Affiliations of authors: A. B. Spurdle, X. Chen, G. Chenevix-Trench (Cancer Unit), C. Mayne (Epidemiology Unit), Joint Experimental Oncology Programme, Queensland Institute of Medical Research, and The University of Queensland, Brisbane, Australia; G. S. Dite, J. L. Hopper, Centre for Genetic Epidemiology, The University of Melbourne, Carlton, Australia; M. C. Southey, L. E. Batten, H. Chy, L. Trute, Department of Pathology and Research, Peter MacCallum Cancer Institute, Melbourne; M. R. E. McCredie, Cancer Epidemiology Research Unit, NSW Cancer Council, Kings Cross, Australia, and Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand; G. G. Giles, Cancer Epidemiology Centre, Anti-Cancer Council of Victoria, Australia; J. Armes, Victorian Breast Cancer Research Consortium and Department of Pathology, Peter MacCallum Cancer Institute, Melbourne; D. J. Venter, Department of Pathology and Research, Peter MacCallum Cancer Institute, and Department of Pathology, The University of Melbourne.
Correspondence to: Amanda B. Spurdle, Ph.D., Cancer Unit, Queensland Institute of Medical Research, P.O. Royal Brisbane Hospital, Queensland, 4029, Australia (e-mail: mandyS{at}qimr.edu.au).
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Therefore, it is possible that there are "low-risk" genetic factors (an order of magnitude more common than the "high-risk" mutations in BRCA1 or BRCA2) that explain a substantial proportion of familial aggregation of breast cancer (2). Candidates for such low-risk genes would include genes likely to be involved in cancer predisposition that contain common but subtle variants. Such genes would include those mediating a range of functions, such as DNA repair, steroid hormone metabolism, signal transduction, and cell cycle control. Variants of particular interest would include genetic polymorphisms that affect gene expression or function through modified transcription of DNA, through altered stability, through processing or translation of messenger RNA, or by amino acid substitution in the expressed protein.
Exposures to endogenous and exogenous steroid hormones are known to influence breast cancer risk, and hormonal signals are manifested via hormone receptors. The androgen receptor (AR) gene is involved in various pathways, including differentiation, development, and regulation of cell growth, and a role of the AR gene in cancer predisposition is indicated by reported associations between prostate cancer risk and the length of the polymorphic exon 1 CAG repeat (CAGn) within the AR transactivation domain (3-7). For prostate cancer, an increased risk for smaller repeat lengths (such as CAGn <20 or <22) has been observed (3-7). One of the larger studies of 269 high-grade prostate cancer case subjects and 588 control subjects found a relative risk of 2.1 (95% confidence interval [CI] = 1.1-4.0) for CAGn <19 (6), while a smaller study of 281 prostate cancer case subjects and 246 control subjects found a relative risk of 2.2 (95% CI = 1.1-4.7) for CAGn <22 in a subgroup of relatively thin individuals (Quetelet index, computed as weight in kilograms divided by height in meters squared, <24.4) (5). Furthermore, case subjects with earlier onset disease have been reported to have shorter repeat lengths (8). Biological significance of the CAG repeat length variation is suggested by in vitro studies (9), which demonstrated that smaller repeat lengths exhibit greater transactivation capabilities. Furthermore, greatly expanded CAG repeat lengths (>39 repeats) are associated with spinal and bulbar muscular atrophy (SBMA) in vivo(10). The biological importance of repeat length variation is emphasized at even the extreme lengths within the SBMA range, with an increase in repeat length correlating with age at onset of this disorder and also with the likelihood of clinical manifestation of gynecomastia (11). In addition, the importance of repeat length variation within the normal range is suggested not only by the prostate cancer studies detailed above, but also by a more recent report of an association between CAG repeat length >28 and increased risk of impaired spermatogenesis (12).
Several lines of evidence suggest a role for the AR gene in breast cancer risk. Inactivating
mutations in the hormone-binding domain in male breast cancer patients have been documented (13,14). In addition, seven different human breast cancer samples and
three different breast cancer cell lines have been shown to express high levels of an AR splice
variant that lacks exon 3, the region encoding the second zinc finger of the DNA-binding
transactivation domain (15). Furthermore, a report in abstract form of a
study of 190 BRCA1 mutation carriers (16) purported to show that the
AR exon 1 CAGn may act as a modifier of BRCA1-associated risk of breast cancer,
with inheritance of at least one allele of CAGn 29 apparently associated with
earlier age at disease onset.
We undertook a population-based study to establish whether there was evidence for an association between early-onset breast cancer and the length of the AR exon 1 CAG repeat.
![]() |
SUBJECTS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
For each case subject and control subject, a detailed family history was systematically recorded for all first- and second-degree relatives and subsequently checked with their living relatives at the time of their interview. Verification of all family cancers reported by case subjects, control subjects, or relatives was sought through cancer registries, pathology reports, hospital records, treating clinicians, and death certificates. Blood samples were collected from all case subjects and control subjects at the time of interview.
Of 644 eligible case subjects, 467 (72.5%) were interviewed. Attrition was due to death (1.7%), refusal (surgeon, 8.4%; patient, 11.8%), no response (surgeon, 0.6%; patient, 1.4%), or having changed residence (3.6%). Of the 633 eligible control subjects, refusals (25.8%) and no responses (9.8%) resulted in 408 control subjects being interviewed (64.4%). Blood samples were available from 393 case subjects (84.2% of participating and 61.0% of eligible case subjects) and from 295 control subjects (72.3% of participating and 46.6% of eligible control subjects).
AR gene analysis was performed for 368 case subjects (78.8% of participating and 57.1% of eligible case subjects) and for 284 control subjects (69.6% of participating and 44.9% of eligible control subjects). Selection of case subjects and control subjects for AR gene analysis was not made on the basis of measured risk information but rather on the basis of DNA availability. For case subjects and control subjects, there was no difference between those included and those not included in the AR gene analysis for factors shown to be associated with breast cancer in the full sample of case subjects and control subjects (17). A greater proportion of Victorian participants than of New South Wales participants was included in the AR gene analysis for resource reasons unrelated to AR genotype. For the case subjects, 151 (69%) of 219 participating Victorian subjects were included in the AR gene analysis versus 217 (88%) of 248 participating New South Wales subjects (P<.001). For control subjects, 154 (86%) of 180 participating Victorian subjects were included in the AR gene analysis versus 130 (57%) of 228 participating New South Wales subjects (P<.001).
With regard to family history of breast cancer, 49 (13.3%) of the 368 case subjects in the AR gene analysis had an affected first-degree relative compared with 58 (12.4%) of all interviewed case subjects. For control subjects, these numbers (percentages) were 17 (6.0%) and 21 (5.2%), respectively. AR gene analysis was performed for 49 (84.5%) case subjects and for 17 (81.0%) control subjects with an affected first-degree relative and for 319 (78.0%) case subjects and for 267 (69.0%) control subjects without an affected first-degree relative.
Molecular Analysis
Collection of peripheral blood and DNA extraction were described previously (18). The AR exon 1 CAG trinucleotide repeat was amplified by polymerase chain reaction (PCR) with the use of primer sequences detailed by La Spada et al. (10), with inclusion of a 5'-6-carboxy-4,7,2',7'-tetrachlorofluorescein (5'-TET)-labeled forward primer to generate a fluorescent product. The 10-µL reaction mixture contained 30 ng of DNA, primers (10 pmol each), deoxynucleotide triphosphates (200 nM), 1x Taq polymerase buffer (The Perkin-Elmer Corp., Foster City, CA), 1 U of Taq polymerase, 1.5 mM MgCl2, and 7% deionized formamide. Amplification conditions were as follows: 2 minutes at 94 °C and 34 cycles at 94 °C for 20 seconds, 62 °C for 20 seconds, and 72 °C for 20 seconds, followed by a 10-minute extension at 72 °C. Amplified samples were diluted 1 : 12 in formamide loading buffer, denatured for 2 minutes at 95 °C, and separated by size on a 6% denaturing polyacrylamide gel. The ABI Prism 373 Genescan and Genotyper systems (The Perkin-Elmer Corp.) were used for detection and sizing of fluorescent products. Separation of the ABI TAMRA-350 size standard in each lane allowed for Genescan automated sizing of 5'-TET-labeled PCR products. In addition, control samples of known size were separated at different positions across each gel. Consistent sizing of samples was indicated by the independent generation of matching size results for a random subset (18.2%) of samples separated on more than one gel. PCR-amplified samples from both case subjects and control subjects were loaded randomly on gels to further avoid any sizing bias.
Immunohistochemical Studies
Immunohistochemical studies were performed on sections obtained from paraffin blocks of tissues fixed in 10% neutral buffered formalin. Sections (3 µm) were cut from paraffin blocks, placed onto silane-coated slides, and dried at 60 °C for 30 minutes. The sections were dewaxed in Histolene\T (Fronine, Riverstone, New South Wales, Australia) and rehydrated through graded alcohols. Antigen was retrieved by heating the sections for 2 minutes at pressure in a pressure cooker in 10 mM sodium citrate (pH 6.0). The Autostrainer (Dako Corp., Carpinteria, CA) was used for subsequent steps. All washes used 50 mM Tris-HCl (pH 7.6), containing 0.05% Tween. The sections were treated with 3% hydrogen peroxide for 10 minutes to inactivate endogenous peroxidase activity. They were then incubated with monoclonal estrogen receptor (ER)- or progesterone receptor (PR)-specific antibodies (Dako Corp.) at a 1 : 50 dilution for 30 minutes at room temperature. Sections were incubated with biotinylated secondary antibody, followed by peroxidase-conjugated streptavidin by use of the Universal DAKO LSAB\R2 kit (Dako Corp.) at 10 minutes for each step. Staining was visualized by use of 3-amino-9-ethyl-carbazole; the sections were washed in water and counterstained with hematoxylin. Crystal Mount (Biomedia, Foster City, CA) was applied to the sections and dried on a 60 °C hot plate. For histology, the sections were then mounted under coverslips with DPX Mountant (Fluka Chemical Corp., Ronkonkoma, NY).
Staining of invasive carcinoma cells with the use of ER- and PR-specific antibodies was
scored for intensity and proportion of positive cells by a modification of the method described by
Allred et al. (19). The proportion score represented the estimated fraction
of positive staining cells (0 = 10%; 1 = 11%-25%; 2
= 26%-50%; 3 = 51%-75%; 4 =
76%-90%; 5 =
91%). The intensity score represented the
estimated average staining intensity of positive cells (0 = none; 1 = weak; 2
= moderate; 3 = strong). The results were then analyzed to give a semiquantitative
estimate of the expression levels of antigen in the tissue. Intensities of 0 or 1 were designated as
negative to weak expression. For intensity scores of 2 and 3, a combined score was derived by
adding the intensity and proportion scores. Combined scores of 2 and 3 were designated as
negative to weak expression; combined scores from 4 to 6 were designated as moderate
expression, and scores of 7 or 8 were designated as strong expression. Only nuclear staining was
scored.
Statistical Methods
The distributions of the average, the smaller, and the larger CAGn alleles were compared between case subjects and control subjects by Student's t test and by analysis of covariance, allowing adjustment for measured risk factors.
Analyses of allele frequencies were carried out as described by Southey et al. (18), with the use of logistic regression. For each analysis, two alleles were defined
according to the CAG repeat length. For example, the major analysis involved defining one allele
as CAGn <22 and the other as CAGn 22. This cutoff point was
chosen because it divides the distribution of CAG repeat lengths in controls approximately in
half and is near to the mode of CAGn = 21. It also happens to be one of the
cutoff points reported to show an association with prostate cancer (5).
Further analyses used other cutoff points, such as CAGn <20 as described by
Ingles et al. (4) and CAGn
29 as described by Rebbeck
et al. (16), although there were few observations in those extreme
categories.
Under Hardy-Weinberg equilibrium, the maximum likelihood estimator of the frequency of a
particular allele is f = (2n11 + n01)/2n, where n = n11 + n01 + n00 and nij is the observed number of
subjects with the "ij" genotype (i = 0; j = 1), where 1 represents presence of the allele or a group of alleles and 0 represents the
absence and has asymptotic standard error (SE) [(f[1 - f])/2n]. The Hardy-Weinberg equilibrium assumption
was assessed by comparing the observed numbers of individuals with different genotypes with
those expected under Hardy-Weinberg equilibrium for the estimated allele frequency and
comparing the Pearson goodness-of-fit statistic with a
2 distribution with 1 df.
Given no evidence of departure from Hardy-Weinberg equilibrium, the allele frequency was analyzed and modeled as a function of risk factors by use of linear logistic regression, by assuming that the number of "1" alleles (as described above) was a binomial variable with n = 2. The influence of the AR genotype on risk of breast cancer was assessed, as in standard case-control analyses, by use of unconditional multiple linear logistic regression, with and without adjustment for measured risk factors. Genotype was modeled six ways: by number of alleles (two parameters), by a linear effect per number of alleles (one parameter), by an effect of any allele, and by linear effects of the average allele (i.e., average of smaller and larger alleles), the smaller allele, and the larger allele sizes (one parameter each).
All analyses were performed with the use of STATA statistical software (20). All statistical tests and P values were two-tailed. Following convention, statistical significance was taken as a nominal P value of less than .05.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
|
|
The frequency of alleles did not differ according to age, country of birth, state of residence, highest level of education (as a surrogate for socioeconomic status), marital status, or any of the other risk factors for breast cancer measured by the questionnaire. After adjustment for these factors, there was no difference in allele frequency between case subjects and control subjects (OR = 1.41; 95% CI = 0.95-2.09; P = .09). There was also no difference between women with or without a family history of breast cancer, whether in case subjects and control subjects combined (OR = 0.99; 95% CI = 0.66-1.50; P = 1.0), in case subjects only (OR = 1.00; 95% CI = 0.58-1.74; P = 1.0), or in control subjects only (OR = 0.98; 95% CI = 0.52-1.88; P = 1.0).
Table 3 shows that, irrespective of how the CAGn
22 allele status was modeled, there was no association with breast cancer, either before or
after adjustment for the risk factors identified in the full dataset (17).
After adjustment, the average effect on log OR per allele
22 CAGn was 0.16
(95% CI = -0.03 to 0.40; P = .2), and the effect of any
allele
22 CAGn was equivalent to an OR of 1.40 (95% CI =
0.94-2.09; P = .1). The SEs on the log OR scale were about 0.12, so that effects
equivalent to an OR of 1.35 or more would have been detectable at the .05 level of significance
with more than 80% power.
|
For women with a reported family history of breast cancer (first- or second-degree relative),
the crude OR for presence of the CAGn 22 allele was 1.26 (95% CI
= 0.63-2.49; P = .5); after adjustment for risk factors as in Table 3,
it was 1.36 (95% CI = 0.63-2.95; P = .4).
For women without a reported family history, the crude OR was 1.24 (95% CI =
0.80-1.91; P = .3); after adjustment, it was 1.35 (95% CI =
0.84-2.15; P = .2). Therefore, there was no evidence that having 22 or more
CAG repeats has an effect on risk of breast cancer in women with a family history of breast
cancer or in women without a family history.
Case subjects were stratified by morphology to assess whether CAG repeat length exhibited
an association with cancers of a particular morphologic subtype. The majority of tumors in case
subjects (83.5%) were classified as ductal cancers, and analysis of this subgroup gave OR
estimates of 1.21 (95% CI = 0.83-1.77; P = .3) and 1.32
(95% CI = 0.88-1.98; P = .2) for unadjusted and adjusted
analyses, respectively, for a linear effect of number of alleles with CAGn 22.
ER and PR status was determined for tumors from a proportion of case subjects. ER and PR
status is a known prognostic indicator, with less differentiated and presumably higher grade
tumors lacking receptors, and is a common basis for stratification of breast cancers. Case subjects
were stratified on the basis of ER and PR status to evaluate the possibility that CAG repeat
length may be a risk factor for breast cancers of a particular ER or PR expression level. There
was no evidence of an association between the presence of the allele CAGn 22
and moderate or strong ER expression (91 case subjects), with unadjusted and adjusted OR
estimates of 1.09 (95% CI = 0.32-3.78; P = .9) and 1.38
(95% CI = 0.31-6.21; P = .7), respectively. Similarly, there was
no evidence of an association between the presence of the allele CAGn
22 and
moderate or strong PR expression (92 case subjects), with unadjusted and adjusted OR estimates
of 0.81 (95% CI = 0.25-2.61; P = .7) and 0.91 (95% CI
= 0.22-3.72; P = .9), respectively.
Most of the samples included in this study have also been typed for the ER codon 325 amino
acid substitution polymorphism (18). Although our study of 388 case
subjects and 294 control subjects found no evidence for an association of the ER codon 325
polymorphism with breast cancer in women under the age of 40 years, we analyzed the data from
the 648 individuals typed for both the ER and AR polymorphisms. There was no evidence for a
gene-gene interaction between the AR CAG repeat polymorphism (as defined by the cut point
22 CAGn) and the ER codon 325 polymorphism in the etiology of breast
cancer before age 40, either with (OR for gene-gene interaction = 0.89; 95% CI
= 0.40-2.01; P = .8) or without (OR = 0.93; 95% CI
= 0.44-1.98; P = 0.9) adjustment for risk factors previously identified (18).
Analyses using the extreme cutoff points of CAGn <20 and CAGn
29 were also undertaken to allow comparison with published data for prostate cancer risk (5) and breast cancer risk (16). The allele
frequency (95% CI) for CAGn <20 was 0.168 (0.142-0.198) in case
subjects, no different from 0.176 (0.146-0.210) in control subjects (P = .7),
whereas the allele frequency (95% CI) for CAGn
29 was 0.026
(0.016-0.040) in case subjects and 0.020 (0.010-0.034) in control subjects (P =
.5). Similar results were also obtained for other cut points, such as 19, 21, and 27.
Finally, of the 11 case subjects we have identified to date who carry a germline
protein-truncating mutation in BRCA1 (Hopper JL, Southey MC, Dite GS, Jolley DJ, Giles GG,
McCredie MR, et al.: manuscript submitted for publication), none carried a CAGn
29. The mean (95% CI) of the average repeat lengths was 21.5 (20.2-22.8) as
compared with 22.0 (21.8-22.2) for case subjects and 22.0 (21.7-22.3) for control subjects,
respectively.
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The hypotheses arising from the studies of prostate cancer (3-8) and
breast cancer (13-16) discussed in the introduction were thoroughly
tested by the analyses carried out. Cutoff points based on the median size in control subjects
(CAGn <22), allele sizes reported to confer risk for prostate cancer (CAGn <20), and modifying risk allele sizes purported for BRCA1 mutation carriers (CAGn 29), and other cut points, all failed to reveal an influence on risk of breast
cancer before the age of 40. This study was sufficiently large to have good statistical power to
detect modest effect sizes, such as a difference in means of 0.2 standard deviation or an effect of
1.35 or more for CAGn
22. Furthermore, the inability to distinguish between
the active and inactive X allele of female case subjects and control subjects was obviated by
testing the risk differences between individuals with no, one, or two alleles within a risk category
group. Neither that analysis nor the analysis of average allele (on the presumption of random X
inactivation of the AR gene in target tissues) showed an association.
We intend to genotype other candidate genes involved in steroid hormone metabolic pathways and to test for gene-gene interactions. We will also test for gene-environment interactions by comparing the distributions of environmental and lifestyle factors measured by questionnaire across case subjects with different genotypes. Because such analyses involve a multitude of statistical tests, nominally "significant" findings must be treated with caution. Replication is essential in establishing credible results. As in this report, we shall publish the data in its raw form, so as to allow pooling with other similar population-based studies, and we encourage others to do likewise.
![]() |
NOTES |
---|
We thank Alana Goldman and Joanne Voisey of the Queensland Institute of Medical Research for technical assistance with this project. Confirmation of the results was possible through the provision of size standards by Steve Edwards of the Institute of Cancer Research, Sutton, Surrey, Najah Nassif of Sydney University, and Wayne Tilley and Grant Buchanan of Flinders University. We are grateful to the physicians, surgeons, and oncologists in Victoria and New South Wales who endorsed this project, to the interviewing staff, and to the many women and their relatives who participated in this research.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
1 Hopper JL, Carlin JB. Familial aggregation of a disease consequent upon correlation between relatives in a risk factor measured on a continuous scale. Am J Epidemiol 1992;136:1138-47.[Abstract]
2 Easton D, Peto J. The contribution of inherited predisposition to cancer incidence. Cancer Surv 1990;9:395-416.[Medline]
3 Irvine RA, Yu MC, Ross RK, Coetzee GA. The CAG and GGC microsatellites of the androgen receptor gene are in linkage disequilibrium in men with prostate cancer. Cancer Res 1995;55:1937-40.[Abstract]
4
Ingles SA, Ross RK, Yu MC, Irvine RA, La Pera G, Haile RW,
et al. Association of prostate cancer risk with genetic polymorphisms in vitamin D receptor and
androgen receptor. J Natl Cancer Inst 1997;89:166-70.
5 Stanford JL, Just JJ, Gibbs M, Wicklund KG, Neal CL, Blumenstein BA, et al. Polymorphic repeats in the androgen receptor gene: molecular markers of prostate cancer risk. Cancer Res 1997;57:1194-8.[Abstract]
6
Giovannucci E, Stampfer MJ, Krithivas K, Brown M, Dahl D,
Brufsky A, et al. The CAG repeat within the androgen receptor gene and its relationship to
prostate cancer [published erratum appears in Proc Natl Acad Sci U S A
1997;94:8272]. Proc Natl Acad Sci U S A 1997;94:3320-3.
7 Hakimi JM, Schoenberg MP, Rondinelli RH, Piantadosi S, Barrack ER. Androgen receptor variants with short glutamine or glycine repeats may identify unique subpopulations of men with prostate cancer. Clin Cancer Res 1997;3:1599-608.[Abstract]
8 Hardy DO, Scher HI, Bogenreider T, Sabbatini P, Zhang ZF, Nanus DM, et al. Androgen receptor CAG repeat lengths in prostate cancer: correlation with age of onset. J Clin Endocrinol Metab 1996;81:4400-5.[Abstract]
9 Chamberlain NL, Driver ED, Miesfeld RL. The length and location of CAG trinucleotide repeats in the androgen receptor N-terminal domain affect transactivation function. Nucleic Acids Res 1994;22:3181-6.[Abstract]
10 La Spada AR, Wilson EM, Lubahn DB, Harding AE, Fischbeck KH. Androgen receptor gene mutations in X-linked spinal and bulbar muscular atrophy. Nature 1991;352:77-9.[Medline]
11 MacLean HE, Choi WT, Rekaris G, Warne GL, Zajac JD. Abnormal androgen receptor binding affinity in subjects with Kennedy's disease (spinal and bulbar muscular atrophy). J Clin Endocrinol Metab 1995;80:508-16.[Abstract]
12
Tut TG, Ghadessy FJ, Trifiro MA, Pinsky L, Yong EL. Long
polyglutamine tracts in the androgen receptor are associated with reduced trans-activation,
impaired sperm production, and male infertility. J Clin Endocrinol Metab 1997;82:3777-82.
13 Wooster R, Mangion J, Eeles R, Smith S, Dowsett M, Averill D, et al. A germline mutation in the androgen receptor gene in two brothers with breast cancer and Reifenstein syndrome. Nat Genet 1992;2: 132-4.[Medline]
14 Lobaccaro JM, Lumbroso S, Belon C, Galtier-Dereure F, Bringer J, Lesimple T, et al. Male breast cancer and the androgen receptor gene. Nat Genet 1993;5:109-10.[Medline]
15 Zhu X, Daffada AA, Chan CM, Dowsett M. Identification of an exon 3 deletion splice variant androgen receptor mRNA in human breast cancer. Int J Cancer 1997;72:574-80.[Medline]
16 Rebbeck TR, Kantoff PW, Krithivas K, Narod SA, Godwin AK, Daly MB, et al. Modification of breast cancer risk in BRCA1 mutation carriers by the androgen receptor CAG repeat polymorphism [abstract]. Proc Am Assoc Cancer Res 1998;39:366.
17 McCredie MR, Dite G, Giles GG, Hopper JL. Breast cancer in Australian women under the age of 40. Cancer Causes Control 1998;9:189-98.[Medline]
18
Southey MC, Batten LE, McCredie MR, Giles GG, Dite G,
Hopper JL, et al. Estrogen receptor polymorphism at codon 325 and risk of breast cancer in
women before age forty. J Natl Cancer Inst 1998;90:532-6.
19 Allred DC, Clark GM, Elledge R, Fuqua SA, Brown RW, Chamness GC, et al. Association of p53 protein expression with tumor cell proliferation rate and clinical outcome in node-negative breast cancer. J Natl Cancer Inst 1993;85:200-6.[Abstract]
20 StataCorp. Stata statistical software: release 5.0. College Station (TX): Stata Corp.; 1997.
Manuscript received November 12, 1999; revised March 5, 1999; accepted April 5, 1999.
This article has been cited by other articles in HighWire Press-hosted journals:
![]() |
||||
|
Oxford University Press Privacy Policy and Legal Statement |