This is by far the largest study of its kind to date, and further suggests that AIB1 does not play a substantial role in modifying the phenotype of BRCA1 and BRCA2 carriers. The AIB1 gene encodes the AIB1/SRC-3 steroid hormone receptor coactivator, and amplification of the gene and/or protein occurs in breast and ovarian tumors. A CAG/CAA repeat length polymorphism encodes a stretch of 17 to 29 glutamines in the HR-interacting carboxyl-terminal region of the protein which is somatically unstable in tumor tissues and cell lines. There is conflicting evidence regarding the role of this polymorphism as a modifier of breast cancer risk in BRCA1 and BRCA2 carriers. To further evaluate the evidence for an association between AIB1 glutamine repeat length and breast cancer risk in BRCA1 and BRCA2 mutation carriers, we have genotyped this polymorphism in 1,090 BRCA1 and 661 BRCA2 mutation carriers from Australia, Europe, and North America. There was no evidence for an increased risk associated with AIB1 glutamine repeat length. Given the large sample size, with more than adequate power to detect previously reported effects, we conclude that the AIB1 glutamine repeat does not substantially modify risk of breast cancer in BRCA1 and BRCA2 mutation carriers. (Cancer Epidemiol Biomarkers Prev 2006;15(1):76–9)

The AIB1 (NCOA3) gene encodes the AIB1/SRC-3 steroid hormone receptor coactivator, and amplification of the gene and/or protein occurs in breast and ovarian tumors (1-4), and is associated with tumor size (2), immunohistochemical profile (including estrogen receptor, progesterone receptor, p53, and HER2 status; ref. 5), and tamoxifen resistance (6). A CAG/CAA repeat length polymorphism encodes a stretch of 17 to 29 glutamines in the HR-interacting carboxyl-terminal region of the protein, and although repeat number has not been directly assessed with respect to its effects on function, the repeat region has been shown to be somatically unstable in tumor tissues and cell lines. One study found that germ line DNA from BRCA1/2 carrier cases have a greater proportion of uncommon sequence patterns compared with normal controls, and a greater proportion of alleles ≥28 repeats compared with sporadic breast cancer cases (7).

Several studies have been undertaken to assess the role of the AIB1 glutamine repeat polymorphism as a modifier of breast cancer risk in BRCA1 and BRCA2 carriers, with the hypothesis-generating study of 448 female BRCA1 or BRCA2 mutation carriers reporting increased breast cancer risk associated with allele length ≥29 glutamines [odds ratio, 2.9; 95% confidence interval (CI), 1.7-5.0], an effect which appeared to be driven by the 370 BRCA1 mutation carriers in the sample (8). Longer repeat length was associated with modestly increased risk in a second study of 222 BRCA1 and 88 BRCA2 mutation carriers [rate ratio (RR) per repeat 1.25 (95% CI, 1.1-1.4) for BRCA1 carriers, and 0.9 (0.8-1.1) for BRCA2 carriers; ref. 9], but not in another much larger study of 851 BRCA1 and 324 BRCA2 mutation carriers [RR per repeat 1.1 (95% CI, 0.8-1.3) and 1.2 (0.9-1.6) for BRCA1 and BRCA2 carriers, respectively; ref. 10].

To further evaluate the evidence for an association between AIB1 glutamine repeat length and breast cancer risk in BRCA1 and BRCA2 mutation carriers, we have genotyped this polymorphism in a series of 1,754 BRCA1 and BRCA2 mutation carriers.

Subjects

The distribution of samples according to source, gene, and cancer status is shown in Table 1. Recruitment and genetic studies were approved by relevant ethics committees at all sites, and written informed consent was obtained from each participant. Mutation carriers were identified as part of clinic-, community-, multiple-case family-, and population-based research studies, as described elsewhere (11-15). Mutation classification was as described previously (11). A small subset of 17 individuals from the Australian Breast Cancer Family Study were also analyzed as part of a previous population-based case control study of AIB1 (16).

Table 1.

Characteristics of study subjects

Sample sources*Mode of ascertainmentGroupingBRCA1
BRCA2
BRCA1 and BRCA2
n (% of total)n (% of total)n
EMBRACE clinic-based United Kingdom 386 (35.4) 175 (26.5)  
kConFaB clinic-based Australia 237 (21.7) 217 (32.8)  
BCFR-Australia-AJBCS community-based Australia 18 (1.7) 22 (3.3)  
BCFR-Australia-ABCFS population-based Australia 20 (1.8) 23 (3.5) 
BCFR-Philadelphia clinic-based North America 60 (5.5) 28 (4.2) 
BCFR-Utah clinic-based North America 36 (3.3) 17 (2.6)  
BCFR-New York clinic-based North America 104 (9.5) 32 (4.8)  
BCFR-Ontario population-based North America 67 (6.1) 39 (5.9)  
BCFR-Northern California population-based North America 31 (2.8) 29 (4.4) 
National Cancer Institute clinic-based North America 81 (7.4) 27 (4.1)  
INHERIT BRCAs-Quebec multiple-case family-based Quebec 50 (4.6) 52 (7.9)  
Total   1,090 661 
Affected with breast cancer   598 (54.9) 392 (59.3) 
Affected with ovarian cancer   83 (7.6) 26 (3.9) 
Number of families   685 390 
Sample sources*Mode of ascertainmentGroupingBRCA1
BRCA2
BRCA1 and BRCA2
n (% of total)n (% of total)n
EMBRACE clinic-based United Kingdom 386 (35.4) 175 (26.5)  
kConFaB clinic-based Australia 237 (21.7) 217 (32.8)  
BCFR-Australia-AJBCS community-based Australia 18 (1.7) 22 (3.3)  
BCFR-Australia-ABCFS population-based Australia 20 (1.8) 23 (3.5) 
BCFR-Philadelphia clinic-based North America 60 (5.5) 28 (4.2) 
BCFR-Utah clinic-based North America 36 (3.3) 17 (2.6)  
BCFR-New York clinic-based North America 104 (9.5) 32 (4.8)  
BCFR-Ontario population-based North America 67 (6.1) 39 (5.9)  
BCFR-Northern California population-based North America 31 (2.8) 29 (4.4) 
National Cancer Institute clinic-based North America 81 (7.4) 27 (4.1)  
INHERIT BRCAs-Quebec multiple-case family-based Quebec 50 (4.6) 52 (7.9)  
Total   1,090 661 
Affected with breast cancer   598 (54.9) 392 (59.3) 
Affected with ovarian cancer   83 (7.6) 26 (3.9) 
Number of families   685 390 
*

Source abbreviations: EMBRACE, Epidemiological Study of Familial Breast Cancer; kConFaB, Kathleen Cunningham Consortium for Research into Familial Breast Cancer; BCFR, Breast Cancer Family Registry, AJBCS, Australian Jewish Breast Cancer study; ABCFS, Australian Breast Cancer Family Study; National Cancer Institute, Cancer Family Registry, Intramural program of the National Cancer Institute; INHERIT BRCAs, Interdisciplinary Health Research International Team on Breast Cancer susceptibility.

Cancer type refers to first primary cancer diagnosis. One BRCA2 carrier with breast cancer was censored as unaffected at age of prior mastectomy.

Molecular Methods

The AIB1 glutamine repeat length was measured by standard fluorescent PCR PAGE methodology, using the ABI Prism 373 Genescan and Genotyper systems. PCR primers used were F primer 5′-CCGACAACAGAGGGTGGCTAT-3′, and R primer 5′-CTGGGGGAAGCAGTCACATTAG-3′. The annealing temperature was 63°C. The AIB1 glutamine repeat length was assayed in the Quebec samples by standard 35S-dATP PCR. PCR primers used were F primer 5′-TCCGACAACAGAGGGTGGCTATG-3′, and R primer 5′-TTAGGAGGTGGGCTGAAGGCCTG-3′. The annealing temperature was 60°C.

Statistical Methods

Subject status characterization, potential confounder categorization, and statistical analysis methods have been described previously (11), with subjects grouped by country or origin (Table 1). Briefly, the primary analyses of association between genotype and disease risk were done using Cox regression with time to breast cancer onset as the end point. Repeat length was defined as either: (a) a binary variable, defined by stated cutpoints, (b) a continuous variable, using the length of the smaller of the two alleles, the larger of the two alleles, or the average length of a subject's two alleles. Confidence limits for the RR were calculated using a robust variance approach to allow for the dependence among individuals in the same family (17). Secondary analyses used the weighted Cox regression approach (11, 18), in which individuals were weighted such that observed breast cancer incidences in the study sample are consistent with established breast cancer risk estimates for BRCA1 and BRCA2 mutation carriers (19). R version 1.9.0 was used for all analyses. S-Plus VI was used for power calculations, as described previously (11, 18).

Genotype distributions were similar to those in previous studies. The glutamine length ranged from 18 to 37 repeats, the most common alleles being 26 repeats (13%), 28 repeats (38%), and 29 repeats (47%). The estimated RRs associated by repeat length are given in Table 2. There was no evidence for an increased risk associated with AIB1 glutamine repeat length, for the ≥28 and ≥29 repeat cutpoints previously shown to be associated with risk (8), or for repeat length considered as a continuous variable. None of the estimated RRs were different from 1 at the 0.05 level of significance, for BRCA1 or BRCA2 mutation carriers. There was little difference between the estimates adjusted only for source group, ethnicity, and year of birth, and those adjusted also for reproductive factors. Risk estimates using the weighted Cox regression approach were similar to the unweighted estimates as expected when the null hypothesis is true (18).

Table 2.

Breast cancer risk associated with AIB1 Gln repeat length

BRCA1 mutation carriers
BRCA2 mutation carriers
Adjusted for group, ethnicity, and year of birth
Adjusted for group, ethnicity, year of birth, and additional variables
Adjusted for group, ethnicity, and year of birth
Adjusted for group, ethnicity, year of birth, and additional variables
PRR (95% CI)PRR (95% CI)PRR (95% CI)PRR (95% CI)
AIB1 repeat as a categorical variable          
    AIB1 ≥28 Gln unweighted analysis 0.2 0.89 (0.75-1.06) 0.1 0.87 (0.73-1.04) 0.6 1.06 (0.85-1.33) 0.2 1.16 (0.91-1.48) 
 weighted analysis 0.1 0.83 (0.65-1.06) 0.03 0.76 (0.59-0.97) 0.3 1.18 (0.84-1.67) 0.1 1.34 (0.92-1.93) 
    AIB1 ≥29 Gln unweighted analysis 0.7 0.96 (0.78-1.18) 0.7 0.96 (0.78-1.17) 0.9 1.02 (0.79-1.32) 0.3 1.12 (0.88-1.43) 
 weighted analysis 0.6 0.93 (0.71-1.21) 0.7 0.95 (0.73-1.24) 0.8 0.96 (0.65-1.41) 0.4 1.18 (0.82-1.70) 
AIB1 repeat as a continuous variable          
    AIB1 small allele size unweighted analysis 0.2 0.95 (0.89-1.02) 0.07 0.94 (0.87-1.01) 0.6 1.02 (0.95-1.10) 0.2 1.06 (0.97-1.15) 
 weighted analysis 0.2 0.94 (0.86-1.03) 0.02 0.90 (0.82-0.99) 0.5 1.04 (0.92-1.17) 0.2 1.10 (0.97-1.26) 
    AIB1 large allele size unweighted analysis 0.3 1.05 (0.96-1.15) 0.3 1.07 (0.94-1.20) 0.6 0.96 (0.82-1.12) 0.9 0.99 (0.84-1.17) 
 weighted analysis 0.3 1.07 (0.95-1.22) 0.2 1.11 (0.94-1.30) 0.2 0.86 (0.68-1.09) 0.6 0.93 (0.72-1.21) 
    AIB1 average allele size unweighted analysis 0.7 0.98 (0.89-1.08) 0.4 0.95 (0.85-1.06) 0.8 1.02 (0.90-1.15) 0.3 1.07 (0.94-1.22) 
 weighted analysis 0.7 0.97 (0.85-1.11) 0.2 0.91 (0.79-1.06) 0.9 1.02 (0.84-1.22) 0.3 1.12 (0.91-1.37) 
BRCA1 mutation carriers
BRCA2 mutation carriers
Adjusted for group, ethnicity, and year of birth
Adjusted for group, ethnicity, year of birth, and additional variables
Adjusted for group, ethnicity, and year of birth
Adjusted for group, ethnicity, year of birth, and additional variables
PRR (95% CI)PRR (95% CI)PRR (95% CI)PRR (95% CI)
AIB1 repeat as a categorical variable          
    AIB1 ≥28 Gln unweighted analysis 0.2 0.89 (0.75-1.06) 0.1 0.87 (0.73-1.04) 0.6 1.06 (0.85-1.33) 0.2 1.16 (0.91-1.48) 
 weighted analysis 0.1 0.83 (0.65-1.06) 0.03 0.76 (0.59-0.97) 0.3 1.18 (0.84-1.67) 0.1 1.34 (0.92-1.93) 
    AIB1 ≥29 Gln unweighted analysis 0.7 0.96 (0.78-1.18) 0.7 0.96 (0.78-1.17) 0.9 1.02 (0.79-1.32) 0.3 1.12 (0.88-1.43) 
 weighted analysis 0.6 0.93 (0.71-1.21) 0.7 0.95 (0.73-1.24) 0.8 0.96 (0.65-1.41) 0.4 1.18 (0.82-1.70) 
AIB1 repeat as a continuous variable          
    AIB1 small allele size unweighted analysis 0.2 0.95 (0.89-1.02) 0.07 0.94 (0.87-1.01) 0.6 1.02 (0.95-1.10) 0.2 1.06 (0.97-1.15) 
 weighted analysis 0.2 0.94 (0.86-1.03) 0.02 0.90 (0.82-0.99) 0.5 1.04 (0.92-1.17) 0.2 1.10 (0.97-1.26) 
    AIB1 large allele size unweighted analysis 0.3 1.05 (0.96-1.15) 0.3 1.07 (0.94-1.20) 0.6 0.96 (0.82-1.12) 0.9 0.99 (0.84-1.17) 
 weighted analysis 0.3 1.07 (0.95-1.22) 0.2 1.11 (0.94-1.30) 0.2 0.86 (0.68-1.09) 0.6 0.93 (0.72-1.21) 
    AIB1 average allele size unweighted analysis 0.7 0.98 (0.89-1.08) 0.4 0.95 (0.85-1.06) 0.8 1.02 (0.90-1.15) 0.3 1.07 (0.94-1.22) 
 weighted analysis 0.7 0.97 (0.85-1.11) 0.2 0.91 (0.79-1.06) 0.9 1.02 (0.84-1.22) 0.3 1.12 (0.91-1.37) 

NOTE: First primary breast cancer diagnosis was considered an event (status affected), whereas first primary ovarian cancers were censored as unaffected at age of diagnosis, and individuals without breast or ovarian cancer were censored as unaffected at age at interview. All individuals were censored at age of prior prophylactic mastectomy. Mean age of BRCA1 carriers was 41 years (20-81) for affected individuals, and 42 years (17-83) for individuals censored as unaffected. Mean age of BRCA2 carriers was 43 years (23-72) for affected individuals, and 45 years (18-84) for individuals censored as unaffected. Individuals carrying both BRCA1 and BRCA2 mutations were included in BRCA1 and BRCA2 analyses. Analyses were adjusted for source group, ethnicity, year of birth, and hormonal variables oophorectomy, parity, age at menarche, and contraceptive pill use. Categorization for group was as shown in Table 1, and for other variables as described in Spurdle et al. (11). Oophorectomy and parity were treated as time-dependent variables from age at first variable event. Questionnaire information on potential confounders for analyses adjusting for additional hormonal variables was available for 964 BRCA1 and 598 BRCA2 carriers.

Risk estimates did not differ materially when women with a first primary diagnosis of ovarian cancer were excluded [e.g., RR (95% CI) for the ≥29 CAG cutpoint of 0.96 (0.79-1.16) for BRCA1 mutation carriers (P = 0.7), and 1.05 (0.83-1.33) for BRCA2 mutation carriers (P = 0.7)], or when carriers ascertained from population-based sites were excluded (data not shown), suggesting that the preferential ascertainment of cases versus controls from these sites did not bias results.

Our sample size was large enough to detect effects reported by Rebbeck et al. (8). Assuming the age distribution of affected and unaffected carriers as shown in Table 1, simulations estimated the power of detecting risk ratios of 1.56 and 2.85 to be 91% and 100%, respectively, for BRCA1 mutation carriers, and 58% and 100% for BRCA2 carriers. The upper 95% confidence limits on the RR in our analysis (1.21 for BRCA1, 1.70 for BRCA2 ≥29 repeats based on the weighted analysis) exclude any substantial risk.

Our study found no evidence to support the previously reported associations of AIB1 glutamine repeat length with increased breast cancer risk in two relatively small studies of BRCA1 carriers (8, 9), supporting recently published negative findings from a much larger study of mutation carriers (10). Given the large sample size, with more than adequate power to detect previously reported effects, we conclude that the AIB1 glutamine repeat does not substantially modify risk of breast cancer in BRCA1 and BRCA2 mutation carriers.

Grant support: kConFaB has been funded by the Kathleen Cunningham Foundation, National Breast Cancer Foundation, National Health and Medical Research Council, Cancer Council of Victoria, Cancer Council of South Australia, Queensland Cancer Fund, Cancer Council of New South Wales, Cancer Foundation of Western Australian, and Cancer Council of Tasmania. The Epidemiological Study of Familial Breast Cancer study, A. Antoniou, S. Peock, and M.R. Cook are funded by a project grant from Cancer Research UK. The Interdisciplinary Health Research International Team on Breast Cancer Susceptibility research program was funded by the Canadian Institutes of Health Research. This research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research, and Division of Epidemiology and Genetics. The Australian Jewish Breast Cancer Study and Australian Breast Cancer Family Study were funded by the National Health and Medical Research Council, the Victorian Health Promotion Foundation, the New South Wales Cancer Council, the Peter MacCallum Cancer Institute, the Inkster-Ross Memorial Fund, and in part by the NIH, as part of the Cancer Family Registry for Breast Cancer Study (CA 69638). This work used data collected from the Breast Cancer Family Registry, funded by the National Cancer Institute under RFA no. CA-95-003 and through cooperative agreements with the Fox Chase Cancer Center, Huntsman Cancer Institute (Saundra Buys and Vicki Venne), Columbia University (no. UO1 CA69398), Northern California Cancer Center (Dee W. West, Alice Whittemore, and Frederick Li), Cancer Care Ontario, and University of Melbourne (Graham Giles, Margaret McCredie and Deon Venter). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Cancer Family Registries, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government or the Cancer Family Registries Centers. The AIB1 genotyping and analysis is supported by a grant from the National Health and Medical Research Council. A.B. Spurdle is funded by a National Health and Medical Research Council Career Development Award, F. Durocher is a recipient of a Research Career Award in the Health Sciences by IRSC/Rx&D Health Research Foundation, J. Simard is Chairholder of the Canada Research Chair in Oncogenetics, and G. Chenevix-Trench and J.L. Hopper are National Health and Medical Research Council Senior and Senior Principle Research Fellows, respectively. P.L. Smith was funded by a grant from the Interdisciplinary Health Research International Team on Breast Cancer Susceptibility program from the Canadian Institute for Health Research. D.F. Easton is a Cancer Research U.K. Principal Research Fellow.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Renee McIlroy for her role in initiating this study, Dr. David Duffy for statistical advice, and Heather Thorne, Sandra Picken, Eveline Niedermayer, Jenny Leary, Tracey Davis, Lesley Andrews, and Sarah Steinborner for supply of kConFab data and DNA for this project. We are grateful to the physicians, surgeons, and oncologists who endorsed this project, the interviewing staff, and the many women who participated in this research.

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