A considerable fraction of prostate cancers harbor a gene fusion between the androgen-regulated TMPRSS2 and ERG, one of the most frequently over-expressed proto-oncogenes in prostate cancer. Here, we investigated if inherited genetic variation upstream of ERG alters prostate cancer risk and survival.
We genotyped 21 haplotype tagging SNPs (htSNPs) covering 123 kb of 5′UTR DNA including exon 3 of ERG in 2,760 incident prostate cancer cases and 1,647 controls from a population-based Swedish case–control study (CAPS). Individual SNPs and haplotypes were tested for association with prostate cancer risk and survival.
One haplotype—′CTCGTATG′ located 100 kb upstream of ERG—was associated with lethal prostate cancer (HR, 1.36; 95% CI, 1.2–1.9, p = 0.006). Carriers of the variant ‘T’ allele of rs2836626 were diagnosed with higher TNM-stage (p = 0.009) and had an increased risk of prostate cancer-specific death (HR = 1.3; 95% CI, 1.1–1.7, p = 0.009). However, this association did not remain statistically significant after adjusting for multiple testing. We found overall no association between ERG variation and prostate cancer risk.
Genetic variation upstream of ERG may alter prostate cancer stage and ultimately prostate cancer-specific death but it is unlikely that it plays a role in prostate cancer development.
Prostate cancer; ERG; Haplotype; Polymorphism; Survival
In spite of intensive efforts, understanding of the genetic aspects of familial prostate cancer remains largely incomplete. In a previous microsatellite-based linkage scan of 1233 prostate cancer (PC) families, we identified suggestive evidence for linkage (i.e. LOD≥1.86) at 5q12, 15q11, 17q21, 22q12, and two loci on 8p, with additional regions implicated in subsets of families defined by age at diagnosis, disease aggressiveness, or number of affected members.
In an attempt to replicate these findings and increase linkage resolution, we used the Illumina 6000 SNP linkage panel to perform a genome-wide linkage scan of an independent set of 762 multiplex PC families, collected by 11 ICPCG groups.
Of the regions identified previously, modest evidence of replication was observed only on the short arm of chromosome 8, where HLOD scores of 1.63 and 3.60 were observed in the complete set of families and families with young average age at diagnosis, respectively. The most significant linkage signals found in the complete set of families were observed across a broad, 37 cM interval on 4q13-25, with LOD scores ranging from 2.02 to 2.62, increasing to 4.50 in families with older average age at diagnosis. In families with multiple cases presenting with more aggressive disease, LOD scores over 3.0 were observed at 8q24 in the vicinity of previously identified common PC risk variants, as well as MYC, an important gene in PC biology.
These results will be useful in prioritizing future susceptibility gene discovery efforts in this common cancer.
High serum levels of macrophage inhibitory cytokine 1 (MIC-1) are strongly associated with metastatic prostate cancer, suggesting MIC-1 is a biomarker for prostate cancer prognosis.
We conducted a prospective cohort study of 1,442 Swedish men with a pathologically verified diagnosis of prostate cancer between 2001 and 2003. Blood was drawn either pretreatment (n = 431) or posttreatment (n = 1,011) and cases were followed for a mean time of 4.9 years (range, 0.1–6.8 years).
MIC-1 serum levels independently predicted poor cancer-specific survival with an almost 3-fold higher cancer death rate in patients with serum levels in the highest quartile compared with men with serum levels in the lowest quartile (adjusted hazard ratio, 2.98; 95% confidence interval, 1.82–4.68). Pretreatment MIC-1 levels revealed an even stronger association with disease outcome with an 8-fold higher death rate in the highest compared with the lowest category (adjusted hazard ratio, 7.98; 95% confidence interval, 1.73–36.86). Among patients considered to have localized disease, MIC-1 significantly increased the discriminative capacity between indolent and lethal prostate cancer compared with the established prognostic markers clinical stage, pathologic grade, and prostate-specific antigen level (P = 0.016). A sequence variant in the MIC-1 gene was associated with decreased MIC-1 serum levels (P = 0.002) and decreased prostate cancer mortality (P = 0.003), suggesting a causative role of MIC-1 in prostate cancer prognosis.
Serum MIC-1 concentration is a novel biomarker capable of predicting prostate cancer prognosis.
About 20% of all human cancers are caused by chronic infection or chronic inflammatory states. Recently, a new hypothesis has been proposed for prostate carcinogenesis. It proposes that exposure to environmental factors such as infectious agents and dietary carcinogens, and hormonal imbalances lead to injury of the prostate and to the development of chronic inflammation and regenerative ‘risk factor’ lesions, referred to as proliferative inflammatory atrophy (PIA). By developing new experimental animal models coupled with classical epidemiological studies, genetic epidemiological studies and molecular pathological approaches, we should be able to determine whether prostate cancer is driven by inflammation, and if so, to develop new strategies to prevent the disease.
Multiple prostate cancer (PCa) risk-related loci have been discovered by genome-wide association studies (GWAS) based on case–control designs. However, GWAS findings may be confounded by population stratification if cases and controls are inadvertently drawn from different genetic backgrounds. In addition, since these loci were identified in cases with predominantly sporadic disease, little is known about their relationships with hereditary prostate cancer (HPC). The association between seventeen reported PCa susceptibility loci was evaluated with a family-based association test using 1,979 hereditary PCa families of European descent collected by members of the International Consortium for Prostate Cancer Genetics, with a total of 5,730 affected men. The risk alleles for 8 of the 17 loci were significantly over-transmitted from parents to affected offspring, including SNPs residing in 8q24 (regions 1, 2 and 3), 10q11, 11q13, 17q12 (region 1), 17q24 and Xp11. In subgroup analyses, three loci, at 8q24 (regions 1 and 2) plus 17q12, were significantly over-transmitted in hereditary PCa families with five or more affected members, while loci at 3p12, 8q24 (region 2), 11q13, 17q12 (region 1), 17q24 and Xp11 were significantly over-transmitted in HPC families with an average age of diagnosis at 65 years or less. Our results indicate that at least a subset of PCa risk-related loci identified by case–control GWAS are also associated with disease risk in HPC families.
During the recent years, rapid development of sequencing technologies and a competitive market has enabled researchers to perform massive sequencing projects at a reasonable cost. As the price for the actual sequencing reactions drops, enabling more samples to be sequenced, the relative price for preparing libraries gets larger and the practical laboratory work becomes complex and tedious. We present a cost-effective strategy for simplified library preparation compatible with both whole genome- and targeted sequencing experiments. An optimized enzyme composition and reaction buffer reduces the number of required clean-up steps and allows for usage of bulk enzymes which makes the whole process cheap, efficient and simple. We also present a two-tagging strategy, which allows for multiplex sequencing of targeted regions. To prove our concept, we have prepared libraries for low-pass sequencing from 100 ng DNA, performed 2-, 4- and 8-plex exome capture and a 96-plex capture of a 500 kb region. In all samples we see a high concordance (>99.4%) of SNP calls when comparing to commercially available SNP-chip platforms.
Prostate cancer has a strong familial component but uncovering the molecular basis for inherited susceptibility for this disease has been challenging. Recently, a rare, recurrent mutation (G84E) in HOXB13 was reported to be associated with prostate cancer risk. Confirmation and characterization of this finding is necessary to potentially translate this information to the clinic. To examine this finding in a large international sample of prostate cancer families, we genotyped this mutation and 14 other SNPs in or flanking HOXB13 in 2,443 prostate cancer families recruited by the International Consortium for Prostate Cancer Genetics (ICPCG). At least one mutation carrier was found in 112 prostate cancer families (4.6 %), all of European descent. Within carrier families, the G84E mutation was more common in men with a diagnosis of prostate cancer (194 of 382, 51 %) than those without (42 of 137, 30 %), P = 9.9 × 10−8 [odds ratio 4.42 (95 % confidence interval 2.56–7.64)]. A family-based association test found G84E to be significantly over-transmitted from parents to affected offspring (P = 6.5 × 10−6). Analysis of markers flanking the G84E mutation indicates that it resides in the same haplotype in 95 % of carriers, consistent with a founder effect. Clinical characteristics of cancers in mutation carriers included features of high-risk disease. These findings demonstrate that the HOXB13 G84E mutation is present in ~5 % of prostate cancer families, predominantly of European descent, and confirm its association with prostate cancer risk. While future studies are needed to more fully define the clinical utility of this observation, this allele and others like it could form the basis for early, targeted screening of men at elevated risk for this common, clinically heterogeneous cancer.
Electronic supplementary material
The online version of this article (doi:10.1007/s00439-012-1229-4) contains supplementary material, which is available to authorized users.
Prostate cancer (PrCa) is the most common non-skin cancer diagnosed among males in developed countries and the second leading cause of cancer mortality, yet little is known regarding its etiology and factors that influence clinical outcome. Genome-wide association studies (GWAS) of PrCa have identified at least 30 distinct loci associated with small differences in risk. We conducted a GWAS in 2782 advanced PrCa cases (Gleason grade ≥ 8 or tumor stage C/D) and 4458 controls with 571 243 single nucleotide polymorphisms (SNPs). Based on in silico replication of 4679 SNPs (Stage 1, P < 0.02) in two published GWAS with 7358 PrCa cases and 6732 controls, we identified a new susceptibility locus associated with overall PrCa risk at 2q37.3 (rs2292884, P= 4.3 × 10−8). We also confirmed a locus suggested by an earlier GWAS at 12q13 (rs902774, P= 8.6 × 10−9). The estimated per-allele odds ratios for these loci (1.14 for rs2292884 and 1.17 for rs902774) did not differ between advanced and non-advanced PrCa (case-only test for heterogeneity P= 0.72 and P= 0.61, respectively). Further studies will be needed to assess whether these or other loci are differentially associated with PrCa subtypes.
Prostate cancer is the second leading cause of cancer-related deaths in men, accounting for over 30,000 deaths annually. The purpose of this study was to test whether variation in selected candidate genes in biological pathways of interest for prostate cancer progression could help distinguish patients at higher risk for fatal prostate cancer.
In this hypothesis-driven study, we genotyped 937 single nucleotide polymorphisms (SNPs) in 156 candidate genes in a population-based cohort of 1,309 prostate cancer patients. We identified 22 top-ranking SNPs (P ≤0.01, FDR ≤0.70) associated with prostate cancer-specific mortality (PCSM). A subsequent validation study was completed in an independent population-based cohort of 2,875 prostate cancer patients.
Five SNPs were validated (P ≤0.05) as being significantly associated with PCSM, one each in the LEPR, CRY1, RNASEL, IL4, and ARVCF genes. Compared to patients with 0–2 of the at-risk genotypes those with 4–5 at-risk genotypes had a 50% (95% CI, 1.2–1.9) higher risk of PCSM and risk increased with the number of at-risk genotypes carried (Ptrend = 0.001), adjusting for clinicopathological factors known to influence prognosis.
Five genetic markers were validated to be associated with lethal prostate cancer.
This is the first population-based study to demonstrate that germline genetic variants provide prognostic information for prostate cancer-specific survival. The clinical utility of this five-SNP panel to stratify patients at higher risk for adverse outcomes should be evaluated.
Prostate cancer-specific mortality; survival; genetic variants; single nucleotide polymorphisms; hazard ratio
Genome-wide association studies have identified prostate cancer susceptibility alleles on chromosome 11q13. As part of the Cancer Genetic Markers of Susceptibility (CGEMS) Initiative, the region flanking the most significant marker, rs10896449, was fine mapped in 10 272 cases and 9123 controls of European origin (10 studies) using 120 common single nucleotide polymorphisms (SNPs) selected by a two-staged tagging strategy using HapMap SNPs. Single-locus analysis identified 18 SNPs below genome-wide significance (P< 10−8) with rs10896449 the most significant (P= 7.94 × 10−19). Multi-locus models that included significant SNPs sequentially identified a second association at rs12793759 [odds ratio (OR) = 1.14, P= 4.76 × 10−5, adjusted P= 0.004] that is independent of rs10896449 and remained significant after adjustment for multiple testing within the region. rs10896438, a proxy of previously reported rs12418451 (r2= 0.96), independent of both rs10896449 and rs12793759 was detected (OR = 1.07, P= 5.92 × 10−3, adjusted P= 0.054). Our observation of a recombination hotspot that separates rs10896438 from rs10896449 and rs12793759, and low linkage disequilibrium (rs10896449–rs12793759, r2= 0.17; rs10896449–rs10896438, r2= 0.10; rs12793759–rs10896438, r2= 0.12) corroborate our finding of three independent signals. By analysis of tagged SNPs across ∼123 kb using next generation sequencing of 63 controls of European origin, 1000 Genome and HapMap data, we observed multiple surrogates for the three independent signals marked by rs10896449 (n= 31), rs10896438 (n= 24) and rs12793759 (n= 8). Our results indicate that a complex architecture underlying the common variants contributing to prostate cancer risk at 11q13. We estimate that at least 63 common variants should be considered in future studies designed to investigate the biological basis of the multiple association signals.
The genetic determinants for aggressiveness of prostate cancer (PCa) are poorly understood. Copy-number variations (CNVs) are one of the major sources for genetic diversity and critically modulate cellular biology and human diseases. We hypothesized that CNVs may be associated with PCa aggressiveness. To test this hypothesis, we conducted a genome-wide common CNVs analysis in 448 aggressive and 500 nonaggressive PCa cases recruited from Johns Hopkins Hospital (JHH1) using Affymetrix 6.0 arrays. Suggestive associations were further confirmed using single-nucleotide polymorphisms (SNPs) that tagged the CNVs of interest in an additional 2895 aggressive and 3094 nonaggressive cases, including those from the remaining case subjects of the JHH study (JHH2), the NCI Cancer Genetic Markers of Susceptibility (CGEMS) Study, and the CAncer of the Prostate in Sweden (CAPS) Study. We found that CNP2454, a 32.3 kb deletion polymorphism at 20p13, was significantly associated with aggressiveness of PCa in JHH1 [odds ratio (OR) = 1.30, 95% confidence interval (CI): 1.01–1.68; P = 0.045]. The best-tagging SNP for CNP2454, rs2209313, was used to confirm this finding in both JHH1 (P = 0.045) and all confirmation study populations combined (P = 1.77 × 10−3). Pooled analysis using all 3353 aggressive and 3584 nonaggressive cases showed the T allele of rs2209313 was significantly associated with an increased risk of aggressive PCa (OR = 1.17, 95% CI: 1.07–1.27; P = 2.75 × 10−4). Our results indicate that genetic variations at 20p13 may be responsible for the progression of PCa.
Recent genome-wide association studies have been successful in identifying common sequence variants associated with prostate cancer risk; however, their importance in prostate cancer prognosis remains unknown. To assess confirmed prostate cancer susceptibility variants with prostate cancer prognosis, we genotyped 16 established susceptibility variants in a Swedish cohort of 2,875 prostate cancer cases, ascertained between 2001 and 2003, with complete follow-up regarding vital status through January 2008. Cox regression models, adjusted for age, clinical stage, pathologic grade, nodal or distant metastases, and diagnostic serum levels of prostate-specific antigen level, were used to assess association between risk variants and prostate cancer–specific survival. During follow-up, 626 men died, and of those, 440 had prostate cancer classified as their underlying cause of death. We found no association between any of the explored sequence variants and prostate cancer–specific mortality, either in exploring individual variants or in assessing the cumulative effect of all variants. We conclude that hitherto established prostate cancer susceptibility variants are not associated with the lethal potential of prostate cancer.
Prostate specific antigen (PSA) is widely used for prostate cancer screening but its levels are influenced by many non cancer-related factors. The goal of the study is to estimate the effect of genetic variants on PSA levels.
We evaluated the association of SNPs that were reported to be associated with prostate cancer risk in recent genome-wide association studies with plasma PSA levels in a Swedish study population, including 1,722 control subjects without a diagnosis of prostate cancer.
Of the 16 SNPs analyzed in control subjects, significant associations with PSA levels (P≤0.05) were found for six SNPs. These six SNPs had a cumulative effect on PSA levels; the mean PSA levels in men were almost twofold increased across increasing quintile of number of PSA associated alleles, P-trend=3.4×10−14. In this Swedish study population risk allele frequencies were similar among T1c case patients (cancer detected by elevated PSA levels alone) as compared to T2 and above prostate cancer case patients.
Results from this study may have two important clinical implications. The cumulative effect of six SNPs on PSA levels suggests genetic-specific PSA cutoff values may be used to improve the discriminatory performance of this test for prostate cancer; and the dual associations of these SNPs with PSA levels and prostate cancer risk raise a concern that some of reported prostate cancer risk-associated SNPs may be confounded by the prevalent use of PSA screening.
genetic; bias; KLK3
Prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) are continuously being discovered. Their ability to identify men at high risk and the impact of increasing numbers of SNPs on predictive performance are not well understood.
Absolute risk for PCa was estimated in a population-based case-control study in Sweden (2,899 cases and 1,722 controls) using family history and three sets of sequentially discovered PCa risk-associated SNPs. Their performance in predicting PCa was assessed by positive predictive values (PPV) and sensitivity.
SNPs and family history were able to differentiate individual risk for PCa and identify men at higher risk; ~18% and ~8% of men in the study had 20-year (55–74 years) absolute risks that were two-fold (0.24) or three-fold (0.36) greater than the population median risk (0.12), respectively. When predictive performances were compared at absolute risk cutoffs of 0.12, 0.24 or 0.36, PPV increased considerably (~20%, ~30% and ~37%, respectively) while sensitivity decreased considerably (~55%, ~20% and ~10%, respectively). In contrast, when increasing numbers of SNPs (5, 11 and 28 SNPs) were used in risk prediction, PPV approached a constant value while sensitivity increased steadily.
SNPs discovered to date are suitable for risk prediction while additional SNPs discovered in the future may identify more subjects at higher risk. Men identified as high-risk by SNP-based testing may be targeted for PCa screening or chemoprevention. The clinical impact on improving the effectiveness of these interventions can be and should be assessed.
Absolute risk; SNPs; association; screening; chemoprevention
A cost-efficient way to increase power in a genetic association study is to pool controls from different sources. The genotyping effort can then be directed to large case series. The Nordic Control database, NordicDB, has been set up as a unique resource in the Nordic area and the data are available for authorized users through the web portal (http://www.nordicdb.org). The current version of NordicDB pools together high-density genome-wide SNP information from ∼5000 controls originating from Finnish, Swedish and Danish studies and shows country-specific allele frequencies for SNP markers. The genetic homogeneity of the samples was investigated using multidimensional scaling (MDS) analysis and pairwise allele frequency differences between the studies. The plot of the first two MDS components showed excellent resemblance to the geographical placement of the samples, with a clear NW–SE gradient. We advise researchers to assess the impact of population structure when incorporating NordicDB controls in association studies. This harmonized Nordic database presents a unique genome-wide resource for future genetic association studies in the Nordic countries.
common controls; genome-wide data; Nordic Control Database; population stratification
Epidemiological and experimental evidence suggests that inflammation plays a role in both prostate cancer (PCa) and benign prostate hyperplasia (BPH). This study evaluates the risk of PC after transurethral resection (TURP) for BPH and estimates the PCa risk related to presence of inflammation in the resected material. The Pathology Department at the University Hospital of Umeå (Umeå, Sweden) identified BPH cases (n = 7,901) that underwent TURP between 1982 and 1997. Using these pathological specimens, we compared the incidence of PCa in the cohort to the population and calculated the standardized incidence and mortality ratios (SIR and SMR). Inflammation, the androgen receptor (AR), and p53 were evaluated in a nested case-control study of 201 cases and controls. Inflammation was graded severe or mild-moderate. In the follow-up period after TURP, cases developed prostate cancer and the controls did not. After TURP, SIR for prostate cancer increased [1.26, CI 95% (1.17–1.35) ], whereas SMR decreased [0.59, CI 95% (0.47–0.73) ]. Presence of inflammation at the time of TURP did not differ between cases and controls nor were there differences in p53 or AR staining. The data suggest a small increased risk of PCa after TURP and decreased PCa mortality. Inflammation at the time of TURP is not associated with PCa risk in this material. The increased PCa risk may be attributed to increased surveillance and PSA screening.
prostate cancer; inflammation; benign prostate hyperplasia; androgen receptor; p53
A fine mapping study in the HNF1B gene at 17q12 among two study populations revealed a second prostate cancer locus, ~26 kb centromeric to the first known locus (rs4430796); these are separated by a recombination hotspot. A SNP in the second locus (rs11649743) was confirmed in five additional populations, and P=1.7×10−9 for an allelic test in the seven combined studies. The association at each SNP remains significant after adjusting for the other SNP.
While PSA is the best biomarker for predicting prostate cancer, its predictive performance needs to be improved. Results from the Prostate Cancer Prevention Trial (PCPT) revealed the overall performance measured by the areas under curve (AUC) of the receiver operating characteristic (ROC) at 0.68. The goal of the present study is to assess the ability of genetic variants as a PSA independent method to predict prostate cancer risk.
We systematically evaluated all prostate cancer risk variants that were identified from genome-wide association studies during the past year in a large population-based prostate cancer case-control study population in Sweden, including 2,893 prostate cancer patients and 1,781 men without prostate cancer.
Twelve SNPs were independently associated with prostate cancer risk in this Swedish study population. Using a cutoff of any 11 risk alleles or family history, the sensitivity and specificity for predicting prostate cancer were 0.25 and 0.86, respectively. The overall predictive performance of prostate cancer using genetic variants, family history, and age, measured by AUC was 0.65 (95% CI: 0.63–0.66), significantly improved over that of family history and age (0.61%, 95% CI: 0.59–0.62), P = 2.3 × 10−10.
The predictive performance for prostate cancer using genetic variants and family history is similar to that of PSA. The utility of genetic testing, alone and in combination with PSA levels, should be evaluated in large studies such as the European Randomized Study for Prostate Cancer trial and PCPT.
prostate cancer; prediction; PSA; association
Multiple SNPs at 17q12 and 17q24.3 were recently identified to be associated with prostate cancer risk using a genome-wide association study. Although these associations reached genome-wide significance level in a combined analysis of several study populations of European descent in the original report, confirmation in independent populations, including African Americans (AA), is critical to increase confidence that they represent true disease associations and whether the results can be generalized. Therefore, we evaluated these 7 SNPs in two populations recruited from Johns Hopkins Hospital, including European Americans (EA) (1,563 cases and 576 controls) and AA (364 cases and 353 controls). Each of the previously reported risk alleles of these 7 SNPs were more common in cases than in controls among EA and AA. The differences were highly significant in EA (P = 10−4) and marginally significant in AA (P = 0.04) for 17q12SNPs. In contrast, the differences were not statistically significant in EA or AA for SNPs at 17q24.3, but were marginally significant for two SNPs (P = 0.04 - 0.06) when subjects from EA and AA were combined. Similar results were obtained for genotype and haplotype frequencies. These risk variants were not associated with aggressiveness of prostate cancer or other clinical variables such as TNM stage, pre-operative PSA, or age at diagnosis. Our results provide the first confirmation of these novel prostate loci and the first demonstration that these two loci may also play roles in prostate cancer risk among AA.
prostate cancer; association; risk; 17q12; 17q24.3
Patterns of genetic diversity have previously been shown to mirror geography on a global scale and within continents and individual countries. Using genome-wide SNP data on 5174 Swedes with extensive geographical coverage, we analyzed the genetic structure of the Swedish population. We observed strong differences between the far northern counties and the remaining counties. The population of Dalarna county, in north middle Sweden, which borders southern Norway, also appears to differ markedly from other counties, possibly due to this county having more individuals with remote Finnish or Norwegian ancestry than other counties. An analysis of genetic differentiation (based on pairwise Fst) indicated that the population of Sweden's southernmost counties are genetically closer to the HapMap CEU samples of Northern European ancestry than to the populations of Sweden's northernmost counties. In a comparison of extended homozygous segments, we detected a clear divide between southern and northern Sweden with small differences between the southern counties and considerably more segments in northern Sweden. Both the increased degree of homozygosity in the north and the large genetic differences between the south and the north may have arisen due to a small population in the north and the vast geographical distances between towns and villages in the north, in contrast to the more densely settled southern parts of Sweden. Our findings have implications for future genome-wide association studies (GWAS) with respect to the matching of cases and controls and the need for within-county matching. We have shown that genetic differences within a single country may be substantial, even when viewed on a European scale. Thus, population stratification needs to be accounted for, even within a country like Sweden, which is often perceived to be relatively homogenous and a favourable resource for genetic mapping, otherwise inferences based on genetic data may lead to false conclusions.
We conducted dense linkage disequilibrium (LD) mapping of a series of 25 genes putatively involved in lipid metabolism in 1567 dementia cases [including 1270 with Alzheimer disease (AD)] and 2203 Swedish controls. Across a total of 448 tested genetic markers, the strongest evidence of association was as anticipated for APOE (rs429358 at P ∼ 10−72) followed by a previously reported association of ABCA1 (rs2230805 at P ∼ 10−8). In the present study, we report two additional markers near the SREBF1 locus on chromosome 17p that were also significant after multiple testing correction (best P = 3.1 × 10−6 for marker rs3183702). There was no convincing evidence of association for remaining genes, including candidates highlighted from recent genome-wide association studies of plasma lipids (CELSR2/PSRC1/SORT1, MLXIPL, PCSK9, GALNT2 and GCKR). The associated markers near SREBF1 reside in a large LD block, extending more than 400 kb across seven candidate genes. Secondary analyses of gene expression levels of candidates spanning the LD region together with an investigation of gene network context highlighted two possible susceptibility genes including ATPAF2 and TOM1L2. Several markers in strong LD (r2 > 0.7) with rs3183702 were found to be significantly associated with AD risk in recent genome-wide association studies with similar effect sizes, providing independent support of the current findings.
Polymorphisms associated with prostate cancer include those in three genes encoding major secretory products of the prostate: KLK2 (encoding kallikrein-related peptidase 2; hK2), KLK3 (encoding prostate-specific antigen; PSA), and MSMB (encoding beta-microseminoprotein). PSA and hK2, members of the kallikrein family, are elevated in serum of men with prostate cancer. In a comprehensive analysis which included sequencing of all coding, flanking, and 2kb of putative promoter regions of all 15 kallikrein (KLK) genes spanning ≈280 Kb on chromosome 19q, we identified novel SNPs and genotyped 104 SNPs in 1419 cancer cases and 736 controls in CAPS1, with independent replication in 1267 cases and 901 controls in CAPS2. This verified prior associations of SNPs in KLK2 and in MSMB (but not in KLK3) with prostate cancer. Twelve SNPs in KLK2 and KLK3 were associated with levels of PSA forms or hK2 in plasma of control subjects. Based on our comprehensive approach, this is likely to represent all common KLK variants associated with these phenotypes. A T allele at rs198977 in KLK2 associated with increased cancer risk and a striking decrease of hK2 levels in blood. We also found a strong interaction between rs198977 genotype and hK2 levels in blood in predicting cancer risk. Based on this strong association, we developed a model for predicting prostate cancer risk from standard biomarkers, rs198977 genotype, and rs198977 x hK2 interaction; this model had greater accuracy than did biomarkers alone (AUC 0.874 vs 0.866), providing proof in principle to clinical application for our findings.
prostate cancer; prostate-specific antigen; human kallikrein-related peptidase 2; genetic variation; case-control study
Genome-wide association studies (GWAS) have identified multiple genetic variants associated with susceptibility to prostate cancer (PrCa). In the two-stage Cancer Genetic Markers of Susceptibility (CGEMS) prostate cancer scan, a single-nucleotide polymorphism (SNP) rs10486567 located within intron 2 of JAZF1 gene on chromosome 7p15.2 showed a promising association with PrCa overall (p = 2.14×10−6) with a suggestion of stronger association with aggressive disease (p = 1.2×10−7).
In the third stage of GWAS, we genotyped 106 JAZF1 SNPs in 10,286 PrCa cases and 9,135 controls of European ancestry.
The strongest association was observed with the initial marker, rs10486567, which now achieves genome-wide significance (p = 7.79×10−11, ORHET 1.19; 95%CI = 1.12 – 1.27 and ORHOM 1.37; 95%CI = 1.20 – 1.56). We did not confirm a previous suggestion of a stronger association of rs10486567 with aggressive disease (p = 1.60×10−4 for aggressive cancer, n=4,597; p = 3.25×10−8 for non-aggressive cancer, n=4,514). Based on a multi-locus model with adjustment for rs10486567, no additional independent signals were observed at chromosome 7p15.2. There was no association between PrCa risk and SNPs in JAZF1 previously associated with height (rs849140, p = 0.587), body stature (rs849141, tagged by rs849136, p = 0.171), risk of type 2 diabetes and systemic lupus erythematosus (rs864745, tagged by rs849142, p = 0.657).
rs10486567 remains the most significant marker for PrCa risk within JAZF1 in individuals of European ancestry.
Future studies should identify all variants in high LD with rs10486567 and evaluate their functional significance for PrCa.
Disease risk-associated single nucleotide polymorphisms (SNPs) identified from genome-wide association studies have the potential to be used for disease risk prediction. An important feature of these risk-associated SNPs is their weak individual effect but stronger cumulative effect on disease risk. Several approaches are commonly used to model the combined effect in risk prediction but their performance is unclear. We compared two methods to model the combined effect of 14 prostate cancer (PCa) risk-associated SNPs and family history for the estimation of absolute risk for PCa in a population-based case-control study in Sweden (2,899 cases and 1,722 controls). Method 1 weighs each risk allele equally using a simple method of counting the number of risk alleles while Method 2 weighs each risk SNP differently based on their respective Odds Ratios. We found considerable differences between the two methods. Absolute risk estimates from Method 1 were generally higher than that of Method 2, especially among men at higher risk. The difference in the overall discriminative performance, measured by area under the curve (AUC) of the receiver operating characteristic was small between Method 1 (0.614) and Method 2 (0.618), P = 0.20. However, the performance of these two methods in identifying high-risk individuals (two-fold or three-fold higher than average risk), measured by positive predictive values (PPV), was higher for Method 2 than Method 1. In conclusion, these results suggest that Method 2 is superior to Method 1 in estimating absolute risk if the purpose of risk prediction is to identify high-risk individuals.
Absolute risk; SNPs; association; prostate cancer; genomic medicine