Publications by authors named "Sara Benlloch"

41 Publications

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Nat Genet 2021 01 4;53(1):65-75. Epub 2021 Jan 4.

Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
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http://dx.doi.org/10.1038/s41588-020-00748-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8148035PMC
January 2021

Shared heritability and functional enrichment across six solid cancers.

Nat Commun 2019 01 25;10(1):431. Epub 2019 Jan 25.

Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Calle de Melchor Fernández Almagro, 3, 28029, Madrid, Spain.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r = 0.57, p = 4.6 × 10), breast and ovarian cancer (r = 0.24, p = 7 × 10), breast and lung cancer (r = 0.18, p =1.5 × 10) and breast and colorectal cancer (r = 0.15, p = 1.1 × 10). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.
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http://dx.doi.org/10.1038/s41467-018-08054-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347624PMC
January 2019

Author Correction: Germline variation at 8q24 and prostate cancer risk in men of European ancestry.

Nat Commun 2019 01 17;10(1):382. Epub 2019 Jan 17.

Department of Medical Genetics, Oslo University Hospital, 0424, Oslo, Norway.

The original version of this Article contained an error in the spelling of the author Manuela Gago-Dominguez, which was incorrectly given as Manuela G. Dominguez. This has now been corrected in both the PDF and HTML versions of the Article.
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http://dx.doi.org/10.1038/s41467-019-08293-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6336778PMC
January 2019

Author Correction: Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.

Nat Genet 2019 02;51(2):363

Dame Roma Mitchell Cancer Research Centre, University of Adelaide, Adelaide, South Australia, Australia.

In the version of this article initially published, the name of author Manuela Gago-Dominguez was misspelled as Manuela Gago Dominguez. The error has been corrected in the HTML and PDF version of the article.
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http://dx.doi.org/10.1038/s41588-018-0330-6DOI Listing
February 2019

Germline variation at 8q24 and prostate cancer risk in men of European ancestry.

Nat Commun 2018 11 5;9(1):4616. Epub 2018 Nov 5.

Department of Medical Genetics, Oslo University Hospital, 0424, Oslo, Norway.

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62-4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification.
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http://dx.doi.org/10.1038/s41467-018-06863-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218483PMC
November 2018

Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study.

Cancer Epidemiol Biomarkers Prev 2019 01 23;28(1):208-216. Epub 2018 Oct 23.

Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.

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 ( < 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.
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http://dx.doi.org/10.1158/1055-9965.EPI-18-0079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746173PMC
January 2019

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants.

Nat Commun 2018 06 11;9(1):2256. Epub 2018 Jun 11.

Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia.

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.
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http://dx.doi.org/10.1038/s41467-018-04109-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995836PMC
June 2018

Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.

Nat Genet 2018 07 11;50(7):928-936. Epub 2018 Jun 11.

Dame Roma Mitchell Cancer Research Centre, University of Adelaide, Adelaide, South Australia, Australia.

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P < 5.0 × 10) with PrCa and one locus significantly associated with early-onset PrCa (≤55 years). Our findings include missense variants rs1800057 (odds ratio (OR) = 1.16; P = 8.2 × 10; G>C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa.
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http://dx.doi.org/10.1038/s41588-018-0142-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568012PMC
July 2018

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

Bioinformatics 2018 12;34(24):4141-4150

Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, UT, USA.

Motivation: The 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.

Results: We 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 Implementation: The '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 Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty461DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289141PMC
December 2018

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Bioinformatics 2017 03;33(6):822-833

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.

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/ .

Contact: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btw762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860469PMC
March 2017

Prostate cancer risk regions at 8q24 and 17q24 are differentially associated with somatic TMPRSS2:ERG fusion status.

Hum Mol Genet 2016 12;25(24):5490-5499

Institute of Human Genetics, University of Ulm, Ulm, Germany.

Molecular and epidemiological differences have been described between TMPRSS2:ERG fusion-positive and fusion-negative prostate cancer (PrCa). Assuming two molecularly distinct subtypes, we have examined 27 common PrCa risk variants, previously identified in genome-wide association studies, for subtype specific associations in a total of 1221 TMPRSS2:ERG phenotyped PrCa cases. In meta-analyses of a discovery set of 552 cases with TMPRSS2:ERG data and 7650 unaffected men from five centers we have found support for the hypothesis that several common risk variants are associated with one particular subtype rather than with PrCa in general. Risk variants were analyzed in case-case comparisons (296 TMPRSS2:ERG fusion-positive versus 256 fusion-negative cases) and an independent set of 669 cases with TMPRSS2:ERG data was established to replicate the top five candidates. Significant differences (P < 0.00185) between the two subtypes were observed for rs16901979 (8q24) and rs1859962 (17q24), which were enriched in TMPRSS2:ERG fusion-negative (OR = 0.53, P = 0.0007) and TMPRSS2:ERG fusion-positive PrCa (OR = 1.30, P = 0.0016), respectively. Expression quantitative trait locus analysis was performed to investigate mechanistic links between risk variants, fusion status and target gene mRNA levels. For rs1859962 at 17q24, genotype dependent expression was observed for the candidate target gene SOX9 in TMPRSS2:ERG fusion-positive PrCa, which was not evident in TMPRSS2:ERG negative tumors. The present study established evidence for the first two common PrCa risk variants differentially associated with TMPRSS2:ERG fusion status. TMPRSS2:ERG phenotyping of larger studies is required to determine comprehensive sets of variants with subtype-specific roles in PrCa.
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http://dx.doi.org/10.1093/hmg/ddw349DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5418832PMC
December 2016

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

Int J Cancer 2017 Jan 26;140(2):322-328. Epub 2016 Oct 26.

Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University Sofia, 2 Zdrave St, Sofia, 1431, Bulgaria.

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.
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http://dx.doi.org/10.1002/ijc.30462DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5132137PMC
January 2017

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers.

Cancer Epidemiol Biomarkers Prev 2017 01 3;26(1):126-135. Epub 2016 Oct 3.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California.

Background: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits.

Methods: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background.

Results: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis.

Conclusions: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures.

Impact: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR.
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http://dx.doi.org/10.1158/1055-9965.EPI-16-0106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5224974PMC
January 2017

Alcohol consumption and prostate cancer incidence and progression: A Mendelian randomisation study.

Int J Cancer 2017 Jan 8;140(1):75-85. Epub 2016 Oct 8.

Division of Cancer Prevention and Control, H. Lee Moffitt Cancer Center, 12902 Magnolia Dr, Tampa, FL.

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, HR  = 0.78; 95% confidence interval (95%CI):0.66,0.91; p values = 0.002); rs12910509, HR  = 0.76; 95%CI:0.64,0.91; p values = 0.003); and rs8041922 (HR  = 0.76; 95%CI:0.64,0.91; p values = 0.002). These SNPs were in linkage disequilibrium with each other. In ALDH1B1, rs10973794 (HR  = 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.
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http://dx.doi.org/10.1002/ijc.30436DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5111609PMC
January 2017

PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS.

J Med Genet 2016 12 5;53(12):800-811. Epub 2016 Sep 5.

Division of Molecular Medicine, Pathology North, Newcastle and University of Newcastle, NSW, Australia.

Background: The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study.

Methods: We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant.

Results: For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants.

Conclusions: This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.
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http://dx.doi.org/10.1136/jmedgenet-2016-103839DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5200636PMC
December 2016

Polyunsaturated fatty acids and prostate cancer risk: a Mendelian randomisation analysis from the PRACTICAL consortium.

Br J Cancer 2016 08 4;115(5):624-31. Epub 2016 Aug 4.

Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, 2 Zdrave Street, 1431 Sofia, Bulgaria.

Background: Prostate cancer is a common cancer worldwide with no established modifiable lifestyle factors to guide prevention. The associations between polyunsaturated fatty acids (PUFAs) and prostate cancer risk have been inconsistent. Using Mendelian randomisation, we evaluated associations between PUFAs and prostate cancer risk.

Methods: We used individual-level data from a consortium of 22 721 cases and 23 034 controls of European ancestry. Externally-weighted PUFA-specific polygenic risk scores (wPRSs), with explanatory variation ranging from 0.65 to 33.07%, were constructed and used to evaluate associations with prostate cancer risk per one standard deviation (s.d.) increase in genetically-predicted plasma PUFA levels using multivariable-adjusted unconditional logistic regression.

Results: No overall association was observed between the genetically-predicted PUFAs evaluated in this study and prostate cancer risk. However, risk reductions were observed for short-chain PUFAs, linoleic (ORLA=0.95, 95%CI=0.92, 0.98) and α-linolenic acids (ORALA=0.96, 95%CI=0.93, 0.98), among men <62 years; whereas increased risk was found among men ⩾62 years for LA (ORLA=1.04, 95%CI=1.01, 1.07). For long-chain PUFAs (i.e., arachidonic, eicosapentaenoic, and docosapentaenoic acids), increased risks were observed among men <62 years (ORAA=1.05, 95%CI=1.02, 1.08; OREPA=1.04, 95%CI=1.01, 1.06; ORDPA=1.05, 95%CI=1.02, 1.08).

Conclusion: Results from this study suggest that circulating ω-3 and ω-6 PUFAs may have a different role in the aetiology of early- and late-onset prostate cancer.
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http://dx.doi.org/10.1038/bjc.2016.228DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4997551PMC
August 2016

Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types.

Cancer Discov 2016 09 17;6(9):1052-67. Epub 2016 Jul 17.

Department of Epidemiology, UCI Center for Cancer Genetics Research and Prevention, School of Medicine, University of California, Irvine, Irvine, California.

Unlabelled: Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.

Significance: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
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http://dx.doi.org/10.1158/2159-8290.CD-15-1227DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010513PMC
September 2016

Assessing the role of insulin-like growth factors and binding proteins in prostate cancer using Mendelian randomization: Genetic variants as instruments for circulating levels.

Int J Cancer 2016 10 23;139(7):1520-33. Epub 2016 Jun 23.

Brigham and Women's Hospital/Dana-Farber Cancer Institute, 45 Francis Street-ASB II-3, Boston, Massachussets.

Circulating insulin-like growth factors (IGFs) and their binding proteins (IGFBPs) are associated with prostate cancer. Using genetic variants as instruments for IGF peptides, we investigated whether these associations are likely to be causal. We identified from the literature 56 single nucleotide polymorphisms (SNPs) in the IGF axis previously associated with biomarker levels (8 from a genome-wide association study [GWAS] and 48 in reported candidate genes). In ∼700 men without prostate cancer and two replication cohorts (N ∼ 900 and ∼9,000), we examined the properties of these SNPS as instrumental variables (IVs) for IGF-I, IGF-II, IGFBP-2 and IGFBP-3. Those confirmed as strong IVs were tested for association with prostate cancer risk, low (< 7) vs. high (≥ 7) Gleason grade, localised vs. advanced stage, and mortality, in 22,936 controls and 22,992 cases. IV analysis was used in an attempt to estimate the causal effect of circulating IGF peptides on prostate cancer. Published SNPs in the IGFBP1/IGFBP3 gene region, particularly rs11977526, were strong instruments for IGF-II and IGFBP-3, less so for IGF-I. Rs11977526 was associated with high (vs. low) Gleason grade (OR per IGF-II/IGFBP-3 level-raising allele 1.05; 95% CI: 1.00, 1.10). Using rs11977526 as an IV we estimated the causal effect of a one SD increase in IGF-II (∼265 ng/mL) on risk of high vs. low grade disease as 1.14 (95% CI: 1.00, 1.31). Because of the potential for pleiotropy of the genetic instruments, these findings can only causally implicate the IGF pathway in general, not any one specific biomarker.
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http://dx.doi.org/10.1002/ijc.30206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4957617PMC
October 2016

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation.

Nat Commun 2016 Apr 7;7:10979. Epub 2016 Apr 7.

Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan 48202, USA.

Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.
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http://dx.doi.org/10.1038/ncomms10979DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4829663PMC
April 2016

Pubertal development and prostate cancer risk: Mendelian randomization study in a population-based cohort.

BMC Med 2016 Apr 4;14:66. Epub 2016 Apr 4.

Division of Cancer Prevention and Control, H. Lee Moffitt Cancer Center, 12902 Magnolia Dr., Tampa, FL, USA.

Background: Epidemiological studies have observed a positive association between an earlier age at sexual development and prostate cancer, but markers of sexual maturation in boys are imprecise and observational estimates are likely to suffer from a degree of uncontrolled confounding. To obtain causal estimates, we examined the role of pubertal development in prostate cancer using genetic polymorphisms associated with Tanner stage in adolescent boys in a Mendelian randomization (MR) approach.

Methods: We derived a weighted genetic risk score for pubertal development, combining 13 SNPs associated with male Tanner stage. A higher score indicated a later puberty onset. We examined the association of this score with prostate cancer risk, stage and grade in the UK-based ProtecT case-control study (n = 2,927), and used the PRACTICAL consortium (n = 43,737) as a replication sample.

Results: In ProtecT, the puberty genetic score was inversely associated with prostate cancer grade (odds ratio (OR) of high- vs. low-grade cancer, per tertile of the score: 0.76; 95 % CI, 0.64-0.89). In an instrumental variable estimation of the causal OR, later physical development in adolescence (equivalent to a difference of one Tanner stage between pubertal boys of the same age) was associated with a 77 % (95 % CI, 43-91 %) reduced odds of high Gleason prostate cancer. In PRACTICAL, the puberty genetic score was associated with prostate cancer stage (OR of advanced vs. localized cancer, per tertile: 0.95; 95 % CI, 0.91-1.00) and prostate cancer-specific mortality (hazard ratio amongst cases, per tertile: 0.94; 95 % CI, 0.90-0.98), but not with disease grade.

Conclusions: Older age at sexual maturation is causally linked to a reduced risk of later prostate cancer, especially aggressive disease.
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http://dx.doi.org/10.1186/s12916-016-0602-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4820939PMC
April 2016

Blood lipids and prostate cancer: a Mendelian randomization analysis.

Cancer Med 2016 06 19;5(6):1125-36. Epub 2016 Mar 19.

Brigham and Women's Hospital/Dana-Farber Cancer Institute, 45 Francis Street- ASB II-3, Boston, Massachusetts, 02115.

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.
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http://dx.doi.org/10.1002/cam4.695DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4924371PMC
June 2016

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

Br J Cancer 2016 04;114(8):945-52

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.
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http://dx.doi.org/10.1038/bjc.2016.50DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5379914PMC
April 2016

A genetic study and meta-analysis of the genetic predisposition of prostate cancer in a Chinese population.

Oncotarget 2016 Apr;7(16):21393-403

Department of Urology, Changhai Hospital, The Second Military Medical University, Shanghai, 200433, China.

Prostate cancer predisposition has been extensively investigated in European populations, but there have been few studies of other ethnic groups. To investigate prostate cancer susceptibility in the under-investigated Chinese population, we performed single-nucleotide polymorphism (SNP) array analysis on a cohort of Chinese cases and controls and then meta-analysis with data from the existing Chinese prostate cancer genome-wide association study (GWAS). Genotyping 211,155 SNPs in 495 cases and 640 controls of Chinese ancestry identified several new suggestive Chinese prostate cancer predisposition loci. However, none of them reached genome-wide significance level either by meta-analysis or replication study. The meta-analysis with the Chinese GWAS data revealed that four 8q24 loci are the main contributors to Chinese prostate cancer risk and the risk alleles from three of them exist at much higher frequencies in Chinese than European populations. We also found that several predisposition loci reported in Western populations have different effect on Chinese men. Therefore, this first extensive single-nucleotide polymorphism study of Chinese prostate cancer in comparison with European population indicates that four loci on 8q24 contribute to a great risk of prostate cancer in a considerable large proportion of Chinese men. Based on those four loci, the top 10% of the population have six- or two-fold prostate cancer risk compared with men of the bottom 10% or median risk respectively, which may facilitate the design of prostate cancer genetic risk screening and prevention in Chinese men. These findings also provide additional insights into the etiology and pathogenesis of prostate cancer.
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http://dx.doi.org/10.18632/oncotarget.7250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008293PMC
April 2016

The effects of height and BMI on prostate cancer incidence and mortality: a Mendelian randomization study in 20,848 cases and 20,214 controls from the PRACTICAL consortium.

Cancer Causes Control 2015 Nov 19;26(11):1603-16. Epub 2015 Sep 19.

Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.

Background: Epidemiological studies suggest a potential role for obesity and determinants of adult stature in prostate cancer risk and mortality, but the relationships described in the literature are complex. To address uncertainty over the causal nature of previous observational findings, we investigated associations of height- and adiposity-related genetic variants with prostate cancer risk and mortality.

Methods: We conducted a case-control study based on 20,848 prostate cancers and 20,214 controls of European ancestry from 22 studies in the PRACTICAL consortium. We constructed genetic risk scores that summed each man's number of height and BMI increasing alleles across multiple single nucleotide polymorphisms robustly associated with each phenotype from published genome-wide association studies.

Results: The genetic risk scores explained 6.31 and 1.46% of the variability in height and BMI, respectively. There was only weak evidence that genetic variants previously associated with increased BMI were associated with a lower prostate cancer risk (odds ratio per standard deviation increase in BMI genetic score 0.98; 95% CI 0.96, 1.00; p = 0.07). Genetic variants associated with increased height were not associated with prostate cancer incidence (OR 0.99; 95% CI 0.97, 1.01; p = 0.23), but were associated with an increase (OR 1.13; 95 % CI 1.08, 1.20) in prostate cancer mortality among low-grade disease (p heterogeneity, low vs. high grade <0.001). Genetic variants associated with increased BMI were associated with an increase (OR 1.08; 95 % CI 1.03, 1.14) in all-cause mortality among men with low-grade disease (p heterogeneity = 0.03).

Conclusions: We found little evidence of a substantial effect of genetically elevated height or BMI on prostate cancer risk, suggesting that previously reported observational associations may reflect common environmental determinants of height or BMI and prostate cancer risk. Genetically elevated height and BMI were associated with increased mortality (prostate cancer-specific and all-cause, respectively) in men with low-grade disease, a potentially informative but novel finding that requires replication.
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http://dx.doi.org/10.1007/s10552-015-0654-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4596899PMC
November 2015

Genome-wide association study of prostate cancer-specific survival.

Cancer Epidemiol Biomarkers Prev 2015 Nov 25;24(11):1796-800. Epub 2015 Aug 25.

Mayo Clinic, Rochester, Minnesota.

Background: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical.

Methods: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR).

Results: We observed no significant association between genetic variants and prostate cancer survival.

Conclusions: Common genetic variants with large impact on prostate cancer survival were not observed in this study.

Impact: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes.
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http://dx.doi.org/10.1158/1055-9965.EPI-15-0543DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5674990PMC
November 2015

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

Prostate 2015 Sep 14;75(13):1467-74. Epub 2015 Jul 14.

Mayo Clinic, Rochester, Minnesota.

Background: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction.

Methods: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls.

Results: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk.

Conclusions: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.
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http://dx.doi.org/10.1002/pros.23037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745998PMC
September 2015

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

Cancer Epidemiol Biomarkers Prev 2015 Jul 2;24(7):1121-9. Epub 2015 Apr 2.

University of Tasmania, Menzies Research Institute Tasmania, Hobart, Tasmania, Australia.

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

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

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

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

Impact: We demonstrate that the risk profiling based on SNPs can identify men at substantially increased or reduced risk that could have useful implications for targeted prevention and screening programs.
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http://dx.doi.org/10.1158/1055-9965.EPI-14-0317DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491026PMC
July 2015
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