1 Obstetrics and Gynecology Epidemiology Center, Brigham and Womens Hospital, Harvard Medical School, Boston, MA.
2 Department of Epidemiology, Harvard School of Public Health, Boston, MA.
3 Channing Laboratory, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA.
4 Norris Cotton Cancer Center and the Departments of Community and Family Medicine and of Medicine, Dartmouth Medical School, Lebanon, NH.
5 Reproductive Endocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA.
Received for publication July 15, 2004; accepted for publication October 1, 2004.
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ABSTRACT |
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haplotypes; ovarian neoplasms; polymorphism, single nucleotide; receptors, progesterone
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INTRODUCTION |
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As demonstrated in progesterone receptor-deficient mice, the physiologic effects of progesterone are completely dependent on the presence of the progesterone receptor gene (PGR) (9). The PGR gene is located on chromosome 11q22 and is a member of the steroid receptor superfamily of nuclear receptors. The eight exons, flanking splice sites, and the promoter region of the PGR gene have been sequenced by one of the coauthors (I. D.) in 68 invasive endometrial cancer cases. Seven variable sites were reported: two promoter single nucleotide polymorphisms (SNPs) (+44C/T and +331G/A), four exonic SNPs (S334T, G393G, V660L, and H770H), and a 306-base pair Alu insertion in intron 7 (10). Pairwise linkage disequilibrium tests revealed that the V660L polymorphism in exon 4, the synonymous H770H polymorphism in exon 5, and the Alu insertion in intron 7 are in complete linkage disequilibrium (D' = 1.00) and that the S334T polymorphism in exon 1 was also strongly linked to these three polymorphisms (D' = 0.99). Consequently, the variants of these polymorphisms occur together. Collectively, the V660L variant, H770H variant, and the Alu insertion form the PROGINS allele (11, 12). Since the polymorphisms included in the PROGINS allele are in complete linkage disequilibrium, the genotype of one polymorphism is the same as the genotypes of the other two polymorphisms. There has been speculation regarding the biologic function of the PROGINS allele, but its influence on progesterone receptor activity remains unclear. To date, only the +331G/A polymorphism has been shown to be functional, as it causes an increase in the expression of the progesterone receptor B (PGR-B) isoform (10).
There has been considerable interest in the relation between the progesterone receptor and ovarian cancer risk (1218). However, the results of epidemiologic studies have been inconsistent because of differences in study design or study populations, as well as small sample sizes. In this report, we genotyped four polymorphisms (+331G/A (reference single nucleotide polymorphism (rs)10895068), +44C/T (rs518162), G393G (rs1379130), V660L (rs1042839)), estimated their haplotypes, and evaluated their associations with ovarian cancer in a large population-based, case-control study.
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MATERIALS AND METHODS |
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Controls in phase 1 were selected using random digit dialing supplemented with lists of residents for older controls, as previously described (19). Briefly, for the random digit dialing, approximately 5,400 calls (not including businesses or nonworking numbers) yielded 10 percent of households in which the answerer declined to provide a household census and 80 percent of households in which an age- and sex-matched control for a case could not be made or was ineligible because of a priori oophorectomy. Of the remaining 10 percent of screened households containing a potentially eligible control, 72 percent agreed to participate. Because random digit dialing proved inefficient for identifying controls over age 60 years in Massachusetts, we identified older controls in Massachusetts by randomly selecting women from lists of residents (town books) matched to cases by community and age within 4 years. Of 328 sampled town-book controls, 21 percent could not be reached, 18 percent were ineligible, and 30 percent declined to participate.
Controls for phase 2 were identified through town books in Massachusetts and drivers license lists in New Hampshire. Age matching was accomplished by sampling controls on the basis of the age distribution of cases in the previous phase of the study, with adjustment as current cases were enrolled. Of the 1,843 potential controls identified in phase 2, 576 were ineligible because they had died, moved, had no telephone, did not speak English, had no ovaries, or were seriously ill; 197 (in Massachusetts) could not be recontacted because subjects returned an "opt out" postcard required by the hospital independent review team; and 349 potential controls declined participation. Of the 1,244 controls that enrolled in the study from the two phases of this study, 1,098 donated a blood specimen. For the purposes of this study, 1,034 control specimens were available for genotyping.
Questionnaire data
Risk factors for ovarian cancer and other potential confounders were collected by questionnaires administered in person. To avoid the possible impact of preclinical disease on exposure status, cases were asked about exposures that occurred at least 1 year prior to diagnosis, and controls were asked about exposures that occurred more than 1 year before the interview date. Both cases and controls were asked to recall long-term habits. Women were considered to have a family history of breast or ovarian cancer if they reported that either their mother or sister had breast or ovarian cancer. Women were considered to have a history of infertility if they reported that they had tried to get pregnant for at least 1 year without success or had seen a doctor about having difficulty getting pregnant or carrying a pregnancy to term. Body mass index was calculated from reported height and weight (kg/m2) at 1 year before diagnosis for cases or 1 year before interview for controls.
Blood specimens
Heparinized blood was collected at the time of enrollment and separated into plasma, red cell, and buffy coat (white cell) components. DNA was extracted from buffy coat components using a QIAamp 96 DNA blood kit (Qiagen, Valencia, California).
We genotyped four SNPs (+44C/T, +331G/A, G393G, and V660L) in the 987 cases and 1,034 controls identified through the population-based, case-control study. Genotyping assays for all four SNPs were performed by the 5'-nuclease assay (Taqman) on the ABI PRIMS 7900HT sequence detection system (Applied Biosystems, Foster City, California). Taqman primers, probes, and conditions for genotyping assays are available upon request. Genotyping was performed by laboratory personnel blinded to case-control status, and blinded quality control samples were inserted to validate genotyping procedures; concordance for the quality control samples was 100 percent. Over 95 percent of the samples were successfully genotyped for each of the four polymorphisms. Genotyping failures were considered missing data; consequently, the total number of samples with genotype data varies between polymorphisms.
Starting in September 1998, we asked control women, at the time of their blood draw, for the date of their last menstrual period. Progesterone levels for 66 controls who were not pregnant, not taking hormones, and at day 14 or more of their menstrual cycle at the time of their blood draw were assayed in the Reproductive Endocrinology Laboratory at Massachusetts General Hospital (Boston, Massachusetts). Progesterone was measured using a chemiluminescence immunoassay system (Immulite; Diagnostic Products Corporation, Los Angeles, California). The limit of detection is 0.6 ng/ml, and the reportable range of the method is 0.640 ng/ml. Specimens containing levels higher than 40 ng/ml were diluted and retested. The interassay coefficients of variance were 14.3, 10.5, and 10.9 percent for laboratory quality control sera containing 1.5, 3.0, and 15 ng/ml, respectively. The reference range (95 percent confidence interval) for reproductively healthy women (cycling) is less than 0.624 ng/ml.
Statistical analysis
We used chi-square tests to assess whether the PGR genotypes were in Hardy-Weinberg equilibrium. We calculated the linkage disequilibrium between all possible pairwise combinations of the SNPs using Lewontins D' statistic (20). The more common allele for all SNPs served as the reference group in the regression models. Each SNP was assessed in three genotype categories (wild type, heterozygote, homozygote variant) and then collapsed into two categories with heterozygotes and homozygote variants combined because of the low prevalence of homozygote variants for these SNPs. Odds ratios and 95 percent confidence intervals were calculated using unconditional logistic regression with adjustment for age (continuous) and study center (Massachusetts or New Hampshire). In addition, we included the following ovarian cancer risk factors in the multivariate-adjusted models to assess the association between PGR genotypes and ovarian cancer risk independent of these variables: parity (0, 1, 2 livebirths), oral contraceptive use (<3 months or never,
3 months), and family history defined as mother or sister with breast or ovarian cancer (yes, no). All tests of statistical significance were two sided.
Since histologic categories of ovarian cancer may have different pathways to disease (21, 22), we evaluated the association between progesterone receptor SNPs and ovarian cancer histologies (serous borderline, serous invasive, mucinous, clear cell, endometrioid, and undifferentiated/other) separately compared with controls, with odds ratios and 95 percent confidence intervals calculated using unconditional logistic regressions with the same covariates as above. Furthermore, we assessed possible effect modification by a priori variables that may influenced hormone levels, including the following: parity and breastfeeding (nulliparous, parous with breastfeeding, parous without breastfeeding); oral contraceptive use (<3 months or never, 3 months); fertility status (fertile, infertile); menopausal status (premenopausal, postmenopausal); body mass index (
24.4 kg/m2, >24.4 kg/m2); age at menopause (<50 years,
50 years); and age at menarche (<13 years,
13 years). To test for the statistical significance of interactions between these a priori exposures and PGR genotypes, we used a likelihood ratio test to compare nested models that included terms for all combinations of the PGR genotype and levels of these a priori exposures with the models having only the main effects. For the histology-specific and interaction analyses, we combined heterozygotes and homozygote variants into one category for more power.
The most likely haplotype frequencies based on the genotype results were estimated using the expectation-maximization method (23), and these estimates were used to assign the probability of each haplotype to each individual (24). Therefore, each participant has a value for each haplotype that represents the probability of having that haplotype, and the haplotype probabilities always sum to one for each participant. To ensure adequate statistical power, we used only haplotypes with greater than 5 percent frequency to evaluate the association between PGR haplotypes and ovarian cancer. The association between haplotypes and disease was assessed by unconditional logistic regression, with adjustment for age and study center, comparing probabilities of each haplotype with that of the most common haplotype. We used the SAS/Genetics (SAS Institute, Inc., Cary, North Carolina) statistical package for haplotype estimation and analyses.
Since ethnic differences in allele frequencies could lead to a spurious association between genotype and disease, we stratified both the SNP and haplotype analyses by ethnicity (Caucasian/non-Caucasian). Because of the small number of non-Caucasian women in our study (n = 82), all analyses were restricted to Caucasian women.
In an exploratory analysis among controls, we used the chi-square test to evaluate differences in progesterone-related hormonal and reproductive characteristics (parity, menstrual cycle pattern, miscarriage, and premenstrual symptoms) between those who carried the PROGINS allele and those who did not. Finally, mean progesterone levels from controls who were not pregnant, not on hormones, and had blood drawn more than 14 days since their last menstrual period were compared by PROGINS carrier status using generalized linear models. In addition, we divided the progesterone level into quartiles and compared the distribution of PROGINS carriers within each quartile with the distribution in the lowest quartile. Women with progesterone levels of less than 1.3 ng/ml were excluded from the progesterone level analysis. We used the SAS version 8.2 statistical package (SAS Institute, Inc.) for all the SNP and progesterone level analyses.
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RESULTS |
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Among controls who were not pregnant, not on hormones, and had blood drawn more than 14 days since their last menstrual period, mean progesterone blood levels were slightly lower in women who carried the V660L variant allele (mean = 14 ng/ml) compared with women who carried the wild type (mean = 17 ng/ml), but this difference was not significant (p = 0.45). The highest quartile of luteal-phase progesterone levels observed was 2451 ng/ml, and there was a significantly larger proportion of carriers of the V660L wild-type allele included in this category compared with carriers of the V660L variant allele, 33 percent versus 6 percent (p = 0.03) (data not shown).
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DISCUSSION |
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Seven studies have evaluated the association between ovarian cancer and the PROGINS allele, which includes the V660L variant, the H770H variant, and the Alu insertion. Most studies found no significant increase in risk associated with carrying or being homozygous for a variant allele (12, 1418, 25). An exception was seen in the study by McKenna et al. (15) in which two populations were combined, resulting in a disproportionate number of controls with an unusually low frequency of the PROGINS allele. Two other studies reported no overall associations but a significant (18) or borderline significant (16) increase in risk associated with the PROGINS allele in nonusers of oral contraceptives (a finding we could not replicate).
Inconsistencies between our study results and those of previous studies concerning the PROGINS allele may be due to the differences in study design (13, 14, 17), restriction to BRCA1/2 carriers (18), and differences in ethnic distributions (16). Results reported by Spurdle et al. (12) were not entirely inconsistent with our own. Given their sample size (558 cases, 298 controls), they would have been able to detect an association with an odds ratio of 0.6 or less. Since both their estimate of the association between carriage of the V660L variant (OR = 0.8) and our own (OR = 0.7) did not fall in this detectable range, lack of a significant association is reasonable.
We should also note that investigators from North Carolina and Australia, who had previously evaluated the PROGINS allele in separate studies (12, 16), combined their populations to evaluate the association between the +331G/A polymorphism and ovarian cancer risk (26). They observed a nonsignificant decrease in overall ovarian cancer risk for women who possessed the +331A allele and, in subgroup analyses, a significantly decreased risk of endometrioid and clear cell cancers (OR = 0.46, 95 percent CI: 0.23, 0.92) (26). We did not observe these associations in our population. Overall, we observed no reduction in ovarian cancer risk for women who carried the +331A allele (OR = 0.98, 95 percent CI: 0.72, 1.34) and a nonsignificant reduction in risk of endometrioid (OR = 0.72, 95 percent CI: 0.36, 1.45) and clear cell (OR = 0.65, 95 percent CI: 0.29, 1.46) ovarian cancers. In general, previous studies of the association between the PROGINS allele and other progesterone receptor polymorphisms have suffered from small sample sizes, the combination of cases and controls with diverse ethnicities, or controls who were not sampled from the general population.
The V660L, H770H, and Alu insertion are all located in or near the hormone-binding domain, which spans exons 4 through 8 and thus might influence progesterone binding (27). Mutational analyses have shown that loss of any part of the hormone-binding domain results in decreased ligand binding and decreased transcriptional regulation of progesterone target genes (28).
If the PROGINS allele decreases PGR binding, then studies of progesterone antagonists, molecules which bind to the progesterone receptor and silence transcription (29), become relevant to both the potential consequences of decreased progesterone binding and the role of progesterone in ovarian cancer risk. If progesterone reduces ovarian epithelial growth and thus decreases the risk of ovarian cancer, then we would expect antiprogestins to be associated with increased ovarian cancer risk, yet the results of antiprogestin studies suggest the opposite (3033). Administration of antiprogestins to ovarian cancer patients in a clinical trial setting resulted in a reduction of tumor burden for some patients (30), and ovarian cancer cell lines exposed to antiprogestins exhibit decreased growth and increased apoptosis (3133). However, as mentioned earlier, the effect of the PROGINS allele is unclear; therefore, the relevance of the antiprogestin data is uncertain.
Other mechanisms by which possession of the PROGINS variant could decrease ovarian cancer risk might involve ovulatory pathways. Our observations of infertility, nulliparity, or irregular menstrual cycles among controls who carried the V660L variant allele suggest that these women, like the PGR gene-deficient mice (9), may have quantitative or qualitative differences in ovulation, which could influence ovarian cancer risk according to the incessant ovulation theory of ovarian cancer. Furthermore, we observed lower progesterone levels in PROGINS carriers than in women who carried wild-type alleles, suggesting a less exuberant corpus luteal response. These same effects, disruption of ovulation and lower progesterone levels, are reported in nonpregnant women who were given antiprogestins (34, 35).
Although the possible biologic pathways through which the PROGINS variant allele may exert its influence on ovarian cancer risk remain unclear, involvement of the PGR gene remains likely on the basis of biologic and epidemiologic data. Advantages of our study include our haplotype approach to assess genetic variation in the progesterone receptor gene and our large sample size. While we have not genotyped every SNP in the progesterone receptor, the SNPs we have chosen to genotype represent regions that are inherited together. With four SNPs, we captured over 99 percent of the variation in the eight exons, flanking splice sites, and promoter region of the progesterone receptor. Haplotype-tagging SNPs have been shown to be an effective and powerful method to assess the association between genetic variation and disease (36). Our study of 987 cases and 1,034 controls provides 80 percent power to detect an odds ratio of 1.30 or 0.76 for the association between the V660L variant and ovarian cancer risk. This large sample size allowed us to evaluate potential effect modifiers of the PGR gene-ovarian cancer associations and possible variation by histologic type.
Limitations of our study include generalizability and potential biases. Since the study population is primarily Caucasian women and our analyses are restricted to Caucasians, our results are not generalizable to other ethnicities. Alternatively, the homogeneity of our population is an advantage since it reduces the possibility for confounding by ethnicity. Since we used a case-control design, we must also consider the possibility of selection bias. Ovarian cancer is an aggressive disease; 9 percent of our cases died before enrollment. If a particular PGR genotype imparted some survival benefit, then we would have an overrepresentation of that genotype among our cases, resulting in a misleading association between genotype and disease.
In conclusion, our results suggest no association between +44C/T, +331G/A, and G393G and ovarian cancer risk but an inverse association between the PROGINS allele and ovarian cancer. Functional studies are needed to determine the role of the PROGINS allele in ovarian cancer development.
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ACKNOWLEDGMENTS |
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The authors thank Hardeep Ranu, Craig Labadie, Pati Soule, Alicia Whittington, and Allison Vitonis for their assistance.
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NOTES |
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REFERENCES |
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