Skip to main content
  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
    • Collections
      • Disparities Collection
      • Editors' Picks
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • OnlineFirst
    • Editors' Picks
    • Citation
    • Author/Keyword
  • News
    • Cancer Discovery News
  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention

Advanced Search

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
    • Collections
      • Disparities Collection
      • Editors' Picks
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • OnlineFirst
    • Editors' Picks
    • Citation
    • Author/Keyword
  • News
    • Cancer Discovery News
Research Articles

Genome-wide Association Study of Prostate Cancer Mortality

Kathryn L. Penney, Saumyadipta Pyne, Fredrick R. Schumacher, Jennifer A. Sinnott, Lorelei A. Mucci, Peter L. Kraft, Jing Ma, William K. Oh, Tobias Kurth, Philip W. Kantoff, Edward L. Giovannucci, Meir J. Stampfer, David J. Hunter and Matthew L. Freedman
Kathryn L. Penney
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Saumyadipta Pyne
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fredrick R. Schumacher
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jennifer A. Sinnott
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lorelei A. Mucci
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter L. Kraft
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jing Ma
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
William K. Oh
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tobias Kurth
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Philip W. Kantoff
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Edward L. Giovannucci
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Meir J. Stampfer
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David J. Hunter
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthew L. Freedman
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI: 10.1158/1055-9965.EPI-10-0601 Published November 2010
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Background: A pressing clinical issue in prostate cancer is to distinguish which men will have an indolent or aggressive course of disease. Clinical variables such as Gleason grade and stage are useful predictors of lethal cancer; however, the low predictive values of the common Gleason scores, changes in grading over time, and earlier diagnosis of patients due to screening limits their clinical utility. Identifying genetic variants associated with lethal prostate cancer could inform clinical decision making.

Methods: We conducted a genome-wide association study, comparing lethal prostate cancer cases to cases surviving at least 10 years beyond their initial diagnosis. Genotyping was done with the Affymetrix 5.0 chip [∼500,000 single nucleotide polymorphisms (SNP) and 1,483 copy number variants (CNV)] on DNA from participants in the Physicians' Health Study and Health Professionals Follow-up Study (196 lethal cases, 368 long-term survivors). After excluding SNPs and individuals based on quality control criteria, logistic regression assuming an additive model was done using the PLINK software.

Results: No SNP reached genome-wide significance (P ≤ 1 × 10−7); however, three independent SNPs had P < 1 × 10−5. One top-ranked SNP replicated (P = 0.05) in an independent follow-up study. Although no CNV had genome-wide significance, 14 CNVs showed nominal association with prostate cancer mortality (P < 0.05).

Conclusions: No variants were significantly associated at a genome-wide level with prostate cancer mortality. Common genetic determinants of lethal prostate cancer are likely to have odds ratios <2.0.

Impact: Genetic markers identified could provide biological insight to improve therapy for men with potentially fatal cancer. Larger studies are necessary to detect the genetic causes of prostate cancer mortality. Cancer Epidemiol Biomarkers Prev; 19(11); 2869–76. ©2010 AACR.

Introduction

One of the most urgent clinical questions in prostate cancer is how to predict an individual's course of disease at the time of diagnosis. Prostate cancer is the most common incident cancer (other than nonmelanoma skin cancer) and the second leading cause of cancer mortality in men in the United States (1). However, the vast majority of prostate cancer patients will not die from their cancer. Although early detection and treatment play a role in cancer survival, some treated individuals still succumb to prostate cancer whereas many survive without medical intervention. A recent large trial found that men randomized to prostatectomy had only a small (although significant) absolute reduction in prostate cancer death compared with those randomized to watchful waiting (2). Albertsen et al. (3) followed 767 men with conservatively treated localized prostate cancer for over 20 years and observed that the majority of men (70%) did not die of prostate cancer.

What causes one prostate cancer patient to develop metastases or die from their cancer while others survive with the disease for many years? At present, the most utilized predictors of outcome at diagnosis are age, clinical stage, prostate-specific antigen level, and Gleason score. Gleason score, a measure based on the histologic patterns of prostate tumors, is currently one of the best predictors. In a study using re-reviewed Gleason score from prostatectomy specimens, those with Gleason 8 cancers had a hazard of lethal cancer (dying from prostate cancer or developing distant metastases) that was 7.4 [95% confidence interval (95% CI), 2.5-22] times higher than those with Gleason 3+4; cases with Gleason 9 to 10 had an even higher risk of lethal cancer (hazard ratio, 19.1; 95% CI, 7.4-49.7; ref. 4). However, the positive predictive value for mortality of a higher Gleason score, including the most common Gleason 7 as well as Gleason 8 to 10, is only 29% (5), and therefore far from optimal. Gleason score has additional limitations as a predictor because of scoring changes over time (6, 7) and interobserver variability (8, 9).

Epidemiologic and experimental evidence supports the hypothesis that aggressive cancer has an inherited component. A recent study showed concordance of survival and prostate cancer mortality among fathers and sons with prostate cancer, implying that prognosis itself may have a hereditary component (10). Laboratory experiments using a highly metastatic mouse mammary model crossed with several different strains showed that the genetic background of an animal can influence the metastatic efficiency (11). Further quantitative trait mapping work identified regions on chromosome 19 that were significantly associated with metastatic efficiency, suggesting that inherited variation may influence metastasis (12). Thus far, genetic studies in humans have focused on Gleason score as a proxy for aggressive disease. Several regions have been implicated in linkage scans, but three of the regions (5q31-33, 7q31-33, and 19q12-q13.3) were strongly significantly associated with high-grade cancer (P < 0.001) and replicated in at least two independent studies, suggesting that a locus may be present under these peaks (13-16).

In prostate cancer genetic association studies for risk, a combined analysis of two genome-wide association studies (GWAS) identified a variant at chromosome 9q33.2 in a putative tumor-suppressor gene (DABP2IP) that was associated with risk of aggressive prostate cancer, defined by Gleason grade and clinical stage (17). Another study found that the TT genotype of rs4054823 at 17p12 increased the risk of aggressive cancer compared with nonaggressive cancer, again defined by clinical variables (18). A germ-line deletion at 2p24.3 was more strongly associated with the risk of aggressive cancer than nonaggressive cancer (19).

Substantial longitudinal follow-up is required to capture information on prostate cancer mortality; thus, this outcome is studied less frequently than Gleason score. However, we believe that a large-scale genetic study for the most important prostate cancer outcome is crucial to improve our understanding of prostate cancer aggressiveness. We therefore performed a GWAS for prostate cancer mortality in the Physicians' Health Study (PHS) and Health Professionals Follow-up Study (HPFS), with a replication study in the Dana-Farber Harvard Cancer Center Specialized Programs of Research Excellence (SPORE; Gelb Center) case series. In addition to examining the association of genotypes, we also evaluated whether copy number variants (CNV) were associated with prostate cancer mortality.

Materials and Methods

Study Population

Physicians' Health Study.

The PHS began as a randomized, double-blind trial of aspirin and β-carotene in the prevention of cardiovascular disease and cancer among 22,071 healthy U.S. physicians. Written consent was obtained from each participant at the time of initial enrollment, and the investigation was approved by the Human Subjects Committee at Brigham and Women's Hospital. Men were excluded if they had any serious medical conditions at baseline, including all cancers (except nonmelanoma skin cancer). Blood samples were collected from 68% of the physicians in 1982 to 1984, as described previously (20).

Participants are followed through annual questionnaires to collect data on diet, health and lifestyle behaviors, and medical history, and biannually through postcards to ascertain health end points, including prostate cancer. All self-reported prostate cancer cases are verified through medical record and pathology review. Through this systematic medical record review, we also abstract data on clinical information, such as Gleason score. Cause of death is determined by review of death certificates, medical records, and information from the family by a panel of three physicians. There is a high follow-up rate for both cancer incidence (96%) and mortality (98%). Metastases are reported on follow-up questionnaires sent to all men living with prostate cancer and are confirmed through medical record review.

For the current study, we included incident prostate cancer diagnosed between 1982 and 2003, and restricted participants to self-reported Caucasians to reduce potential population stratification. Due to cost restraints, we were unable to genotype all PHS prostate cancer patients from whom blood had been collected. We therefore examined the two extremes of prostate cancer cases: long-term survivors (patients who survived a minimum of 10 years after diagnosis until death or end of follow-up (March 1, 2008) and did not develop metastases to bone or organs or die from prostate cancer; n = 415) and lethal prostate cancer cases (patients who developed metastases to bone or organs after diagnosis or died from prostate cancer; n = 176).

Health professionals follow-up study.

The HPFS, an ongoing prospective cohort study on the causes of cancer and heart disease in men, consists of 51,529 U.S. health professionals who were of ages 40 to 75 years in 1986 (21). At baseline and then biennially, participants respond to a mailed questionnaire that included questions on demographics, lifestyle, and medical history. Between 1993 and 1995, 18,018 of the men provided a blood specimen. When a participant reports a prostate cancer diagnosis, medical and pathology records are obtained. Study investigators review these records to confirm the diagnosis and to abstract stage at diagnosis and Gleason grade. Deaths among cohort members are identified by reports from next-of-kin, the postal service, or searches of the National Death Index. To increase the number of lethal cancers in this study, we included 46 prostate cancer deaths from the HPFS (self-reported Caucasian) among cases diagnosed between 1993 and 2000; these were selected from a larger nested case-control study and had the most available DNA from a total of 53 prostate cancer deaths.

Dana-Farber Harvard Cancer Center SPORE (Gelb Center).

The Gelb Center is a case series of prostate cancer patients diagnosed between 1976 and 2007. A detailed description of this study has been published previously (22). The study captures detailed clinical information from multiple sources, including medical records and patient registration, and a blood sample collected after diagnosis. Follow-up of the participants occurs at clinic visits to the Dana-Farber Cancer Institute and by searching the National Death Index. Because cause of death is not always available or known, if an individual was known to have metastases before their death, they were assumed to have died from prostate cancer. For this study, after restricting to self-reported Caucasians, we included 155 long-term survivors (end of follow-up July 1, 2007) and 500 lethal cases as a replication set.

Genome-wide association study

Affymetrix scan.

The samples from the PHS and HPFS were included in the genome scan. DNA was extracted from peripheral blood samples for all participants. Genotyping was done with the Affymetrix 5.0 single nucleotide polymorphism (SNP) chip, which contains probes for 500,568 SNPs. Briefly, ∼500 ng of DNA from each sample is digested with Nsp and Sty restriction enzymes. The digested segments were ligated to enzyme-specific adaptors that incorporate a universal PCR priming sequence; PCR amplification by using universal primers was done in a reaction optimized to amplify fragments. The products are fragmented, end-labeled with biotinylated nucleotides, and hybridized to a chip and detected (23). The resulting intensities for each allele are used to make a genotype call. The “Birdseed” calling algorithm, an updated version of the Robust Linear Modeling using Mahalanobis distance calling algorithm developed at the Broad Institute of Harvard and Massachusetts Institute of Technology, was used for this study (24). More information on the technology, calling algorithm, and SNP coverage can be found in ref. (25).

Samples and quality control.

A total of 637 unique samples from PHS and HPFS were included in this study. Deaths and long-term survivors were interspersed across seven 96-well plates, and laboratory personnel were blinded to outcome. Each plate had two empty wells (negative controls) as well as two duplicates to be used for quality control.

We assessed the genotype concordance of 458 SNPs from 500-kb regions of chromosomes 1, 5, 10, 15, and 20 for the 14 duplicate pairs (concordance 99.9%). We also compared the genotype calls for 31 SNPs that had previously been genotyped on a subset of these PHS participants; concordance was 99.3% for >14,000 genotypes.

Data analysis.

The PLINK program (26) was used to analyze these genome scan data (27). Forty-six individuals (33 long-term survivors, 13 deaths) with <90% of genotype calls made were removed from the analysis; the average call rate in the remaining individuals was 98.8%. Of the SNPs genotyped, SNPs missing >10% of genotypes (14, 704), with minor allele frequency <1% (68, 603), or with Hardy-Weinberg equilibrium P < 1 × 10−6 (1, 979) were excluded, leaving 419,613 SNPs for analysis.

To address potential remaining population stratification, we used the Eigenstrat program (28). We ran this program for all participants with the default parameters (5 outlier iterations across the top 10 eigenvectors, with outliers exceeding 6 SD along a top principal component excluded), and output the first two eigenvector values. Several individuals were not assigned values along these eigenvectors due to missing data (as described above) or were designated outliers (14 long-term survivors, 12 deaths); these individuals were excluded from further analysis. Using PLINK, for the main analysis, we ran an unconditional logistic regression model adjusting for the first two eigenvectors (excluding one HPFS death missing age at diagnosis), outputting the additive model results for the association of each SNP with lethal prostate cancer (n = 196) versus long-term survival (n = 368). We then ran secondary analyses additionally adjusting for age at diagnosis and restricting to men with a Gleason score of 7.

Follow-up study

The Gelb Center samples were used for a genetic replication study. We selected and designed assays for SNPs with P < 1 × 10−3 that fell in previously identified linkage peaks for Gleason score (chr5q31-33, n = 6; chr7q31-33, n = 1). We then selected markers to capture the independent variation with P < 5 × 10−4 (n = 72). Genotyping was done with Sequenom iPLEX matrix-assisted laser desorption/ionization-time of flight mass spectrometry technology (see ref. 29 for reaction details). The association of the additive model of these SNPs with lethal prostate cancer versus long-term survival was done using unconditional logistic regression. SNPs were excluded from analysis if they had a <90% genotyping success rate. Of the 79 SNPs genotyped in the Gelb samples, 11 failed genotyping quality control. Replication was declared only if P ≤ 0.05 and the direction of the effect was the same as in the GWAS; for the replicated SNP, a joint analysis with the original GWAS data was done as a meta-analysis with a random-effects model. Analysis was done with SAS v9.1 statistical software.

Copy number polymorphism analysis

We analyzed SNP chip-based copy number polymorphism data as generated by the CNV detection software Canary (30) in the form of summarized intensity scores for 1,483 CNVs and 565 subjects. We followed the subject filtering criteria as described above in our genotype analysis; individuals who were missing considerable data or were found to be outliers by Eigenstrat were excluded. Then, we followed a likelihood ratio approach for testing association between each CNV and the binary status of mortality considered as a trait. The approach jointly fits two linear models, as outlined in Barnes et al. (31), and is described as follows. The first model classifies the summarized intensities for each CNV by fitting a finite mixture of Gaussian densities via an Expectation-maximization-based algorithm that uses Bayesian information criterion to select the optimal number of classes. Upon convergence, the classification assigns every individual subject to a copy number genotype. Given an optimal model with multiple copy number classes, we tested for its association with the subject's trait with this joint model. This is done by fitting of a generalized logit linear model to test the null hypothesis H0 that there is no association between a subject's copy number genotype and his binary prostate cancer mortality trait (in this case, lethal/indolent). If the fitting is correct and there is indeed no association, then the computed likelihood ratio statistic is χ2 distributed with 1 degree of freedom, which leads to a corresponding P value of association. The plots and statistics for CNV classification and the associated distribution of trait were generated with the BioConductor package CNVtools.

Results

GWAS results

A description of the PHS and HPFS participants is provided in Table 1. Although participants were restricted to self-reported Caucasians, residual population stratification was addressed with the Eigenstrat program (28). The correlation of eigenvectors 1 and 2 with outcome status was 0.046 and 0.009, respectively, demonstrating that the overall population structure was not strongly related to outcome; the first two eigenvectors for the lethal prostate cancer cases and long-term survivors are shown in Supplementary Fig. S1.

View this table:
  • View inline
  • View popup
Table 1.

Description of GWAS and replication study participants

A set of 419,613 SNPs passed quality control and were used for subsequent analyses (see Materials and Methods). A quantile-quantile plot of the results compares the χ2 values obtained in this study with the expected distribution under the null hypothesis of no association between genetic variation and mortality (Fig. 1). Although no SNPs reached genome-wide significance (P ≤ 1 × 10−7), three independent SNPs had P < 1 × 10−5; the plot of P values (Fig. 2) shows that there are peaks on chromosome 2q31.2, 11q12.2, and 11q14.1. The results for all SNPs with P < 1 × 10−3 (n = 277) are provided in Supplementary Table S1.

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Quantile-quantile plot comparing observed statistics for all results to those expected based on the null distribution.

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

P values for the association of SNPs with prostate cancer mortality plotted by chromosome and position.

To determine the associations of SNPs on mortality independent of their possible associations with age at diagnosis, we ran the analysis adjusting for age at diagnosis (continuous) in addition to the top 2 eigenvectors. When adjusting for age at diagnosis, there are 3,767 results with P < 0.01; 19% of these results are not among the 3,803 results with P < 0.01 from our main analysis. However, the top SNPs from the non–age-adjusted results (Supplementary Table S1) all have P < 0.005 in the adjusted analysis, suggesting that the overall effect of SNPs on mortality through age at diagnosis may not be substantial. We also examined the association of SNPs with lethal cancer restricting to cases with Gleason 7; again, no SNPs reached genome-wide significance. With this much smaller number of participants, half of the SNPs with P < 0.001 had P < 0.05 in the main analysis.

We examined the results for previously identified prostate cancer risk SNPs in our scan. Sixteen of the 31 confirmed risk SNPs compiled by Varghese and Easton (32) were either directly genotyped in our scan or had a proxy with R2 > 0.8. The most significant finding was for rs16901979 for which the risk allele decreased the probability of lethal cancer [odds ratio (OR), 0.35; P = 0.006]; all results are reported in Supplementary Table S2.

Replication study results

Because the majority of the top-ranked SNPs from the scan will be false positives, we performed a replication study in the Dana-Farber Harvard Cancer Center SPORE Gelb Center (500 lethal cases, 155 indolent). We selected top-ranked SNPs (P < 10−3) that were located in previously identified Gleason linkage peaks (n = 7). We then selected markers to capture the independent variation with P < 5 × 10−4 (n = 72). Of the 79 SNPs selected, 68 were successfully genotyped. Six of these had P ≤ 0.05, but for five the direction of the effect was not consistent with the scan. The one SNP that replicated with the effect in the same direction, rs6973814 (OR, 1.95; 95% CI, 1.01-3.79; P = 0.05), was ranked 66th in the original GWAS (OR, 3.07; P = 0.0003) and is located on chromosome 7q11.2 (nearest gene, AUTS2, 600 kb away). In a joint analysis with the scan results, the combined OR was 2.50 (95% CI, 1.60-3.90; P = 6 × 10−5). All Gelb Center results are in Supplementary Table S3.

CNV results

The model fitting results and number of classes for all 1,483 CNVs are provided in Supplementary Table S4. For the CNVs for which the classification (based on iterative Expectation-maximization modeling) converged and produced more than one CNV genotype class (n = 341), we examined the association between the number of copies an individual carries and lethal prostate cancer. Fourteen CNVs had a nominal P < 0.05; however, none remained significant after correction for multiple testing (Supplementary Fig. S2; Table 2).

View this table:
  • View inline
  • View popup
Table 2.

Significant associations (P < 0.05) between CNVs and prostate cancer mortality

Discussion

A number of recent GWAS and follow-up replication studies have identified over 20 bona fide genetic prostate cancer risk loci (33-40). Importantly, these studies have provided a new look into the biology of developing the disease. Some of these variants have been tested for association with aggressiveness, typically using the Gleason grade as a proxy for aggressive disease. However, identifying genetic determinants of lethal cancer could improve on the current clinical predictive ability at diagnosis. Understanding who would and who would not benefit from intervention could affect the selection of appropriate medical therapy for the individual, preventing unnecessary treatments and the physical and psychological side effects. In addition, the markers themselves may provide biological insight that could lead to improved therapy for those with potentially fatal cancer.

In this GWAS for lethal cancer, although no SNPs reached genome-wide significance, we identified one top-ranked SNP that replicated in an independent population. The closest gene to the one SNP that was replicated is AUTS2. A recent study based on mRNA expression data reported that this gene was included in the top 100 potential genetic mediators for nonrecurrent primary prostate cancer (41), suggesting a possible biological function.

As noted by McCaroll (42), it is increasingly possible to extend GWAS to examine CNVs and their association with disease phenotypes. In recent years, the SNP arrays have been redesigned to contain probes at the majority of CNVs, which in turn take advantage of the recent high-resolution maps of the CNV locations (43, 44). In this direction, the present GWAS was extended to study CNV in the same SNP array data based on 1,483 mapped CNVs by using a robust statistical modeling algorithm for classification. Although no CNV achieved genome-wide significance, we identified 14 CNVs nominally associated with prostate cancer mortality. Subsequent data mining with alternate modeling strategies or larger studies may reveal further associations.

Prostate cancer mortality is one of the most important phenotypes of this disease. Unfortunately, due to the long follow-up time and the cost necessary to obtain this information, few studies have information on survival and cause of death or the numbers of lethal cases necessary to study this outcome. A major strength of this study is its ability to examine the primary prostate cancer end point, lethal disease, with a substantial number of participants from cohorts that have been followed for decades. The top results were then evaluated in a large case series that also captures survival data.

Figure 3 shows that we are only powered to detect relatively strong effects (e.g., OR >2 with minor allele frequency >20%). Although this is a limitation of our study, it also provides insight into the genetic variants involved in prostate cancer aggressiveness. Based on our data, no common variant will have a large effect on aggressiveness, but rather will most likely have the same magnitude of effect as the alleles that have previously been identified for risk. Although our one SNP that replicated had a larger combined OR of 2.5, in the replication dataset alone the OR was 1.95, suggesting that the initial finding is likely overestimating the magnitude of the effect.

Figure 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3.

Power for genome scan. Power was calculated using the number of cases included in the final analysis (196 lethal prostate cancer, 368 long-term survivors) with a α level of 1 × 10−7 across a range of allele frequencies and additive model ORs.

Another possible limitation (albeit one that exists in all studies of prostate cancer mortality that are conducted in screened and treated populations) is misclassification of the outcome. Individuals who were labeled as having indolent cancer because they survived at least 10 years without developing metastases or dying of cancer may only be in this category because they received aggressive medical treatment, without which they would have died. However, as the results of the Swedish randomized trial of prostatectomy versus watchful waiting suggest, the number needed to treat to save the life of one man with prostate cancer is 19 (45); thus, the potential effect of misclassification is likely to be minimal. Additionally, it is important to investigate if these genetic variants predict prostate cancer mortality independent of clinical variables such as treatment or Gleason score; however, missing data limits our ability to conduct these analyses. We performed an analysis restricting to the most common Gleason score of 7. Although the results were somewhat similar to the overall analysis, a larger future study examining these associations among men with Gleason 7 would be interesting and could identify SNPs that are associated with lethal cancer independent of their effects on Gleason. A limitation in the CNV analysis is the number of probes included on this Affymetrix chip; a more comprehensive study of CNVs with prostate cancer mortality should be done.

Although several SNPs have been identified that are associated with risk of prostate cancer, these SNPs in general have not been found to confer an increased risk of aggressive compared with indolent disease. If lethal prostate cancer indeed have a genetic component, this suggests that genetic variants determining aggressive disease are different from those that confer overall risk. It would be of clinical utility if future studies specifically focused on attempting to differentiate lethal from indolent cancer by using germline genetic scans and follow-up studies.

Disclosure of Potential Conflicts of Interest

T. Kurth, received within the last 2 years investigator-initiated research funding from the French National Research Agency, the NIH, Merck, and the Migraine Research Foundation; consultant to i3 Drug Safety and World Health Information Science Consultants, LLC; received honoraria from Genzyme, Merck, and Pfizer for educational lectures; and received travel funds from the Restless Legs Syndrome Foundation.

Acknowledgments

Grant Support: Grant from the Doris Duke Clinical Scientist Development Award. The laboratory work at the Broad Institute Center for Genotyping and Analysis was supported by grant U54 RR020278 from the National Center for Research Resources. The PHS was supported by grants CA-34944, CA-40360, and CA-097193 from the National Cancer Institute and grants HL-26490 and HL-34595 from the National Heart, Lung, and Blood Institute, Bethesda, MD. The HPFS was supported by CA-55075 from the National Cancer Institute. Additional funding was provided by Dana-Farber/Harvard Cancer Center Prostate Cancer SPORE (P50 CA090381-08). K.L. Penney was supported by a National Research Service Awards (T32 CA009001-34).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

  • Received June 8, 2010.
  • Revision received August 3, 2010.
  • Accepted August 10, 2010.

References

  1. ↵
    American Cancer Society. Cancer facts and figures, 2008. Atlanta, GA: American Cancer Society; 2008.
  2. ↵
    1. Bill-Axelson A,
    2. Holmberg L,
    3. Ruutu M,
    4. et al
    . Radical prostatectomy versus watchful waiting in early prostate cancer. N Engl J Med 2005;352:1977–84.
    OpenUrlCrossRefPubMed
  3. ↵
    1. Albertsen PC,
    2. Hanley JA,
    3. Fine J
    . 20-Year outcomes following conservative management of clinically localized prostate cancer. JAMA 2005;293:2095–101.
    OpenUrlCrossRefPubMed
  4. ↵
    1. Stark JR,
    2. Perner S,
    3. Stampfer MJ,
    4. et al
    . Gleason score and lethal prostate cancer: does 3 + 4 = 4 + 3? J Clin Oncol 2009;27:3459–64.
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Andren O,
    2. Fall K,
    3. Franzen L,
    4. Andersson SO,
    5. Johansson JE,
    6. Rubin MA
    . How well does the Gleason score predict prostate cancer death? A 20-year follow up of a population based cohort in Sweden. J Urol 2006;175:1337–40.
    OpenUrlCrossRefPubMed
  6. ↵
    1. Albertsen PC,
    2. Hanley JA,
    3. Barrows GH,
    4. et al
    . Prostate cancer and the Will Rogers phenomenon. J Natl Cancer Inst 2005;97:1248–53.
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Mitchell RE,
    2. Shah JB,
    3. Desai M,
    4. et al
    . Changes in prognostic significance and predictive accuracy of Gleason grading system throughout PSA era: impact of grade migration in prostate cancer. Urology 2007;70:706–10.
    OpenUrlCrossRefPubMed
  8. ↵
    1. Allsbrook WC Jr..,
    2. Mangold KA,
    3. Johnson MH,
    4. et al
    . Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. Hum Pathol 2001;32:74–80.
    OpenUrlCrossRefPubMed
  9. ↵
    1. Allsbrook WC Jr..,
    2. Mangold KA,
    3. Johnson MH
    . Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. Hum Pathol 2001;32:81–8.
    OpenUrlCrossRefPubMed
  10. ↵
    1. Hemminki K,
    2. Ji J,
    3. Forsti A,
    4. Sundquist J,
    5. Lenner P
    . Concordance of survival in family members with prostate cancer. J Clin Oncol 2008;26:1705–9.
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Lifsted T,
    2. Le Voyer T,
    3. Williams M,
    4. et al
    . Identification of inbred mouse strains harboring genetic modifiers of mammary tumor age of onset and metastatic progression. Int J Cancer 1998;77:640–4.
    OpenUrlCrossRefPubMed
  12. ↵
    1. Hunter KW,
    2. Broman KW,
    3. Voyer TL,
    4. et al
    . Predisposition to efficient mammary tumor metastatic progression is linked to the breast cancer metastasis suppressor gene Brms1. Cancer Res 2001;61:8866–72.
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Witte JS,
    2. Goddard KA,
    3. Conti DV,
    4. et al
    . Genomewide scan for prostate cancer-aggressiveness loci. Am J Hum Genet 2000;67:92–9.
    OpenUrlCrossRefPubMed
    1. Witte JS,
    2. Suarez BK,
    3. Thiel B,
    4. et al
    . Genome-wide scan of brothers: replication and fine mapping of prostate cancer susceptibility and aggressiveness loci. Prostate 2003;57:298–308.
    OpenUrlCrossRefPubMed
    1. Slager SL,
    2. Schaid DJ,
    3. Cunningham JM,
    4. et al
    . Confirmation of linkage of prostate cancer aggressiveness with chromosome 19q. Am J Hum Genet 2003;72:759–62.
    OpenUrlCrossRefPubMed
  14. ↵
    1. Slager SL,
    2. Zarfas KE,
    3. Brown WM,
    4. et al
    . Genome-wide linkage scan for prostate cancer aggressiveness loci using families from the University of Michigan Prostate Cancer Genetics Project. Prostate 2006;66:173–9.
    OpenUrlCrossRefPubMed
  15. ↵
    1. Duggan D,
    2. Zheng SL,
    3. Knowlton M,
    4. et al
    . Two genome-wide association studies of aggressive prostate cancer implicate putative prostate tumor suppressor gene DAB2IP. J Natl Cancer Inst 2007;99:1836–44.
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Xu J,
    2. Zheng SL,
    3. Isaacs SD,
    4. et al
    . Inherited genetic variant predisposes to aggressive but not indolent prostate cancer. Proc Natl Acad Sci U S A;107:2136–40.
  17. ↵
    1. Liu W,
    2. Sun J,
    3. Li G,
    4. et al
    . Association of a germ-line copy number variation at 2p24.3 and risk for aggressive prostate cancer. Cancer Res 2009;69:2176–9.
    OpenUrlAbstract/FREE Full Text
  18. ↵
    Steering Committee of the Physicians' Health Study Research Group. Final report on the aspirin component of the ongoing Physicians' Health Study. N Engl J Med 1989;321:129–35.
    OpenUrlCrossRefPubMed
  19. ↵
    1. Giovannucci E,
    2. Pollak M,
    3. Liu Y,
    4. et al
    . Nutritional predictors of insulin-like growth factor I and their relationships to cancer in men. Cancer Epidemiol Biomarkers Prev 2003;12:84–9.
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Oh WK,
    2. Hayes J,
    3. Evan C,
    4. et al
    . Development of an integrated prostate cancer research information system. Clin Genitourin Cancer 2006;5:61–6.
    OpenUrlPubMed
  21. ↵
    1. Matsuzaki H,
    2. Loi H,
    3. Dong S,
    4. et al
    . Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array. Genome Res 2004;14:414–25.
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Rabbee N,
    2. Speed TP
    . A genotype calling algorithm for Affymetrix SNP arrays. Bioinformatics 2006;22:7–12.
    OpenUrlAbstract/FREE Full Text
  23. ↵
    Affymetrix. Genome-Wide Human SNP Array 5.0. 2009 [cited 2010; Available from: http://www.affymetrix.com/estore/browse/products.jsp?productId=131532&categoryId=35906#1_3.
  24. ↵
    PLINK. 2009 [cited 2010; Available from: http://pngu.mgh.harvard.edu/purcell/plink/.
  25. ↵
    1. Purcell S,
    2. Neale B,
    3. Todd-Brown K,
    4. et al
    . PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559–75.
    OpenUrlCrossRefPubMed
  26. ↵
    1. Price AL,
    2. Patterson NJ,
    3. Plenge RM,
    4. Weinblatt ME,
    5. Shadick NA,
    6. Reich D
    . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006;38:904–9.
    OpenUrlCrossRefPubMed
  27. ↵
    1. Ross RW,
    2. Oh WK,
    3. Xie W,
    4. et al
    . Inherited variation in the androgen pathway is associated with the efficacy of androgen-deprivation therapy in men with prostate cancer. J Clin Oncol 2008;26:842–7.
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Korn JM,
    2. Kuruvilla FG,
    3. McCarroll SA,
    4. et al
    . Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat Genet 2008;40:1253–60.
    OpenUrlCrossRefPubMed
  29. ↵
    1. Barnes C,
    2. Plagnol V,
    3. Fitzgerald T,
    4. et al
    . A robust statistical method for case-control association testing with copy number variation. Nat Genet 2008;40:1245–52.
    OpenUrlCrossRefPubMed
  30. ↵
    1. Varghese JS,
    2. Easton DF
    . Genome-wide association studies in common cancers—what have we learnt? Curr Opin Genet Dev 2010;20:201–9.
    OpenUrlCrossRefPubMed
  31. ↵
    1. Freedman ML,
    2. Haiman CA,
    3. Patterson N,
    4. et al
    . Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men. Proc Natl Acad Sci U S A 2006;103:14068–73.
    OpenUrlAbstract/FREE Full Text
    1. Amundadottir LT,
    2. Sulem P,
    3. Gudmundsson J,
    4. et al
    . A common variant associated with prostate cancer in European and African populations. Nat Genet 2006;38:652–8.
    OpenUrlCrossRefPubMed
    1. Gudmundsson J,
    2. Sulem P,
    3. Manolescu A,
    4. et al
    . Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet 2007;39:631–7.
    OpenUrlCrossRefPubMed
    1. Gudmundsson J,
    2. Sulem P,
    3. Steinthorsdottir V,
    4. et al
    . Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet 2007;39:977–83.
    OpenUrlCrossRefPubMed
    1. Yeager M,
    2. Orr N,
    3. Hayes RB,
    4. et al
    . Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet 2007;39:645–9.
    OpenUrlCrossRefPubMed
    1. Gudmundsson J,
    2. Sulem P,
    3. Rafnar T,
    4. et al
    . Common sequence variants on 2p15 and Xp11.22 confer susceptibility to prostate cancer. Nat Genet 2008;40:281–3.
    OpenUrlCrossRefPubMed
    1. Eeles RA,
    2. Kote-Jarai Z,
    3. Giles GG,
    4. et al
    . Multiple newly identified loci associated with prostate cancer susceptibility. Nat Genet 2008;40:316–21.
    OpenUrlCrossRefPubMed
  32. ↵
    1. Thomas G,
    2. Jacobs KB,
    3. Yeager M,
    4. et al
    . Multiple loci identified in a genome-wide association study of prostate cancer. Nat Genet 2008;40:310–5.
    OpenUrlCrossRefPubMed
  33. ↵
    1. Ergun A,
    2. Lawrence CA,
    3. Kohanski MA,
    4. Brennan TA,
    5. Collins JJ
    . A network biology approach to prostate cancer. Mol Syst Biol 2007;3:82.
    OpenUrlAbstract/FREE Full Text
  34. ↵
    1. McCarroll SA
    . Extending genome-wide association studies to copy-number variation. Hum Mol Genet 2008;17:R135–42.
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. McCarroll SA,
    2. Kuruvilla FG,
    3. Korn JM,
    4. et al
    . Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet 2008;40:1166–74.
    OpenUrlCrossRefPubMed
  36. ↵
    1. Cooper GM,
    2. Zerr T,
    3. Kidd JM,
    4. Eichler EE,
    5. Nickerson DA
    . Systematic assessment of copy number variant detection via genome-wide SNP genotyping. Nat Genet 2008;40:1199–203.
    OpenUrlCrossRefPubMed
  37. ↵
    1. Bill-Axelson A,
    2. Holmberg L,
    3. Filen F,
    4. et al
    . Radical prostatectomy versus watchful waiting in localized prostate cancer: the Scandinavian prostate cancer group-4 randomized trial. J Natl Cancer Inst 2008;100:1144–54.
    OpenUrlAbstract/FREE Full Text
View Abstract
PreviousNext
Back to top
Cancer Epidemiology Biomarkers & Prevention: 19 (11)
November 2010
Volume 19, Issue 11
  • Table of Contents
  • Table of Contents (PDF)

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Cancer Epidemiology, Biomarkers & Prevention article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Genome-wide Association Study of Prostate Cancer Mortality
(Your Name) has forwarded a page to you from Cancer Epidemiology, Biomarkers & Prevention
(Your Name) thought you would be interested in this article in Cancer Epidemiology, Biomarkers & Prevention.
Citation Tools
Genome-wide Association Study of Prostate Cancer Mortality
Kathryn L. Penney, Saumyadipta Pyne, Fredrick R. Schumacher, Jennifer A. Sinnott, Lorelei A. Mucci, Peter L. Kraft, Jing Ma, William K. Oh, Tobias Kurth, Philip W. Kantoff, Edward L. Giovannucci, Meir J. Stampfer, David J. Hunter and Matthew L. Freedman
Cancer Epidemiol Biomarkers Prev November 1 2010 (19) (11) 2869-2876; DOI: 10.1158/1055-9965.EPI-10-0601

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Genome-wide Association Study of Prostate Cancer Mortality
Kathryn L. Penney, Saumyadipta Pyne, Fredrick R. Schumacher, Jennifer A. Sinnott, Lorelei A. Mucci, Peter L. Kraft, Jing Ma, William K. Oh, Tobias Kurth, Philip W. Kantoff, Edward L. Giovannucci, Meir J. Stampfer, David J. Hunter and Matthew L. Freedman
Cancer Epidemiol Biomarkers Prev November 1 2010 (19) (11) 2869-2876; DOI: 10.1158/1055-9965.EPI-10-0601
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosure of Potential Conflicts of Interest
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • Associations of Tobacco and Alcohol with SINT Risk in Utah
  • A Prognostic ATG-Based Scoring System in Ovarian Cancer
  • Antiparietal Cell Antibody and Esophageal and Gastric Cancer
Show more Research Articles
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook   Twitter   LinkedIn   YouTube   RSS

Articles

  • Online First
  • Current Issue
  • Past Issues

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians
  • Reviewers

About Cancer Epidemiology, Biomarkers & Prevention

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2019 by the American Association for Cancer Research.

Cancer Epidemiology, Biomarkers & Prevention
eISSN: 1538-7755
ISSN: 1055-9965

Advertisement