Travis, Ruth C

Importance Germline gene panel testing is recommended for men with advanced prostate cancer (PCa) or a family history of cancer. While evidence is limited for some genes currently included in panel testing, gene panels are also likely to be incomplete and missing genes that influence PCa risk and aggressive disease. Objective To identify genes associated with aggressive PCa. Design, Setting, and Participants A 2-stage exome sequencing case-only genetic association study was conducted including men of European ancestry from 18 international studies. Data analysis was performed from January 2021 to March 2023. Participants were 9185 men with aggressive PCa (including 6033 who died of PCa and 2397 with confirmed metastasis) and 8361 men with nonaggressive PCa. Exposure Sequencing data were evaluated exome-wide and in a focused investigation of 29 DNA repair pathway and cancer susceptibility genes, many of which are included on gene panels. Main Outcomes and Measures The primary study outcomes were aggressive (category T4 or both T3 and Gleason score ≥8 tumors, metastatic PCa, or PCa death) vs nonaggressive PCa (category T1 or T2 and Gleason score ≤6 tumors without known recurrence), and metastatic vs nonaggressive PCa. Results A total of 17 546 men of European ancestry were included in the analyses; mean (SD) age at diagnosis was 65.1 (9.2) years in patients with aggressive PCa and 63.7 (8.0) years in those with nonaggressive disease. The strongest evidence of association with aggressive or metastatic PCa was noted for rare deleterious variants in known PCa risk genes BRCA2 and ATM ( P ≤ 1.9 × 10 −6 ), followed by NBN ( P = 1.7 × 10 −4 ). This study found nominal evidence ( P < .05) of association with rare deleterious variants in MSH2 , XRCC2 , and MRE11A . Five other genes had evidence of greater risk (OR≥2) but carrier frequency differences between aggressive and nonaggressive PCa were not statistically significant: TP53 , RAD51D , BARD1 , GEN1 , and SLX4 . Deleterious variants in these 11 candidate genes were carried by 2.3% of patients with nonaggressive, 5.6% with aggressive, and 7.0% with metastatic PCa. Conclusions and Relevance The findings of this study provide further support for DNA repair and cancer susceptibility genes to better inform disease management in men with PCa and for extending testing to men with nonaggressive disease, as men carrying deleterious alleles in these genes are likely to develop more advanced disease.

Abstract Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3–T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.

AbstractEpidemiologic studies show an increased risk of non‐Hodgkin lymphoma (NHL) in patients with autoimmune disease (AD), due to a combination of shared environmental factors and/or genetic factors, or a causative cascade: chronic inflammation/antigen‐stimulation in one disease leads to another. Here we assess shared genetic risk in genome‐wide‐association‐studies (GWAS).Secondary analysis of GWAS of NHL subtypes (chronic lymphocytic leukemia, diffuse large B‐cell lymphoma, follicular lymphoma, and marginal zone lymphoma) and ADs (rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis). Shared genetic risk was assessed by (a) description of regional genetic of overlap, (b) polygenic risk score (PRS), (c)"diseasome", (d)meta‐analysis.Descriptive analysis revealed few shared genetic factors between each AD and each NHL subtype. The PRS of ADs were not increased in NHL patients (nor vice versa). In the diseasome, NHLs shared more genetic etiology with ADs than solid cancers (p = .0041). A meta‐analysis (combing AD with NHL) implicated genes of apoptosis and telomere length.This GWAS‐based analysis four NHL subtypes and three ADs revealed few weakly‐associated shared loci, explaining little total risk. This suggests common genetic variation, as assessed by GWAS in these sample sizes, may not be the primary explanation for the link between these ADs and NHLs.

Abstract Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

AbstractMotivationThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP–SNP interactions.ResultsWe tested two candidate approaches: the ‘Five-Full’ and ‘AA9int’ method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP–SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP–SNP interactions in large-scale studies.Availability and implementationThe ‘AA9int’ and ‘parAA9int’ functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary informationSupplementary data are available at Bioinformatics online.

Abstract A growing number of loci within the human leukocyte antigen (HLA) region have been implicated in non-Hodgkin lymphoma (NHL) etiology. Here, we test a complementary hypothesis of “heterozygote advantage” regarding the role of HLA and NHL, whereby HLA diversity is beneficial and homozygous HLA loci are associated with increased disease risk. HLA alleles at class I and II loci were imputed from genome-wide association studies (GWAS) using SNP2HLA for 3,617 diffuse large B-cell lymphomas (DLBCL), 2,686 follicular lymphomas (FL), 2,878 chronic lymphocytic leukemia/small lymphocytic lymphomas (CLL/SLL), 741 marginal zone lymphomas (MZL), and 8,753 controls of European descent. Both DLBCL and MZL risk were elevated with homozygosity at class I HLA-B and -C loci (OR DLBCL = 1.31, 95% CI = 1.06–1.60; OR MZL = 1.45, 95% CI = 1.12–1.89) and class II HLA-DRB1 locus (OR DLBCL = 2.10, 95% CI = 1.24–3.55; OR MZL = 2.10, 95% CI = 0.99–4.45). Increased FL risk was observed with the overall increase in number of homozygous HLA class II loci (P trend < 0.0001, FDR = 0.0005). These results support a role for HLA zygosity in NHL etiology and suggests that distinct immune pathways may underly the etiology of the different NHL subtypes. Significance: HLA gene diversity reduces risk for non-Hodgkin lymphoma. Cancer Res; 78(14); 4086–96. ©2018 AACR.

Abstract Motivation Testing SNP–SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP–SNP interactions are underdeveloped. Results We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR, EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP–SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/. Supplementary information Supplementary data are available at Bioinformatics online.

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.

Prostate cancer is the most common cancer in men in developed countries, and is a target for risk reduction strategies. The effects of alcohol consumption on prostate cancer incidence and survival remain unclear, potentially due to methodological limitations of observational studies. In this study, we investigated the associations of genetic variants in alcohol‐metabolising genes with prostate cancer incidence and survival. We analysed data from 23,868 men with prostate cancer and 23,051 controls from 25 studies within the international PRACTICAL Consortium. Study‐specific associations of 68 single nucleotide polymorphisms (SNPs) in 8 alcohol‐metabolising genes (Alcohol Dehydrogenases (ADHs) and Aldehyde Dehydrogenases (ALDHs)) with prostate cancer diagnosis and prostate cancer‐specific mortality, by grade, were assessed using logistic and Cox regression models, respectively. The data across the 25 studies were meta‐analysed using fixed‐effect and random‐effects models. We found little evidence that variants in alcohol metabolising genes were associated with prostate cancer diagnosis. Four variants in two genes exceeded the multiple testing threshold for associations with prostate cancer mortality in fixed‐effect meta‐analyses. SNPs within ALDH1A2 associated with prostate cancer mortality were rs1441817 (fixed effects hazard ratio, HRfixed = 0.78; 95% confidence interval (95%CI):0.66,0.91; p values = 0.002); rs12910509, HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.003); and rs8041922 (HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.002). These SNPs were in linkage disequilibrium with each other. In ALDH1B1, rs10973794 (HRfixed = 1.43; 95%CI:1.14,1.79; p values = 0.002) was associated with prostate cancer mortality in men with low‐grade prostate cancer. These results suggest that alcohol consumption is unlikely to affect prostate cancer incidence, but it may influence disease progression.