Germline genetic variation in prostate susceptibility does not predict outcomes in the chemoprevention trials PCPT and SELECT

Background The development of prostate cancer can be influenced by genetic and environmental factors. Numerous germline SNPs influence prostate cancer susceptibility. The functional pathways in which these SNPs increase prostate cancer susceptibility are unknown. Finasteride is currently not being used routinely as a chemoprevention agent but the long term outcomes of the PCPT trial are awaited. The outcomes of the SELECT trial have not recommended the use of chemoprevention in preventing prostate cancer. This study investigated whether germline risk SNPs could be used to predict outcomes in the PCPT and SELECT trial. Methods Genotyping was performed in European men entered into the PCPT trial (n = 2434) and SELECT (n = 4885). Next generation genotyping was performed using Affymetrix® Eureka™ Genotyping protocols. Logistic regression models were used to test the association of risk scores and the outcomes in the PCPT and SELECT trials. Results Of the 100 SNPs, 98 designed successfully and genotyping was validated for samples genotyped on other platforms. A number of SNPs predicted for aggressive disease in both trials. Men with a higher polygenic score are more likely to develop prostate cancer in both trials, but the score did not predict for other outcomes in the trial. Conclusion Men with a higher polygenic risk score are more likely to develop prostate cancer. There were no interactions of these germline risk SNPs and the chemoprevention agents in the SELECT and PCPT trials

Bayesian approach to assessing population differences in genetic risk of disease with application to prostate cancer

Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry

Bromodomain protein 4 discriminates tissue-specific super-enhancers containing disease-specific susceptibility loci in prostate and breast cancer.

Background Epigenetic information can be used to identify clinically relevant genomic variants single nucleotide polymorphisms (SNPs) of functional importance in cancer development. Super-enhancers are cell-specific DNA elements, acting to determine tissue or cell identity and driving tumor progression. Although previous approaches have been tried to explain risk associated with SNPs in regulatory DNA elements, so far epigenetic readers such as bromodomain containing protein 4 (BRD4) and super-enhancers have not been used to annotate SNPs. In prostate cancer (PC), androgen receptor (AR) binding sites to chromatin have been used to inform functional annotations of SNPs.Results Here we establish criteria for enhancer mapping which are applicable to other diseases and traits to achieve the optimal tissue-specific enrichment of PC risk SNPs. We used stratified Q-Q plots and Fisher test to assess the differential enrichment of SNPs mapping to specific categories of enhancers. We find that BRD4 is the key discriminant of tissue-specific enhancers, showing that it is more powerful than AR binding information to capture PC specific risk loci, and can be used with similar effect in breast cancer (BC) and applied to other diseases such as schizophrenia.Conclusions This is the first study to evaluate the enrichment of epigenetic readers in genome-wide associations studies for SNPs within enhancers, and provides a powerful tool for enriching and prioritizing PC and BC genetic risk loci. Our study represents a proof of principle applicable to other diseases and traits that can be used to redefine molecular mechanisms of human phenotypic variation

Blood lipids and prostate cancer: a Mendelian randomization analysis

Genetic risk scores were used as unconfounded instruments for specific lipid traits (Mendelian randomization) to assess whether circulating lipids causally influence prostate cancer risk. Data from 22,249 prostate cancer cases and 22,133 controls from 22 studies within the international PRACTICAL consortium were analyzed. Allele scores based on single nucleotide polymorphisms (SNPs) previously reported to be uniquely associated with each of low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride (TG) levels, were first validated in an independent dataset, and then entered into logistic regression models to estimate the presence (and direction) of any causal effect of each lipid trait on prostate cancer risk. There was weak evidence for an association between the LDL genetic score and cancer grade: the odds ratio (OR) per genetically instrumented standard deviation (SD) in LDL, comparing high- (≥7 Gleason score) versus low-grade (<7 Gleason score) cancers was 1.50 (95% CI: 0.92, 2.46; P = 0.11). A genetically instrumented SD increase in TGs was weakly associated with stage: the OR for advanced versus localized cancer per unit increase in genetic risk score was 1.68 (95% CI: 0.95, 3.00; P = 0.08). The rs12916-T variant in 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) was inversely associated with prostate cancer (OR: 0.97; 95% CI: 0.94, 1.00; P = 0.03). In conclusion, circulating lipids, instrumented by our genetic risk scores, did not appear to alter prostate cancer risk. We found weak evidence that higher LDL and TG levels increase aggressive prostate cancer risk, and that a variant in HMGCR (that mimics the LDL lowering effect of statin drugs) reduces risk. However, inferences are limited by sample size and evidence of pleiotropy

Investigating the possible causal role of coffee consumption with prostate cancer risk and progression using Mendelian randomization analysis.

Coffee consumption has been shown in some studies to be associated with lower risk of prostate cancer. However, it is unclear if this association is causal or due to confounding or reverse causality. We conducted a Mendelian randomisation analysis to investigate the causal effects of coffee consumption on prostate cancer risk and progression. We used two genetic variants robustly associated with caffeine intake (rs4410790 and rs2472297) as proxies for coffee consumption in a sample of 46,687 men of European ancestry from 25 studies in the PRACTICAL consortium. Associations between genetic variants and prostate cancer case status, stage and grade were assessed by logistic regression and with all-cause and prostate cancer-specific mortality using Cox proportional hazards regression. There was no clear evidence that a genetic risk score combining rs4410790 and rs2472297 was associated with prostate cancer risk (OR per additional coffee increasing allele: 1.01, 95% CI: 0.98,1.03) or having high-grade compared to low-grade disease (OR: 1.01, 95% CI: 0.97,1.04). There was some evidence that the genetic risk score was associated with higher odds of having nonlocalised compared to localised stage disease (OR: 1.03, 95% CI: 1.01, 1.06). Amongst men with prostate cancer, there was no clear association between the genetic risk score and all-cause mortality (HR: 1.00, 95% CI: 0.97,1.04) or prostate cancer-specific mortality (HR: 1.03, 95% CI: 0.98,1.08). These results, which should have less bias from confounding than observational estimates, are not consistent with a substantial effect of coffee consumption on reducing prostate cancer incidence or progression.British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration Cancer Research UK. Grant Number: C18281/A19169 RMM and Caroline Relton (Integrative Cancer Epidemiology Programme) Canadian Institutes of Health Research the European Commission's Seventh Framework Programme. Grant Numbers: 223175, HEALTH-F2-2009-223175 Cancer Research UK. Grant Numbers: C5047/A7357, C1287/A10118, C5047/A3354, C5047/A10692, C16913/A6135 National Institute of Health (NIH) Cancer Post-Cancer GWAS. Grant Number: 1 U19 CA 148537-01 the GAME-ON initiative the European Community's Seventh Framework Programme. Grant Numbers: 223175, HEALTH-F2-2009-223175 Cancer Research UK. Grant Numbers: C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692 the National Institutes of Health. Grant Number: CA128978 Post-Cancer GWAS initiative. Grant Numbers: 1U19 CA148537, 1U19 CA148065, 1U19 CA148112 the GAME-ON initiative the Department of Defence. Grant Number: W81XWH-10-1-0341 the Canadian Institutes of Health Research (CIHR) CIHR Team in Familial Risks of Breast Cancer Komen Foundation for the Cure Breast Cancer Research Foundation. Grant Number: Ovarian Cancer Research Fund VicHealth and Cancer Council Victoria Australian NHMRC. Grant Numbers: 209057, 251553, 504711 Cancer Council Victoria Australian Institute of Health and Welfare (AIHW) National Death Index and the Australian Cancer Database U.K. Health Technology Assessment (HTA) Programme of the NIH Research. Grant Numbers: HTA 96/20/99, ISRCTN20141297 Prodigal study and the ProMPT (Prostate Mechanisms of Progression and Treatment) National Cancer Research Institute (NCRI) Department of Health, the Medical Research Council and Cancer Research UK. Grant Number: G0500966/75466 Cancer Research UK. Grant Number: C5047/A7357 NIHR Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden NHS Foundation Trust National Institute for Health Research Bristol Nutrition Biomedical Research Unit based at University Hospitals Bristol NHS Foundation Trust and the University of Bristol FCH, DEN and JLD are NIHR Senior Investigators MRC and the University of Bristol. Grant Numbers: G0600705, MC_UU_12013/6This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1002/ijc.3046

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

BACKGROUND: Germline mutations within DNA-repair genes are implicated in susceptibility to multiple forms of cancer. For prostate cancer (PrCa), rare mutations in BRCA2 and BRCA1 give rise to moderately elevated risk, whereas two of B100 common, low-penetrance PrCa susceptibility variants identified so far by genome-wide association studies implicate RAD51B and RAD23B. METHODS: Genotype data from the iCOGS array were imputed to the 1000 genomes phase 3 reference panel for 21 780 PrCa cases and 21 727 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. We subsequently performed single variant, gene and pathway-level analyses using 81 303 SNPs within 20 Kb of a panel of 179 DNA-repair genes. RESULTS: Single SNP analyses identified only the previously reported association with RAD51B. Gene-level analyses using the SKAT-C test from the SNP-set (Sequence) Kernel Association Test (SKAT) identified a significant association with PrCa for MSH5. Pathway-level analyses suggested a possible role for the translesion synthesis pathway in PrCa risk and Homologous recombination/Fanconi Anaemia pathway for PrCa aggressiveness, even though after adjustment for multiple testing these did not remain significant. CONCLUSIONS: MSH5 is a novel candidate gene warranting additional follow-up as a prospective PrCa-risk locus. MSH5 has previously been reported as a pleiotropic susceptibility locus for lung, colorectal and serous ovarian cancers.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/bjc.2016.5