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Division of Molecular Genetic Epidemiology (A.V., K.H., K.W., B.B., A.F.), German Cancer Research Center, D-69120 Heidelberg, Germany; Department of Biosciences at Novum (K.H., A.F.), Karolinska Institute, SE-141 57 Huddinge, Sweden; Institute of Human Genetics (C.R.B., R.K.), University of Heidelberg, D-69120 Heidelberg, Germany; Division of Molecular Gynaeco-Oncology (B.W., R.K.S.), Department of Gynaecology and Obstetrics, Clinical Center University of Cologne, D-50931 Cologne, Germany; Center of Molecular Medicine of Cologne (B.W., R.K.S.), University Hospital of Cologne, D-50924 Cologne, Germany; Department of Gynaecology and Obstetrics (A.M.), Klinikum rechts der Isar, Technical University of Munich, D-81675 Munich, Germany; and Department of Gynaecology and Obstetrics (M.U.), Ludwig-Maximilians-University, D-81377 Munich, Germany
Address all correspondence and requests for reprints to: Annika Vaclavicek, Division of Molecular Genetic Epidemiology C050, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. E-mail: a.vaclavicek{at}dkfz.de.
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Abstract |
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 Top Abstract IntroductionPatients and MethodsResultsDiscussionReferences |
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Objective: We investigated whether genetic variation in the PRL and PRLR genes is associated with the risk of breast cancer (BC).
Design: We conducted a case-control study with a total of seven single nucleotide polymorphisms (SNPs).
Setting: The study was conducted at an academic research laboratory and university clinics.
Patients and Other Participants: A total of 441 German familial, unrelated BC cases and 552 controls matched by age, ethnicity, and geographical region participated in the study.
Intervention(s): There were no interventions.
Main Outcome Measures(s): SNP genotype and haplotype distributions and haplotype interactions were correlated with the risk of BC.
Results: Two SNPs (rs1341239 and rs12210179) within the PRL promoter regions were significantly associated with increased risk in homozygotes for the variant alleles [odds ratio (OR), 1.67 and 95% confidence interval (CI), 1.11–2.50; and OR, 2.09 and 95% CI, 1.23–3.52, respectively]. The PRL haplotype containing the variant alleles of the promoter SNPs increased significantly the risk of BC (OR 1.42, 95%CI 1.07–1.90). A PRLR haplotype was associated with a significant decrease in BC risk (OR 0.69, 95% CI 0.54–0.89). An increasing number of PRL and PRLR risk haplotypes led to a significant trend of increasing risk for BC (
2 = 12.15; P = 0.007).
Conclusions: Genetic variation in the PRL and PRLR genes was shown to influence BC risk. Additional studies are needed to further clarify the role of the PRL and PRLR genes in the risk of BC.
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Introduction |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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Whereas pituitary PRL is controlled by a proximal promoter, which requires the Pit-1 transcription factor for trans-activation, extrapituitary PRL is regulated by an alternative distal promoter, located 5.8 kb upstream of the pituitary transcription start site (5). The 5' region of the human PRLR gene contains two alternative first exons that are transcribed from alternative promoters (6). Differential promoter utilization of the PRLR gene, as well as several PRLR isoforms, may lead to diverse effects.
There is increasing evidence that PRL is involved in the development of mammary cancer. PRL is expressed in both normal and malignant breast tissue (2, 3, 4, 7), but a higher PRLR expression in malignant than adjacent tissue has been reported (8, 9). In the rodent model systems, PRL plays a key role in the development of mammary cancer (2, 4); in these, hyperprolactinemia correlates with increased mammary tumorigenesis. Transgenic female mice overexpressing the PRL gene develop mammary carcinomas (10). In contrast, the role of PRL in humans is not clear. Recently, two large prospective studies (11, 12), conducted within the Nurses’ Health Study cohort, have shown a significant positive association between the plasma PRL levels and the risk of BC in postmenopausal women. A third study (13), within a Swedish cohort, showed no association. In this last study, the time of blood collection was not controlled, contrary to the Nurses’ Health Study, which may explain the conflicting results. All studies performed among premenopausal women have been small, and no association between the PRL levels and the risk of BC has been observed (7).
In this study, we investigated whether genetic variation in the PRL and PRLR gene regions is associated with BC by performing a case-control study on a German study population. The gene regions, including the promoters, were first screened for polymorphisms. A total of seven single nucleotide polymorphisms (SNPs) were chosen for genotype and haplotype analysis (PRL gene, rs1341239, rs12210179, rs2244502, and rs1205960; PRLR gene, rs13354826, rs9292573, and rs37389).
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Patients and Methods |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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A case-control study was performed using a German study population consisting of 441 familial, unrelated BC cases of women (mean age, 43.8 yr; range, 19–82 yr). The cases were collected by the German Consortium for Hereditary Breast and Ovarian Cancer at the Institute of Human Genetics (Heidelberg, Germany), the Department of Gynaecology and Obstetrics (Cologne, Germany), and the Department of Medical Genetics (Munich, Germany). The inclusion criteria for the cases were as follows: F1, families with two or more BC cases, including at least two cases with onset before the age of 50 yr (131 cases); F2, families with at least one BC and at least one ovarian cancer (68 cases); F3, families with at least two BC cases not included in F1 or F2 (201 cases); and F4, families with a single case of BC diagnosed before the age of 35 yr (16 cases). Additionally, three cases had a family history of both female and male BC, and 13 cases had bilateral BC diagnosed below the age of 50 yr; from nine cases, the data of the inclusion criteria were missing. Family histories covered three generations, with the index case having at least one first-degree relative diagnosed with BC or ovarian cancer. The entire coding regions of the BRCA1 and BRCA2 genes were screened, and cases carrying deleterious BRCA1/2 mutations were excluded (14). For this study, familial cases were chosen because it has been shown previously that the use of familial cases significantly increases the power to detect rare alleles contributing to risk for BC (15, 16). The age-matched control series included 552 healthy and unrelated female blood donors (mean age, 50.7 yr; range, 26–68 yr) collected by the Institute of Transfusion Medicine and Immunology (Mannheim, Germany) having the ethnic and geographic background of the BC patients. According to the German guidelines for blood donation, all blood donors were examined by a standard questionnaire and consented to the use of their samples for research purposes. The study was approved by the ethical committee of the University of Heidelberg (Heidelberg, Germany).
SNP selection
In the present study, we screened the promoter regions of the PRL and PRLR genes for polymorphisms. We also screened the coding sequences for SNPs published in the National Center for Biotechnology Information (NCBI) dbSNP database(http://www.ncbi.nlm.nih.gov/) and reported by Canbay et al. (17). Additionally, the SNPbrowser software (Applied Biosystems, Foster City, CA) was used to select tagging SNPs within the gene regions. SNPs with a minor allele frequency in Caucasians less than 10% were excluded, as well as validated SNPs whose genotypes matched more than 80% with the selected tagging SNPs. The promoter regions and the regions surrounding the coding and tagging SNPs that were screened are shown in Fig. 1
. They were sequenced in 23 BC samples as described below. A total of seven SNPs were selected for additional analyses.
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The PCR was performed with 5 ng genomic DNA in a total reaction volume of 10 µl using 1x PCR buffer, 1.5 mM MgCl2, 0.11 µM dNTP mixture (Invitrogen, Paisley UK), 0.15 µM of each primer (Thermo Electron, Ulm, Germany), and 0.3 U Platinum Taq DNA polymerase (Invitrogen). Amplification was performed using a GeneAmp PCR System 9700 thermocycler (Applied Biosystems) under the following conditions: initial denaturation at 94 C for 4 min; three cycles of denaturation at 94 C for 1 min, annealing at optimum temperature for 1 min, extension at 72 C for 1 min; and 32 cycles at 94 C for 30 sec, optimum annealing temperature at –1 C for 30 sec, 72 C for 30 sec, final extension at 72 C for 6 min. The PCR products were separated by electrophoresis on a 1.5% agarose gel stained with ethidium bromide and visualized on a UV transilluminator. Primer sequences and annealing temperatures are available on request.
DNA sequencing
A set of 23 samples with BC cases was investigated by sequencing to confirm the presence and frequency of the polymorphisms and to obtain standards for genotyping with TaqMan (Applied Biosystems). The PCR was performed in a 10-µl reaction volume using the same primers and conditions as described above. The PCR product was purified with ExoSapIT (USB Amersham Biosciences, Uppsala, Sweden) at 37 C for 40 min, followed by 85 C for 15 min. The Big Dye Terminator Cycle Sequencing Ready Reaction kit (Applied Biosystems) was used to perform the sequencing reaction. The sequencing reaction was performed separately for forward and reverse primer, respectively, under the following PCR conditions: 96 C for 1 min, 27 cycles at 96 C for 16 sec, 54 C for 5 sec, and 60 C for 4 min. The sequencing products were precipitated with isopropanol, washed with 70% ethanol, diluted in 25 µl water, and loaded onto an ABI PRISM 3100 Genetic analyzer (Applied Biosystems). The obtained sequences were aligned using DNAStar SeqMan II software (DNAStar, Madison, WI).
Genotyping
The SNPs were investigated using the allelic discrimination method. TaqMan assays on demand were ordered from Applied Biosystems: C_2497031 (rs1341239), C_16283940 (rs2244502), C_2497046 (rs1205960), C_1438961 (rs13354826), C_1439007 (rs9292573), and C_2935914 (rs37389). Primer and probe sequences for rs12210179 (assay by design) are available on request. The reaction was performed in 10 µl using 225 nM each primer, 50 nM each probe, and 5 µl TaqMan Universal 2x PCR Master Mix (Applied Biosystems). PCR conditions were as follows: 50 C for 2 min, 95 C for 10 min, followed by 40–50 cycles at 92 C for 15 sec, and 60 C for 1 min. The PCR was performed in a GeneAmp PCR System 9700 thermocycler (Applied Biosystems), and the number of cycles was dependent on the genotype clustering. The samples were read and analyzed in an ABI Prism 7900HT sequence detection system using SDS 1.2 software (Applied Biosystems). Five to 10% of the samples were sequenced to confirm the results. All of the sequencing results were concordant with the original results.
Haplotype analysis
Haplotypes for the PRL and PRLR genes were determined using SNPHAP software (David Clayton, Cambridge Institute of Medical Research, Cambridge, UK; http://www-gene.cimr.cam.ac.uk/clayton/). The linkage disequilibrium (LD) was calculated with Haploview software (18).
Statistical analysis
Genotype frequencies in BC cases and controls were tested for the Hardy-Weinberg equilibrium (HWE), and any difference between the observed and expected frequencies was tested for significance using the 2 test. Statistical significance for the differences in the genotype and haplotype frequencies between the BC cases and controls was determined by the 2 test. Whenever the expected number of cases was smaller than five, Fisher’s exact test was used. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for associations between genotypes and BC. Armitage’s trend test with one degree of freedom was performed to evaluate the trend across the genotypes. The statistical analyses were performed using the HWE test tool offered by the Institute of Human Genetics, Technische Universität Munich (Munich, Germany; http://ihg.gsf.de/cgi-bin/hw/hwa1.pl) and the Epi Info 2000 software (http://www.cdc.gov/epiinfo). The power to detect the observed ORs was calculated using the PS software for power and sample size calculation (http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize).
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Results |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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and Table 1) and to select positive controls for subsequent TaqMan allelic discrimination assays. First, the PRL proximal promoter region from –2600 to +150 bp (numbering relative to ATG in exon 1), as well as the distal promoter from –8500 to –5800 bp, including the alternative first exon 1a, were sequenced. Also, regions surrounding the coding SNPs published by NCBI were sequenced to verify the existence of the SNPs. One new SNP, T-1523C, in the PRL gene proximal promoter was detected. For the Arg117Stop SNP (rs6238) in exon 4 of the PRL gene, we sequenced 96 BC samples, but the existence of this SNP could not be confirmed.
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and Table 1). Although the coding SNP in exon 5 could not be confirmed in our sample set, we detected the SNP in exon 6 in 1 of the 23 samples. Additionally, two tagging SNPs in the PRL gene and six in the PRLR gene, selected using the SNPbrowser software, were screened. They were confirmed in our sample set. A total of seven SNPs (PRL gene, rs1341239, rs12210179, rs2244502, and rs1205960; PRLR gene, rs13354826, rs9292573, and rs37389) were selected for genotyping in the German cohort. Whereas the two PRL promoter SNPs were chosen because of putative functional effects, the other SNPs were selected because they seemed not to be in LD and therefore suitable for haplotype analysis. The genotype distributions of all of the chosen polymorphisms were consistent with the HWE.
PRL promoter SNPs show significant association with BC risk
The variant allele frequencies of the [G/T] SNP (rs1341239) in the distal promoter and the [A/G] SNP (rs12210179) in the proximal promoter were higher in the cases than the controls, which led to a significantly increased BC risk among the homozygous individuals (rs1341239: OR, 1.67; 95% CI, 1.11–2.50; P = 0.01; rs12210179: OR, 2.09; 95% CI, 1.23–3.52; P = 0.005) (Table 2). The association between these genotypes and BC risk remained unchanged after adjusting the results for age below and over 50 yr (data not shown). When we divided the cases into the subgroups F1 to F4 based on the family history of BC and ovarian cancer, the genotype distributions were mainly very similar. However, one has to take into consideration that there is a potential overlap between the different subgroups. The only statistically significant difference was observed at the PRL SNP rs12210179 between the subgroup F2 (families with at least one BC and one ovarian cancer) and the subgroups F1 and F3 (families with only BC). However, the subgroup F2 was rather small (68 cases), and the difference led only to a more pronounced increase in the risk of BC in the subgroup F2 compared with the other subgroups [OR for the GG genotype carriers, 4.08; 95% CI, 1.67–9.97 vs. 1.98 (0.96–4.11) and 1.81 (0.94–3.51), respectively].
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). The calculated allele frequencies agreed with the published data in Caucasians (NCBI, Applied Biosystems).
Haplotypes were created for all individuals, and they were compared with the most common haplotype, which contained the most frequent alleles of each SNP (Table 2). The five most common haplotypes accounted for approximately 90% of the total haplotypes. Data of haplotypes with a frequency lower than 5% are not shown. Whereas the promoter polymorphisms rs1341239 and rs12210179 showed a high LD of |D'| = 0.91, the other selected PRL polymorphisms were not as highly linked with each other (|D'| = 0.1–0.72). We observed a significant association between the TGTG haplotype and BC risk (OR, 1.42; 95% CI, 1.07–1.90; P = 0.02), as well as an increase in OR for the TGAG haplotypes, which was of borderline significance (OR, 1.36; 95% CI, 0.99–1.87; P = 0.06). Both haplotypes contained the variant alleles of the promoter SNPs, which also individually were associated with an increased risk. An adjustment for age below and over 50 yr did not change the association between these haplotypes and BC risk (data not shown).
TCC haplotype in the PRLR gene is associated with decreased risk in BC
The observed allele frequencies of the SNPs within the PRLR gene agreed with the published data in Caucasians (NCBI, Applied Biosystems) (Table 3). For the [T/C] SNP (rs9292573), a decreased OR with an increasing number of variant alleles was observed. The OR for the homozygous variant allele carriers was 0.69 (95% CI, 0.45–1.06), but this association was not statistically significant (P = 0.09). The haplotype analysis identified four common haplotypes that accounted for approximately 95% of the total haplotypes (Table 3). Data of haplotypes with a frequency lower than 5% are not shown. The SNPs rs13354826 and rs9292573 showed a high LD of |D'| = 0.94. This explains why no PRLR haplotypes containing the variant C alleles of these SNPs were observed. The LD between rs13354826 and rs37389 and the SNPs rs9292573 and rs37389 was lower (|D'| = 0.66 and 0.27, respectively). The haplotype that contained the most frequent alleles was used as a reference, although it was only the second most common haplotype. The TCC haplotype, which contained the C allele of the SNP rs9292573, was associated with a significantly decreased BC risk (OR, 0.69; 95% CI, 0.54–0.89; P = 0.004). An adjustment for age below and over 50 yr did not change the association between these haplotypes and BC risk (data not shown).
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An increasing number of both PRL and PRLR risk haplotypes (TGTG/TGAG and TTC, respectively) tended to lead to an increased risk of BC (data not shown). When the total number of PRL and PRLR risk haplotypes were combined in each individual and compared against the reference with no risk haplotypes, a significant trend with an increasing number of risk haplotypes was observed (2 = 12.15; P = 0.007) (Table 4).
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Discussion |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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Promoter polymorphisms may alter gene expression attributable to altered transcription factor binding. The [G/T] SNP (rs1341239) in the distal PRL promoter has been shown to be functionally significant (19, 20). The variant T allele creates a consensus GATA transcription factor binding site, which we confirmed using TESS (Transcription Element Search System) database (http://www.cbil.upenn.edu/tess/). An altered GATA-related transcription factor binding at the SNP position has been indicated by the electrophoretic mobility shift assay (19). Furthermore, the SNP has been shown to affect the promoter activity and mRNA levels (19). GATA-3 is expressed in the breast (21, 22, 23). However, its exact function is unknown. Our study revealed a significant effect of increased BC risk among the rare TT genotype carriers of the SNP rs1341239. Also, the rare GG genotype carriers of the other promoter SNP (rs12210179) were at increased risk of BC. This SNP is not located within any transcription binding site. However, it is in high LD (|D'| = 0.91) with the functional SNP rs1341239, which may explain its effect on BC risk in our study.
For the PRLR gene, we first screened the promoter regions and one SNP in each of the exons 5 (rs16871473, NCBI) and 6 (17), respectively, which were supposed to change amino acid. Because these SNPs did not exist or they were too rare, they were not considered in this study. Instead, tagging SNPs were selected using the SNPbrowser software. Three of them were included in the subsequent haplotype analysis, and a significantly protective TCC haplotype (OR, 0.69; 95% CI, 0.54–0.89; P = 0.004) was identified. The PRLR gene region is not equally well characterized in the public databases NCBI and HapMap as the PRL gene region, and the available haplotype data did not include the SNPs that we selected for our study using SNPbrowser. Thus, although the four most common haplotypes in our study accounted for approximately 95% of all haplotypes, we cannot guarantee that these haplotypes would account for all haplotypes within the PRLR gene region.
Furthermore, a significantly increased risk with an increasing number of both PRL and PRLR risk haplotypes was observed. Thus, our studies indicate a role for the PRL and PRLR genes in the etiology of BC. In a previous study, Glasow et al. (24) screened all coding exons of the PRLR gene in 30 BC patients, but no mutations were identified. In another study among 38 BC patients, only one novel SNP was observed in two patients but not in 100 healthy controls (17). To our knowledge, no mutation studies in the PRL gene have been performed.
The strength of our study was the use of familial cases, because any heritable genetic changes would be expected to be enriched among them (15, 16). With the present sample size of the German population in our study, we had a power of more than 80% to detect the observed ORs of 1.67 and 2.09 for the PRL promoter gene polymorphisms rs1341239 and rs12210179, respectively. For the PRLR gene, we had a power of more than 90% to detect the OR of 0.69 in the TCC haplotype carriers. Although the present study included multiple comparisons, the consistency of the findings with SNPs and haplotypes increases their credibility. First, the effect of the heterozygous and homozygous genotypes on the risk of BC was consistent. Second, the effect of haplotypes supported the effects of individual genotypes. Finally, with the increasing number of risk haplotypes, an increasing risk of BC was observed.
In summary, we observed a significantly increased risk for carriers of the variant alleles of the PRL promoter SNPs and for the TGTG haplotype, which contains these variant alleles. A significantly decreased risk for carriers of the PRLR TCC haplotype was also noted. As additional evidence, individuals’ increasing number of the PRL and PRLR risk haplotypes increased the BC risk significantly. Additional studies are needed to confirm our data and to clarify further the role of the PRL and PRLR genes in the risk of BC.
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Acknowledgments |
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Footnotes |
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The authors have nothing to declare.
First Published Online January 24, 2006
Abbreviations: BC, Breast cancer; CI, confidence interval; HWE, Hardy-Weinberg equilibrium; LD, linkage disequilibrium; OR, odds ratio; PRL, prolactin; PRLR, PRL receptor; SNP, single nucleotide polymorphism.
Received August 24, 2005.
Accepted January 17, 2006.
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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