Affiliations of authors: F. A. Firgaira, R. Seshadri, C. R. E. McEvoy, Department of Haematology and Genetic Pathology, Flinders University and Flinders Medical Centre, Bedford Park, South Australia; G. S. Dite, J. L. Hopper, The University of Melbourne, Centre for Genetic Epidemiology, Carlton, Victoria, Australia; G. G. Giles, Cancer Epidemiology Centre, Anti-Cancer Council of Victoria, Carlton, Australia; M. R. E. McCredie, Cancer and Epidemiology Research Unit, New South Wales Cancer Council, Kings Cross, Australia, and Department of Preventative and Social Medicine, University of Otago, New Zealand; M. C. Southey, D. J. Venter, Department of Pathology and Research, Peter MacCallum Cancer Institute, Melbourne, Victoria, Australia, and Department of Pathology, The University of Melbourne, Parkville, Australia.
Correspondence to: John L. Hopper, Ph.D., The University of Melbourne, Centre for Genetic Epidemiology, 200 Berkeley St., Carlton, Victoria 3053, Australia (e-mail: j.hopper{at}gpph.unimelb.edu.au).
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ABSTRACT |
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INTRODUCTION |
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The HRAS1 minisatellite is derived from a tandemly repeated 28-base-pair sequence. The four common alleles, designated a1 to a4, have approximate sizes of 1.0 kilobase (kb), 1.5 kb, 2.1 kb, and 2.5 kb, respectively, and have been reported to account for approximately 94% of HRAS1 alleles in Caucasians (1,2,4). About 40 other allelic variants have been described, and these constitute what are termed the "rare alleles." Many of these variants, however, differ from a common allele by only one or a few repeat units.
Previous studies, from which the putative association of the rare alleles with breast cancer was derived, have relied on poorly resolving electrophoretic systems and visual sizing of the HRAS1 minisatellite alleles. This procedure may have led to the underreporting of rare alleles, and the consequent misclassifications may have influenced their results and contributed to the conflicting results in many studies (2).
In this study, we have used an Applied Biosystems model 373 automated DNA sequencer and GENESCANTM technology to more precisely size HRAS1 minisatellite alleles (5) in a population-based sample of Australian women with breast cancer diagnosed before the age of 40 years and in a randomly selected sample of women without breast cancer, who were frequency matched for age. The frequencies of HRAS1 minisatellite alleles in case subjects and control subjects have been compared to determine whether the rare alleles are associated with an increased risk of breast cancer.
If inheriting at least one rare allele does increase the risk of breast cancer, the frequency of rare alleles should be greater in women with a family history of the disease (see "Appendix" section). HRAS1 allele association analyses are also presented separately for women with and women without a family history of breast cancer.
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METHODS |
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As described in the protocol outlined by Hopper et al. (7), a population-based, case-control-family study of early-onset breast cancer was carried out in Melbourne and Sydney, Australia, from 1992 through 1995 (8,9). Case subjects were women under the age of 40 years at diagnosis of a first primary breast cancer, identified through the Victorian and New South Wales cancer registries. Control subjects were women without breast cancer, selected from the electoral roll (adult registration for voting is compulsory in Australia) by use of stratified random sampling and frequency matched for age. Case subjects, control subjects, and relatives were administered the same risk factor questionnaire (8).
For each case subject and control subject, a detailed family history was systematically recorded for all first-degree and second-degree relatives and subsequently checked with their living relatives at the time of their interview. Unless otherwise stated, women who reported having at least one first-degree or second-degree female relative with breast cancer were considered to have a family history of breast cancer. Verification of family cancers reported by case subjects or relatives was obtained through cancer registries, pathology reports, hospital records, treating clinicians, and death certificates (8). Blood samples were collected from case subjects and control subjects at the time of interview.
Of 644 eligible case subjects, 467 (72.5%) case subjects were interviewed. Attrition was due to death (1.7%), refusal (surgeon = 8.4% or patient = 11.8%), nonresponse (surgeon = 0.6% or patient = 1.4%), or a change in place of residence (3.6%). Of the 632 eligible control subjects, refusals (25.8%) and nonresponse (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 294 control subjects (72.1% of participating and 46.5% of eligible control subjects).
Analyses of HRAS1 genotypes were performed for a subset of 249 case subjects (53.3% of participating and 38.7% of eligible case subjects) and 234 control subjects (57.4% of participating and 37.0% of eligible control subjects). Selection of case subjects and control subjects for these analyses was not made on the basis of measured risk factor information. For case subjects and control subjects, there were no differences between those included and those not included in the study for the following factors associated with breast cancer in the full sample of case subjects and control subjects (8): age, marital status, level of education, parity, height, weight, age at menarche, and country of birth. Genetic analyses were performed for 60.3% of case subjects and 63.6% of control subjects who had a first-degree relative with breast cancer and for 52.3% of case subjects and 57.0% of control subjects who did not have a first-degree relative with breast cancer.
Written informed consent was obtained from all case subjects and control subjects, and the study was approved by institutional review boards of The University of Melbourne, The Anti-Cancer Council of Victoria, the New South Wales Cancer Council, and Flinders University.
Molecular Analysis
DNA was prepared as previously described (9). The HRAS1 minisatellite region was amplified by polymerase chain reaction as described (5). The fluorescently labeled products were precisely sized for genotyping of HRAS1 alleles by use of native polyacrylamide gels on a Perkin-Elmer Applied Biosystems model 373 DNA sequencer and GENESCANTM software (Perkin-Elmer Corp., Foster City, CA) as detailed previously (5). Genotyping was performed in a blinded fashion for source (case subject or control subject) of DNA.
Statistical Methods
Under the assumptions of Hardy-Weinberg equilibrium, the maximum likelihood estimator of
the frequency of rare alleles is f = (2n11 +
n01)/2n, where n = n11 +
n01 + n00 and nij is the observed
number of subjects
with the "ij" genotype (i,j = 0,1), where 1 represents the
presence of a rare allele and 0 represents the absence of a rare allele (i.e., presence of one of the
four common alleles) and has asymptotic standard error (SE) [(f [1 -
f ])/2n]1/2 and approximate 95% CI =
(f - 1.96SE - f + 1.96SE). Estimates of allele frequency for
different
groups were compared by assuming that they were each normally distributed with standard
deviation equal to SE derived from that group alone. 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 then comparing the Pearson goodness-of-fit statistic with a 2
distribution with 1 df.
Given no evidence of departure from Hardy-Weinberg equilibrium, we analyzed and modeled the frequency of rare alleles as a function of potential covariates by use of linear logistic regression, by assuming that the number of rare alleles was the sum of two independent binomial variables.
The influence of HRAS1 genotype on the risk of breast cancer was assessed by standard case subject/control subject analyses by use of multiple linear logistic regression, with and without adjustment for the risk factors identified in this study (8). Genotype was modeled the following three ways: 1) by number of rare alleles (two parameters), 2) by a linear effect per number of rare alleles (one parameter), and 3) by the presence or absence of any rare allele (one parameter).
Logistic regression analyses were performed with STATA (10). All statistical tests and P values are two-sided. For logistic regression analyses, P values were calculated by use of the likelihood ratio test.
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RESULTS |
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Table 3 shows that the frequency of rare
alleles did not differ by the
presence of a reported or verified family history of breast cancer overall, among case subjects, or
among control subjects, irrespective of the definition of family history. The frequency of rare
alleles was actually lower among case subjects with a verified first-degree relative with breast
cancer (P = .05).
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Table 4 shows that, irrespective of how the
putative effect of rare
alleles was modeled, there was no association of rare HRAS1 allele status with risk of breast
cancer, either before or after adjustment for the risk factors identified in the full dataset
(8). Although the point estimates for the effect of
having two rare alleles
were greater than for having one rare allele, as was also found by the meta-analysis for all cancers
(2), none of the estimates was statistically
significant. The differences in
effects were also not statistically significant (all P>.5). When modeled as a linear
effect on the logarithmic OR scale, the crude effect per number of rare alleles was 0.06
(95% CI = -0.28 to 0.40; P = .7), equivalent to predicted
ORs of 1.06 and 1.12 for one and two rare alleles, respectively. After adjustment, the effect on
the logarithmic OR was 0.09 (95% CI = -0.29 to 0.47 per allele
[P = .6]), equivalent to 1.09 and 1.19 for one and two alleles,
respectively. On the
logarithmic odds scale, the estimated effect for having any rare allele was 0.04 (95% CI
= -0.36 to 0.44) with no adjustment and 0.05 (95% CI = -0.37 to
0.47) after adjustment (P = .8). These two estimates were less than the
corresponding estimate for breast cancer of 0.52 (95% CI = 0.20-0.84) found by
the meta-analysis (2) (P = .06 and
P =
.08, respectively). Given that the 95% CIs of these logarithmic OR estimates were about
0.8, effects equivalent to a logarithmic OR of 0.5 (i.e., OR = 1.65) or more would have
been detectable at the .05 level of statistical significance with more than 80% power.
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DISCUSSION |
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The difference, however, is consistent with the automated sizing method that can distinguish
more clearly between a common allele and the rare alleles that differ by one or a few repeats,
especially for the larger alleles. For example, from Table 1, we can
calculate that the allele frequency for the combined alleles of 2 - 1, 2, and 2 + 1 was 0.121
(0.108 in case subjects and 0.135 in control subjects; compare with 0.119 for the a2 allele derived
from the meta-analysis). For 3 - 2, 3 - 1, 3, 3 + 1, and 3 + 2, it was 0.096 (0.104 in
case subjects and 0.088 in control subjects; compare with 0.110 for the a3 allele derived from the
meta-analysis). For 4 - 3, . . ., 4, . . ., and 4 + 3, it was 0.089 (0.098 in case subjects and
0.079 in control subjects; compare with 0.090 for the a4 allele derived from the meta-analysis).
(In none of these instances was the frequency different between case subjects and control
subjects.) We suggest, therefore, that a major difficulty as well as a possibly contributory cause of
conflicting association results in past studies has been the inability of the methods, based on
Southern blotting, to distinguish the larger rare alleles from similarly sized common alleles.
We failed to detect an association between the presence of the rare alleles and an increased risk of breast cancer before the age of 40 years by comparing case subjects with control subjects or by comparing individuals with and without a family history of breast cancer. Given the estimated frequency of rare alleles and that we studied more than 200 case subjects and 200 control subjects, our study had more than 80% power to detect an effect equivalent to an OR of the size found by the meta-analysis. A smaller effect, however, cannot be dismissed. Because typically less than 10% of cases of breast cancer in Western societies occur before the age of 40 years, it is possible that the effect of the rare HRAS1 alleles may be confined to or may be stronger in cancers occurring at a later age.
If the rare alleles were associated with an increased risk of breast cancer, carriers would be more likely to have a family history of breast cancer. For example, if the frequency of rare alleles is 0.17 and the associated risk of breast cancer really is 1.7 as was found by the meta-analysis (2), the probability that the mother is affected is 1.32 times higher if the daughter is a carrier, compared with her not being a carrier (see "Appendix" section). In our study, however, the probability of having a family history of breast cancer was actually less, though not statistically significantly so, in case subject carriers and in carriers overall. On the logarithmic odds scale, the 95% CIs were about 1.2 among case subjects and 0.8 overall. Therefore, although there was less than 50% power to detect the predicted effect of about 0.25 on the logarithmic odds scale, for both case subjects and overall, the predicted effect lies outside the 95% CIs for the observed effects of -0.5 (95% CI = -1.1 to 0.1) and -0.15 (95% CI = -0.55 to 0.25), respectively. That is, examination of data on family history of breast cancer provided no support for an association between the rare alleles and risk of breast cancer.
The findings presented in this report for early-onset breast cancer suggest that the putative
associations of HRAS1 minisatellite alleles with cancers of the breast and other sites need to be
re-evaluated with newer methods of sizing alleles. We have presented our raw data in Table
1 so that pooling with similar studies may yet
reveal genotypes or alleles
that discriminate between case subjects and control subjects.
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APPENDIX |
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Let Y = 1, if an individual has breast cancer, and otherwise Y
= 0, and RR represent the increase in risk of breast cancer associated with having at least
one of the rare alleles. That is, P(Yi = 1 | Xi
= 1) = RR x P(Yi = 1 | Xi
= 0), where i = m or d. Also, assume that if i
j, P(Yi| Xi, Xj)
=
P(Yi| Xi), so that if the individual's
genotype is
known, risk is independent of the relation's genotype. Then,
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Therefore, when p = 0.17 and RR = 1.7, the ratio P(Ym = 1 | Xd = 1)/P(Ym = 1 | Xd = 0) = [RR(1 + pq) + q2]/[RRp + q](2 - p) is equal to 1.32.
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NOTES |
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REFERENCES |
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Manuscript received June 23, 1999; revised October 6, 1999; accepted October 12, 1999.
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