Affiliations of authors: Department of Preventive Medicine, Norris Comprehensive Cancer Center, University of Southern California, Keck School of Medicine, Los Angeles, CA (CLP, AHW, DOS, BEH, MCP); Program in Medical and Population Genetics, Broad Institute/MIT Center for Genome Research, Cambridge, MA (JNH, NPB, DA); Department of Pediatric Endocrinology, Harvard Medical School and Boston Childrens Hospital, Boston, MA (JNH); Departments of Genetics and Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, MA (SY, DA); Cancer Research Center, University of Hawaii, Honolulu, HI (LNK)
Correspondence to: Dr. Celeste Leigh Pearce, USC/Norris Comprehensive Cancer Center, 1441 Eastlake Ave., Rm. 4411A, Los Angeles, CA 90089 (e-mail: cpearce{at}usc.edu)
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ABSTRACT |
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INTRODUCTION |
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By contrast, progesterone acts as a mitogen for breast tissue (17), and adding progestins to menopausal estrogen therapy increases mammographic density, which is in turn associated with breast cancer risk (18). There is considerable evidence from casecontrol and cohort studies that adding progestins to menopausal estrogen therapy substantially increases risk of breast cancer (1922), a result that has been confirmed by results from the Womens Health Initiative randomized trial (23).
Studies of the possible association of genetic variation in PGR with risk of ovarian (2429) and breast cancer (25,26,3034) have concentrated on a variant known as PROGINS.PROGINS was first described as an Alu element insertion in intron 7 (35), but it is also marked by a missense single-nucleotide polymorphism (SNP) in exon 4 (rs1042838) and a silent SNP in exon 5 (rs1042839) (36). Any of these three alleles appears to uniquely identify the presence of the other two. There is some evidence that women with two copies of the PROGINS allele may be at increased risk of ovarian cancer (2428) but at decreased risk of breast cancer (25,26,31,34,37). A second variant in PGR, the putatively functional promoter SNP +331G/A, was associated with an increased risk of breast cancer in one study (38) but not in another (39). This variant has not been studied in relation to ovarian cancer.
How these few variants relate to the underlying complexity of genetic variation across the entire functional PGR locus has not been characterized extensively. Here we report a comprehensive description of common sequence variation across the PGR and the results of a haplotype-based risk analysis in relation to ovarian and breast cancer risk.
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METHODS |
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All studies reported here were approved by the Institutional Review Boards of the University of Southern California and the University of Hawaii, as appropriate. Written informed consent was obtained from each case patient and each control subject before her blood was drawn.
Ovarian Cancer Study
The ovarian cancer case patients and control subjects were participants in two population-based casecontrol studies conducted in Los Angeles County from 1992 to 1999 (2,40). In both studies, case patients were identified from the local National Cancer Institutes Surveillance, Epidemiology and End Results (SEER) registry1, and neighborhood control subjects were identified by physically canvassing the neighborhood of the case patient, using a systematic algorithm (2). Control subjects were matched to case patients by ethnicity and neighborhood of residence (2,40). We included only non-Latina (white) subjects because these studies had only a small number of participants of other ethnicities. The participation rate among case patients was 80% and 67% for the first and second studies, respectively (2,40); the participation rate among control subjects was approximately 70% for each of the two studies. Blood was donated by approximately 75% of participants in the two studies, and approximately 67% of those subjects who donated blood had a sample available for the current analysis. Subjects who donated blood and had a sample available for the current analysis did not differ from the case patients and control subjects interviewed with respect to age, socioeconomic status, parity, oral contraceptive use, or other ovarian cancer risk factors (data not shown; all P>.05). A total of 267 case patients and 397 control subjects had samples available for genetic analysis.
Blood was collected at the interview location and transported on cold packs to the Norris Comprehensive Cancer Center. Blood components were separated and stored at 80 °C. DNA was isolated using either a chloroform extraction process (41) or the Qiagen Blood Kit (Qiagen, Chatsworth, CA). Following extraction an aliquot of the DNA was sent to Molecular Staging, Inc. (New Haven, CT), for whole-genome amplification (42).
Breast Cancer Study
The invasive breast cancer case patients and control subjects were female participants in the Hawaii and Los Angeles Multiethnic Cohort Study (MEC) (43). Details of the MEC study, which targeted African American, Hawaiian, Japanese, Latina, and non-Latina (white) women, have been published previously (43,44). Briefly, more than 200 000 men and women between the ages of 45 and 75 years, residing in Hawaii or California, completed a questionnaire that included data on demographic, lifestyle, and health characteristics, as well as a comprehensive dietary survey (43).
Participants in the MEC are followed for incident cancers. Incident case ascertainment is completed by computer linkage of the cohort with the SEER cancer registries in Hawaii and Los Angeles and with the California Cancer Registry. All incident breast cancer case patients and a 3% random sample of female MEC participants without prevalent breast cancer were asked to provide a blood sample and participate in studies of genetic susceptibility to breast cancer. The participation rates were 74% for case patients and 66% for control subjects. The women who provided a blood sample did not differ from those who refused by either demographic or established breast cancer risk factors (data not shown). A total of 1715 incident breast cancer case patients and 2505 control subjects were available for analysis.
The blood samples were processed within 4 hours of collection. Blood components were separated and stored in 0.5 mL volumes at 80 °C. The DNA of each case patient and control subject was isolated using the Qiagen Blood Kit.
PGR SNP Selection, Haplotype Block Determination, and Haplotype-Tagging SNP Selection
Seventy-four SNPs were selected from the public (http://www.ncbi.nlm.nih.gov/SNP/) and Celera (http://www.celera.com) databases to provide dense coverage across the PGR and its putative regulatory regions, using the procedures described in Haiman et al. (44). SNPs were genotyped in a sample of 349 women without a history of cancer who were randomly selected from the MEC: African American (n = 70), Hawaiian (n = 69), Japanese (n = 70), Latina (n = 70), and white (n = 70) (collectively known as the MEC haplotype panel). The Sequenom platform was used for this gene characterization phase (Sequenom, San Diego, CA).
After eliminating SNPs with poor genotyping reliability (n = 8) and SNPs that were monomorphic in our populations (n = 12), 54 SNPs remained for determination of block structure (see below). These 54 SNPs spanned a 122.8-kb region of the PGR gene, from 23.0 kb upstream of exon 1 to 7.6 kb downstream of the 3' untranslated region. The SNP IDs, locations, and frequencies in the MEC haplotype panel are given in Supplementary Table 1 (Available at: http://jncicancerspectrum.oupjournals.org/jnci/content/vol97/issue1); these data can also be accessed on the MEC Web site (http://www.uscnorris.com/MECgenetics). The alleles listed in Supplementary Table 1 (Available at: http://jncicancerspectrum.oupjournals.org/jnci/content/vol97/issue1) are designated as major and minor based on the forward sequence orientation listed on the University of Santa Cruz Genome Bioinformatics Web site (http://genome.ucsc.edu). The major allele designation is based on the most common allele in the African American samples.
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Within-block haplotype frequency estimates were determined from the complete genotype data from the MEC haplotype panel one ethnicity at a time, using the expectation-maximization (EM) algorithm of Excoffier and Slatkin (47). The minimum set of haplotype-tagging SNPs (htSNPs) needed to identify the common haplotypes (i.e., those with a frequency of at least 5% in any ethnic group) within a block was selected as described by Stram et al. (48). In this approach, the minimum set is determined by considering the squares of the correlations between the true haplotypes and their estimates (Rh2s). The minimum set of htSNPs within each block was selected to ensure an Rh2 of at least 0.80 for all haplotypes observed within any specific ethnic group at a frequency of at least 5%. With this set of htSNPs, the minimum Rh2 for the whites, which comprise the majority of this study population, was always greater than or equal to 0.90. To achieve an Rh2 of greater than or equal to 0.80 for the African Americans, four additional htSNPs were genotyped in this ethnic group (see Results). Haplotype relationships across the blocks were determined using the publicly available Haploview software (http://www.broad.mit.edu/personal/jcbarret/haplo/). The PROGINS allele was defined by the exon 4 missense SNP, rs1042838, and was an htSNP. Although the putative functional SNP +331G/A fell in an interblock region, it was genotyped in the case patients and control subjects because it had previously been reported to be associated with risk of breast cancer (38).
Genotyping in the CaseControl Samples
The htSNPs were genotyped in the ovarian and breast cancer case patients and control subjects, using the 5' nuclease Taqman allelic discrimination assay (Applied Biosystems, Foster City, CA). Blinded 10% replicate samples were included, and the laboratory personnel were blinded to casecontrol status. Reproducibility was 98%.
Statistical Analysis
HardyWeinberg equilibrium was confirmed in the control subjects in each ethnic group for each htSNP. Unconditional logistic regression analyses were conducted for the individual htSNPs, using SAS statistical software (SAS Institute, Cary, NC, version 8.0). Risk among those heterozygous and homozygous for each mutation was estimated relative to those carrying the wild-type allele at that locus.
Using all ovarian cancer case patients and control subjects, haplotype frequencies were computed from the htSNP genotypes as described by Stram et al. (49). For each subject and each haplotype, a haplotype dosage estimate (i.e., an estimate of the number of copies of that haplotype) was computed using that individuals genotype data and the overall (i.e., ignoring casecontrol status) haplotype frequency estimates obtained from the EM algorithm (47). (It should be noted that haplotype dosage can be estimated even if the genotype data are not completely known.) The genotype data were also phased according to the method of Stephens and colleagues (50,51). Unconditional logistic regression was used to analyze the resulting case patient and control subject data by both unphased (49) and phased (50,51) methods (SAS Institute). The results of these two approaches were nearly identical.
Although the ovarian cancer studies used a matched casecontrol design, the match was broken for the genetic analyses because in many cases both members of the matched pair did not provide a blood sample. Adjustment for age and socioeconomic status (the matching factors) and ovarian cancer risk factors, including oral contraceptive use, parity, and a family history of ovarian cancer (in a first-degree relative) did not substantially alter the genotype or haplotype risk estimates (data not shown), and therefore we did not include such adjustments in the final models. The P values reported are two sided and have not been corrected for multiple hypothesis testing.
The same analytic approach was adopted with the data from the breast cancer study participants, except that in this case we did adjust for race/ethnicity; adjusting for age and other breast cancer risk factors did not materially change the risk estimates, and therefore these adjustments were not retained in the final models. By contrast, adjusting for ethnicity was essential because of the variability in allele and haplotype frequencies across ethnic groups. We also conducted ethnic-specific analyses to determine whether there was heterogeneity of effects.
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RESULTS |
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The 54 SNPs showed strong intermarker linkage disequilibrium, with four regions of strong linkage disequilibrium (blocks). The interblock distances were less than 5 kb, with the exception of the region between blocks 3 and 4 (Fig. 1). This region, of 20.8 kb measured with four SNPs, fell entirely within intron 3, and because the linkage disequilibrium spanning this region and the two adjacent blocks was strong, this region was assumed to be adequately covered by the genotyped SNPs. The putatively functional promoter SNP +331G/A fell into the first interblock region and was in low linkage disequilibrium with all of the SNPs in blocks 1 and 2.
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Ovarian Cancer Risk Analysis
Women who carried two copies of the PROGINS variant allele (i.e., the minor allele of the rs1042838 SNP) were at increased risk of ovarian cancer relative to noncarriers (odds ratio [OR] = 3.23, 95% confidence interval [CI] = 1.19 to 8.75, P = .022; Table 1). One other htSNP in block 4, rs608995, was associated with a statistically significantly increased risk of ovarian cancer; women who carried two copies of the minor allele of this SNP were 3.10 times as likely to have ovarian cancer as noncarriers of this allele (95% CI = 1.63 to 5.89, P<.001; Table 1).
The PROGINS allele at rs1042838 was almost always seen in combination with the minor allele of rs608995. However, the converse was not true: the rs608995 minor allele frequently occurred in the absence of the rs1042838 minor allele. Increased risk of ovarian cancer was observed in women carrying two copies of the minor rs608995 allele, regardless of the presence of the PROGINS allele (Fig. 3; single SNPs, columns 4, 5, and 6). After estimating the risk of ovarian cancer associated with the best single marker (rs608995), adding rs1042838 to the model did not improve the fit (P = .94).
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We next examined haplotype-specific risks; these haplotypes could represent either combinations of alleles or common ancestral states that are not captured by individual htSNPs. Analyses conducted using a log-additive model revealed no linearly increasing risk associated with any haplotype in the four blocks (data not shown). Phased data showed that each block contained one haplotype that was associated with a statistically significant increased risk of ovarian cancer among carriers of two copies of that haplotype, compared with noncarriers (Fig. 4). All of these haplotypes were part of the PROGINS long-range haplotype. Individuals who carried only one copy of any of these haplotypes were not at increased risk of ovarian cancer.
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Haplotypes 4-D (which carries the PROGINS allele at rs1042838 and the minor allele at rs608995) and 4-E (which carries the rs608995 minor allele, but not the PROGINS allele) share nine of 12 alleles in common in the full haplotypes shown in Fig. 2, suggesting that they are derived from a common ancestor. We thus conducted additional analyses (Fig. 3) to examine whether this common ancestral state might actually be the best model for association with disease risk. For ease of interpretation across analyses, we defined a common reference group that included individuals who carried no copies of the minor allele of rs608995, and therefore no copies of the PROGINS allele of rs1042838.
It was apparent that carrying either two copies of 4-D or 4-E or one copy of each was associated with increased risk of ovarian cancer of a similar magnitude (Fig. 3). When individuals who carried one copy of 4-D and one copy of 4-E or two copies of either one were combined for analysis, this group had a 3.44-fold (95% CI = 1.67 to 7.08, P<.001) increased risk of ovarian cancer (Fig. 3). Individuals who carried only one copy of either 4-D or 4-E were not at increased risk of ovarian cancer (Fig. 3).
The magnitude of the OR associated with carrying two copies of 4-D or 4-E or one of each was indistinguishable from that associated with carrying two copies of rs608995. The role of the haplotype compared with the single SNP cannot be determined from these data.
Breast Cancer Risk Analysis
Whereas the PROGINS allele was statistically significantly associated with increased risk of ovarian cancer, it showed a non-statistically significant association with reduced risk of breast cancer (Fig. 2). (It should be noted that the first four SNPs in Table 1 [rs474320, rs3740753, Hcv3182868, and rs1042838] represent the PROGINS allele because these four SNPs are in perfect linkage disequilibrium, and differences in risk associations among them are due solely to missing data.) In addition, rs608995 was not associated with the risk of breast cancer (Table 1).
In the haplotype analysis (Fig. 4), the four SNPs marking the PROGINS allele lie on haplotypes 1-D, 2-C, 3-C, and 4-D, respectively. Haplotypes 1-D, 2-C, and 4-D were associated with reduced risk of breast cancer, but the results were not statistically significant. However, the association of haplotype 3-C with breast cancer risk was statistically significant (P = .04). No other haplotypes were associated with risk of breast cancer.
We found no association between the putative functional SNP +331G/A and risk of breast cancer when considering all case patients (Supplementary Table 2, available at: http://jncicancerspectrum.oupjournals.org/jnci/content/vol97/issue1) or after stratifying by body mass index (data not shown), as was found by De Vivo et al. (38).
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DISCUSSION |
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This study combined data from two independent ovarian cancer casecontrol studies (2,40). When examining these effects separately by study we observed similar results (data not shown); with rs608995, the odds ratio for the first study (2) was 2.92, and the odds ratio for the second study (40) was 2.86.
Women who carried two copies of the PROGINS allele had a 37% lower risk of breast cancer than women who carried no copies, but this decrease was not statistically significant (P =.11). Subgroup analyses (data not shown) of each of the five racial/ethnic groups revealed that a decrease was seen in all groups. Our findings are consistent with those of a meta-analysis (37) showing that risk of breast cancer was decreased among homozygous carriers of the PROGINS allele compared with noncarriers (OR = 0.41, 95% CI = 0.15 to 0.95). Our findings are also consistent with two (31,34) of four (3134) papers on the PROGINSbreast cancer association published after the meta-analysis was performed. However, rs608995, which was associated with increased risk of ovarian cancer and explained the ovarian cancerPROGINS risk relationship, was not associated with breast cancer risk.
The putatively functional +331G/A SNP, which was previously found by De Vivo et al. (38) but not by Feigelson et al. (39) to be associated with breast cancer, was not associated with breast cancer in our study (Supplementary Table 2, available at: http://jncicancerspectrum.oupjournals.org/jnci/content/vol97/issue1). In subgroup analysis we did observe a 27% increased risk of breast cancer among whites who carried either one or two copies of the minor allele of this SNP (data not shown); however the increase was not statistically significant. Moreover, given the functional nature of this SNP, one would expect the association to be observed in all ethnic groups if it were biologically meaningful.
Although our study included a relatively large number of ovarian and breast cancer patients and control subjects, the risk associated with PGR variation was restricted to homozygous carriers of the haplotypes, thereby reducing our power to detect an association. This limitation of our study underscores the need for even larger collections of patient samples as well as the role of collaborative studies, such as the Breast and Prostate Cancer Cohort Consortium (http://cancercontrol.cancer.gov/bb/cohort_conso.html). In particular, the consortiums collection of more than 10 000 breast cancer case patients should help to clarify the association of PROGINS with risk of breast cancer. Another possible limitation of the study arises from the fact that the proportion of women who provided a blood sample in both the ovarian and breast cancer studies ranged from 66% to 74%. However, we have no reason to believe that the women who provided a blood sample are genetically different from those who did provide a blood sample; these women did not differ on known ovarian or breast cancer risk factors.
Under the most parsimonious model supported by our datai.e., a single PGR causal allele for ovarian cancer, currently marked by rs608995 and carried on both haplotypes 4-D and 4-Eany germline variation in the PGR that is associated with ovarian cancer is likely to lie in block 4 or in a region downstream of this block. This proposed location is supported by evidence of recombination observed between haplotype 4-E and haplotypes within blocks lying upstream of block 4 (Fig. 1).
If our findings with respect to ovarian cancer are replicated in other studies, this would provide important evidence that direct modulation of progesterone signaling (as caused by allelic variation) influences ovarian cancer risk. Understanding how this variation influences risk of ovarian cancer should give further insight into ways this difficult-to-diagnose disease could be prevented in the future. The next step in investigating how germline variation in the PGR gene influences the risk of ovarian cancer requires direct sequencing of the PGR gene in patients to identify novel variants within the block 4 region as well as functional studies of the specific SNPs comprising haplotypes 4-D and 4-E and the haplotypes themselves.
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NOTES |
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J.N.H. is a recipient of a Career Development Award of the Burroughs Wellcome Fund. D.A. is a Charles E. Culpeper of the Rockefeller Brothers Fund and a Burroughs Wellcome Fund Clinical Scholar in Translational Research.
We thank Peggy Wan for data management assistance, Dr. David Van Den Berg for laboratory expertise, and Loreall Pooler, David Wong, and Stephanie Riley for genotyping assistance. We are deeply indebted to the ovarian cancer case patients and control subjects who we interviewed.
This work was supported by the California Cancer Research Program grant 00-01389V-20170; National Cancer Institute R01-CA63464; Public Health Service grants CA 17054, CA14089, CA 61132, and N01-PC-67010; and Subcontract 050-E8709 from the California Public Health Institute, which is supported by the California Department of Health Services as part of its statewide cancer reporting program mandated by Health and Safety Code Section 210 and 211.3. The ideas and opinions expressed herein are those of the authors, and no endorsement by the funding agencies is intended or should be inferred.
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Manuscript received March 10, 2004; revised October 21, 2004; accepted October 25, 2004.
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