ARTICLE

Clarifying the PROGINS Allele Association in Ovarian and Breast Cancer Risk: A Haplotype-Based Analysis

Celeste Leigh Pearce, Joel N. Hirschhorn, Anna H. Wu, Noël P. Burtt, Daniel O. Stram, Stanton Young, Laurence N. Kolonel, Brian E. Henderson, David Altshuler, Malcolm C. Pike

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 Children’s 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)


    ABSTRACT
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Background: The PROGINS allele of the progesterone receptor (PGR) gene has been associated with an increased risk of ovarian cancer and a decreased risk of breast cancer. We set out to refine the association between common variation at the PGR gene locus and these two diseases. Methods: We characterized the haplotype structure of the PGR gene by genotyping 54 single-nucleotide polymorphisms (SNPs) in 349 women. We then selected a subset of 17 haplotype-tagging SNPs that captured variation across the locus and typed them in 267 ovarian cancer case patients and 397 control subjects from two case–control studies and in 1715 breast cancer case patients and 2505 control subjects from a cohort study. Results: The PGR locus was characterized by four blocks of strong linkage disequilibrium. Two SNPs in block 4 were associated with an increased risk of ovarian cancer among homozygous carriers as compared with noncarriers: rs1042838 (PROGINS allele; odds ratio [OR] = 3.23, 95% confidence interval [CI] = 1.19 to 8.75, P = .022) and rs608995 (minor allele; OR = 3.10, 95% CI = 1.63 to 5.89, P<.001). The PROGINS allele was observed on a subset of chromosomes carrying the minor allele at rs608995, and its association with ovarian cancer was fully explained by its association with rs608995. In addition, rs608995 fell on two common haplotypes (4-D and 4-E), whose association with ovarian cancer was the same as that of rs608995. These same two haplotypes were associated with a non–statistically significantly reduced risk of breast cancer. Conclusions: Variation in PGR was associated with ovarian cancer risk, although the strongest result was not with the PROGINS allele. Instead, any causal allele(s) are likely in or downstream of block 4 and carried on haplotypes 4-D and 4-E. There was some evidence that the same variation was associated with a reduced risk of breast cancer, but the association was not statistically significant.



    INTRODUCTION
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Multiple lines of evidence indicate that progestin signaling is involved in the risk of ovarian and breast cancer. Pregnancy and oral contraceptive use are associated with a reduced risk of invasive epithelial ovarian cancer (ovarian cancer) (111), suggesting a key role for progesterone in its etiology, as initially described by Risch (12). Rodriguez et al. (13) conducted a long-term study of extended exposure of macaques to oral contraceptives or to the individual components of oral contraceptives and found that the progestin component of oral contraceptives, given alone without the estrogen component, showed an increased apoptotic effect on the ovarian surface epithelium (the purported cell of origin of ovarian cancer) similar to that seen with oral contraceptives. A protective role for progestins is also supported by experimental data showing that progesterone reduces in vitro proliferation of ovarian tumor cells (both benign and malignant) (14). Indirect evidence of a protective role for progestins is provided by the finding that the progesterone receptor (PR) is expressed at much lower levels in ovarian cancer tumor tissue than in normal surface epithelium (15) or benign tumors (15,16).

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 case–control 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 Women’s 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.


    METHODS
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Approval and Consent

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 case–control studies conducted in Los Angeles County from 1992 to 1999 (2,40). In both studies, case patients were identified from the local National Cancer Institute’s 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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Table 1. Odds ratios (OR) and 95% confidence intervals (CI) for associations of progesterone receptor htSNPs with risk of ovarian and breast cancer*

 
D' was used as a pairwise measure of linkage disequilibrium to test for nonrandom association between the SNPs (45). D' can take a value between 0 and 1, with low values providing evidence of recombination and high values providing evidence of no recombination between any two SNPs. Block structure for each ethnic group in the MEC haplotype panel was determined by the method of Gabriel et al. (46), in which the 90% confidence bounds of D' are used to define sites of historical recombination between SNPs with a minor allele frequency of at least 10%. Blocks, or regions of strong linkage disequilibrium, are those in which no more than 5% of informative pairs (based on high D' and tight confidence bounds) show evidence of recombination. A series of contiguous SNPs in high linkage distribution was considered a block if it contained at least six SNPs with minor allele frequencies of at least 10%, because within such regions untyped markers have a greater than 80% chance of showing a strong correlation (r2>0.8) with the set of markers used to define the block (46). SNPs with a minor allele frequency of less than 10% that fell within a block were considered to be part of that block, regardless of their D' relationship with the higher-frequency SNPs in the block. The optimal placement of SNPs with a frequency of less than 10% that fell on a block boundary was assessed by applying the method of Gabriel et al., as described above (46); if they fell in neither flanking block they were considered to be part of an interblock region. The block structure of the PGR gene was the same for each ethnic group (data not shown), with the exception of African Americans, for whom there was some evidence of recombination in blocks 3 and 4. In the analysis presented here, however, we have used the same block definition for all populations.

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 Case–Control 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 case–control status. Reproducibility was 98%.

Statistical Analysis

Hardy–Weinberg 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 individual’s genotype data and the overall (i.e., ignoring case–control 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 case–control 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.


    RESULTS
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Genomic Structure of the PGR

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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Fig. 1. A cartoon of the progesterone receptor gene showing the linkage disequilibrium blocks (top) and associated haplotypes at a frequency of at least 5% (bottom) in all ethnic groups combined. The exons are shaded rectangles. The putative regulatory regions are shown with dotted lines and the introns with solid lines. The haplotype IDs corresponding to the tables are shown to the left of each haplotype for each block. The frequency of each haplotype within a block is shown to the right of the haplotype. The number above each base (from 1 to 54) is the single-nucleotide polymorphism (SNP) number. The thickness of the lines connecting the haplotypes across blocks represents the relative frequency (i.e., high [thick] versus low [thin]) with which a given haplotype is associated with the haplotype in the adjacent block. * = haplotype-tagging SNP (htSNP) in all ethnic groups; {dagger} = African American–specific htSNP.

 
The haplotype frequencies observed in the MEC haplotype panel are shown in Fig. 2. There were from four to nine common (≥5%) haplotypes in each block, and these haplotypes accounted for at least 90% of all haplotypes within these blocks. These common haplotypes across all groups could be defined by 17 htSNPs: three each in blocks 1 and 2, six in block 3, and five in block 4, with four additional htSNPs required for African Americans (Fig. 2).



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Fig. 2. Haplotype frequencies by block and ethnicity in the Multiethnic Cohort Study haplotype panel. The haplotype-tagging single-nucleotide polymorphisms (htSNPs) for all ethnic groups are shaded. * = PROGINS alleles in block 4; {dagger} = African American–specific htSNP. Dashes indicate that the haplotype was not observed in that ethnic group.

 
The PROGINS allele (A allele of the rs1042838 SNP) was in block 4 and fell on a single common haplotype, 4-D. When examining the haplotype relationships across the four blocks, we observed that the PROGINS allele was on a single long-range haplotype. This long-range haplotype consists of haplotypes 1-D, 2-C, 3-C, and 4-D, which were always inherited together. This long-range haplotype occurred at a frequency of 8% in all ethnic groups combined and 16% in whites. This was the only long-range haplotype conserved across the entire locus at a frequency greater than 1%.

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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Fig. 3. Comparison between the PROGINS and rs608995 htSNPs and haplotypes 4D and 4E with risk of ovarian cancer. The reference group included individuals who carried no copies of the minor allele of rs608995. * = No rs1042838 and no rs608995 minor alleles; {dagger} = One control subject carried one copy of rs1042838 and one copy of the rs608995 minor allele but no copies of haplotype 4-D or 4-E; eight control subjects and one case patient carried no copies of rs1042838 but one copy of the rs608995 minor allele and no copies of haplotype 4-D or 4-E.

 
The three additional htSNPs associated with increased ovarian cancer risk (Table 1) are all carried on the PROGINS long-range haplotype and therefore do not represent independent associations. None of the remaining htSNPs or the putative promoter SNP +331G/A was associated with a statistically significantly increased risk of ovarian cancer (Supplementary Table 2, available at: http://jncicancerspectrum.oupjournals.org/jnci/content/vol97/issue1). There was no statistically significant interaction between oral contraceptive use and the PROGINS allele on ovarian cancer risk (data not shown).

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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Fig. 4. The common haplotype risk associations with ovarian cancer cases and controls and breast cancer cases and controls, based on the haplotype-tagging single-nucleotide polymorphisms for each block. The PROGINS allele is marked by rs1042838 in block 4. The breast cancer analysis is adjusted for ethnicity. * = Marker of the PROGINS allele; {dagger} = adjusted for race/ethnicity.

 
Women carrying two copies of haplotype 4-D were 4.47 times as likely to have ovarian cancer as noncarriers of this haplotype (95% CI = 1.40 to 14.24, P = .011; Fig. 4). A second haplotype in block 4, haplotype 4-E, was also associated with increased risk among carriers of two copies of that haplotype; however, this state was uncommon, and the result was not statistically significant (OR = 4.68, 95% CI = 0.48 to 45.29, P = .18).

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).


    DISCUSSION
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Previous reports have suggested that the PROGINS allele of PGR is associated with increased risk of ovarian cancer among homozygous carriers (2428). However, our data suggest that any influence of genetic variation in the PGR on risk of ovarian cancer is related not to the variant form of PROGINS itself but, rather, to two common haplotypes (4-D and 4-E), only one of which harbors the PROGINS allele. These two haplotypes were jointly associated with a more than threefold increased risk of ovarian cancer among individuals carrying a combination of two copies of 4-D or 4-E, or one of each. In our data, this effect is marked best by the single SNP (rs608995) carried by both haplotypes; however, we have no reason to believe that the minor allele of rs608995, rather than any of the other alleles shared uniquely by the two haplotypes, is the causal allele.

This study combined data from two independent ovarian cancer case–control 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 PROGINS–breast cancer association published after the meta-analysis was performed. However, rs608995, which was associated with increased risk of ovarian cancer and explained the ovarian cancer–PROGINS 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 consortium’s 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 data—i.e., a single PGR causal allele for ovarian cancer, currently marked by rs608995 and carried on both haplotypes 4-D and 4-E—any 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.


    NOTES
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
1 Editor’s note: SEER is a set of geographically defined, population-based, central cancer registries in the United States, operated by local nonprofit organizations under contract to the National Cancer Institute (NCI). Registry data are submitted electronically without personal identifiers to the NCI on a biannual basis, and the NCI makes the data available to the public for scientific research. Back

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.


    REFERENCES
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 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

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Manuscript received March 10, 2004; revised October 21, 2004; accepted October 25, 2004.


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