Affiliations of authors: Department of Radiation Sciences/Oncology, Umeå University, Umeå, Sweden (FL, FW, HG); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (KAB, MH, HOA); Department of Urology and Andrology, Umeå University Hospital, Umeå, Sweden (PS); Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC (SLZ, JB, BC, JC, DAM, JX); Department of Urology, Johns Hopkins Medical Institutions, Baltimore, MD (WI)
Correspondence to: Henrik Grönberg, PhD, Department of Radiation Sciences/Oncology, Umeå University, S-901 87 Umeå, Sweden (e-mail: henrik.gronberg{at}oc.umu.se)
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ABSTRACT |
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INTRODUCTION |
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Inflammatory changes have long been recognized in prostate tissues and have more recently led to speculation that inflammation might favor prostate cancer development. However, the role of inflammation in the development of prostate cancer is still unclear. Accumulating epidemiologic and molecular evidence suggest that prostatic inflammation may be an important component in the pathogenesis of prostate cancer (4). For example, McCarron et al. reported an association between prostate cancer risk and sequence variants in cytokine genes such as tumor necrosis factor alpha (TNF-), interleukin 8 (IL-8), and IL-10 (5). In addition, two of the prostate cancer susceptibility genes identified to date, RNASEL and MSR1, encode proteins with important functions in the host response to infection (6, 7).
Macrophage inhibitory cytokine1 (MIC-1) is a member of the transforming growth factor (TGF-
) superfamily: proteins that play an important role in the pro- and anti-inflammatory response to infection. The gene that encodes MIC-1 is also known as growth/differentiation factor 15 (GDF-15) (8), placental bone morphogenetic protein (PLAB) (9,10), and prostate-derived factor (PDF) (11). Although structural similarities strongly suggest that MIC-1 belongs to the TGF-
superfamily, the function of MIC-1 is not clear. A study by Bootcov et al. (12) suggests that MIC-1 may be a regulator of macrophage activation that is designed to limit later phases of macrophage activation by responding to secreted proinflammatory monokines. The MIC-1 protein is synthesized as a 308-amino-acid propeptide. The propeptide is cleaved by a furin-like protease at a conserved site (amino acids 193196). Following cleavage, the mature, 112-amino-acid protein is secreted as a disulfide-linked homodimer. Similar to TGF-
s 15, MIC-1 has two cysteine residues near the amino terminus of the mature protein. A GC polymorphism, identified by Fairlie et al. (13), changes the basic amino acid histidine (H) to aspartic acid (D) at position 6 of the mature MIC-1 protein. The neighboring cysteine, at position 7, is important for the stability of MIC-1. Because of the different biochemical properties of aspartic acid and histidine, the H6D polymorphism may alter MIC-1's stability and function.
Several lines of evidence suggest that changes in MIC-1 function might affect the prostate. Several studies have reported that the MIC-1 gene is expressed at higher levels in prostate cancer tissue than in normal prostate tissue (14,15). Furthermore, a recent study by Liu et al. (16) indicated that dihydrotestosterone and -estradiol, important factors in prostate cancer progression, markedly reduced MIC-1 secretion in hormone-sensitive LNCaP human prostate cancer cells. In addition, the authors also observed a reduction of cellcell adhesion and an induction of apoptosis in DU-145 cells (which do not secrete MIC-1) treated with MIC-1. Also, MIC-1 serum levels were markedly higher in patients with metastatic prostate cancer than in patients with breast or colorectal cancer, consistent with observations that MIC-1 gene expression is higher in prostate cancer tissues than in breast and colorectal cancer tissues (17). Finally, two independent genome-wide scans for prostate cancer susceptibility genes in families affected with hereditary prostate cancer identified a region on chromosome 19 with suggestive evidence for linkage; the strongest evidence for linkage was on 19p13 (18,19), where the MIC-1 gene is located. Altogether, these observations indicate an important function of MIC-1 in the development and progression of prostate cancer and suggest that the MIC-1 gene may be a good candidate gene for prostate cancer susceptibility.
The aim of this study was to test the hypothesis that there is an association between prostate cancer risk and MIC-1 gene sequence variants. We examined this question in a large population-based study in Sweden. Our goal was to evaluate whether polymorphisms in a gene that regulates inflammatory processes might influence the risk of prostate cancer.
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SUBJECTS AND METHODS |
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Cases studied came from Cancer Prostate in Sweden (CAPS), a large, population-based casecontrol study. The case patients were recruited from four of the six regional cancer registries that cover the entire population of Sweden. Each of these registries serves one health care region (northern, central, Stockholm, and southeastern) and altogether encompass approximately 6 million inhabitants (67% of Sweden's population). Swedish law requires that both the attending physician and pathologist report newly diagnosed cancer cases to the cancer registries. Therefore, the registries include almost 100% of all cancer diagnosed in Sweden.
In the CAPS study, the sourcepersontime comprised men living in the area of Örebro and the northern part of Sweden (Västernorrland, Jämtland, Västerbotten, Norrbotten) from January 1, 2001, as well as men living in the areas of Västmanland, Södermanland, Gävleborg, Dalarna, Värmland, Uppland, Stockholm, Östergötland, and Småland from July 1, 2001, until September 2002 (except for Jämtland and the county of Lycksele in Västerbotten, where the sourcepersontime ended March 1, 2002). The sourcepersontime was divided into two age-specific study groups. The first group included men 3565 years of age, living in all the regions mentioned above. The second study base included men 6679 years of age at the time of study entry, living only in Örebro, Västmanland, Södermanland, and the northern part of Sweden.
Recruitment of Case Patients and Control Subjects
The inclusion criterion for cases in CAPS was pathologically or cytologically verified adenocarcinoma of the prostate. After being notified about a new case, the administrator at the regional cancer registry mailed a letter to the treating physician to inform him or her about the study. In the letter, the physician was asked to approve the patient's participation in the study. If approval was given, the physician mailed a letter to the patient describing the study and asked the patient to send a reply letter to the administrator at the cancer registry. After approval from both the physician and the patient, the study secretaries sent a questionnaire and a kit with tubes for blood sampling to each eligible case patient. The self-administered questionnaire included questions regarding diet (validated food questionnaire), family history, smoking, and physical activity.
In total, 1961 prostate cancer patients were invited to participate; of those, 1444 (73.6%) agreed to participate by donating a blood sample and/or answering the questionnaire. Clinical data that are not included in the Cancer Registry were obtained from the National Prostate Cancer Registry (NPCR; http://www.roc.se [last accessed: June 21, 2004]). The case patients were linked to the NPCR, and clinical information such as TNM (tumornodemetastasis) stage, Gleason sum, PSA (prostate-specific antigen) level at the time of diagnosis and method of diagnosis, and primary treatment were obtained for 95.3% of the cases. The case patients were thereafter classified as having either localized diseaseT1-2 and N0/NX and M0/MX and Grade III/Gleason sum 27, and PSA level less than 100 µg/L or advanced (prone to progressive) diseaseT3/4 or N+ or M+ or Grade III or Gleason sum 810 or PSA level greater than 100 µg/L.
Control subjects were randomly selected from the updated Swedish Population Registry, identified by their unique citizen personal identification number, and frequency matched according to the expected age distribution (within 5 years) and geographic origin of the case patients. After the control subjects had been identified, they were mailed an introduction letter describing the study. Three to four weeks later, the same questionnaire and kit with tubes for blood sampling as those for the case patients were mailed to the control subjects. Of the 1697 randomly selected control subjects invited, 866 (51.0%) agreed to participate by blood donation and/or questionnaire. Eight potential control subjects were excluded after linkage to the National Cancer Register revealed that they had had a prostate cancer diagnosis prior to study inclusion.
To improve the response rate, we recontacted case patients and control subjects three times: after 34 weeks with a follow-up letter, after 68 weeks with a new questionnaire and blood draw kit, and after about 12 weeks with a phone call. The clinical characteristics of the study participants are presented in Table 1. Mean age (age at diagnosis for case patients and age at inclusion for control subjects) for the case patients and control subjects was 66.6 and 67.9 years, respectively. Written informed consent was obtained from each participant, and the study was approved by the ethics committees at the two participating academic institutions, Karolinska Institute and Umeå University.
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All case patients in CAPS who reported at least one family member with prostate cancer in the initial questionnaire were contacted again. A more detailed family history was obtained in these selected families through a second questionnaire and a subsequent telephone interview. All prostate cancer cases in first-, second-, and, if possible, third-degree relatives were independently verified through the Cancer Registry or medical records. The families were subsequently classified as hereditary prostate cancer, defined as at least three verified first- or second-degree relatives with prostate cancer (49 case patients), or as familial prostate cancer, defined as two first- or second-degree relatives with prostate cancer (116 case patients).
Blood Samples and DNA Extraction
We followed strict procedures for blood handling and transport to ensure consistency. All study participants were instructed to donate blood (four 10-mL samples) at the nearest health clinic or hospital. Samples were stored at room temperature and mailed overnight to the Medical Biobank at Umeå University. After blood samples arrived at the Biobank, leukocytes, erythrocytes, plasma, and serum were separated into different tubes. Samples were stored at 70 °C until analysis. DNA was extracted from leukocytes by using a Puregene Kit (Gentra Systems, Minneapolis, MN). DNA was available for 1383 (95.8%) of the case patients and for 780 (90.9%) of the control subjects that agreed to participate.
Genotyping
Eight single-nucleotide polymorphisms (SNPs) were selected from the MIC-1 gene, including the promoter, exons, introns, and the 3' untranslated region (3' UTR) and were genotyped in 94 randomly selected control subjects using a 5' nuclease TaqMan assay together with fluorescently labeled Minor Grove Binders probes. The SNP genotyping assays were designed using the Assay-by-Design service (Applied Biosystems, Foster City, CA). All reactions were performed in a 25-µL volume consisting of 10 ng of genomic DNA, 900 nM of each primer, 200 nM of each probe, and 12.5 µL of TaqMan universal master mix. Polymerase chain reaction (PCR) cycling conditions were as follows: 50 °C for 2 minutes, 95 °C for 10 minutes followed by 40 cycles of 92 °C for 15 seconds, and 60 °C for 1 min. The samples were analyzed on an ABI 7700 sequence detection system.
Four haplotype-tagging SNPs (Exon1+25, Exon1+142, IVS1+1809, and Exon2+2423), representing 98.6% of the haplotype variation of the eight most common haplotypes, were selected from the eight SNPs above and were genotyped using the MassARRAY system (SEQUENOM, Valencia, CA). The DNA samples were labeled in a blinded manner and shipped from Umeå University in Sweden to the core genotyping laboratory in the Center for Human Genomics, Wake Forest University. In total, 37 controls from the CEPH foundation (http://www.cephb.fr [last accessed: July 14, 2004]) (1331-01, 1331-02) and 29 blind repeats were spread among the DNA samples. In addition, every DNA plate contained two water blanks. PCR was performed in a total volume of 5 µL with 10 ng of genomic DNA, 2.5 mM of MgCl2, 0.1 U of HotStart Taq polymerase (QIAGEN, Valencia, CA), 200 µM of deoxynucleoside triphosphates (dNTPs), and 200 nM of primer. Cycling conditions were 95 °C for 15 minutes, followed by 45 cycles of 95 °C for 20 seconds, 50 °C for 30 seconds, and 72 °C for 1 minute with a final extension step of 72 °C for 3 minutes. The homogeneous MassEXTEND reactions with the four SNPs were performed in a total volume of 9 µL with 50 µM d/ddNTP each, 0.063 U of Thermo Sequenase (both from SEQUENOM) per microliter, and 600 nM of extension primer. The cycling conditions were 94 °C for 2 minutes, followed by 55 cycles of 94 °C for 5 seconds, 52 °C for 5 seconds and 72 °C for 5 seconds. The homogenous MassEXTEND reaction products were purified with SpectroCLEAN, transferred to SpectroCHIP using SpectroPOINT, and then scanned through SpectroREADER. Genotyping was done using SpectroTYPER (all from SEQUENOM). A full list of PCR primers and probes is available at http://www.wfubmc.edu/genomics (last accessed: June 21, 2004).
Sequencing Analysis
To identify MIC-1 sequence variants, we sequenced the 2-kb MIC-1 promoter region in 24 randomly selected control subjects. PCR was performed in a 10-µL volume containing 30 ng of genomic DNA, 0.2 µM of each primer, 0.2 mM of each dNTP, 1.5 mM of MgCl2, and 0.25 U of HotStart Taq DNA polymerase (QIAGEN). PCR cycling conditions were as follows: 95 °C for 15 minutes, followed by 35 cycles of 95 °C for 30 seconds, 63 °C for 30 seconds, and 72 °C for 50 seconds, with a final extension step of 72°C for 6 minutes. All PCR products were purified using the QuickStep PCR purification kit (Edge BioSystems, Gaithersburg, MD) to remove dNTPs and excess primers. All sequencing reactions were performed using dye-terminator chemistry (BigDye; ABI, Foster City, CA) and precipitated using 63% (±5%) ethanol. Samples were loaded onto an ABI 3730 DNA analyzer after adding 2025 µL of H2O. SNPs were identified using Sequencher software version 4.0.5 (Gene Codes Corporation). A list of sequencing primers is available at http://www.wfubmc.edu/genomics.
Statistical Analysis
HardyWeinberg equilibrium tests for each sequence variant and pairwise linkage disequilibrium tests for all sequence variants were performed using a replication method, as previously described (20). For each test, 10 000 permutations were performed, and the Fisher's probability test statistic of each replicate was calculated from the new corresponding multilocus table. Empirical P values for each test were estimated as the proportion of replicate datasets found to be as probable as or less probable than the observed dataset, using the Genetic Data Analysis software package (20).
Associations between genotypes and prostate cancer were assessed by the score test in conditional logistic regression of a covariate equal to number of rare alleles (0, 1, 2). Genotype-specific risks were estimated as odds ratios (ORs) with associated 95% confidence intervals (CIs) with conditional logistic regression. When testing for association and estimating odds ratios, we stratified the conditional logistic regression by each combination of age (5-year age groups) and geographical region (two regions, as described in the Study Population section) to adjust for the matching conducted in collecting control subjects. Besides age and geographical region, no other factors were included in the regression analysis. Haplotypes were estimated using a model proposed by Stephens, using the PHASE software package (http://www.stats.ox.ac.uk/mathgen/software.html [last accessed: June 21, 2004]). This method uses a Markov Chain Monte Carlo approach to estimate haplotypes and incorporates a statistical model for the distribution of unresolved haplotypes based on coalescence theory. A subset of haplotype-tagging SNPs (htSNPs) that retained at least 95% of the haplotype information observed among the 94 control individuals were selected using the software program htSNP2 (http://www-gene.cimr.cam.ac.uk/clayton/software/stata [last accessed: June 21, 2004]). When comparing different subsets of candidate htSNPs, the overall proportion of diversity explained was used as a selection criterion. Tests for association between the haplotypes and prostate cancer risk were performed with a score test developed by Schaid et al. (21) using the HAPLO.SCORE program (http://lib.stat.cmu.edu/R/CRAN/index.html [last accessed: June 21, 2004]) for the R programming language. This method, based on the generalized linear model framework, allows adjustment for possible confounding variables and provides both global and haplotype-specific tests. In these analyses, age and geographic region were adjusted for through indicator variables representing each combination of age category (5-year age groups) and geographical region (northern part of Sweden versus southeastern part of Sweden and the area of Stockholm). Haplotypes with estimated frequencies of less than 0.005 were pooled into a single group. Empirical P values, based on 10 000 simulations, were computed for the global score test and each of the haplotype-specific score tests. All reported P values are two-sided.
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RESULTS |
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To comprehensively evaluate a potential association between sequence variants in the MIC-1 gene and prostate cancer, we used a haplotype-tagging SNP approach in the selection of sequence variants. First, we defined the MIC-1 gene target region as 2 kb of the promoter, all exons and introns, and the 3' UTR. We then selected a subset of eight common SNPs in this target region by using the criteria of SNPs with minor allele frequencies of 5% or greater at a density of 1 SNP per kilobase, with additional attention paid to potentially functional SNPs. These SNPs were identified either through public databases (NCBI dbSNP [http://www.ncbi.nlm.nih.gov/SNP] and SNPer [http://snpper.chip.org {both last accessed: July 14, 2004}], or when detailed public SNP information was not available, from our resequencing data in 24 control subjects). Six SNPs in the promoter region with minor allele frequencies of 5% or greater were identified by the resequencing of the 24 control subjects, from which two SNPs (MIC-11576 and MIC-1893) were selected for genotyping. From the public database, six SNPs (Exon1+25, Exon1+142, IVS1+904, IVS1+1809, Exon2+2423, and 3' UTR+2816) were selected in the exons, the introns, and the 3' UTR. Among these six, the SNPs Exon1+25, Exon1+142, and Exon2+2423 cause amino acid changes at codons 9 (V to L), 48 (S to T), and 202 (H to D), respectively, in the precursor protein. The last SNP (Exon2+2423) is also referred to as H6D because the amino acid change is located at position 6 of the mature MIC-1 protein.
These eight SNPs were genotyped in 94 randomly selected control subjects. The SNP MIC11576 deviated statistically significantly from HardyWeinberg equilibrium (P = .003) and was therefore excluded from further analysis. Four SNPs (Exon1+25, Exon1+142, IVS1+1809, and Exon2+2434 [H6D]), which captured 98.6% of the haplotype variation (eight haplotypes) among the 94 controls, were selected as htSNPs. These four htSNPs were genotyped for all 1383 case patients and 780 control subjects. Each of these four SNPs was in HardyWeinberg equilibrium among case patients and control subjects, respectively (all P>.05). These SNPs were in strong linkage disequilibrium because most of the pairwise D' estimates were 1.0, with the lowest occurring at 0.94.
Association Analysis
No indication of genotyping error was observed. Genotype consistency was 100% among the CEPH control DNA, and results from duplicated samples were 100% concordant, giving an estimated error rate of 0%. A statistically significant difference (P = .006) in genotype frequency was observed for the H6D variant between prostate cancer patients and control subjects, with a higher proportion of homozygotes for the common CC allele, which encodes the wild-type protein, among prostate cancer case patients (53.0% versus 48.5%) (Table 2). The estimated odds ratio for prostate cancer was lower for carriers of the GC or GG genotype, which encodes the H6D mutant, compared with CC genotype carriers (OR = 0.83, 95% CI = 0.69 to 0.99) (Table 3). The decreased risk for carriers of the GC or GG genotype compared with that of the CC genotype carriers was further accentuated both in patients with advanced disease (OR = 0.79 [95% CI = 0.63 to 0.99]) and among patients with a positive family history of prostate cancer (OR = 0.68 [95% CI = 0.48 to 0.96]). In the Swedish population, the population attributable risk [estimated by F(OR 1)/OR, where F denotes the proportion of cases with CC genotype] for the H6D variant was estimated to be 8.7% (7.2% for sporadic cancer and 19.2% for familial cancer). No statistically significant differences in genotype frequencies between case patients and control subjects were observed for the other three htSNPs (Exon1+25, Exon1+142, or IVS1+1809) (Table 2).
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DISCUSSION |
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Theoretically, the statistically significant association observed between prostate cancer risk and genotype could arise due to the confounding effect of different genetic backgrounds in case patients and control subjects (i.e., population stratification). Although the Swedish population is genetically homogeneous and control subjects were frequency matched to prostate cancer patient age and residence, which was accounted for by stratified analysis, it seems unlikely that the observed association is due to population stratification. Supporting this homogeneity, screening for three specific mutations in the cystic fibrosis gene (CFTR) provides a 97.6% detection rate in the Swedish population (23). In contrast, in a U.S. population, 50 to 70 mutations are needed for 81% detection (24). Furthermore, the diagnosis of prostate cancer in Sweden is minimally influenced by PSA screening, due to the minimal emphasis on such screening in this population (25). In this study (25), more than 40% of prostate cancer cases were diagnosed with clearly advanced disease. The corresponding proportion is only 10%20% in populations where PSA screening is highly prevalent (e.g., the United States) (26). The stronger association of the H6D variant with advanced disease is of particular note. Genetic markers capable of predicting increased risk of aggressive disease compared with increased risk of any prostate cancer diagnosis are urgently needed, given the difficulties in predicting prostate cancer outcome at the time of diagnosis.
In conclusion, this study provided strong genetic data supporting an association between a nonsynonymous sequence variant in the MIC-1 and prostate cancer risk. Very little is known about the biologic function of MIC-1, but suppression of proinflammatory monokines is a potential function of MIC-1. The amino acid change at position 6 has the potential to alter the function of the protein. If it abolishes or reduces MIC-1 activity, inflammation in the prostate may go unchecked in carriers with this polymorphism, leading to an increased risk for tumor development. More studies are needed to replicate our finding in independent populations and to understand the mechanism by which sequence variants in the gene affect the expression and function of the MIC-1 protein in the signaling pathways that control macrophage regulation. However, our findings should stimulate additional research interest on the possible role of genetic variants in the inflammation process and on how this variation may affect prostate cancer susceptibility.
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NOTES |
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We thank all study participants in the CAPS study, Ulrika Lund for skillfully coordinating the study center at Karolinska Institute, all urologists and their patients in the CAPS study, and all urologists providing clinical data to the national registry of prostate cancer. We also thank Karin Andersson, Susan Lindh, Gabriella Thorén, and Margareta Åswärd at the Regional Cancer Registries. In addition, we thank Sören Holmgren and the personnel at the Medical Biobank in Umeå for skillfully handling the blood samples.
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Manuscript received December 3, 2003; revised June 15, 2004; accepted June 22, 2004.
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