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. Author manuscript; available in PMC: 2016 Apr 1.
Published in final edited form as: Hum Genet. 2015 Feb 26;134(4):439–450. doi: 10.1007/s00439-015-1534-9

Associations of Prostate Cancer Risk Variants with Disease Aggressiveness: Results of the NCI-SPORE Genetics Working Group Analysis of 18,343 Cases

Brian T Helfand 1, Kimberly A Roehl 2, Phillip R Cooper 3, Barry McGuire 4, Liesel M Fitzgerald 5, Geraldine Cancel-Tassin 6, Jean-Nicolas Cornu 7, Scott Bauer 8, Erin L Van Blarigan 9, Xin Chen 10, David Duggan 11, Elaine A Ostrander 12, Mary Gwo-Shu 13, Zuo-Feng Zhang 14, Shen-Chih Chang 15, Somee Jeong 16, Sonja Berndt 17, Shannon K McDonnell 18, Rick Kittles 19, Benjamin A Rybicki 20, Matthew Freedman 21, Phil Kantoff 22, Mark Pomerantz 23, Joan P Breyer 24, Jeffrey R Smith 25, Timothy R Rebbeck 26, Dan Mercola 27, William B Isaacs 28, Fredrick Wiklund 29, Olivier Cussenot 30, Stephen N Thibodeau 31, Daniel J Schaid 32, Lisa Cannon-Albright 33, Kathy A Cooney 34, Stephen J Chanock 35, Janet L Stanford 36, June M Chan 37, John Witte 38, Jianfeng Xu 39, Jeanette T Bensen 40, Jack A Taylor 41, William J Catalona 42
PMCID: PMC4586077  NIHMSID: NIHMS720413  PMID: 25715684

Introduction

Current serum prostate-specific antigen (PSA)-based screening practices cannot reliably distinguish between men with indolent disease versus those with life-threatening disease. In this regard, PSA-based screening has been associated with an “over-diagnosis” of prostate cancer (PC), with some men possibly being diagnosed and treated for a seemingly indolent tumor that may never have been detected nor caused symptoms (Andriole et al. 2009; Schroder et al. 2009). Accordingly, there is an urgent need for new biomarkers that can better differentiate tumor behavior and clinical outcome.

Genome-wide association studies (GWAS) of PC patients and controls have identified approximately 100 different single nucleotide polymorphisms (SNPs) associated with the overall risk of being diagnosed with PC (Al Olama et al. 2014; Amundadottir et al. 2006; Choudhury et al. 2012; Eeles et al. 2008b; Eeles et al. 2013; Gudmundsson et al. 2009; Gudmundsson et al. 2007a; Gudmundsson et al. 2008; Gudmundsson et al. 2007b; Haiman et al. 2007; Thomas et al. 2008; Yeager et al. 2007). Most of these initial discovery studies have compared men with non-aggressive or moderately-aggressive tumors to controls without known PC. Although some of these studies have been performed or validated in different racial populations (Cook et al. 2014; Freedman et al. 2006; Haiman et al. 2011; Han et al. 2014; Wang et al. 2012; Zheng et al. 2010), the great majority have been limited to men of European ancestry (Ishak and Giri 2011). Thus, while the results advance the knowledge of genetic factors associated with PC risk, in general, they have not been focused on clinically-significant disease nor directed towards ethnic groups at greatest risk of dying of PC, such as men of African ancestry.

Some of the initial discovery GWAS attempted to evaluate the associations between specific PC-risk SNPs and various aspects of disease aggressiveness (e.g., Gleason score). However, these studies were generally performed as post hoc analyses and involved heterogeneous definitions of disease aggressiveness. In addition, most of these analyses compared the frequency of genetic variants in men with high-grade disease to controls and men with low-grade disease to controls. Only a relatively small proportion of GWAS in PC have been designed to evaluate the associations between genetic variants and clinically significant outcomes (e.g., disease aggressiveness) as their primary outcome measure. Xu et al. (2010) reported that the minor allele of single nucleotide polymorphism (SNP), rs4054823 on chromosome 17p12 was present at a significantly greater frequency in men with Gleason score ≥ 8 tumors and higher-stage disease (≥pT3b). Lin et al (2011) used a candidate gene approach to identify a panel of 5 SNPs associated with PC-specific mortality. However, other studies have not found robust associations with PC-specific mortality (Penney et al. 2010). Other research groups have reported associations between individual SNPs on chromosomes 3p12, 8q24, 10q11, and 15q13 and the pathology features characteristic of aggressive PC (Ahn et al. 2011; Bensen et al. 2013; FitzGerald et al. 2011). A meta-analysis aimed at determining whether genetic variants were associated with adverse pathology features reported that SNP rs11672691 showed associations with more aggressive tumors (Amin Al Olama et al. 2013). Additionally, results from other GWAS and linkage analyses have reported risk loci associated with aggressive disease amongst familial cases (Casey et al. 2006; Chang et al. 2005; Gudmundsson et al. 2008; Kirkland et al. 2010; Liu et al. 2011; Nam et al. 2011; Nurminen et al. 2011; Schaid et al. 2006; Schaid et al. 2007; Slager et al. 2006; Stanford et al. 2006; Witte et al. 2000). In addition, Shui et al. reported that 8 SNPs were associated with lethal PC (Shui et al. 2014). The results of these studies are limited by the relatively small cohorts of PC patients of European ancestry studied, the heterogeneous definitions of aggressive disease used, reliance upon clinical (versus surgical) grading and staging of tumors, and lack of validation in diverse racial populations. Validation is essential to provide generalizability of results, especially since most of the genetic variants have only modest effects on disease risk and aggressiveness (OR 1.1–1.3).

The National Cancer Institute Prostate Cancer Genetics Working Group (GWG) was assembled to conduct a case-case association study of aggressive and non-aggressive PC (Catalona et al. 2011) using a panel of the then 36 validated SNPs associated with PC risk. In order to eliminate some of the variability in definitions of disease aggressiveness, only PC cases with complete information on disease aggressiveness and clinical follow-up were included in the analyses. Herein, we report our findings from a retrospective evaluation of more than 18,000 men with PC, including >8,000 men with aggressive disease, 5,000 men with non-aggressive disease, and >1800 African American men.

Methods and Materials

Study Samples

Nineteen PC research groups participated in this study and contributed clinical and genotype data (Supplemental Table 1). All institutions provided de-identified genotype and clinical information regarding pathologic tumor staging and grading to a central Data Coordinating Center (Northwestern University). The genotypes of 36 SNPs previously validated to be associated with PC risk were collected on a total of 25,674 cases with PC, including 23,278 men of self-reported European ancestry, 2,129 of African ancestry, and 267 of unknown ancestry. This panel of SNPs was chosen for evaluation because at the time of the 2010 NCI-SPORE GWG conference, it included the most comprehensive list of validated PC-risk SNPs. While information was collected on all 36 SNPs, there were varying numbers of SNPs available for analysis from each site (Supplemental Table 1). Details on the methodologies for genotyping at each individual institution are presented in Supplemental Table 2.

For men treated with surgery for PC, the pathology tumor grade and stage were used in the analysis. For men who underwent non-surgical treatments, the clinical stage and biopsy Gleason score were used. In addition, biochemical (PSA) evidence of tumor recurrence status and PC-specific mortality was documented for both cohorts of men. For the purposes of the present study, disease aggressiveness was defined in two ways: First, “aggressive disease” was indicated by PC-specific death, or distant metastasis, or lymph node involvement, or seminal vesicle invasion, or extracapsular tumor extension or Gleason score ≥8. “Non-aggressive” disease was defined strictly as Gleason ≤ 6 and clinically localized and organ-confined disease. For men who underwent radiation therapy, their clinical stage and grade was used to define organ-confined disease. “Intermediate disease” was defined as non-lethal with either Gleason 7 disease or biochemical recurrence. All cohorts required documentation of at least 3 years follow-up from the time of diagnosis and/or treatment, no high-risk pathology features, metastases or PC-specific mortality. A second overlapping definition of “aggressive disease” was based solely on the Gleason grade of the tumor. For this analysis, high-, intermediate- and low-grade tumors were defined by Gleason scores ≥8, 7, and ≤6, respectively. Because there was incomplete data on the primary and secondary Gleason patterns, analyses that separated Gleason scores 3+4 and 4+3 could not be performed.

Exclusion Criteria

Men were excluded from the analysis if they did not have documentation of pathology Gleason score for men undergoing prostatectomy (n = 48, 0.2%), clinical Gleason score for men undergoing radiation therapy (147, 0.6%), data on PSA (n = 1481, 5.8%), staging (n = 3408, 13.3%), and/or clinical follow-up information (n = 4878, 19.0%). Men were also excluded if they did not have documentation of either European or African-American ancestry (n = 267, 1.0%).

Statistical Analyses

Sensitivity analyses were performed comparing the genotypes of men fulfilling and those not meeting inclusion criteria. With the exception of one SNP, there were no differences between the included and excluded groups. However, the allele counts of SNP rs16902094 on chromosome 8q24 were significantly different between excluded and included groups of men of European ancestry. This difference was eliminated after adjusting for length of clinical follow-up, i.e., when outcomes were compared among patients who had similar follow-up intervals.

Analyses were performed for the entire cohort and separately by self-reported race (European and African-American). The reference allele used for all analyses was defined by the allele previously associated with PC risk. We used logistic regression analysis to test for the association between the allele counts for each individual SNP and the presence of PC within the included cohorts. Alleles were coded in a log-additive manner, whereby the counts reflected the number of previously associated alleles. For some analyses, we stratified cases into those with aggressive, intermediate, or non-aggressive disease, and we treated these case groups as ordinal outcomes. The institutional site was documented to adjust for possible differences in genotyping methodology. As such, institutional site was included in the logistic regression model as a covariate using Northwestern University and the referent site

Multinomial logistic regression analysis was used to examine the association between allele counts (again for the previously-reported PC risk allele) and tumor aggressiveness as well as the potential association between allele counts and Gleason score. The Bonferroni correction was used to adjust for multiple testing.

Results

A total of 18,343 men (16,515 of European ancestry and 1,828 of African ancestry) met inclusion criteria. The clinical and pathology features of these men are shown (Table 1). Of the Caucasian men, 49.8%, 21.6% and 28.6% had aggressive, intermediate and non-aggressive disease, respectively. Similarly, 39.4%, 34.2% and 26.4% men of African heritage were categorized as having aggressive, intermediate or non-aggressive disease. The numbers of men genotyped for each of the 36 SNPs are shown in Table 1 and Supplemental Table 3.

Table 1.

Demographic and Clinico-pathologic Information

Overall Non-Aggressive Intermediate Aggressive Aggressive
N % N % N % N %
N 18343 100 5213 28.4 4188 22.8 8942 48.8
Median Age at Diagnosis 61 34–93 60 34–84 60 35–79.7 63 36–93
Mean Age at Diagnosis 61.3 8.1 59.8 7.7 59.9 7.6 62.9 8.2
 <55 3636 19.8 1260 24.2 1025 24.5 1351 15.1
 55–64 8183 44.6 2563 49.2 2007 47.9 3613 40.4
 GW65–74 5054 27.6 1246 23.9 1037 24.8 2771 31
 >74 969 5.3 144 2.8 119 2.8 706 7.9
 Unknown 501 2.7 0 0 0 0 501 5.6
Race
 European ancestry 16515 90 4731 90.8 3563 85.1 8221 91.9
 African-American ancestry 1828 10 482 9.2 625 14.9 721 8.1
Median PSA Level 6.3 0–5300 5.2 0–20 5.3 0.1–20 9.4
PSA Level
 <2.5 1052 5.7 544 10.4 302 7.2 206 2.3
 2.5–4 1892 10.3 791 15.2 612 14.6 489 5.5
 4–10 9376 51.1 3161 60.6 2752 65.7 3463 38.7
 10–20 2533 13.8 597 11.5 499 11.9 1437 16.1
 >20 2403 13.1 0 0 0 0 2403 26.9
 Unknown 1087 5.9 120 2.3 23 0.6 944 10.6
Clinical Stage
 T1c 7727 42.1 2694 51.7 2786 66.5 2247 25.1
 T2–T3 3542 19.3 769 14.7 986 23.5 1787 20
 Unknown 7074 38.6 1750 33.6 416 9.9 4908 54.9
Pathologic Stage
 T1–T2 10266 56 4663 89.4 3569 85.2 2034 22.8
 T3–T4 5001 27.3 0 0 0 0 5001 55.9
 Unknown 3076 16.8 550 10.6 619 14.8 1907 21.3
Clinical Gleason Score
 ≤6 1041 37.5 497 100 212 39.3 340 19.6
 7 663 24 0 0 328 60.7 327 18.9
 8–10 1064 38.5 0 0 0 0 1064 61.5
Pathologic Gleason Score
 ≤l6 7729 50.9 4716 100 1568 43.1 1451 21.2
 7 5058 33.3 0 0 2068 56.9 2984 43.7
 8–10 2400 15.8 0 0 0 0 2400 35.1
Unknown Gleason 388 2.1 0 0 12 0.2 376 4.2
Death from Prostate Cancer 986 5.4 0 0 0 0 986 11
Median Followup Time (years) 4.7 0–29.9 4 0–20 4 0–22 5 0–29.9
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After adjusting for multiple testing, case-case logistic analyses comparing the genotypes of the entire cohort with aggressive, intermediate and non-aggressive disease showed that only the minor allele of rs2735839 (G) on chromosome 19q13 remained significantly (and inversely) associated with aggressive disease (p = 9.343 ×10−8; Table 2). Similarly, after correction for multiple testing, only the same SNP was significantly (and inversely) associated with the presence of aggressive disease in the European (p = 1.042 ×10−5) and African-American (p = 2.0 ×10−4; Table 2) cohorts

Table 2. Case-Case Study Comparing the Genotypes of Men with Aggressive to Non-Aggressive Disease.

The OR (95% CI) was calculated from multinomial logistic regression analyses using a saturated model comparing aggressive and non-aggressive disease. The P value is derived from a cumulative logit model comparing aggressive, intermediate and non-aggressive disease.

European Ancestry African American Ancestry
SNP Location Risk Allele OR (95% CI) P value OR (95% CI) P value
rs721048 2p15 A 1.09 (0.98–1.20) 0.105 0.80 (0.49–1.30) 0.353
rs1465618 2p21 A 1.09 (1.00–1.20) 0.053 0.71 (0.41–1.22) 0.210
rs12621278 2q31.1 G 0.95 (0.79–1.15) 0.656 0.51 (0.09–2.84) 0.436
rs2660753 3p12.1 T 0.92 (0.84–1.00) 0.071 0.83 (0.68–1.01) 0.047
rs10934853 3q21 A 1.03 (0.94–1.13) 0.507 0.79 (0.26–2.35) 0.617
rs12500426 4q22.3 A 1.02 (0.94–1.10) 0.649 1.15 (0.67–1.96) 0.816
rs17021918 4q22.3 T 1.03 (0.94–1.14) 0.555 1.00 (0.57–1.77) 0.997
rs7679673 4q24 A 1.04 (0.96–1.13) 0.378 1.28 (0.84–1.96) 0.238
rs2736098 5p15 A 1.10 (0.88–1.37) 0.249 1.82 (0.62–5.33) 0.285
rs401681 5p16 (TERT) C 0.89 (0.77–1.02) 0.120 1.12 (0.42–2.99) 0.724
rs9364554 6q25.3 (SLC22A3) T 1.03 (0.95–1.12) 0.436 0.86 (0.58–1.27) 0.429
rs10486567 7p15.2 (JAZF1) G 0.94 (0.87–1.01) 0.060 0.92 (0.74–1.15) 0.436
rs6465657 7q21.3 (LMTK2) C 0.95 (0.90–1.01) 0.155 0.91 (0.50–1.66) 0.733
rs1512268 8p21.2 A 1.03 (0.95–1.11) 0.569 1.07 (0.70–1.62) 0.785
rs16901979 8q24 A 1.15 (1.00–1.32) 0.051 1.12 (0.90–1.40) 0.355
rs16902094 8q24 G 1.02 (0.92–1.14) 0.643 1.22 (0.24–6.31) 0.836
rs445114 8q24 T 0.98 (0.89–1.08) 0.714 1.46 (0.53–4.02) 0.558
rs6983267 8q24 G 1.00 (0.94–1.06) 0.832 1.23 (0.84–1.78) 0.234
rs1447295 8q24 A 1.02 (0.94–1.11) 0.621 0.87 (0.71–1.08) 0.249
rs10086908 8q24 C 1.07 (0.97–1.18) 0.187 0.73 (0.20–2.63) 0.693
rs1571801 9q33.2 A 0.97 (0.90–1.03) 0.304 1.14 (0.86–1.51) 0.380
rs10993994 10q11 (MSMB) T 0.96 (0.91–1.02) 0.180 0.85 (0.69–1.03) 0.116
rs4962416 10q26.13 C 0.92 (0.84–1.00) 0.052 0.98 (0.76–1.27) 0.805
rs7127900 11p15.5 A 0.92 (0.84–1.01) 0.108 0.78 (0.51–1.20) 0.287
rs11228565 11q13 A 1.01 (0.91–1.13) 0.774 1.30 (0.16–10.90) 0.733
rs10896450 11q13 G 0.95 (0.82–1.09) 0.450 0.62 (0.24–1.58) 0.376
rs12418451 11q13.3 A 1.00 (0.90–1.11) 0.936 0.77 (0.08–7.75) 0.761
rs4054823 17p12 T 1.09 (1.00–1.19) 0.088 --
rs11649743 17q12 G 0.99 (0.91–1.07) 0.755 0.96 (0.62–1.49) 0.898
rs4430796 17q12 A 1.00 (0.95–1.06) 0.948 1.05 (0.86–1.29) 0.610
rs1859962 17q24 G 0.99 (0.94–1.05) 0.803 1.06 (0.86–1.31) 0.493
rs8102476 19q13 C 1.07 (0.98–1.16) 0.097 0.93 (0.33–2.62) 0.958
rs2735839 19q13.3 (KLK2/KLK3) G 0.77 (0.69–0.87) 1.042×10−5 0.72 (0.58–0.89) 2.0×10−4
rs9623117 22q13.1 C 1.00 (0.93–1.09) 0.994 1.00 (0.17–6.01) 0.648
rs5759167 22q13.2 T 0.96 (0.88–1.04) 0.294 1.35 (0.70–2.59) 0.536
rs5945572 Xp11 A 0.93 (0.83–1.04) 0.270 0.94 (0.68–1.30) 0.687
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We also compared the allele count frequencies in men of European ancestry with high (Gleason score ≥8), intermediate (Gleason score =7), and low-grade disease (Gleason score =6). After correction for multiple testing, only the minor allele (G) of rs2735839 on 19q13 remained significantly (and inversely) associated with high-grade disease in the entire cohort (p=1.389 ×10−8). Again, after correction for multiple testing, only the same SNP retained its significance within European (p=1.862 ×10−5) and African-American men (p=4.667×10−4; Table 3).

Table 3. Analysis of Genotype Association with Gleason Score.

The OR (95% CI) was calculated from multinomial logistic regression analyses using a saturated model comparing high-grade and low-grade disease. The P value is derived from a cumulative logit model comparing high-, intermediate- and low-grade disease.

European Ancestry African American Ancestry
SNP Location Risk Allele OR (95% CI) P value OR (95% CI) P value
rs721048 2p15 A 1.08 (0.77–1.11) 0.259 0.76 (0.41–1.41) 0.686
rs1465618 2p21 A 1.08 (0.98–1.20) 0.047 1.22 (0.72–2.07) 0.919
rs12621278 2q31.1 G 0.93 (0.73–1.17) 0.538 --
rs2660753 3p12.1 T 0.99 (0.88–1.10) 0.510 0.95 (0.76–1.18) 0.137
rs10934853 3q21 A 0.95 (0.86–1.05) 0.454 1.94 (0.65–5.84) 0.447
rs12500426 4q22.3 A 1.01 (0.93–1.11) 0.992 1.04 (0.64–1.67) 0.395
rs17021918 4q22.3 T 1.06 (0.95–1.18) 0.122 0.91 (0.55–1.48) 0.514
rs7679673 4q24 A 1.03 (0.94–1.13) 0.310 1.17 (0.79–1.72) 0.407
rs2736098 5p15 A 1.10 (0.80–1.50) 0.578 0.87 (0.25–3.04) 0.811
rs401681 5p16 (TERT) C 0.94 (0.82–1.07) 0.293 1.77 (0.65–4.77) 0.194
rs9364554 6q25.3 (SLC22A3) T 0.90 (0.82–0.98) 0.114 0.96 (0.61–1.51) 0.904
rs10486567 7p15.2 (JAZF1) G 0.89 (0.82–0.97) 0.003 0.86 (0.68–1.10) 0.091
rs6465657 7q21.3 (LMTK2) C 0.92 (0.86–1.00) 0.014 0.80 (0.47–1.38) 0.444
rs1512268 8p21.2 A 0.97 (0.89–1.06) 0.785 0.86 (0.58–1.28) 0.776
rs16901979 8q24 A 1.18 (1.00–1.39) 0.072 1.07 (0.83–1.36) 0.836
rs16902094 8q24 G 1.02 (0.89–1.16) 0.709 0.89 (0.18–4.36) 0.695
rs445114 8q24 T 0.96 (0.86–1.07) 0.330 1.28 (0.46–3.55) 0.764
rs6983267 8q24 G 0.94 (0.88–1.00) 0.076 1.16 (0.76–1.79) 0.209
rs1447295 8q24 A 1.04 (0.93–1.15) 0.104 1.05 (0.82–1.33) 0.548
rs10086908 8q24 C 1.06 (0.94–1.18) 0.439 0.94 (0.34–2.61) 0.960
rs1571801 9q33.2 A 0.98 (0.91–1.07) 0.951 1.41 (1.04–1.91) 0.198
rs10993994 10q11 (MSMB) T 0.94 (0.88–1.01) 0.127 0.89 (0.71–1.11) 0.289
rs4962416 10q26.13 C 0.95 (0.86–1.04) 0.348 1.04 (0.77–1.40) 0.863
rs7127900 11p15.5 A 0.82 (0.73–0.92) 0.002 1.12 (0.75–1.67) 0.816
rs11228565 11q13 A 1.05 (0.92–1.19) 0.765 4.49 (0.78–26.00) 0.054
rs10896450 11q13 G 0.98 (0.84–1.16) 0.829 0.76 (0.28–2.09) 0.708
rs12418451 11q13.3 A 0.98 (0.85–1.13) 0.869 0.83 (0.22–3.16) 0.868
rs4054823 17p12 T 1.03 (0.94–1.14) 0.352 4.72 (1.24–17.89) 0.025
rs11649743 17q12 G 0.95 (0.86–1.05) 0.096 1.05 (0.63–1.75) 0.684
rs4430796 17q12 A 1.05 (0.98–1.13) 0.513 0.94 (0.74–1.19) 0.956
rs1859962 17q24 G 0.95 (0.88–1.02) 0.093 0.97 (0.77–1.22) 0.297
rs8102476 19q13 C 1.00 (0.91–1.10) 0.717 1.53 (0.53–4.41) 0.477
rs2735839 19q13.3 (KLK2/KLK3) G 0.77 (0.68–0.86) 1.862×10−5 0.69 (0.54–0.87) 4.667×10−4
rs9623117 22q13.1 C 0.96 (0.87–1.07) 0.746 1.74 (0.72–4.19) 0.117
rs5759167 22q13.2 T 0.98 (0.89–1.07) 0.599 0.99 (0.54–1.80) 0.267
rs5945572 Xp11 A 0.87 (0.77–0.99) 0.128 1.17 (0.82–1.68) 0.655
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To determine whether the association between rs2735839 and high-risk and high-grade disease was due to a PSA screening bias, we performed subset analyses using various PSA cutoffs (Table 4). There was a significant and inverse association between rs2735839 and aggressive disease at nearly every PSA cutoff in both cohorts of European and African-American men.

Table 4. Subgroup analysis based upon PSA level comparing the association between SNP rs2735839 and high-risk disease.

Of note, there were not enough African-African men with very low PSA values to make meaningful comparisons.

European Ancestry African American Ancestry
SNP Location Risk Allele OR (95% CI) P value OR (95% CI) P value
PSA ≤ 20.0 ng/ml rs2735839 19q13.3 (KLK2/KLK3) G 0.73 (0.64–0.83) 1.622×10−5 0.76 (0.59–0.98) 0.019
PSA ≤ 10.0 ng/ml rs2735839 19q13.3 (KLK2/KLK3) G 0.68 (0.59–0.79) 1.302×10−5 0.82 (0.61–1.10) 0.067
PSA ≤ 4.0 ng/ml rs2735839 19q13.3 (KLK2/KLK3) G 0.70 (0.52–0.94) 0.0092 N/A N/A
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Discussion

Most previous studies of PC-risk alleles were not designed to identify genetic variants associated with aggressive disease because they were largely focused on men with a diagnosis of PC, irrespective of disease aggressiveness. In contrast, our study was aimed at identifying variants that are associated with PC aggressiveness rather than overall risk of PC (Helfand et al. 2010).

Previous studies have identified associations between SNPs within or near the PSA gene (KLK3) and high-grade tumors and adverse clinical outcomes (Bensen et al. 2013; Cramer et al. 2008; Gudmundsson et al. 2010; Lindstrom et al. 2011; Reinhardt et al. 2013; Slager et al. 2003; Xu et al. 2008). For example, using cohorts of European and Ashkenazi Jewish ancestry, two studies found that SNP rs2735839 in KLK3 was associated with PC-specific mortality (Gallagher et al. 2010; Pomerantz et al. 2011). In addition, the SNP rs2735839 on chromosome 19q13 within the KLK3 (PSA) gene has been previously evaluated in men of European ancestry for its association with clinico-pathologic features of prostate tumors (Bensen et al. 2013; He et al. 2014; Kader et al. 2009; Lindstrom et al. 2011; Nobata et al. 2012; Pomerantz et al. 2011; Xu et al. 2008). Taken together, the G allele has been associated with PC risk and increased serum PSA levels, but also with significantly lower disease aggressiveness (Bensen et al. 2013). However, results have been inconsistent. In the present study of large cohorts of both European and African-American men, this SNP was present at significantly different frequencies amongst men with aggressive and high-grade disease compared to those with non-aggressive or low-grade disease. Since this genetic variant lies within the KLK3 gene, it is not surprising that variants within or around this gene could influence PC aggressiveness (Gudmundsson et al. 2010; Gudmundsson et al. 2009; He et al. 2014; Hsu et al. 2009; Kader et al. 2009; Lange et al. 2012; Pal et al. 2007; Penney et al. 2011; Schaid et al. 2007; Xu et al. 2008). Although the mechanism(s) of this association are unclear, it is possible that it may reflect, at least in part, a PSA detection bias (e.g., G allele of rs2735839 is associated with lower PSA expression and a delay in PC diagnosis). However, a PSA detection bias may not be sufficient to explain all of the current findings. For example, data from non-PSA-screened cohorts (Eeles et al. 2008a) and from men with low PSA values (Table 4) and functional studies (Lai et al. 2007) support the possibility of other potential roles for this locus, including PSA production, and the intrinsic risk of PC overall and of aggressive disease. Regardless, this SNP appears to have the potential ability to discriminate between aggressive and non-aggressive disease. Specifically, in men of European ancestry, this SNP discriminated between aggressive and non-aggressive disease within men with relatively low PSA values < 4ng/mL (Table 4). Because the clinical and pathology features were used to define the groups, we were not able to determine whether this allele adds independent prognostic information. Therefore, additional studies aimed at fully evaluating its clinical utility are needed.

Our results reveal similar findings with SNP rs2735839, whether we defined aggressive disease as high-grade disease (Gleason score ≥8) alone or a more comprehensive definition (PC-specific death, distant metastasis, lymph node involvement, seminal vesicle invasion, extracapsular tumor extension) (Tables 2 and 3). Taken together, our results suggest that Gleason score may be the driving force for determining the genetic basis of aggressive disease. While the mechanisms largely underlying their association with aggressive disease has largely remains elusive, it appears that PC-risk SNPs may influence gene expression including PSA(Chen et al. 2015; Jin et al. 2013).

It is well established that men of African descent are at significantly increased risk of PC, with a greater proportion being diagnosed at an earlier age with aggressive disease (Moul 2000; Zeliadt et al. 2003). Specifically, African- American men have a 50% higher incidence and more than a 240% higher mortality rate of PC than Caucasian men (Hsing and Chokkalingam 2006) (Taksler et al. 2012). Unfortunately, the majority of genetic studies have not included large cohorts of African-American men. The present study is strengthened by the fact that it highlights both similarities and differences between African-American and European men. For example, there were many more SNPs that were marginally associated with high-risk and high–grade disease in the European compared to African-American men (Tables 2 and 3). As stated above, this is likely related to the mechanisms of their initial discovery in Caucasian cohorts (Han et al. 2014). Other nearby or related SNPs may also be associated with aggressive disease in African-Americans, and more studies within this population are needed. Interestingly, both racial populations showed an association between the aggressive phenotype and the minor allele of rs2735839 (G). This suggests a robustness of the association in other racial populations as well as a common genetic mechanism for PC aggressiveness.

Our study has several strengths, including its involvement of large cohorts with aggressive prostate tumors. This allowed the identification of PC-risk SNPs with at least marginally significant associations with aggressive disease. These small relative risks of aggressive disease are somewhat expected given similar associations between these SNPs and overall disease risk. However, based upon the fact that these SNPs are common within the general population and have a low penetrance, we cannot exclude the possibility of false positive results. Additionally, we used a widely validated subset of PC risk variants in the present study. This relatively limited subset of 36 SNPs allowed us to evaluate more fully the SNPs in a new context without having the same statistical constraints as many GWAS studies that involve millions of other SNPs. However, this same subset also limited the scope of our results, as it did not permit the identification of other more recently validated risk SNPs or novel genetic variations that may better predict PC aggressiveness. Furthermore, we did not have genotype data on all 36 SNPs in all patients included in the study. This limited the statistical power. Therefore, future case-case GWAS involving large cohorts of men with complete genotype data are needed. In addition, future complementary case-control studies evaluating these same SNPs would be needed to better define the direction and magnitude of the associations with aggressive disease. Additionally, although our study population represents one of the largest cohorts of men with African-American ancestry, the statistical power remains limited, and the results require replication in larger, independent datasets. Furthermore, it should be noted that there may have been a selection bias present since the proportion of African-American men with aggressive disease included in the cohort was significantly less than among European-Americans. Furthermore, our results may have been confounded by the fact that race was self-reported and lacked associated genetic information on ancestry. While our results are limited by the definition of disease aggressiveness used, it emphasizes the need for replication, as the majority of prior aggressiveness loci have failed to be replicated.

In summary, we provide further evidence that a single PC risk SNP (rs2735839) on chromosome 19q13 may be associated with high-risk and high-grade PC. Future prospective designed, case-case GWAS should be performed to identify additional SNPs associated with PC aggressiveness

Supplementary Material

55FAF6E3230907C66E4B8111E1B18C83

Contributor Information

Brian T. Helfand, Department of Surgery, Division of Urology, NorthShore University Health System, John and Carol Walter Center for Urological Health, Evanston, Illinois

Kimberly A. Roehl, Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois

Phillip R. Cooper, Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois

Barry McGuire, Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Liesel M. Fitzgerald, Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria 3004, Australia

Geraldine Cancel-Tassin, CeRePP ICPCG Group, Hopital Tenon, Assistance Publique-Hopitaux de Paris, 75020 Paris, France.

Jean-Nicolas Cornu, CeRePP ICPCG Group, Hopital Tenon, Assistance Publique-Hopitaux de Paris, 75020 Paris, France.

Scott Bauer, Genome Analysis Core Facility, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California.

Erin L. Van Blarigan, Genome Analysis Core Facility, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California

Xin Chen, Department of Pathology and Laboratory Medicine, University of California, Irvine.

David Duggan, Integrated Cancer Genomics Division, TGen, Phoenix, Arizona.

Elaine A. Ostrander, FHCRC/NHGRI ICPCG Group, Seattle, Washington

Mary Gwo-Shu, Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts.

Zuo-Feng Zhang, Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California.

Shen-Chih Chang, Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California.

Somee Jeong, Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California.

Sonja Berndt, Division of Cancer Epidemiology & Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, Bethesda, Maryland.

Shannon K. McDonnell, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota

Rick Kittles, Department of Surgery, Division of Urology, University of Arizona, Tucson, Arizona.

Benjamin A. Rybicki, Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan

Matthew Freedman, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.

Phil Kantoff, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.

Mark Pomerantz, Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts.

Joan P. Breyer, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee

Jeffrey R. Smith, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee

Timothy R. Rebbeck, Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania

Dan Mercola, Department of Pathology and Laboratory Medicine, University of California, Irvine, California.

William B Isaacs, Department of Urology, Johns Hopkins University, Baltimore, Maryland.

Fredrick Wiklund, University of Umeå ICPCG Group, Umeå, Sweden.

Olivier Cussenot, CeRePP ICPCG Group, Hopital Tenon, Assistance Publique-Hopitaux de Paris, 75020 Paris, France.

Stephen N. Thibodeau, Department of Lab Medicine and Pathology, Mayo Clinic, Rochester, Minnesota

Daniel J. Schaid, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota

Lisa Cannon-Albright, Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah.

Kathy A. Cooney, Department of Urology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan

Stephen J. Chanock, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland

Janet L. Stanford, FHCRC/NHGRI ICPCG Group, Seattle, Washington

June M. Chan, Genome Analysis Core Facility, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California

John Witte, Genome Analysis Core Facility, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California.

Jianfeng Xu, Program for Personalized Cancer Care and Department of Surgery, NorthShore University HealthSystem, Evanston, Illinois.

Jeanette T. Bensen, Department of Epidemiology, Gillings School of Global Public Health, and the Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill, North Carolina

Jack A. Taylor, Epidemiology Branch and Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina

William J. Catalona, Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois

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