Affiliations of authors: Cancer Research UK, Department of Oncology (AMD, CSH, LT, KLN, SO, PDPP, BAJP), European Prospective Investigation of Cancer (RNL, NED), and Cancer Research UK, Genetic Epidemiology Group (DFE), University of Cambridge, Strangeways Research Laboratory, Cambridge, U.K.; Academic Department of Biochemistry, Royal Marsden Hospital, London, U.K. (MD, EF); Queensland Institute for Medical Research, Brisbane, Australia (LK)
Correspondence to: Alison M. Dunning, PhD, Cancer Research UK, Department of Oncology, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN U.K. (e-mail: alisond{at}srl.cam.ac.uk)
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ABSTRACT |
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INTRODUCTION |
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In postmenopausal women, ovarian estrogen synthesis is negligible and estrogens are synthesized in peripheral tissues, such as subcutaneous fat (4). The final stage of estrogen synthesis is catalyzed by the aromatase enzyme (encoded by the CYP19 gene), which converts the androgens androstenedione and testosterone to the estrogens estradiol and estrone, respectively (Fig. 1). Estrogen synthesis is therefore limited by circulating levels of androstenedione and testosterone, which are synthesized from 17-hydroxypregnenolone and 17-hydroxyprogesterone. 17-hydroxypregnenolone is quantitatively more important in the adrenal synthesis of androgens that circulate predominately in postmenopausal women than 17-hydroxyprogesterone (Fig. 1). These reactions are catalyzed by 17-hydroxylase/17,20-lyase (encoded by the CYP17 gene). The interconversions of the less biologically active hormones androstenedione and estrone to their more active analogs testosterone and estradiol, respectively, are catalyzed by 17
-hydroxysteroid dehydrogenase type 2 (encoded by EDH17B2), which is thus also a key candidate enzyme for controlling estradiol levels.
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To establish whether genetic variants in CYP17, CYP19, EDH17B2, SHBG, COMT, or CYP1B1 genes are associated with sex hormone levels, we determined the circulating levels of six hormones in 1975 normal postmenopausal women not taking hormone replacement therapy (HRT) and tested their association with 15 single nucleotide polymorphisms (SNPs) in these genes (Figs. 1 and 2). We further investigated the association of the same SNPs with risk of breast cancer in a casecontrol study of 15586258 women.
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SUBJECTS AND METHODS |
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From the 25 000 participants in the European Prospective Investigation of CancerNorfolk (EPIC-Norfolk) cohort study (8), we selected, at random, 2115 women from a subset of the participants older than 55 years who had not menstruated for 1 year or more and who had not taken hormone replacement therapy for at least 3 months prior to sampling. Plasma, serum, and whole blood samples were previously obtained from postmenopausal women participating in the EPIC-Norfolk cohort study. The plasma and sera were stored at 70 °C until analysis. Whole blood was collected and stored at 30 °C prior to DNA extraction. Anthropometric measurements and information about dietary habits have also been collected from this cohort. Ethical approval was obtained from the Norwich Local Research Ethics Committee, LREC 98CN01. All study participants provided written informed consent.
Breast Cancer Case Patients and Control Subjects
Patients were drawn from the Anglian Breast Cancer Study (9). The case collection is ongoing, resulting in a change in sample size over time. A minimum of 812 to a maximum of 2850 patients, younger than 70 years, had been recruited at the time of analysis. Control subjects, matched for age, ethnicity, and residence in the East Anglian region of the U.K. were recruited through the EPIC study (8). Characteristics of the case patients and control subjects are shown in Table 1. Whole blood was collected and stored at 30 °C prior to DNA extraction. Ethical approval was obtained from the Anglia and Oxford Multicentre Research Ethics Committee: MREC 02/5/42 and the Norwich Local Research Ethics Committee: LREC 98CN01. All study participants provided written informed consent.
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For the first 151 patients, sex hormone measurements were made from stored serum samples. After conducting comparative studies, which established that there were only minor differences between values obtained from plasma or serum from the same individual, the remaining measurements were made on plasma samples.
Sex hormone analyses were carried out sequentially (in this order: estradiol, testosterone, SHBG, 17-hydroxyprogesterone, androstenedione, and estrone) on the available plasma or serum from each subject. More than 94% of the subjects had sufficient plasma or serum to complete the estradiol, testosterone, SHBG, and 17
-hydroxyprogesterone measurements; thereafter, 71% of the subjects had sufficient plasma or serum for androstenedione measurements; in only 59% of subjects was there sufficient plasma or serum for estrone measurements.
Estradiol was measured from plasma or serum samples by radioimmunoassay after ether extraction (10). The within- and between-batch coefficients of variation were 8.6% and 13%, respectively, at a concentration of 18 pmol/L, and the sensitivity limit was 3.0 pmol/L. Testosterone was measured by using a solid-phase radioimmunoassay kit (Diagnostic Products, Gwynedd, U.K.). Within- and between-batch coefficients of variation were 6.1% and 10%, respectively, at a concentration of 3.1 nmol/L, and the sensitivity limit was 0.14 nmol/L. SHBG was measured by using a liquid-phase immunoradiometric kit (Orion Diagnostica, Espoo, Finland). Within- and between-batch coefficients of variation were 2.1% and 7.4%, respectively, at a concentration of 11 nmol/L, and the sensitivity limit was 0.5 nmol/L. Androstenedione, 17-hydroxyprogesterone, and estrone were measured as described below by using Diagnostic Systems Laboratories detection kits (Oxford, U.K.) with an antibody dilution (1 : 1) modification for 17
-hydroxyprogesterone. Androstenedione was estimated using solid-phase radioimmunoassay. Within- and between-batch coefficients of variation were 5.6% and 11%, respectively, at a concentration of 3.5 nmol/L, and the sensitivity was 0.1 nmol/L. 17
-hydroxyprogesterone levels were measured by radioimmunoassay. Within- and between-batch coefficients of variation were 4.2% and 6.2%, respectively, at a concentration of 1.5 nmol/L, and the detection limit was 0.06 nmol/L. Estrone was measured by radioimmunoassay after extraction with ether and liquid column chromatography on a Lipidex 5000 (PerkinElmer, Boston, MA) with elution using chloroform : hexane : methanol (50 : 50 : 1). Within- and between-batch coefficients of variation were 14% and 22% at 55 pmol/L, and the sensitivity limit was 15 pmol/L.
Genotyping
DNA for genotyping was extracted from the whole blood samples of all subjects (normal postmenopausal women, breast cancer case patients, and control subjects) by Whatman Biosciences (Ely, U.K.). A total of 15 SNPs were analyzed (Fig. 3). All genotyping was carried out by using end-point Taqman assays (Applied Biosystems, Warrington, U.K.) in 96- or 384-well arrays that included blank wells as negative controls, according to the manufacturer's instructions. Primer and probe sequences and assay temperatures are shown in Fig. 3. Assays were run on MJ Tetrad thermal cyclers (Genetics Research Instrumentation, Braintree, U.K.); genotypes were subsequently read on a 7700 or 7900 Sequence Detector (Applied Biosystems), according to the manufacturer's instructions. SNP assays were validated in the casecontrol studies, in which 2%10% of the DNA samples were duplicated in the arrays. Greater than 99% concordance between duplicated samples was required for assays to be accepted for analysis. As a further quality control step, deviation of the genotype distributions from those expected under HardyWeinberg equilibrium were also tested, and the P values are presented in Supplemental Table A1 (available at http://jncicancerspectrum.oupjournals.org/jnci/content/vol96/issue12/). No statistically significant deviations were observed. Genotypes were obtained for more than 95% of all subjects for all assays, except in the normal postmenopausal women, in which genotypes at CYP19 IVS4 [TCT]+/ and EDH17B2 Ser313Gly were obtained for 54% and 81% of subjects, respectively. The lower proportions for the last two assays were the result of there being insufficient reagents to complete the study rather than of any systematic technical failure.
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For the normal postmenopausal women, multiple linear regression was used to evaluate the effects of each polymorphism on sex hormone levels. Regression analyses were performed using Stata7 (Stata Corporation, College Station, TX). Sex hormone levels were log-transformed prior to comparison with genotypes to achieve approximately normal distributions. Analyses were adjusted for the following covariates, which are known to be or are potentially associated with sex hormone levels: body mass index (defined as weight [kg] divided by the square of height [m2]); number of live births (0, 1, 2, 3, or 4); menopausal status (1 year from last period, 25 years from last period, >5 years from last period); and age, in years, when blood was taken (5559, 6064, 6569, 7074, 7579,
80). In 22 of the 2115 samples assayed, sex hormone levels were inconsistent with postmenopausal status (17 had estradiol levels greater than 150 pmol/L, five had testosterone levels greater than 3.5 nmol/L), and these samples were excluded; an additional 118 subjects were excluded because of a self-report of premenopausal status or HRT use that conflicted with the report provided at the time of blood sampling. The analyses are therefore based on a total of 1975 women.
We computed adjusted geometric means for each genotype by running the regression analyses without an overall mean parameter. A two-degree-of-freedom likelihood ratio test for homogeneity of the effect of each SNP was performed. We also performed a one-degree-of-freedom test for trend over the ordered allele categories by fitting a model in which genotype was coded according to the number of rare alleles. The percentage of the phenotypic variance explained by each genotype was estimated by subtracting the r2 value for a model that included the parameters for each SNP from the r2 for a model that excluded the SNP. Haplotype frequencies were estimated using the estimation-maximization algorithm implemented in the estimation-maximization program (11).
In the breast cancer casecontrol studies, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by unconditional logistic regression. All statistical tests were two-sided.
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RESULTS |
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We began by analyzing the association of 15 SNPs in the CYP17, CYP19, EDH17B2, SHBG, CYP1B1, and COMT genes with circulating hormone levels in the normal postmenopausal women. The adjusted geometric means for each hormone level, by SNP genotype, together with the significance levels and the percentage of the total variance explained (r2) by each SNP, are shown in Supplemental Table A2 (available at http://jncicancerspectrum.oupjournals.org/ jnci/content/vol96/issue12/).
We found no statistically significant associations between levels of any of the hormones and SNPs in the CYP17, EDH17B2, CYP1B1, and COMT genes. Each of these variants explained less than 0.6% of the trait variance.
Statistically significant associations with hormone levels were found for two common bi-allelic polymorphisms in the CYP19 gene: a 3' untranslated region (3'UTR) t-c change (rs10046) and the silent [TCT]+/ in IVS4. The 3'UTR t-c SNP was associated with differences in estradiol levels (means: tt, 17.07 pmol/L; tc, 15.70 pmol/L; cc, 14.99 pmol/L; P = .0006; Table 2). Reduced estradiol levels were associated with the presence of the c allele, and this SNP accounted for 0.8% of the total variance in estradiol. This SNP was also associated with differences in estrone levels (P = .004, r2 = .9%), in the estradiol : testosterone ratio (P = .000001, r2 = 1.6%), in the estrone : androstenedione ratio (P = .002, r2 = 1.2%), and in the estradiol : SHBG ratio (P = .02, r2 = .4%), but not with absolute SHBG levels. Comparison of statistical models indicated that the effect of the 3'UTR SNP is consistent with an additive (or co-dominant) mode of action, with the c allele being associated with reduced estradiol and estrone levels and with reduced estradiol : testosterone, estrone : androstenedione, and estradiol : SHBG ratios. When compared with the most general model for estradiol, an additive model was an adequate fit (P = .8), whereas the recessive (P = .02) and dominant (P = .006) models fit poorly. The [TCT]+/ polymorphism was similarly associated with reduced estradiol levels (means: [TCT]+/+, 17.72 pmol/L; [TCT]+/, 15.82 pmol/L; [TCT]/, 14.60 pmol/L; P = .0003, r2 = 1.6%; Table 2), with reduced estrone levels (P = .002, r2 = 2.7%), and with reduced ratios of estradiol : testosterone (P = .002, r2 = 1.4%), estrone : androstenedione (P = .01, r2 = 1.7%), and estradiol : SHBG (P = .02, r2 = .7%). Again, the effect was most consistent with an additive genetic model.
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Figure 2 illustrates the common haplotypes for each of the genes, based on the SNPs we assayed, with haplotype frequencies as estimated by the estimation-maximization algorithm. For every gene, we found that all SNPs were in strong linkage disequilibrium, and in each case, there were just three common haplotypes (the frequencies of all other haplotypes combined were less than 2% for all genes). For CYP17, CYP1B1, and COMT, comprehensive SNP searches of the coding regions, and 5' and 3'UTRs have been completed (12,13); the common SNPs identified by these searches generate the same three common haplotypes, which are tagged by the SNPs used in this study.
The two SNPs in CYP19 that we studied distinguish three common haplotypes, which are derived from one another by single mutational events (Fig. 2). The rarer IVS4 [TCT] allele appears to have arisen on a haplotype that already carried the relatively more common 3'UTR c allele. A fourth haplotype (not shown), which has probably arisen by recombination, has a frequency of only 2%. These haplotypes give rise to eight combined genotypes (Fig. 4, A). Homozygotes for the 3'UTR c allele (regardless of IVS4 [TCT] genotype) have reduced mean estradiol : testosterone ratios (Fig. 4, A) as well as reduced estrone, estradiol, and estrone : androstenedione and estradiol : SHBG ratios (data not shown) compared with homozygotes for the t allele. Model fitting suggests that the primary association is with the 3'UTR t-c SNP and that the effect of the IVS4 [TCT]+/ may be explained by its linkage disequilibrium with the 3'UTR SNPafter adjusting for the effect of 3'UTR t-c, there was no significant effect of IVS4 [TCT]+/, whereas a model without 3'UTR t-c fitted poorly (P = .02 compared with the full model).
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SNPs and Breast Cancer Risk
All 15 SNPs were also investigated in the East Anglian breast cancer casecontrol study and the detailed results are shown in Supplementary Table A1 (available at http://jncicancerspectrum.oupjournals.org/jnci/content/vol96/issue12/). Odds ratios are illustrated in Fig. 5. No statistically significant associations were observed between any SNP and differences in susceptibility to breast cancer. The maximum point estimate was for SHBG D356N (OR for NN versus DD = 1.53, 95% CI = 0.90 to 2.59; P = .2).
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DISCUSSION |
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Our data indicate that the CYP19 3'UTR t-c change was more strongly associated with estradiol levels than the IVS4 [TCT]+/SNP. This result is consistent with the results of Kristensen et al. (14), who have reported that the c allele is associated with lower levels of CYP19 mRNA in tumors. Although it remains possible that the association with estradiol levels may be due to another noncoding region polymorphism that is in linkage disequilibrium with the 3'UTR change, a more probable explanation is that the c allele generates less aromatase enzyme than the t allele and hence confers reduced overall enzyme activity.
The haplotype block structure and tagging SNPs of the CYP19 gene were recently published (15). The entire coding region of CYP19 lies within a single block (although its numerous promoters fall into other blocks) that has just four common haplotypes in the U.S. Caucasian population. The two CYP19 SNPs examined here tag three of these four haplotypes. The 3'UTR t allele uniquely tags the most common haplotype, which we found to be associated with the highest estradiol levels. It remains to be seen whether all three haplotypes carrying the 3'UTR c allele, or just a subset of them, are associated with reduced estradiol levels. If all three haplotypes are found to be associated with similarly reduced estradiol levels, this would provide further evidence that the 3'UTR t-c change directly affects the activity of the aromatase enzyme.
Both SHBG SNPs that we examined were associated with differences in circulating SHBG levels and estradiol : SHBG ratios, but not total estradiol levels. Again, the proportions of the variance explained, although highly statistically significant, were small. The 5'UTR g-a SNP, which had the larger effect of the two SNPs, accounted for 2.4% of the variance in SHBG levels and 1.1% of the variance in the estradiol : SHBG ratio. The effects of these two SNPs appear to be independent, because their rare alleles occur on different haplotypes and have different modes of action (one dominant, one recessive). The N residue at codon 356 is believed to be the anchor site for an additonal carbohydrate group, which reduces the rate of clearance of SHBG from the circulation; the D allele cannot link to the carbohydrate group (16). The 5'UTR change is more likely to affect the rate of production of SHBG than the rate of clearance, but again, it may be in linkage disequilibrium with another, functional change.
The high degrees of statistical significance obtained for these SNPs, despite the small r2 values, serve to demonstrate the statistical power of this type of study, which is based on quantitative phenotypes close to the mode of action of the gene. These results also show that SNPs in the other genes we have studied (CYP17, EDH17B2, COMT, and CYP1B1) have little if any effect on the hormone variables we have measured. In the only similar study we know of, Haiman et al. (17) suggested an association of the CYP17 t-34c SNP with circulating hormone levels in a group of 297 postmenopausal women. The cc homozygotes had elevated (by 9%17%) levels of estrone, estradiol, testosterone, and androstenedione compared with the tt homozygotes, and the difference in estrone levels was statistically significant (P<.01). It is possible that we have not assayed the functionally significant SNPs in these genes. However, the complete coding regions of CYP17, COMT, and CYP1B1 have been examined for variants (12,13) and, although there are multiple SNPs in each gene, the SNPs in the coding sequence generate only three common haplotypes. Although we cannot exclude the possibility that other haplotypes tagged by noncoding SNPs exist, it seems likely that any common coding variant could explain only a small proportion of the total variance in estradiol levels. In the case of EDH17B2, which has a highly conserved pseudogene, it was possible to assay only one SNP: S313G. Again, the entire coding sequence of this gene has been examined for variants (18), and this was the only common polymorphic amino acid substitution found. Because we were not able to use the haplotype tag approach on this gene, it is possible that there are other variants that substantially affect levels or function of this enzyme, particularly if they were not in strong linkage disequilibrium with the S313G substitution.
Our findings, coupled with the established associations between estradiol and SHBG levels and breast cancer risk, suggest that the polymorphisms in CYP19 and SHBG genes should be associated with the risk of breast cancer (and potentially other hormone-related diseases, such as osteoporosis). However, despite numerous studies, no clear associations of these genes with disease risk have emerged. Two studies have reported that the bi-allelic CYP19 [TCT]+/ polymorphism is not associated with breast cancer risk (19,20), and we have obtained odds ratios very close to unity in our set of 2526 cases and 2594 controls (Fig. 5; Supplementary Table A1 available at http://jncicancerspectrum.oupjournals.org/jnci/content/vol96/issue12/). For the CYP19 3'UTR c-t SNP, which is more strongly associated with hormone levels, Kristensen et al. (14) did report a significant association between the t allele and breast cancer risk (OR for tt/cc = 2.00, 95% CI = 1.28 to 3.11), but Haiman et al. (21) found no association (OR for tt/cc = 0.87, 95% CI = 0.67 to 1.27). Here we also failed to find a significant association, based on 2635 cases and 3630 controls (OR for tt/cc = 1.07, 95% CI = 0.93 to 1.23; Pgenotype distribution = .6 for CYP19 3'UTR c-t). The two SNPs that we found to be independently associated with raised SHBG levels might be expected to be associated with a reduced risk of breast cancer by reducing circulating active estradiol. However, we found no statistically significant change in risk for the SHBG SNPs 5'UTR a-g (OR for aa/gg = 1.02, 95% CI = 0.75 to 1.38; P = .7) and D356N (OR for NN/DD = 1.53, 95% CI = 0.90 to 2.59; P = .2). There are presently no other published casecontrol studies on these two SHBG SNPs.
What would explain the apparent discrepancy? These SNPs show significant associations with circulating sex hormone levels, known to be risk factors for breast cancer, but the same SNPs fail to show a significant direct association with breast cancer risk. A recent study has calculated that a doubling of circulating estradiol levels is associated with a 29% increase in breast cancer risk (3). If we assume that estradiol levels within a genotype class are log-normally distributed, then on the basis of the mean values of estradiol associated with the CYP19 3'UTR c-t genotypes, the tt genotype (mean estradiol level = 17.1 pmol/L) would confer an odds ratio for breast cancer of 1.05 and the tc genotype (mean estradiol level = 15.7 pmol/L) would confer an odds ratio of 1.02, relative to the cc genotype (mean 15.0 pmol/L). In practice, the true relationship between estradiol levels and breast cancer risk is likely to be stronger than these calculated values (we have measured levels at only one point in time, and true exposure will be the integral sum of levels over a lifetime). Thomas et al. (22) report an intra-class correlation of .56 between measurements of estradiol levels made in the same individual at different times in their prospective study, suggesting that approximately 50% of variance in estradiol levels is essentially random fluctuation. Consequently, we would predict odds ratios of 1.10 and 1.03 in women with the tt and tc genotypes, respectively. A study of approximately 34 000 cases and a similar number of matched controls would be required to detect a risk of this magnitude (50% power, significance level = .0001)more than an order of magnitude larger than the genetic association studies that have been published to date.
Our data help explain why the results obtained in breast cancer casecontrol studies have been largely inconclusive. The assumption on which they were basedthat genetic variability in factors controlling the bioavailability of estrogens is likely to confer differential risk of developing breast cancerremains correct. We have detected highly statistically significant associations between common genetic variants in both the CYP19 and SHBG genes and circulating sex hormone levels, but the small magnitude of these effects would only marginally increase the risk of developing breast cancer.
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NOTES |
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The authors are indebted to Caroline Baynes, Joanna Camus, Clare Jordan, Hannah Munday, Suzy Oakes, Barbara Perkins, Joan Russell, and Mitul Shah for sample collection, identification, and preparation.
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REFERENCES |
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1 Henderson BE, Bernstein L. The international variation in breast cancer rates: an epidemiological assessment. Breast Cancer Res Treat 1991;18 Suppl 1:S117.[ISI][Medline]
2 Baum M, Budzar AU, Cuzick J, Forbes J, Houghton JH, Klijn JG, et al. Anastrozole alone or in combination with tamoxifen versus tamoxifen alone for adjuvant treatment of postmenopausal women with early breast cancer: first results of the ATAC randomised trial (Erratum in: Lancet 2002;360:1520). Lancet 2002;359:21319.[CrossRef][ISI][Medline]
3 Endogenous Hormones and Breast Cancer Collaborative Group. Endogenous sex hormones and breast cancer in postmenopausal women: reanalysis of nine prospective studies. J Natl Cancer Inst 2002;94:60616.
4 Miller WR, Hawkins RA, Forrest AP. Significance of aromatase activity in human breast cancer. Cancer Res 1982;42(8 Suppl):3365s8s.[Abstract]
5 Siiteri PK, Nisker JA. Increased availablility of serum estrogens in breast cancer: a new hypothesis. In: Pike MC, Siiteri PK, Welsch CK, editors. Hormones and breast cancer. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 1981. p. 87101.
6 Li JJ, Li SA. Estrogen carcinogenesis in Syrian hamster tissues: role of metabolism. Fed Proc 1987;46:185863.[ISI][Medline]
7 Jefcoate CR, Liehr JG, Santen RJ, Sutter TR, Yager JD, Yue W, et al. Tissue-specific synthesis and oxidative metabolism of estrogens. J Natl Cancer Inst Monogr 2000;(27):95112.[Medline]
8 Day N, Oakes S, Luben R, Khaw KT, Bingham S, Welch A, et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br J Cancer 1999;80 Suppl 1:95103.[ISI][Medline]
9 Prevalence and penetrance of BRCA1 and BRCA2 mutations in a population-based series of breast cancer cases. Anglian Breast Cancer Study Group. Br J Cancer 2000;83:13018.[CrossRef][ISI][Medline]
10 Dowsett M, Goss PE, Powles TJ, Hutchinson G, Brodie AM, Jeffcoate SL, et al. Use of the aromatase inhibitor 4-hydroxyandrostenedione in postmenopausal breast cancer: optimization of therapeutic dose and route. Cancer Res 1987;47:195761.[Abstract]
11 Terwilliger JD, Ott J. Handbook of human genetic linkage. Chapter 23. Baltimore (MD): The Johns Hopkins University Press; 1994. p. 188.
12 Inoue K, Asao T, Shimada T. Ethnic-related differences in the frequency distribution of genetic polymorphisms in the CYP1A1 and CYP1B1 genes in Japanese and Caucasian populations. Xenobiotica 2000;30:28595.[CrossRef][ISI][Medline]
13 Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, Patil N, et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes (Erratum in: Nat Genet 1999;23:373). Nat Genet 1999;22:2318.[CrossRef][ISI][Medline]
14 Kristensen VN, Harada N, Yoshimura N, Haraldsen E, Lonning PE, Erikstein B, et al. Genetic variants of CYP19 (aromatase) and breast cancer risk. Oncogene 2000;19:132933.[CrossRef][ISI][Medline]
15 Haiman CA, Stram DO, Pike MC, Kolonel LN, Burtt NP, Altshuler D, et al. A comprehensive haplotype analysis of CYP19 and breast cancer risk: the Multiethnic Cohort. Hum Mol Genet 2003;12:267992.
16 Cousin P, Dechaud H, Grenot C, Lejeune H, Pugeat M. Human variant sex hormone-binding globulin (SHBG) with an additional carbohydrate chain has a reduced clearance rate in rabbit. J Clin Endocrinol Metab 1998;83:23540.
17 Haiman CA, Hankinson SE, Spiegelman D, Colditz GA, Willett WC, Speizer FE, et al. The relationship between a polymorphism in CYP17 with plasma hormone levels and breast cancer. Cancer Res 1999;59:101520.
18 Mannermaa A, Peltoketo H, Winqvist R, Ponder BA, Kiviniemi H, Easton DF, et al. Human familial and sporadic breast cancer: analysis of the coding regions of the 17 beta-hydroxysteroid dehydrogenase 2 gene (EDH17B2) using a single-strand conformation polymorphism assay. Hum Genet 1994;93:31924.[ISI][Medline]
19 Healey CS, Dunning AM, Durocher F, Teare D, Pharoah PD, Luben RN, et al. Polymorphisms in the human aromatase cytochrome P450 gene (CYP19) and breast cancer risk. Carcinogenesis 2000;21:18993.
20 Probst-Hensch NM, Ingles SA, Diep AT, Haile RW, Stanczyk FZ, Kolonel LN, et al. Aromatase and breast cancer susceptibility. Endocr Relat Cancer 1999;6:16573.
21 Haiman CA, Hankinson SE, Spiegelman D, Brown M, Hunter DJ. No association between a single nucleotide polymorphism in CYP19 and breast cancer risk. Cancer Epidemiol Biomarkers Prev 2002;11:2156.
22 Thomas HV, Key TJ, Allen DS, Moore JW, Dowsett M, Fentimen IS, et al. A prospective study of endogenous serum hormone concentrations and breast cancer risk in postmenopausal women on the island of Guernsey. Br J Cancer 1997;76:4015.[ISI][Medline]
Manuscript received August 25, 2003; revised April 8, 2004; accepted April 15, 2004.
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