follow-up is needed.

CONCLUSIONS

At a median follow-up point of 10 years, prostate cancer-specific mortality was low, irrespective of the assigned treatment. Radical prostatectomy and radical radiotherapy reduced disease progression and metastases, but with side effects. Further work is needed to follow up participants at a median of 15 years.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN20141297.

FUNDING

This project was funded by the National Institute for Health Research Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 37. See the National Institute for Health Research Journals Library website for further project information.

Assessing thyroid cancer risk using polygenic risk scores

Genome-wide association studies (GWASs) have identified at least 10 single-nucleotide polymorphisms (SNPs) associated with papillary thyroid cancer (PTC) risk. Most of these SNPs are common variants with small to moderate effect sizes. Here we assessed the combined genetic effects of these variants on PTC risk by using summarized GWAS results to build polygenic risk score (PRS) models in three PTC study groups from Ohio (1,544 patients and 1,593 controls), Iceland (723 patients and 129,556 controls), and the United Kingdom (534 patients and 407,945 controls). A PRS based on the 10 established PTC SNPs showed a stronger predictive power compared with the clinical factors model, with a minimum increase of area under the receiver-operating curve of 5.4 percentage points (P ≤ 1.0 × 10^-9). Adding an extended PRS based on 592,475 common variants did not significantly improve the prediction power compared with the 10-SNP model, suggesting that most of the remaining undiscovered genetic risk in thyroid cancer is due to rare, moderate- to high-penetrance variants rather than to common low-penetrance variants. Based on the 10-SNP PRS, individuals in the top decile group of PRSs have a close to sevenfold greater risk (95% CI, 5.4-8.8) compared with the bottom decile group. In conclusion, PRSs based on a small number of common germline variants emphasize the importance of heritable low-penetrance markers in PTC.

The ternary complex factor protein ELK1 is an independent prognosticator of disease recurrence in prostate cancer

BACKGROUND

Both hormone-sensitive and castration- and enzalutamide-resistant prostate cancers (PCa) depend on the ternary complex factor (TCF) protein ELK1 to serve as a tethering protein for the androgen receptor (AR) to activate a critical set of growth genes. The two sites in ELK1 required for AR binding are conserved in other members of the TCF subfamily, ELK3 and ELK4. Here we examine the potential utility of the three proteins as prognosticators of disease recurrence in PCa.

METHODS

Transcriptional activity assays; Retrospective analysis of PCa recurrence using data on 501 patients in The Cancer Genome Atlas (TCGA) database; Unpaired Wilcoxon rank-sum test and multiple comparison correction using the Holm's method; Spearman's correlations; Kaplan-Meier methods; Univariable and multivariable Cox regression analyses; LASSO-based penalized Cox regression models; Time-dependent area under the receiver operating characteristic (ROC) curve.

RESULTS

ELK4 but not ELK3 was coactivated by AR similar to ELK1. Tumor expression of neither ELK3 nor ELK4 was associated with disease-free survival (DFS). ELK1 was associated with higher clinical T-stage, pathology T-stage, Gleason score, prognostic grade, and positive lymph node status. ELK1 was a negative prognosticator of DFS, independent of ELK3, ELK4, clinical T-stage, pathology T-stage, prognostic grade, lymph node status, age, and race. Inclusion of ELK1 increased the abilities of the Oncotype DX and Prolaris gene panels to predict disease recurrence, correctly predicting disease recurrence in a unique subset of patients.

CONCLUSIONS

ELK1 is a strong, independent prognosticator of disease recurrence in PCa, underscoring its unique role in PCa growth. Inclusion of ELK1 may enhance the utility of currently used prognosticators for clinical decision making in prostate cancer.

Mapping the Germline and Somatic Mutation Interaction Landscape in Indolent and Aggressive Prostate Cancers

BACKGROUND

A majority of prostate cancers (PCas) are indolent and cause no harm even without treatment. However, a significant proportion of patients with PCa have aggressive tumors that progress rapidly to metastatic disease and are often lethal. PCa develops through somatic mutagenesis, but emerging evidence suggests that germline genetic variation can markedly contribute to tumorigenesis. However, the causal association between genetic susceptibility and tumorigenesis has not been well characterized. The objective of this study was to map the germline and somatic mutation interaction landscape in indolent and aggressive tumors and to discover signatures of mutated genes associated with each type and distinguishing the two types of PCa.

MATERIALS AND METHODS

We integrated germline mutation information from genome-wide association studies (GWAS) with somatic mutation information from The Cancer Genome Atlas (TCGA) using gene expression data from TCGA on indolent and aggressive PCas as the intermediate phenotypes. Germline and somatic mutated genes associated with each type of PCa were functionally characterized using network and pathway analysis.

RESULTS

We discovered gene signatures containing germline and somatic mutations associated with each type and distinguishing the two types of PCa. We discovered multiple gene regulatory networks and signaling pathways enriched with germline and somatic mutations including axon guidance, RAR, WINT, MSP-RON, STAT3, PI3K, TR/RxR, and molecular mechanisms of cancer, NF-kB, prostate cancer, GP6, androgen, and VEGF signaling pathways for indolent PCa and MSP-RON, axon guidance, RAR, adipogenesis, and molecular mechanisms of cancer and NF-kB signaling pathways for aggressive PCa.

CONCLUSION

The investigation revealed germline and somatic mutated genes associated with indolent and aggressive PCas and distinguishing the two types of PCa. The study revealed multiple gene regulatory networks and signaling pathways dysregulated by germline and somatic alterations. Integrative analysis combining germline and somatic mutations is a powerful approach to mapping germline and somatic mutation interaction landscape.

Genetic risk score raises the risk of incidence of chronic kidney disease in Korean general population-based cohort

BACKGROUND

Chronic kidney disease (CKD) is a common disease, affecting about 10% of the general population. The genetic component about CKD incidence in Asian population is not well known. The aim of the study is to find the genetic loci associated with incident CKD and to figure out the effect of genetic variation on the development of CKD.

METHODS

We conducted a genome-wide association (GWA) study regarding the development of CKD based on two population-based cohorts of Korean Genome Epidemiology Study. 3617 Koreans from two different cohorts, aged 40-49 years without CKD at initial visit, were included in our analysis. We used 2510 individuals in Ansan as the discovery set and another 1107 individuals from Ansung as the replication set. At baseline, members of both cohorts provided information on creatinine, and DNA samples were collected for genotyping. Single nucleotide polymorphisms that surpassed a significance threshold of P < 5 × 10^-3 were selected.

RESULTS

A total of 281 among 3617 developed CKD during the follow-up period. Incident CKD group was older (P < 0.001), included more female (P < 0.001), and had more hypertension and diabetes (P < 0.001). We identified 12 SNPs that are associated with incident CKD in the GWA study and made genetic risk score using these SNPs. In multiple Cox regression analysis, genetic risk score was still a significant associated factor (HR 1.311, CI 1.201, 1.431, P < 0.001).

CONCLUSIONS

We identified several loci highly associated with incident CKD. The findings suggest the need for further investigations on the genetic propensity for incident CKD.

Interactions of PVT1 and CASC11 on Prostate Cancer Risk in African Americans

BACKGROUND

African American (AA) men have a higher risk of developing prostate cancer than white men. SNPs are known to play an important role in developing prostate cancer. The impact of PVT1 and its neighborhood genes (CASC11 and MYC) on prostate cancer risk are getting more attention recently. The interactions among these three genes associated with prostate cancer risk are understudied, especially for AA men. The objective of this study is to investigate SNP-SNP interactions in the CASC11-MYC-PVT1 region associated with prostate cancer risk in AA men.

METHODS

We evaluated 205 SNPs using the 2,253 prostate cancer patients and 2,423 controls and applied multiphase (discovery-validation) design. In addition to SNP individual effects, SNP-SNP interactions were evaluated using the SNP Interaction Pattern Identifier, which assesses 45 patterns.

RESULTS

Three SNPs (rs9642880, rs16902359, and rs12680047) and 79 SNP-SNP pairs were significantly associated with prostate cancer risk. These two SNPs (rs16902359 and rs9642880) in CASC11 interacted frequently with other SNPs with 56 and 9 pairs, respectively. We identified the novel interaction of CASC11-PVT1, which is the most common gene interaction (70%) in the top 79 pairs. Several top SNP interactions have a moderate to large effect size (OR, 0.27-0.68) and have a higher prediction power to prostate cancer risk than SNP individual effects.

CONCLUSIONS

Novel SNP-SNP interactions in the CASC11-MYC-PVT1 region have a larger impact than SNP individual effects on prostate cancer risk in AA men.

IMPACT

This gene-gene interaction between CASC11 and PVT1 can provide valuable information to reveal potential biological mechanisms of prostate cancer development.

Assessing the Incremental Contribution of Common Genomic Variants to Melanoma Risk Prediction in Two Population-Based Studies

It is unclear to what degree genomic and traditional (phenotypic and environmental) risk factors overlap in their prediction of melanoma risk. We evaluated the incremental contribution of common genomic variants (in pigmentation, nevus, and other pathways) and their overlap with traditional risk factors, using data from two population-based case-control studies from Australia (n = 1,035) and the United Kingdom (n = 1,460) that used the same questionnaires. Polygenic risk scores were derived from 21 gene regions associated with melanoma and odds ratios from published meta-analyses. Logistic regression models were adjusted for age, sex, center, and ancestry. Adding the polygenic risk score to a model with traditional risk factors increased the area under the receiver operating characteristic curve (AUC) by 2.3% (P = 0.003) for Australia and by 2.8% (P = 0.002) for Leeds. Gene variants in the pigmentation pathway, particularly MC1R, were responsible for most of the incremental improvement. In a cross-tabulation of polygenic by traditional tertile risk scores, 59% (Australia) and 49% (Leeds) of participants were categorized in the same (concordant) tertile. Of participants with low traditional risk, 9% (Australia) and 21% (Leeds) had high polygenic risk. Testing of genomic variants can identify people who are susceptible to melanoma despite not having a traditional phenotypic risk profile.

The Genetic Sphygmomanometer: an argument for routine genome-wide genotyping in the population and a new view on its use to inform clinical practice

Initial genomewide association studies were exceptional owing to an ability to yield novel and reliable evidence for heritable contributions to complex disease and phenotype. However the top results alone were certainly not responsible for a wave of new predictive tools. Despite this, even studies small by contemporary standards were able to provide estimates of the relative contribution of all recorded genetic variants to outcome. Sparking efforts to quantify heritability, these results also provided the material for genomewide prediction. A fantastic growth in the performance of human genetic studies has only served to improve the potential of these complex, but potentially informative predictors. Prompted by these conditions and recent work, this letter explores the likely utility of these predictors, considers how clinical practice might be altered through their use, how to measure the efficacy of this and some of the potential ethical issues involved. Ultimately we suggest that for common genetic variation at least, the future should contain an acceptance of complexity in genetic architecture and the possibility of useful prediction even if only to shift the way we interact with clinical service providers.

Association of polygenic risk score with the risk of chronic lymphocytic leukemia and monoclonal B-cell lymphocytosis

Inherited loci have been found to be associated with risk of chronic lymphocytic leukemia (CLL). A combined polygenic risk score (PRS) of representative single nucleotide polymorphisms (SNPs) from these loci may improve risk prediction over individual SNPs. Herein, we evaluated the association of a PRS with CLL risk and its precursor, monoclonal B-cell lymphocytosis (MBL). We assessed its validity and discriminative ability in an independent sample and evaluated effect modification and confounding by family history (FH) of hematological cancers. For discovery, we pooled genotype data on 41 representative SNPs from 1499 CLL and 2459 controls from the InterLymph Consortium. For validation, we used data from 1267 controls from Mayo Clinic and 201 CLL, 95 MBL, and 144 controls with a FH of CLL from the Genetic Epidemiology of CLL Consortium. We used odds ratios (ORs) to estimate disease associations with PRS and c-statistics to assess discriminatory accuracy. In InterLymph, the continuous PRS was strongly associated with CLL risk (OR, 2.49; P = 4.4 × 10-94). We replicated these findings in the Genetic Epidemiology of CLL Consortium and Mayo controls (OR, 3.02; P = 7.8 × 10-30) and observed high discrimination (c-statistic = 0.78). When jointly modeled with FH, PRS retained its significance, along with FH status. Finally, we found a highly significant association of the continuous PRS with MBL risk (OR, 2.81; P = 9.8 × 10-16). In conclusion, our validated PRS was strongly associated with CLL risk, adding information beyond FH. The PRS provides a means of identifying those individuals at greater risk for CLL as well as those at increased risk of MBL, a condition that has potential clinical impact beyond CLL.

Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether polygenic risk scores (PRS) for common cancers are associated with multiple phenotypes in a phenome-wide association study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRS exhibited strong association for several cancer traits they were designed for, including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma, and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.

Schizophrenia polygenic risk score predicts mnemonic hippocampal activity

The use of polygenic risk scores has become a practical translational approach to investigating the complex genetic architecture of schizophrenia, but the link between polygenic risk scores and pathophysiological components of this disorder has been the subject of limited research. We investigated in healthy volunteers whether schizophrenia polygenic risk score predicts hippocampal activity during simple memory encoding, which has been proposed as a risk-associated intermediate phenotype of schizophrenia. We analysed the relationship between polygenic risk scores and hippocampal activity in a discovery sample of 191 unrelated healthy volunteers from the USA and in two independent replication samples of 76 and 137 healthy unrelated participants from Europe and the USA, respectively. Polygenic risk scores for each individual were calculated as the sum of the imputation probability of reference alleles weighted by the natural log of odds ratio from the recent schizophrenia genome-wide association study. We examined hippocampal activity during simple memory encoding of novel visual stimuli assessed using blood oxygen level-dependent functional MRI. Polygenic risk scores were significantly associated with hippocampal activity in the discovery sample [P = 0.016, family-wise error (FWE) corrected within Anatomical Automatic Labeling (AAL) bilateral hippocampal-parahippocampal mask] and in both replication samples (P = 0.033, FWE corrected within AAL right posterior hippocampal-parahippocampal mask in Bari sample, and P = 0.002 uncorrected in the Duke Neurogenetics Study sample). The relationship between polygenic risk scores and hippocampal activity was consistently negative, i.e. lower hippocampal activity in individuals with higher polygenic risk scores, consistent with previous studies reporting decreased hippocampal-parahippocampal activity during declarative memory tasks in patients with schizophrenia and in their healthy siblings. Polygenic risk scores accounted for more than 8% of variance in hippocampal activity during memory encoding in discovery sample. We conclude that polygenic risk scores derived from the most recent schizophrenia genome-wide association study predict significant variability in hippocampal activity during memory encoding in healthy participants. Our findings validate mnemonic hippocampal activity as a genetic risk associated intermediate phenotype of schizophrenia, indicating that the aggregate neurobiological effect of schizophrenia risk alleles converges on this pattern of neural activity.awy004media15749593779001.

Genetic factors influencing prostate cancer risk in Norwegian men

Norway has one of the highest rates of death due to prostate cancer (PCa) in the world. To assess the contribution of both common and rare single nucleotide variants (SNPs) to the prostate cancer burden in Norway, we assessed the frequency of the established prostate cancer susceptibility allele, HOXB13 G84E, as well as a series of validated, common PCa risk SNPs in a Norwegian PCa population of 779 patients. The G84E allele was observed in 2.3% of patients compared to 0.7% of control individuals, OR = 3.8, P = 1 × 10-4. While there was a trend toward an earlier age at diagnosis, overall the clinicopathologic features of PCa were not significantly different in G84E carriers and non-carriers. Evaluation of 32 established common risk alleles revealed significant associations of risk alleles at 13 loci, including SNPs at 8q24, and near TET2, SLC22A3, NKX3-1, CASC8, MYC, DAP2IP, MSMB, HNF1B, PPP1R14A, and KLK2/3. When the data for each SNP are combined into a genetic risk score (GRS), Norwegian men within the top decile of GRS have over 5-fold greater risk to be diagnosed with PCa than men with GRS in the lowest decile. These results indicate that risk alleles of HOXB13 and common variant SNPs are important components of inherited PCa risk in the Norwegian population, although these factors appear to contribute little to the malignancy's aggressiveness.

New biomarkers for diagnosis and prognosis of localized prostate cancer

The diagnostics and management of localized prostate cancer is complicated because of cancer heterogeneity and differentiated progression in various subgroups of patients. As a prostate cancer biomarker, FDA-approved detection assay for serum prostate specific antigen (PSA) and its derivatives are not potent enough to diagnose prostate cancer, especially high-grade disease (Gleason ≥7). To date, a collection of new biomarkers was developed. Some of these markers are superior for primary screening while others are particularly helpful for cancer risk stratification, detection of high-grade cancer, and prediction of adverse events. Two of those markers such as proPSA (a part of the Prostate Health Index (PHI)) and prostate specific antigen 3 (PCA3) (a part of the PCA3 Progensa test) were recently approved by FDA for clinical use. Other markers are not PDA-approved yet but are available from Clinical Laboratory Improvement Amendment (CLIA)-certified clinical laboratories. In this review, we characterize diagnostic performance of these markers and their diagnostic and prognostic utility for prostate cancer.

Risk Model for Prostate Cancer Using Environmental and Genetic Factors in the Spanish Multi-Case-Control (MCC) Study

Prostate cancer (PCa) is the second most common cancer among men worldwide. Its etiology remains largely unknown compared to other common cancers. We have developed a risk stratification model combining environmental factors with family history and genetic susceptibility. 818 PCa cases and 1,006 healthy controls were compared. Subjects were interviewed on major lifestyle factors and family history. Fifty-six PCa susceptibility SNPs were genotyped. Risk models based on logistic regression were developed to combine environmental factors, family history and a genetic risk score. In the whole model, compared with subjects with low risk (reference category, decile 1), those carrying an intermediate risk (decile 5) had a 265% increase in PCa risk (OR = 3.65, 95% CI 2.26 to 5.91). The genetic risk score had an area under the ROC curve (AUROC) of 0.66 (95% CI 0.63 to 0.68). When adding the environmental score and family history to the genetic risk score, the AUROC increased by 0.05, reaching 0.71 (95% CI 0.69 to 0.74). Genetic susceptibility has a stronger risk value of the prediction that modifiable risk factors. While the added value of each SNP is small, the combination of 56 SNPs adds to the predictive ability of the risk model.

Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial

BACKGROUND

Although the clinical validity of risk-associated single nucleotide polymorphisms (SNPs) for assessment of disease susceptibility has been consistently established, risk reclassification from increasing numbers of implicated risk-associated SNPs raises concern that it is premature for clinical use. Our objective is to assess the degree and impact of risk reclassification with the increasing number of SNPs.

METHODS

A total of 3239 patients from the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial were included. Four genetic risk scores (GRSs) were calculated based on sets of sequentially discovered prostate cancer (PCa) risk-associated SNPs (17, 34, 51, and 68 SNPs).

RESULTS

Pair-wise correlation coefficients between sets of GRSs increased as more SNPs were included in the GRS: 0.80, 0.86, and 0.95 for 17 versus 34 SNPs, 34 versus 51 SNPs, and 51 versus 68 SNPs, respectively. Using a GRS of 1.5 as a cutoff for higher versus lower risk, reclassification rates of PCa risk decreased: 14.11%, 12.04%, and 8.15% for 17 versus 34 SNPs, 34 versus 51 SNPs, and 51 versus 68 SNPs, respectively. Evolving GRSs, nevertheless, provide a tool for further refining risk assessment. When all four sequential GRSs were considered, the detection rates of PCa for men whose GRSs were consistently <1.5, reclassified, and consistently ≥1.5 were 20.8%, 29.67%, and 39.26%, respectively (Ptrend  = 1.12 × 10-8 ). In comparison, the detection rates of PCa in men with negative or positive family history were 23.75% and 31.78%, respectively.

CONCLUSIONS

Risk assessment using currently available SNPs is justified. Multiple GRS values from evolving sets of SNPs provide a valuable tool for better refining risk.

Exome-based genome-wide association study and risk assessment using genetic risk score to prostate cancer in the Korean population

PURPOSE

To investigate exome-wide genetic variants associated with prostate cancer (PCa) in Koreans and evaluate the discriminative ability by the genetic risk score (GRS).

PATIENTS AND METHODS

We prospectively recruited 1,001 PCa cases from a tertiary hospital and conducted a case-control study including 2,641 healthy men (Stage I). Participants were analyzed using HumanExome BeadChip. For the external validation, additionally enrolled 514 PCa cases and 548 controls (independent cohort) were analyzed for the identified single nucleotide polymorphisms (SNPs) of Stage I (Stage II). The GRS was calculated as a non-weighted sum of the risk allele counts and investigated for accuracy of prediction of PCa.

RESULTS

the mean age was 66.3 years, and the median level of prostate specific antigen (PSA) was 9.19 ng/ml in PCa cases. In Stage I, 4 loci containing 5 variants (rs1512268 on 8p21.2; rs1016343 and rs7837688 on 8q24.21; rs7501939 on 17q12, and rs2735839 on 19q13.33) were confirmed to reach exome-wide significance (p<8.3x10-7). In Stage II, the mean GRS was 4.23 ± 1.44 for the controls and 4.78 ± 1.43 for the cases. As a reference to GRS 4, GRS 6, 7 and 8 showed a statistically significant risk of PCa (OR=1.85, 2.11 and 3.34, respectively).

CONCLUSIONS

The five variants were validated to associate with PCa in firstly performed exome-wide study in Koreans. The addition of individualized calculated GRS effectively enhanced the accuracy of prediction. These results need to be validated in future studies.

Estimating Heritability of Prostate Cancer-Specific Survival Using Population-Based Registers

BACKGROUND

There is a strong genetic component in prostate cancer development with an estimated heritability of 58%. In addition, recent epidemiological assessments show a familial component in prostate cancer-specific survival, which could be due to either common genetics or environment. In this study we sought to estimate the heritability of prostate cancer-specific survival by studying brothers and father-son pairs in Sweden.

METHODS

We used linkage records from three Swedish national registers: the Multi-Generation Register, the Cancer Register, and the Cause of Death Register. One thousand seven hundred twenty-eight brother pairs and 6,444 father-son pairs, where both family members were diagnosed with prostate cancer, were followed for prostate cancer mortality. By assuming that (i) brothers on average share 50% of their segregating alleles and 100% environment and (ii) fathers and sons share 50% of their segregating alleles and no environment, we implemented a model including influences of additive genetics (heritability), shared environment and non-shared environment for survival data. A conditional likelihood estimation procedure was developed to fit the model. Data simulation was applied to validate model assumptions.

RESULTS

In a model that adjusted for age at diagnosis and calendar period, the estimated heritability of prostate cancer-specific survival was 0.10 (95% CI = 0.00-0.20) that was borderline significantly different from zero (P = 0.057). The shared environment component was not significantly different from zero with a point estimate of 0.00 (95% CI = 0.00-0.13). Simulation studies and sensitivity analysis revealed that the estimated heritability component was robust, whereas the shared environmental component may be underestimated.

CONCLUSIONS

Heritability of prostate cancer-specific survival is considerably lower than for prostate cancer incidence. This supports a hypothesis that susceptibility of disease and progression of disease are separate mechanisms that involve different genes. Further assessment of the genetic basis of prostate cancer-specific survival is warranted. Prostate 77:900-907, 2017. © 2017 Wiley Periodicals, Inc.

Genetic risk score to predict biochemical recurrence after radical prostatectomy in prostate cancer: prospective cohort study

Purpose

To investigate the genetic risk score (GRS) from a large-scale exome-wide association study as a tool of prediction for biochemical recurrence (BCR) after radical prostatectomy (RP) in prostate cancer (PCa).

Results

The 16 SNPs were selected as significant predictors of BCR. The GRS in men experiencing BCR was -1.21, significantly higher than in non-BCR patients (–2.43) (p < 0.001). The 10-year BCR-free survival rate was 46.3% vs. 81.8% in the high-versus low GRS group, respectively (p < 0.001). The GRS was a significant factor after adjusting for other variables in Cox proportional hazard models (HR:1.630, p < 0.001). The predictive ability of the multivariate model without GRS was 84.4%, increased significantly to 88.0% when GRS was included (p = 0.0026).

Materials and Methods

Total 912 PCa patients were enrolled who had received RP and genotype analysis using Exome chip (HumanExome BeadChip). Genetic results were obtained by the methods of logistic regression analysis which measured the odds ratio (OR) to BCR. The GRS was calculated by the sum of each weighted-risk allele count multiplied by the natural logarithm of the respective ORs. Survival analyses were performed using the GRS. We compared the accuracy of separate multivariate models incorporating clinicopathological factors that either included or excluded the GRS.

Conclusions

GRS had additional predictive gain of BCR after RP in PCa. The addition of personally calculated GRS significantly increased the BCR prediction rate. After validation of these results, GRS of BCR could be potential biomarker to predict clinical outcomes.

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

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

Clinical validity and utility of genetic risk scores in prostate cancer

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

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

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

EMA and EFSA Joint Scientific Opinion on measures to reduce the need to use antimicrobial agents in animal husbandry in the European Union, and the resulting impacts on food safety (RONAFA)

EFSA and EMA have jointly reviewed measures taken in the EU to reduce the need for and use of antimicrobials in food-producing animals, and the resultant impacts on antimicrobial resistance (AMR). Reduction strategies have been implemented successfully in some Member States. Such strategies include national reduction targets, benchmarking of antimicrobial use, controls on prescribing and restrictions on use of specific critically important antimicrobials, together with improvements to animal husbandry and disease prevention and control measures. Due to the multiplicity of factors contributing to AMR, the impact of any single measure is difficult to quantify, although there is evidence of an association between reduction in antimicrobial use and reduced AMR. To minimise antimicrobial use, a multifaceted integrated approach should be implemented, adapted to local circumstances. Recommended options (non-prioritised) include: development of national strategies; harmonised systems for monitoring antimicrobial use and AMR development; establishing national targets for antimicrobial use reduction; use of on-farm health plans; increasing the responsibility of veterinarians for antimicrobial prescribing; training, education and raising public awareness; increasing the availability of rapid and reliable diagnostics; improving husbandry and management procedures for disease prevention and control; rethinking livestock production systems to reduce inherent disease risk. A limited number of studies provide robust evidence of alternatives to antimicrobials that positively influence health parameters. Possible alternatives include probiotics and prebiotics, competitive exclusion, bacteriophages, immunomodulators, organic acids and teat sealants. Development of a legislative framework that permits the use of specific products as alternatives should be considered. Further research to evaluate the potential of alternative farming systems on reducing AMR is also recommended. Animals suffering from bacterial infections should only be treated with antimicrobials based on veterinary diagnosis and prescription. Options should be reviewed to phase out most preventive use of antimicrobials and to reduce and refine metaphylaxis by applying recognised alternative measures.

Prostate cancer biomarkers: Are we hitting the mark?

Purpose

Localised prostate cancer diagnosis and management is increasingly complex due to its heterogeneous progression and prognostic subgroups. Pitfalls in current screening and diagnosis have prompted the search for accurate and invasive molecular and genetic biomarkers for prostate cancer. Such tools may be able to distinguish clinically significant cancers from less aggressive variants to assist with prostate cancer risk stratification and guide decisions and healthcare algorithms. We aimed to provide a comprehensive review of the current prostate cancer biomarkers available and in development.

Methods

MEDLINE and EMBASE databases searches were conducted to identify articles pertaining to the use of novel biomarkers for prostate cancer.

Results

A growing number of novel biomarkers are currently under investigation. Such markers include urinary biomarkers, serology-based markers or pathological tissue assessments of molecular and genetic markers. While limited clinical data is present for analysis, early results appear promising. Specifically, a combination of serum and urinary biomarkers (Serum PSA + Urinary PCA3 + Urinary TMPRSS2-ERG fusion) appears to provide superior sensitivity and specificity profiles compared to traditional diagnostic approaches (AUC 0.88).

Conclusion

The accurate diagnosis and risk stratification of prostate cancer is critical to ensure appropriate intervention. The development of non-invasive biomarkers can add to the information provided by current screening practices and allows for individualised risk stratification of patients. The use of these biomarkers appears to increase the sensitivity and specificity of diagnosis of prostate cancer. Further studies are necessary to define the appropriate use and time points of each biomarker and their effect on the management algorithm of prostate cancer.

Personalized prostate cancer care: from screening to treatment

Unprecedented progress has been made in genomic personalized medicine in the last several years, allowing for more individualized healthcare assessments and recommendations than ever before. However, most of this progress in prostate cancer (PCa) care has focused on developing and selecting therapies for late-stage disease. To address this issue of limited focus, we propose a model for incorporating genomic-based personalized medicine into all levels of PCa care, from prevention and screening to diagnosis, and ultimately to the treatment of both early-stage and late-stage cancers. We have termed this strategy the "Pyramid Model" of personalized cancer care. In this perspective paper, our objective is to demonstrate the potential application of the Pyramid Model to PCa care. This proactive and comprehensive personalized cancer care approach has the potential to achieve three important medical goals: reducing mortality, improving quality of life and decreasing both individual and societal healthcare costs.

Polygenic Epidemiology

Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.

Reclassification of genetic-based risk predictions as GWAS data accumulate

Background

Disease risk assessments based on common genetic variation have gained widespread attention and use in recent years. The clinical utility of genetic risk profiles depends on the number and effect size of identified loci, and how stable the predicted risks are as additional loci are discovered. Changes in risk classification for individuals over time would undermine the validity of common genetic variation for risk prediction. In this analysis, we quantified reclassification of genetic risk based on past and anticipated future GWAS data.

Methods

We identified disease-associated SNPs via the NHGRI GWAS catalog and recent large scale genome-wide association study (GWAS). We calculated the genomic risk for a simulated cohort of 100,000 individuals based on a multiplicative odds ratio model using cumulative GWAS-identified SNPs at four time points: 2007, 2009, 2011, and 2013. Individuals were classified as Higher Risk (population adjusted odds >2), Average Risk (between 0.5 and 2), and Lower Risk (<0.5) for each time point and we compared classifications between time points for breast cancer (BrCa), prostate cancer (PrCa), diabetes mellitus type 2 (T2D), and cardiovascular heart disease (CHD). We estimated future reclassification using the anticipated number of undiscovered SNPs.

Results

Risk reclassification occurred for all four phenotypes from 2007 to 2013. During the most recent interval (2011-2013), the degree of risk reclassification ranged from 16.3 % for CHD to 24.4 % for PrCa. Many individuals classified as Higher Risk at earlier time points were subsequently reclassified into a lower risk category. From 2011 to 2013, the degree of such downward risk reclassification ranged from 24.9 % for T2D to 55 % for CHD. The percent of individuals classified as Higher Risk increased as more SNPs were discovered, ranging from an increase of 5 % for CHD to 9 % for PrCa from 2007 to 2013. Reclassification continued to occur when we modeled the discovery of anticipated SNPs based on doubling current sample size.

Conclusion

Risk estimates from common genetic variation show large reclassification rates. Identifying disease-associated SNPs facilitates the clinically relevant task of identifying higher-risk individuals. However, the large amount of reclassification that we demonstrated in individuals initially classified as Higher Risk but later as Average Risk or Lower Risk, suggests that caution is currently warranted in basing clinical decisions on common genetic variation for many complex diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0272-5) contains supplementary material, which is available to authorized users.

Genomics of Cancer and a New Era for Cancer Prevention

A primary justification for dedicating substantial amounts of research funding to large-scale cancer genomics projects of both somatic and germline DNA is that the biological insights will lead to new treatment targets and strategies for cancer therapy. While it is too early to judge the success of these projects in terms of clinical breakthroughs, an alternative rationale is that new genomics techniques can be used to reduce the overall burden of cancer by prevention of new cases occurring and also by detecting them earlier. In particular, it is now becoming apparent that studying the genomic profile of tumors can help to identify new carcinogens and may subsequently result in implementing strategies that limit exposure. In parallel, it may be feasible to utilize genomic biomarkers to identify cancers at an earlier and more treatable stage using screening or other early detection approaches based on prediagnostic biospecimens. While the potential for these techniques is large, their successful outcome will depend on international collaboration and planning similar to that of recent sequencing initiatives.