Cumulative Association of Five Genetic Variants with Prostate Cancer
From: The New England Journal of Medicine
Volume 358:910-919 February 28, 2008 Number 9

S. Lilly Zheng, M.D., Jielin Sun, Ph.D., Fredrik Wiklund, Ph.D., Shelly Smith, M.S., Pär Stattin, M.D., Ph.D., Ge Li, M.D., Hans-Olov Adami, M.D., Ph.D., Fang-Chi Hsu, Ph.D., Yi Zhu, B.S., Katarina Bälter, Ph.D., A. Karim Kader, M.D., Ph.D., Aubrey R. Turner, M.S., Wennuan Liu, Ph.D., Eugene R. Bleecker, M.D., Deborah A. Meyers, Ph.D., David Duggan, Ph.D., John D. Carpten, Ph.D., Bao-Li Chang, Ph.D., William B. Isaacs, Ph.D., Jianfeng Xu, M.D., D.P.H., and Henrik Grönberg, M.D., Ph.D.

ABSTRACT

Background Single-nucleotide polymorphisms (SNPs) in five chromosomal regions — three at 8q24 and one each at 17q12 and 17q24.3 — have been associated with prostate cancer. Each SNP has only a moderate association, but when SNPs are combined, the association may be stronger.

Methods We evaluated 16 SNPs from five chromosomal regions in a Swedish population (2893 subjects with prostate cancer and 1781 control subjects) and assessed the individual and combined association of the SNPs with prostate cancer.

Results Multiple SNPs in each of the five regions were associated with prostate cancer in single SNP analysis. When the most significant SNP from each of the five regions was selected and included in a multivariate analysis, each SNP remained significant after adjustment for other SNPs and family history. Together, the five SNPs and family history were estimated to account for 46% of the cases of prostate cancer in the Swedish men we studied. The five SNPs plus family history had a cumulative association with prostate cancer (P for trend, 3.93x10–28). In men who had any five or more of these factors associated with prostate cancer, the odds ratio for prostate cancer was 9.46 (P=1.29x10–8), as compared with men without any of the factors. The cumulative effect of these variants and family history was independent of serum levels of prostate-specific antigen at diagnosis.

Conclusions SNPs in five chromosomal regions plus a family history of prostate cancer have a cumulative and significant association with prostate cancer.

Genomewide association studies of complex diseases have identified sequence variants that are consistently associated with the risk of such diseases.1 Often such variants have limited use in the assessment of disease risk in an individual patient, since most of them confer a relatively small risk. Whether combinations of individual variants confer larger, more clinically useful associations with increased risk remains to be shown.

Age, race, and family history are three factors that have a consistent association with the risk of prostate cancer.2 A meta-analysis showed a pooled odds ratio of 2.5 for men who had a first-degree relative with the disease.3 Recently, genomewide analysis has identified variants in five chromosomal regions that are significantly associated with a risk of prostate cancer. These variants occur in three independent regions at 8q244,5,6,7 and in one region at 17q12 and another at 17q24.3.8 These five regions probably harbor genes that confer susceptibility to prostate cancer or regulate factors affecting critical genes, but the specific genes in these regions have not been identified.

Individually, single-nucleotide polymorphisms (SNPs) in each of the five chromosomal regions were shown to have only a moderate association with prostate cancer in previous studies. In our study, we investigated whether a combination of SNPs would have a stronger association with prostate cancer than any individual SNP. For this purpose, we assessed the joint associations of SNPs in the five chromosomal regions with prostate cancer in a large-scale study of Swedish men.

Methods

Study Subjects

The study population has been described in detail elsewhere.9 Briefly, we conducted a population-based, case–control study in Sweden, called CAPS (Cancer Prostate in Sweden). Subjects with prostate cancer were identified and recruited from four of the six regional cancer registries in Sweden. The inclusion criterion for case subjects was biopsy-confirmed or cytologically verified adenocarcinoma of the prostate, diagnosed between July 2001 and October 2003. Among 3648 identified subjects with prostate cancer, 3161 (87%) agreed to participate. DNA samples from blood, tumor–node–metastasis (TNM) stage, Gleason grade (as determined by biopsy), and levels of prostate-specific antigen (PSA) at diagnosis were available for 2893 subjects (92%). Case subjects were classified as having advanced disease if they met any of the following criteria: a grade 3 or 4 tumor, spread to nearby lymph nodes and metastasis, a Gleason score of 8 or more, or a PSA level of more than 50 ng per milliliter; otherwise, subjects were classified as having localized disease.

Control subjects, who were recruited concurrently with case subjects, were randomly selected from the Swedish Population Registry and matched according to the expected age distribution of cases (groups of 5-year intervals) and geographic region. A total of 2149 of 3153 control subjects (68%) who were invited subsequently agreed to participate in the study. DNA samples from blood were available for 1781 control subjects (83%). Serum PSA levels were measured for all control subjects but were not used as an exclusionary variable. A history of prostate cancer among first-degree relatives was obtained from a questionnaire for both case subjects and control subjects.

Table 1 presents the demographic and clinical characteristics of the study subjects. Recruitment of the study population was completed in two phases, each with a similar number of subjects; the first phase (CAPS-1) ended October 31, 2002, and the second phase (CAPS-2) ended November 1, 2002. Each subject provided written informed consent. The study received institutional approval from the Karolinska Institutet, Umeå University, and Wake Forest University School of Medicine.

View this table: Table 1. Clinical and Demographic Characteristics of the Subjects.

Selection of SNPs for Genotyping

We selected 16 SNPs from five chromosomal regions (three at 8q24 and one each at 17q12 and 17q24.3) that have been reported to be associated with prostate cancer.6,7,8,10 Polymerase-chain-reaction (PCR) assays and extension primers for these SNPs were designed with the use of MassARRAY software, version 3.0 (Sequenom). (The primer information is available at www.wfubmc.edu/genomics.) PCR and extension reactions were performed according to the manufacturer's instructions, and extension product sizes were determined by mass spectrometry with the use of the iPLEX system (Sequenom). Duplicate test samples and two water samples (PCR-negative controls), of which the technician was unaware, were included in each 96-well plate. The rate of concordant results between duplicate samples was more than 99%.

Statistical Analysis

Tests for Hardy–Weinberg equilibrium were performed for each SNP separately among case subjects and control subjects with the use of Fisher's exact test. Pairwise linkage disequilibrium was tested for SNPs within each of the five chromosomal regions in control subjects with the use of SAS/Genetics software, version 9.0 (SAS Institute).

Differences in allele frequencies between case subjects and control subjects were tested for each SNP with the use of a chi-square test with 1 degree of freedom. Allelic odds ratios and 95% confidence intervals were estimated on the basis of a multiplicative model. For genotypes, a series of tests assuming an additive, dominant, or recessive genetic model were performed for each of the five SNPs with the use of unconditional logistic regression with adjustment for age and geographic region; the model that had the highest likelihood was considered to be the best-fitting genetic model for the respective SNP.

We tested the independent effect of each of the five previously implicated regions by including the most significant SNP from each of the five regions in a logistic-regression model with the use of a backward-selection procedure. Multiplicative interactions were tested for each pair of SNPs by including both main effects and an interaction term (a product of two main effects) in a logistic-regression model. We tested the cumulative effects of the five SNPs on prostate cancer by counting the number of genotypes associated with prostate cancer (on the basis of the best-fitting genetic model from single-SNP analysis) for these five SNPs in each subject. The odds ratio for prostate cancer for men carrying any combination of one, two, three, or four or more genotypes associated with prostate cancer was estimated by comparing them with men carrying none of the prostate-cancer–associated genotypes with the use of logistic-regression analysis. We also performed tests for the cumulative effect on prostate-cancer association, which included five SNPs and family history.

Population attributable risk (PAR) was estimated for SNPs that remained significant after adjustment for other covariates with the use of the following equation:

PAR% = 100xp(odds ratio–1) ÷ [p(odds ratio–1)+1].

In this equation, p is the prevalence of genotypes associated with prostate cancer among control subjects.11 The joint PAR was calculated on the basis of the individual PAR of each associated SNP, assuming no multiplicative interaction among the SNPs, with the use of the following equation:

Figure 1



In this equation, PARi is the individual PAR for each associated SNP calculated under the full model. For the model that included five SNPs and a family history of prostate cancer, the joint PAR for the associated factors was calculated in a similar manner.

Associations of these five SNPs with TNM stages, aggressiveness of prostate cancer (advanced or localized), and family history (yes or no) were tested only among case subjects with the use of a chi-square test of a 2xK table, in which K is the number of possible categories within each variable. A test for trend was used to assess the proportion of genotypes associated with prostate cancer with each increasing Gleason score, from 4 or less to 10. Associations of SNPs with the mean age at diagnosis were tested only among case subjects with the use of a two-sample t-test. Because serum PSA levels were not normally distributed, a nonparametric analysis (Wilcoxon rank-sum test) was used to assess the association between SNPs and preoperative serum PSA levels in case subjects or PSA levels at the time of sampling in control subjects. All reported P values are based on a two-sided test.

Results

Sixteen SNPs in five chromosomal regions (three at 8q24 and two at 17q), which were previously implicated in harboring genes that confer susceptibility to prostate cancer, were evaluated. In the control group, each SNP was in Hardy–Weinberg equilibrium (P=0.05). Significant pairwise linkage disequilibrium (P<0.05) was observed for the SNPs within each region.

Table 2 lists allele frequencies of the 16 SNPs among case and control subjects and shows the results of allelic and genotypic tests. Significantly different frequencies (P<0.05) between case and control subjects were observed for SNPs in each of the five chromosomal regions. At 17q12, SNP rs4430796 had the strongest association with prostate cancer; the frequency of allele T (SNP rs4430796) was 0.61 in case subjects and 0.56 in control subjects (P=6.0x10–7). Of the four SNPs at 17q24.3, three were associated with prostate cancer, but only rs1859962 had a highly significant association (P=2.1x10–4). The results for 17q12 and 17q24.3 were similar to those that were reported previously.8 For SNPs at 8q24, significant associations with prostate cancer were found for all SNPs examined across the three independent regions at 8q24. Of the 16 SNPs, 13 remained significant at P<0.05 after adjustment for 16 tests with the use of a Bonferroni correction.

View this table: Table 2. Association of SNPs at Five Chromosomal Regions with Prostate Cancer.

Carriers of previously reported risk-associated alleles for SNPs at 17q12, 17q24.3, and 8q24 were significantly more likely to have prostate cancer than were control subjects (Table 2). When various genetic models were tested for SNPs at each region, a recessive model was the best-fitting genetic model for SNPs at 17q12 and 17q24.3, and a dominant model was the best-fitting genetic model for SNPs at regions 1, 2, and 3 of 8q24.

Strong genetic dependence (linkage disequilibrium) among SNPs within each region allowed for a combined analysis in which we were able to select one SNP (the most significant SNP from single SNP analysis) to represent each of the five regions in tests for an independent association with prostate cancer (Table 3). When these five SNPs were included in a multivariate logistic-regression model, each of the five remained significantly associated with prostate cancer after adjustment for other SNPs, and each continued to be highly significant when family history was included in the model. On the basis of adjusted odds ratios for each of these five SNPs and a positive family history, PARs were estimated to account for 4 to 21% of prostate-cancer cases in the Swedish population we studied. The estimated joint PAR for prostate cancer of the five associated SNPs plus family history was 46% in the studied population.

View this table: Table 3. Adjusted Odds Ratios and Population Attributable Risks (PARs) for Representative SNPs at Five Chromosomal Regions and Family History.

When multiplicative interaction was tested for each possible pair of these five SNPs with the use of an interaction term in logistic regression, none were significant at P<0.05. However, the five SNPs appeared to have a cumulative association with prostate cancer, after adjustment for age, geographic region, and family history (Table 4). Men who carried one, two, three, or four or more of the five SNPs had an increasing likelihood of having prostate cancer, as compared with men who did not carry any of the five SNPs (P for trend, 6.75x10–27). When family history was included as another risk factor (coded as 0 or 1) for a total of six possible prostate-cancer associated factors, we observed a stronger cumulative effect after adjustment for age and geographic region (P for trend, 4.78x10–28). For example, men who carried any five or more of these six factors had an odds ratio of 9.46 (95% confidence interval [CI], 3.62 to 24.72) for prostate cancer, as compared with men who carried none of the six factors (P=1.29x10–8). This cumulative effect was similarly observed in two subgroups of study subjects, with a P for trend of 1.36x10–10 in CAPS-1 and of 9.03x10–20 in CAPS-2 (data not shown).

View this table: Table 4. Cumulative Effect of Associated Factors on the Risk of Prostate Cancer.

We calculated the specificity and sensitivity of the regression model by constructing receiver-operating-characteristic (ROC) curves and calculated statistics for the area under the curve (AUC) to estimate the ability of each of three models to distinguish case subjects from control subjects. The AUC was 57.7 (95% CI, 56.0 to 59.3) for model 1 (age and region alone), 60.8 (95% CI, 59.1 to 62.4) for model 2 (age, region, and family history), and 63.3 (95% CI, 61.7 to 65.0) for model 3 (age, region, family history, and the number of genotypes associated with prostate cancer at the five SNPs). The AUC was significantly higher for model 3 than for model 2 (P=6.12x10–6). It is important to note that overfitting could have influenced our results, and for this reason the models require verification in independent populations.

Table 5 shows that none of the five SNPs were significantly associated with the aggressiveness of prostate cancer, the Gleason score, the presence or absence of family history, the serum PSA level at diagnosis, or the age at diagnosis. Furthermore, no associations with these clinical variables were found when multiple SNPs associated with prostate cancer were considered simultaneously. For example, the 154 case subjects who carried four or more of the five SNPs were not significantly different from the 162 case subjects who had none of the SNPs with regard to the following clinical variables: positive family history (17% with four or more SNPs and 21% with no SNPs, P=0.39), the proportion with advanced disease (54% and 48%, respectively; P=0.33), and the median serum PSA level at diagnosis (15 ng and 14 ng per milliliter, respectively; P=0.27). A lack of association between the SNPs at 8q24 and clinical characteristics was also reported previously,7,12,13,14 but in other studies a trend was found between 8q24 SNPs and a high Gleason grade, tumor stage, and aggressive disease.4,5,6,15,16 Thus, the association of these SNPs with clinical features of prostate cancer remains an open question.

View this table: Table 5. Association of Five SNPs with Clinical Characteristics.

Discussion

In genomewide studies, multiple chromosomal regions at 8q24 and 17q have been associated with prostate cancer.4,5,6,7,8 All three regions at 8q24 have been replicated in all published studies,10,12,13,14,15,16 but no study has yet replicated the associations in regions at 17q. The highly significant findings at 17q12 and 17q24.3 in our study independently confirm the association of these two regions with prostate cancer. In addition, we confirmed the association of SNPs at regions 1, 2, and 3 of 8q24 with prostate cancer. This independent confirmation of the association of these five chromosomal regions with prostate cancer supports the validity of genetic association studies in complex diseases.

Although each of the SNPs in the five chromosomal regions was only moderately associated with prostate cancer, we found that they had a strong cumulative association with the disease. We estimated that men who have five or more of the six factors associated with prostate cancer (specific genotypes at five SNPs and a positive family history for the disease) have an odds ratio of 9.46 for prostate cancer. The cumulative effect is highly significant in our overall study sample (P for trend, 4.78x10–28) and consistent between the two subgroups in CAPS-1 and CAPS-2. It may be possible to use the combined information from the five SNPs and family history to assess an individual patient's risk of prostate cancer, but this strategy will have to be tested in a prospective study before proceeding with any such risk assessments.

We found that the presence of the five prostate-cancer–associated SNPs was independent of PSA levels in both case subjects (Table 5) and control subjects (data not shown), which suggests that some men with low PSA levels may have an increased risk of prostate cancer if they carry one or more of the prostate-cancer–associated genotypes described here. However, this proposition also requires testing in a prospective trial, particularly one that uses PSA in combination with the associated SNPs and family history.

We do not know the mechanism by which the SNPs we analyzed could affect the risk of prostate cancer. Other than SNP rs4430796, which is located within the TCF2 gene, the specific genes that are affected by the rest of the SNPs have not been identified. Since the five SNPs in our study appear to be associated with a risk of prostate cancer in general, rather than with a more or less aggressive form, we suspect that the genetic variants act at an early stage of carcinogenesis.

Our study is only a first step toward defining a genetic association with prostate cancer in populations. Future investigations will need to test the value of these findings in assessing the risk of prostate cancer in individual men.

Supported by grants (CA105055, CA106523, and CA95052, to Dr. Xu, and CA112517 and CA58236, to Dr. Isaacs) from the National Cancer Institute; a grant (PC051264, to Dr. Xu) from the Department of Defense; grants (to Dr. Grönberg) from the Swedish Cancer Society and the Swedish Academy of Sciences; an endowment from William T. Gerrard, Mario A. Duhon, and John and Jennifer Chalsty (to Dr. Isaacs); and a David H. Koch award (to Dr. Isaacs) from the Prostate Cancer Foundation.

A patent application has been filed by the Wake Forest University School of Medicine, Johns Hopkins University School of Medicine, and Dr. Henrik Grönberg at Karolinska Institutet, Stockholm, to preserve patent rights for the technology and results described in this study. No other potential conflict of interest relevant to this article was reported.

We thank all the study subjects who participated in the CAPS study and urologists who included their patients in the CAPS study, the Regional Cancer Registries, and the CAPS steering committee, including Drs. Jan Adolfsson, Jan-Erik Johansson, and Eberhart Varenhorst.

Source Information

From the Center for Human Genomics (S.L.Z., J.S., S.S., G.L., F.-C.H., Y.Z., A.R.T., W.L., E.R.B., D.A.M., B.-L.C., J.X.) and the Departments of Biostatistical Sciences (F.-C.H.) and Urology (A.K.K.), Wake Forest University School of Medicine, Winston-Salem, NC; the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (F.W., H.-O.A., K.B., H.G.); the Department of Urology, Umeå University Hospital, Umeå, Sweden (P.S.); the Department of Epidemiology, Harvard School of Public Health, Boston (H.-O.A.); Translational Genomics Research Institute, Phoenix, AZ (D.D., J.D.C.); and Johns Hopkins Medical Institutions, Baltimore (W.B.I.).

This article (10.1056/NEJMoa075819) was published at www.nejm.org on January 16, 2008.

Address reprint requests to Dr. Xu at the Center for Human Genomics, Medical Center Blvd., Winston-Salem, NC 27157, or at jxu@wfubmc.edu; or to Dr. Isaacs at Marburg 115, Johns Hopkins Hospital, 600 N. Wolfe St., Baltimore, MD 21287, or at wisaacs@jhmi.edu.

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