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Breast Cancer Research

  • Research article
  • Open Access

Comprehensive analysis of the ATM, CHEK2 and ERBB2genes in relation to breast tumour characteristics and survival: a population-based case-control and follow-up study

  • Kristjana Einarsdóttir1Email author,
  • Lena U Rosenberg1,
  • Keith Humphreys1,
  • Carine Bonnard2,
  • Juni Palmgren1, 3,
  • Yuqing Li2,
  • Yi Li4,
  • Kee S Chia5,
  • Edison T Liu2,
  • Per Hall1,
  • Jianjun Liu2 and
  • Sara Wedrén1
Breast Cancer Research20068:R67

https://doi.org/10.1186/bcr1623

©  Einarsdóttir et al.; licensee BioMed Central Ltd. 2006

Received: 03 October 2006

Accepted: 28 November 2006

Published: 28 November 2006

Abstract

Background

Mutations in the ataxia-telangiectasia mutated (ATM) and checkpoint kinase 2 (CHEK2) genes and amplification of the v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2) gene have been suggested to have an important role in breast cancer aetiology. However, whether common variation in these genes has a role in the development of breast cancer or breast cancer survival in humans is still not clear.

Methods

We performed a comprehensive haplotype analysis of the ATM, CHEK2 and ERBB2 genes in a Swedish population-based study, which included 1,579 breast cancer cases and 1,516 controls. We followed the cases for 8.5 years, on average, and retrieved information on the date and cause of death during that period from the nationwide Swedish causes of death registry. We selected seven haplotype-tagging SNPs (tagSNPs) in the ATM gene, six tagSNPs in the CHEK2 gene and seven tagSNPs in the ERBB2 gene that predicted both haplotypic and single locus variations in the respective genes with R 2 values ≥ 0.8. These tagSNPs were genotyped in the complete set of cases and controls. We computed expected haplotype dosages of the tagSNP haplotypes and included the dosages as explanatory variables in Cox proportional hazards or logistic regression models.

Results

We found no association between any genetic variation in the ATM, CHEK2 or ERBB2 genes and breast cancer survival or the risk of developing tumours with certain characteristics.

Conclusion

Our results indicate that common variants in the ATM, CHEK2 or ERBB2 genes are not involved in modifying breast cancer survival or the risk of tumour-characteristic-defined breast cancer.

Keywords

Breast CancerBreast Cancer SurvivalBreast Cancer CaseCommon HaplotypeBreast Cancer Risk Factor

Introduction

Twin and family studies consistently indicate that the risk of breast cancer is influenced, in part, by hereditary factors, but high-risk mutations seem to account for only 1–2% of all breast cancer cases in the general population [1]. A polygenic model has been proposed to account for the residual familial risk [2], which anticipates small effects of several low-penetrance genetic risk variants in combination with environmental influence. Although no human data exist for the affect of germ-line polymorphisms on tumour outcome, there is strong evidence that strain background is a significant determinant of the clinical behaviour of experimental mammary carcinomas in mice [3–5]. We thus set out to study the role of common variation in key breast cancer candidate genes in relation to breast cancer aetiology, survival and tumour characteristics.

The ataxia-telangiectasia mutated (ATM; MIM 607585), checkpoint kinase 2 (CHEK2; MIM 604373) and v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2; also named HER2; MIM 164870) genes have been suggested to have an important role in breast cancer aetiology. The ATM protein is activated in response to ionizing radiation and triggers phosphorylation of CHEK2 and other proteins that promote cell cycle arrest and activation of DNA repair [6–12]. The ATM gene is mutated in the rare autosomal recessive disorder ataxia-telangiectasia (A-T) and the risk of breast cancer has been found to be increased in relatives of A-T patients [13, 14], in addition to A-T heterozygotes [15, 16]. Mutations in the CHEK2 gene have been found in patients with Li-Fraumeni syndrome [17] and one such mutation – the rare 1100delC gene mutation – has been found to increase breast cancer susceptibility at the population level [18] and in families without BRCA1 or BRCA2 gene mutations [19, 20]. ERBB2 is a transmembrane glycoprotein, with tyrosine kinase activity [21–25], that has a major role in signal transduction, thereby affecting cell proliferation, differentiation, motility and survival [26–28]. The ERBB2 gene is amplified and/or overexpressed in approximately 30% of breast tumours, a phenomenon that is associated with a poor prognosis [29–31].

Until now, common variation in the ATM, CHEK2 and ERBB2 genes has mainly been studied in relation to the overall risk of breast cancer, but the results have been inconclusive. It is worthwhile studying common variation in the ATM, CHEK2 and ERBB2 genes in relation to breast cancer progression because defects in the genes could increase the risk of developing tumours with an unfavourable prognosis through their role in the regulation of cell cycle checkpoints and amplification. Variation in the genes could also affect cancer survival through increased radiosensitivity [32–34]. One group found a relationship between poor breast cancer prognosis and common haplotypes in the ERBB2 gene [35], but to our knowledge, nothing has been reported regarding the association between common haplotypes in the ATM and CHEK2 genes and breast cancer survival or tumour characteristics. Hence, many questions regarding the role of these genes in breast cancer survival and the progression of breast cancers are unanswered.

We performed a haplotype analysis of the ATM, CHEK2 and ERBB2 genes by genotyping a dense set of markers in each gene in 92 randomly selected controls, thus acquiring a comprehensive coverage of the common variation in each entire gene. We genotyped selected haplotype-tagging SNPs (tagSNPs) in a well-defined, Swedish population. We then assessed the association of the tagSNPs in the ATM, CHEK2 and ERBB2 genes and their haplotypes with breast cancer survival and the risk of tumour-characteristic-defined breast cancer. We also studied the tagSNPs and haplotypes in the ATM and ERBB2 genes, in addition to two mis-sense mutations in the ATM gene (2572 T→C and 4258 C→T), in relation to the overall risk of breast cancer.

Materials and methods

Study population

The study base included all Swedish-born women between 50 and 74 years of age who were resident in Sweden between October 1993 and March 1995. During that period, we identified all breast cancer cases at diagnosis through the six regional cancer registries in Sweden, which provide virtually complete data on incident cancers in Sweden [36]. We randomly selected controls, who matched the cases in 5-year age strata, from the Swedish registry of the total population. Of the eligible women, 3,345 (84%) breast cancer cases and 3,454 (82%) controls participated in this initial questionnaire-based study, providing detailed information on their use of menopausal hormone therapy, their reproductive history and other lifestyle factors. Results from the study have been published [37–41].

From this initial study, we randomly selected 1,500 breast cancer cases and 1,500 age–frequency-matched controls among the postmenopausal participants without any previous malignancy (except carcinoma in situ of the cervix or nonmelanoma skin cancer). With the intention of increasing the statistical power in subgroup analyses, we further selected all of the remaining breast cancer cases and controls who had used menopausal hormones (oestrogen alone or any combination of oestrogen and progestin) for at least 4 years (191 cases and 108 controls) and all women with self-reported diabetes mellitus (110 cases and 104 controls). Additionally, 345 controls, who were included in both the initial breast cancer study and an endometrial cancer study with the same study base and inclusion criteria, were added to the control group. In total, we selected 1,801 breast cancer cases and 2,057 controls.

After informed consent was obtained, participants donated whole blood. For deceased breast cancer patients and women who declined to donate blood but consented to the use of tissue samples, we collected archived paraffin-embedded, noncancerous tissue samples. We acquired 70% of the requested tissue samples; the main reason for nonparticipation was unwillingness or a lack of time at the respective pathology department to provide the tissue blocks. In total, we obtained 1,321 blood samples and 275 archived tissue samples from the breast cancer patients and 1,524 blood samples from the controls. The mean time from diagnosis to the arrival of the blood and tissue samples at our department was 5 years. Reasons for nonparticipation included a lack of interest in research, a negative attitude towards genetic research, old age and severe disease or death. Population-based participation rates (taking into account the proportion of individuals who did not participate in the questionnaire-based study) for the breast cancer cases and controls were 75% and 61%, respectively.

This study was approved by the Institutional Review Boards in Sweden and the National University of Singapore.

DNA isolation

The Swegene laboratories in Malmö (Sweden) extracted DNA from 4 ml of whole blood using the QIAamp DNA Blood Maxi Kit (Qiagen, Solna, Sweden) according to the manufacturer's instructions. From nonmalignant cells in paraffin-embedded tissue, we extracted DNA using a standard phenol/chloroform/isoamyl alcohol protocol [42]. We successfully isolated DNA from 1,318 blood samples and 272 tissue samples from the breast cancer patients and 1,518 blood samples from the controls.

SNP markers and genotyping

The ATM gene covers 146.3 kb of genomic sequence on chromosome 11, the CHEK2 gene spans 54.1 kb on chromosome 22 and the ERBB2 gene covers 33.7 kb on chromosome 17 (build 125 in the dbSNP (Single Nucleotide Polymorphism database). We selected SNPs in the ATM, CHEK2 and ERBB2 genes and their 10 kb flanking sequences from dbSNP (build 124) and Celera databases aiming for an initial marker density of at least one SNP per 5 kb. SNPs were genotyped using the Sequenom primer extension-based assay (San Diego, CA, USA) and the BeadArray system (Illumina, San Diego, CA, USA) according to the manufacturers' instructions. All genotyping results were generated and checked by laboratory staff unaware of case–control status. Only SNPs for which >85% of the samples gave a genotype call were analysed further. As a quality control, we genotyped 200 randomly selected SNPs (not including SNPs in the ATM, CHEK2 or ERBB2 genes) in the 92 control samples using both the Sequenom system and the BeadArray system. The genotype concordance was >99.5%, suggesting high genotyping accuracy.

Characterization of linkage disequilibrium and haplotype-tagging SNP selection

We produced linkage disequilibrium (LD) plots of the D' values for ATM and ERBB2 genes (supplementary Figures 1 and 2, respectively) using the Haploview program [43]. We reconstructed haplotypes for all three genes using the partition-ligation-expectation-maximization (PLEM) algorithm [44] implemented in the tagSNPs program [45] and selected tagSNPs according to the R 2 coefficient, which quantifies how well the tagSNP haplotypes predict the SNPs or the number of copies of haplotypes that an individual carries. We chose tagSNPs so that common SNP genotypes (minor allele frequency ≥0.03) and common haplotypes (frequency ≥0.03) were predicted by R 2 values ≥ 0.8 [46]. To evaluate the performance of our tagSNPs in capturing unobserved SNPs within the genes and to assess whether a denser set of markers was needed, we performed a SNP-dropping analysis [47, 48]. In brief, each of the genotyped SNPs was dropped in turn and tagSNPs were selected from the remaining SNPs so that their haplotypes predicted the remaining SNPs with an R 2 value of 0.85. We then estimated how well the tagSNP haplotypes of the remaining SNPs predicted the dropped SNP, an evaluation that can provide an unbiased and accurate estimate of tagSNP performance [47, 48].

Tumour characteristics and follow-up

We retrieved information on the date and cause of death (until 31 December 2003) from the Swedish causes of death registry and the date of emigration from the Swedish national population registry. Information from the causes of death registry in Sweden has been found to be of high quality [49]. The follow-up time began at the date of diagnosis and ended on 31 December 2003 or at the date of death or emigration, whichever came first. We collected information on tumour characteristics, such as tumour size, lymph-node involvement, grade (tumour differentiation), histological type and date of the first distant metastasis, from medical records. We obtained information on the oestrogen and progesterone receptor content and S-phase fraction (i.e. the proportion of tumour cells in the DNA synthesis phase of the cell cycle) of the tumours from seven laboratories around Sweden that routinely perform these tumour measurements for the whole country. At the time of the study, all seven laboratories used an enzyme immunoassay (Abbott Laboratories, Solna, Sweden) on cytosol samples for analysing the oestrogen and progesterone receptor content. This method was oestrogen receptor type α specific [50]. The laboratories reported either quantitative measurements (fmol receptor per μg DNA or mg protein and the percentage of cells in S-phase) or categorical measurements (strongly positive, positive, weakly positive or negative for receptor status and high, intermediate or low S-phase fraction). A rather high proportion of this information was missing, which was owing to the fact that these measurements were not routinely performed in the mid-1990s. We classified the tumour characteristics as follows:

1. TNM stage:

1.a. Stage 1 – tumour size ≤20 mm and no regional lymph-node metastases.

1.b. Stage 2 – tumour size ≤20 mm and lymph-node metastases, or tumour size 20–≤50 mm, or tumour size >50 mm and no lymph-node metastases.

1.c. Stage 3 – an inflammatory breast tumour, or tumour size >50 mm and lymph-node metastases.

1.d. Stage 4 – distant metastasis within 90 days of diagnosis.

2. Lymph-node involvement:

2.a. Yes – at least one metastasised lymph node.

2.b. No – no metastasised lymph node.

3. Grade:

3.a. High differentiation.

3.b. Intermediate differentiation.

3.c. Low differentiation.

4. Oestrogen and progesterone receptor status:

4.a. Positive – ≥0.05 fmol/μg DNA, ≥10 fmol/mg protein or categorically strongly positive, weakly positive or positive.

4.b. Negative – <0.05 fmol/μg DNA, <10 fmol/mg protein or categorically negative.

5. S-phase fraction:

5.a. High – ≥9% or categorically high.

5.b. Low – <9% or categorically low.

We combined TNM stage 3 and TNM stage 4 in all association analyses because of small numbers.

Statistical analyses

In assessing the association with tumour-characteristic-defined breast cancer, we stratified the cases on breast cancer subtypes and compared each group with the controls. Our testing strategy was to fit a single model and assess within each stratum of risk-factor subgroup and for different tumour characteristics, haplotype-trait associations as a global likelihood ratio test [51]. We first computed expected haplotype dosage using the tagSNPs program [45], with haplotype frequencies estimated for the breast cancer cases and controls combined, assuming Hardy–Weinberg equilibrium (HWE) of haplotypes. We then, included the haplotype dosages as explanatory variables in the regression models. To estimate the power in the risk component of the study, we used a method described by Chapman and colleagues [52], which assumes co-dominant effects at an unobserved locus. To calculate the power for log-additive effects in the survival component of the study, we used the Quanto program [53] in a similar manner to that described by Manolio and colleagues [54].

We applied unconditional logistic regression models adjusted for age (in 5-year age-groups) to assess the relationship between the ATM, CHEK2 and ERBB2 tagSNP haplotypes and the overall risk of breast cancer, in addition to breast cancer subtypes. We estimated the hazard ratio of death owing to breast cancer in relation to the genes' tagSNP haplotypes using Cox proportional hazards models. The appropriateness of these approaches is supported by Stram and colleagues [45]. That is, if R 2 values are high, such as here, the point and interval estimates obtained by this approach will be approximately accurate. To assess the proportional hazards assumptions of the Cox models, we examined scaled Schoenfeld residuals and found no evidence against proportionality.

'Confounding' has been defined as the presence of a common cause of the exposure and outcome [55]. We believe that lifestyle and reproductive breast cancer risk factors are unlikely to cause genetic variation in the genes, but they could be intermediates in the causal pathway between the genes and both breast cancer and tumour-characteristic-defined breast cancer. For completeness, we assessed whether the tagSNPs were associated with known breast cancer risk factors (age at menarche, age at menopause, body mass index, age at first birth, parity, menopausal hormone use and diabetes mellitus) among the randomly selected controls using Kruskal-Wallis and Chi square tests. Analyses were performed using the SAS system (release 9.1; SAS Institute Inc., Cary, NC, USA).

Results

Study population

Characteristics of breast cancer cases in the parent case–control study and the current genetic association study are described in Table 1. With respect to nongenetic factors among cases and controls [56] and tumour characteristics among cases (Table 1), the current study was representative of the parent study. Breast-cancer-specific death rates among the cases in the current study reflected known prognostic information on various tumour characteristics (Table 1). Tumours with unknown hormone receptor status or S-phase fraction were smaller in both parent and current studies (P < 0.0001 for both receptor status and S-phase fraction). Missing information on tumour grade depended significantly on tumour size in the parent study (P = 0.01) but not in the current study (P = 0.15).
Table 1

Characteristics of the breast cancer cases in the parent study and the present genetic study

 

Parent study

Present study

Present study

Characteristic

n (%)

n (%)

Breast cancer deathsa

Mortality rateb

Total cases

2,817

1,579

198

0.016

Tumour size (mm)

    

≤20

1,858 (71.4)

1,087 (73.6)

100

0.010

21–30

489 (18.8)

270 (18.3)

57

0.026

>30

254 (9.8)

120 (8.1)

38

0.043

Unknown

216

102

3

0.022

P valuec

 

0.13

  

TNM stage

    

1

1,392 (54.4)

804 (55.3)

40

0.006

2

1,074 (42.0)

606 (41.7)

126

0.026

3

58 (2.3)

31 (2.1)

15

0.076

4

34 (1.3)

12 (0.8)

11

0.262

Unknown

259

126

6

0.019

P valuec

 

0.39

  

Lymph-node involvement

    

No

1,741 (68.2)

1,002 (68.9)

68

0.008

Yes

813 (31.8)

453 (31.1)

122

0.035

Unknown

263

124

8

0.027

P valuec

 

0.23

  

Grade

    

1

259 (14.7)

143 (14.2)

4

0.003

2

739 (41.9)

444 (44.1)

37

0.010

3

766 (43.4)

419 (41.7)

93

0.027

Unknown

1,053

573

64

0.016

P valuec

 

0.61

  

Oestrogen and progesterone receptor status

    

ER+/PR+

1,162 (62.7)

677 (65.4)

72

0.012

ER+/PR-

285 (15.4)

133 (12.9)

28

0.026

ER-/PR+

69 (3.7)

33 (3.2)

7

0.027

ER-/PR-

338 (18.2)

192 (18.6)

52

0.037

Unknown

963

544

39

0.010

P valuec

 

0.37

  

Histological type

    

Ductal

1,891 (72.5)

1,070 (72.4)

147

0.016

Lobular

304 (11.7)

185 (12.5)

19

0.012

Other

414 (15.9)

223 (15.1)

28

0.014

Unknown

208

101

4

0.039

P valuec

 

0.48

  

S-phase fraction

    

Low

689 (68.1)

401 (70.0)

40

0.011

High

323 (31.9)

172 (30.0)

52

0.040

Unknown

1805

1,006

106

0.013

P valuec

 

0.71

  

aFrom the date of diagnosis until 31 December 2003.

bFrom the date of diagnosis until 31 December 2003 or until the date of death or emigration, whichever came first. Calculated as breast cancer deaths per person-year of follow-up.

cChi-squared test of independence between the parent and the present studies.

ER, oestrogen receptor; PR, progesterone receptor

Cases who participated through tissue sample donation were, on average, 1.5 years older (P = 0.0003) and more likely to have been diagnosed with TNM stage 2 or more advanced cancers (P < 0.0001) compared with cases who donated a blood sample. Importantly, no significant differences in the genotype frequencies were evident between cases who participated through blood or tissue sample donation.

Estimation of the pattern of linkage disequilibrium and coverage

We summarize the statistics for the genotyping results and SNP coverage in the ATM, CHEK2 and ERBB2 genes in supplementary Table 1. The SNPs in the ATM, ERBB2 and CHEK2 genes that were successfully genotyped in 92 randomly selected controls are listed in supplementary Table 2 (ATM) and 3 (ERBB2), and have been previously published in Table 2 by Einarsdóttir and colleagues [56] (CHEK2). In brief, our study included 51 SNPs in the ATM gene, 14 SNPs in the CHEK2 gene and 13 SNPs in the ERBB2 gene that were successfully genotyped – and were in HWE – in the 92 controls. The mean spacing between the SNPs included in our study was 2.9 kb in the ATM gene, 4.0 kb in the CHEK2 gene and 2.8 kb in the ERBB2 gene. We detected strong LD across all three genes [supplementary Figure 1, Figure 1 in Einarsdóttir and colleagues [56] and supplementary figure 2, respectively]. Using the SNP dropping method [47], we found that the tagSNPs selected from the SNPs included in our study could capture nongenotyped SNPs as efficiently as the SNPs included in our study (supplementary Table 1).
Table 2

Characteristics of the ATM, CHEK2 and ERBB2 tagSNPs and their association with breast cancer survival

SNP ID

SNP name

Number of cases/controls

Minor allele frequencya

Breast cancer deaths/person-yearsb

HR (95% CI)b,c

ATM

     

TAG1d

rs4987886

1,220/1,440

0.06

70/10,660

0.86 (0.42–1.76)

TAG2d

rs3092991

1,119/1,318

0.14

65/9,717

1.12 (0.71–1.77)

TAG3d

rs1800057

1,144/1,346

0.03

66/9,958

0.62 (0.16–2.46)

TAG4

rs1801516

1538/1,500

0.15

185/12,421

0.99 (0.75–1.30)

TAG5

rs17107917

1,546/1,493

0.04

192/12,466

1.01 (0.63–1.64)

TAG6d

rs227060

1,152/1,350

0.28

68/10,004

0.77 (0.52–1.14)

TAG7d

rs664143

1,227/1,408

0.48

73/10,684

1.24 (0.89–1.73)

CHEK2

     

TAG1

rs8135424

1,539/1,478

0.13

197/12,367

1.08 (0.83–1.41)

TAG2

rs5762749

1,516/1,472

0.35

191/12,172

0.78 (0.62–0.97)

TAG3

rs743185

1,547/1,491

0.12

192/12,471

1.21 (0.91–1.62)

TAG4

rs738722

1,501/1,444

0.25

187/12,083

0.72 (0.56–0.93)

TAG5

rs5762765

1,510/1,456

0.38

192/12,118

1.15 (0.94–1.41)

TAG6

rs2236142

1,541/1,471

0.31

192/12,398

1.25 (1.02–1.54)

ERBB2

     

TAG1

rs2643195

1,494/1,458

0.32

186/12,052

1.09 (0.88–1.35)

TAG2

rs4252596

1,530/1,481

0.13

189/12,330

0.95 (0.70–1.30)

TAG3

rs2952155

1,459/1,407

0.25

184/11,666

1.02 (0.80–1.29)

TAG4

rs2952156

1,546/1,481

0.32

194/12,445

1.11 (0.89–1.37)

TAG5e

rs1801200e

1,548/1,485

0.26

193/12,463

1.00 (0.80–1.25)

TAG6

rs4252665

1,527/1,486

0.05

182/12,401

0.76 (0.45–1.27)

TAG7

rs3809717

1,532/1,478

0.31

194/12,310

0.96 (0.77–1.20)

aIn controls.

bAmong women with breast cancer.

cHRs are assessed assuming co-dominance and show the increase/decrease in risk of death from breast cancer with each addition of the minor allele compared with homozygotes of the major allele.

dNot genotyped in the patients who participated through tissue sample donation.

eAlso named I655V.

ATM, ataxia-telangiectasia mutated; CHEK2, checkpoint kinase 2; CI, confidence interval; ERBB2, v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2; HR, hazard ratio; SNP, single nucleotide polymorphism; tagSNP, haplotype-tagging SNP.

Breast cancer survival

Table 2 summarizes the information on the tagSNPs in the ATM, CHEK2 and ERBB2 genes that we genotyped in breast cancer cases and controls. All of the tagSNPs in the CHEK2 and ERBB2 genes and two of the tagSNPs in the ATM gene were not only genotyped in breast cancer cases and controls who participated through blood sample donation, but were also genotyped in breast cancer cases who participated through tissue sample donation. None of the tagSNPs deviated significantly from HWE among the controls or showed a meaningful association with known breast cancer risk factors. Only one of the tagSNPs – TAG5 (also named I655V) in the ERBB2 gene – conferred an amino acid change in the protein product.

We estimated the risk of death from breast cancer associated with the tagSNPs (Table 2) in the ATM, CHEK2 and ERBB2 genes or their haplotypes (Table 3). We found a decreased risk of death from breast cancer associated with each addition of the rare TAG2 allele in the CHEK2 gene (P = 0.026) and an elevated risk of death from breast cancer associated with the rare TAG6 allele in the CHEK2 gene (P = 0.03), compared with homozygotes of the common allele for each variant. The associations did not, however, withstand Bonferroni correction. Carriers of haplotype 2 in the CHEK2 gene seemed to have a decreased risk of death from breast cancer (P = 0.038) compared with haplotype 1 carriers, whereas carriers of the rare ERBB2 haplotypes seemed to have an increased risk of death from breast cancer (P = 0.009). Neither association carried over to the global test (P = 0.15 and P = 0.45, respectively).
Table 3

tagSNP haplotypes in the ATM, CHEK2 and ERBB2 genes in relation to breast cancer survival

Haplotype no.

Haplotypes

Haplotype proportions (cases)

HR (95% CI)

ATM

 

n = 1,574 a

 

1

AACGCCT

0.414

1.00 (Reference)

2

AACGCTC

0.231

0.85 (0.65–1.13)

3

AGCACCC

0.150

0.89 (0.66–1.21)

4

AACGCCC

0.062

0.86 (0.52–1.44)

5

TACGCCT

0.064

0.88 (0.55–1.40)

6

AACGGTC

0.043

0.95 (0.57–1.57)

 

Rareb

0.037

0.95 (0.53–1.68)

Global P valuec

 

0.95

CHEK2

 

n = 1,571 a

 

1

GCCCCC

0.223

1.00 (Reference)

2

GGCTGC

0.231

0.72 (0.52–0.98)

3

GCCCCG

0.140

1.02 (0.72–1.43)

4

ACCCGC

0.113

1.00 (0.70–1.41)

5

GCTCGG

0.089

1.08 (0.73–1.57)

6

GGCCGC

0.052

0.61 (0.34–1.10)

7

GCCCGC

0.027

0.60 (0.26–1.41)

 

Rared

0.125

0.95 (0.67–1.35)

Global P valuec

 

0.15

ERBB2

 

n = 1,579 a

 

1

GGCGACT

0.296

1.00 (Reference)

2

AGTAACG

0.166

0.98 (0.71–1.34)

3

GGCGGCG

0.135

1.01 (0.73–1.40)

4

GACGACG

0.116

0.98 (0.69–1.39)

5

AGTAGCG

0.075

1.21 (0.81–1.81)

6

AGCAACG

0.068

1.16 (0.76–1.77)

7

GGCGACG

0.079

1.06 (0.69–1.63)

8

GGCGGTG

0.048

0.81 (0.47–1.39)

 

Raree

0.018

2.21 (1.22–4.02)

Global P valuec

 

0.45

aInformation on at least one tagSNP.

b11 rare haplotypes combined. Each haplotype has frequency <3% among the controls.

cProbability ratio test.

d19 rare haplotypes combined. Each haplotype has frequency <3% among the controls.

e19 rare haplotypes combined. Each haplotype has frequency <3% among the controls.

ATM, ataxia-telangiectasia mutated; CHEK2, checkpoint kinase 2; CI, confidence interval; ERBB2, v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2; HR, hazard ratio; tagSNP, haplotype-tagging SNP.

We noticed in Table 3 that all of the ATM haplotypes conferred a decrease in risk of death from breast cancer compared with haplotype 1. We therefore assessed the association of haplotype 1 with the risk of death from breast cancer and found a nonsignificantly elevated risk compared with noncarriers [odds ratio (OR), 1.13; 95% confidence interval (CI), 0.92–1.40].

Tumour characteristics

We calculated global P values for the association between the tagSNP haplotypes in the ATM, CHEK2 and ERBB2 genes and the risk of tumour-characteristic-defined breast cancer from logistic regression models. Cases were divided into groups according to their tumour characteristics and each group contrasted against all controls. Each logistic regression model included the common haplotypes and the combined group of rare haplotypes, with the most common haplotype used as a reference standard. None of the global P values reached significance (supplementary Table 4), which indicates that none of the individual haplotypes affected the risk of developing tumours with certain characteristics.

We genotyped the CHEK2 1100delC gene mutation in our study population and have previously reported its effect on the overall risk of breast cancer [56]. The deletion was very rare in our study population, with a frequency of 0.7% among the cases and 0.4% among the controls. We could therefore not perform a meaningful analysis of the association between the deletion and breast cancer characteristics or survival in the current study.

Overall risk of breast cancer: the ATM and ERBB2genes

We found no effect of the ATM or ERBB2 tagSNPs (supplementary Table 5) or haplotypes (supplementary Table 6) on the overall risk of breast cancer, which was not altered after conditioning on the selection variables (menopausal hormone therapy and diabetes mellitus) or restricting the analyses to the randomly selected cases and controls. Stratifying the haplotype results by known breast cancer risk factors did not yield any additional compelling findings (supplementary Table 7).

We genotyped two mis-sense mutations in the ATM gene in the complete sample set: 4258 C→T (rs1800058; L1420F) and 2572 T→C (rs1800056; F858L). Neither mutation deviated significantly from HWE in controls. They were both rare in our study population, with a minor allele frequency of 1.9% for 4258 C→T and 1.4% for 2527 T→C in the controls. In exploring the change in the risk of breast cancer with each addition of the rare allele compared with noncarriers (assuming co-dominance), an elevated – but not significant – risk for the 4258 C→T (OR, 1.36; 95% CI, 0.91–2.04) was found, but no association emerged between the 2527 T→C and the risk of breast cancer (OR, 1.05; 95% CI, 0.65–1.71).

Discussion

Although the gene products of ATM, CHEK2 and ERBB2 are involved in various aspects of breast cancer development and progression, our results suggest that common variation in these genes does not affect survival, tumour-characteristic-defined risk or the overall risk of breast cancer. We carefully studied these associations, both overall and in subgroups of known nongenetic breast cancer risk factors, using large population-based case–control material, and conclude that on the population level, common genetic variation in these genes is not of great importance for these outcomes. This does not preclude the possibility that more crucial – and rare – variation is influential in selected patient groups.

Our study was population-based. All participants were born in Sweden between 1919 and 1944, a time at which foreign immigration to Sweden was still rare [57], which means population stratification is of limited concern in our study. To minimize exposure misclassification, we applied genotyping methods with low error rates (the Sequenom and Illumina methods have genotyping error rates of 0.5% and 0.3%, respectively), DNA samples were randomly assigned to the genotyping plates and the genotyping personnel were blinded to case–control status. Furthermore, we replicated genotype calls of 200 randomly selected SNPs for a subset of samples using a separate genotyping method with >99.5% concordance.

The oestrogen and progesterone receptor status of tumours and S-phase fraction were assessed at seven different laboratories in Sweden, but it is doubtful that the genotype frequencies could be related to any interlaboratory differences. A large proportion of the information on receptor status, S-phase fraction and grade was missing. Assessment of receptor status and S-phase fraction was, to a large extent, dependent on the size of the tumour, but evaluation of the tumour grade was mostly dependent on the pathologist's decision. Because genotype frequencies were not related to tumour size, bias owing to the missing information on these factors seems unlikely.

Survival bias could be a concern in our study because nonparticipation was related to severe disease or death. However, we obtained the majority of the tissue samples requested for deceased patients with breast cancer or breast cancer cases who had declined donation of a blood sample. The lack of tissue accessibility is unlikely to be related to ATM, ERBB2 or CHEK2 genetic variation because it depended on the inability of the respective pathology department to retrieve the samples. The genotype frequencies of the tagSNPs in the ERBB2 and CHEK2 genes did not differ between blood and tissue samples, suggesting that the survival bias was negligible. We were not able to genotype TAG1, TAG2, TAG3, TAG6 and TAG7 in the ATM gene in the tissue samples, but because the results were not different in the analyses restricted to the most severe cases among those who donated blood samples indicates that this was not a major problem in our study.

The loss of power from testing SNPs indirectly is thought to be related to the R 2 measure [46, 58], but exceptions to these assumptions have been reported [59]. We assessed the capability of the tagSNPs to convey an association signal from unobserved, in addition to observed, SNPs. We captured the unobserved SNPs with average R 2 values of 0.92, 0.72 and 0.93 in the ATM, ERBB2 and CHEK2, genes, respectively, and thus suffered minimal loss of power owing to indirect testing. We performed standard power calculations assuming α = 0.05, thus giving an indication regarding the general power of our study. We acknowledge, however, that the power is reduced in the subgroup analyses (an effect cannot be excluded in the smallest groups) and at lower α-levels (required when multiple tests are carried out). For the ability of haplotypes to predict the allele count at a causal locus with a minor allele frequency of 0.20, we had 89% power for ATM, 73% power for ERBB2 and 87% power for CHEK2 to detect an odds ratio of 1.3 in the risk component of the study. To detect a hazard ratio of 1.4, with an α-level of 0.05, in the survival component of the study, we had 50% power for TAG1 in CHEK2, which had a minor allele frequency of 0.13, and 76% power for TAG5 in CHEK2, which had a minor allele frequency of 0.38.

Our data did not support an association between common variation in the ATM, CHEK2 and ERBB2 genes and breast cancer survival or the risk of developing tumours of different characteristics. Hence, we did not confirm the finding of Han and colleagues who found that an ERBB2 haplotype composed of two nonsynonymous tagSNPs – I655V and P1170A – increased the risk of breast cancer death or recurrence [35]. We included the I655V as a tagSNP in our study and genotyped the P1170A in the 92 controls. We found no effect of the I655V on breast cancer survival or tumour characteristics defined breast cancer.

To our knowledge, no study has investigated ATM or CHEK2 common haplotypes in relation to breast cancer survival or tumour characteristics, although one study explored the effect of three common polymorphisms in ATM and two polymorphisms in CHEK2 on breast cancer survival [60]. This study found no association, which is in agreement with our findings. The rare 1100delC mutation in the CHEK2 gene has been associated with breast tumours of high grade [61], in addition to steroid receptor-positive breast tumours, but not with overall survival [62]. The mutation was too rare in our study population to be investigated in relation to breast cancer survival or tumour characteristics.

We previously found no association between common variation in the CHEK2 gene and overall risk of breast cancer [56], which was in agreement with earlier findings [63]. In this study, we, correspondingly, found no effect of common variation in the ATM or ERBB2 genes on the risk of breast cancer, even when the results were stratified by known breast cancer risk factors. One study of the ATM gene [64] and two studies of the ERBB2 gene [35, 65] are in agreement with our findings. Tamimi and colleagues found no association between the haplotypes of five Hapmap tagSNPs (one of which was TAG7 in our study) in the ATM gene and the risk of breast cancer [64]. Benusiglio and colleagues explored ERBB2 haplotypes composed of five tagSNPs [three of which were TAG2, TAG3 and TAG5 in our study] – including the nonsynonymous I655V and P1170A – in relation to the risk of breast cancer [65], whereas Han and colleagues solely studied the I655V and P1170A as tagSNPs [35]. Neither study found any effect of the haplotypes on the risk of breast cancer. Common haplotypes in the ERBB2 gene thus do not seem to affect the risk of breast cancer, although results regarding the I655V common variant in the ERBB2 gene have been conflicting [35, 65–67]. We found no association of the I655V with the risk of breast cancer.

Three groups have found an association between specific ATM common haplotypes and the risk of breast cancer [68–70]. Lee and colleagues [70] and Koren and colleagues [68] reconstructed haplotypes in the ATM gene from five and eight randomly selected common SNPs, respectively, whereas Angele and colleagues [69] included 11 common SNPs in the ATM gene for their haplotype estimation that had been either previously reported in the literature or detected by sequencing. SNP selection overlapped somewhat between the three studies, but none of them reported the probability of their SNPs being able to predict underlying variation in the gene. Furthermore, findings from two of the three groups [68, 69] were derived from small sample sizes.

The 4258 C→T and 2527 T→C mutations in the ATM gene have been detected in breast cancer patients [69, 71–73], but to the best of our knowledge, these mutations have not been reported in A-T patients. In line with two [69, 72] out of three reports [69, 71, 72], we found an elevated – but not significant – risk of breast cancer in carriers of the rare 4258 T allele in the ATM gene. We did not, however, find such an association in carriers of the 2527 C allele. One study found a twofold increase in the risk of breast cancer that was related to the 2527 T→C mutation in a population from USA, but did not confirm the finding in a Polish population [73]. Three other groups did not detect any significant effect of the 2527 T→C mutation on the risk of breast cancer [69, 71, 72], although one of the groups found elevated point estimates [72].

Although the 4258 C→T and 2527 T→C mis-sense variants do not seem to target residues known to be crucial for the function of the ATM protein [72], an increasing amount of evidence suggests that mis-sense variants in the ATM gene cause chromosomal instability and abolish the radiation-induced kinase activity of ATM [74]. Mutant ATM protein also seems to interfere with normal ATM function in a dominant-negative manner [74]. Previous mutation screening studies have indicated that mis-sense mutations in the ATM gene – rather than protein-truncating mutations – are over-represented in patients with breast cancer compared with the general population [72, 75–79]. A recent publication refuted this and found that ATM gene mutations that cause A-T – that is, truncating, splicing and mis-sense mutations – are breast cancer susceptibility alleles [80]. They found a greater than twofold increase in the risk of breast cancer related to a combination of 12 mutations, including six truncating mutations [80]. Thus, controversy remains both regarding which type of mutations in the ATM gene are involved in breast cancer aetiology and which mutations actually drive the association with the risk of breast cancer [69–73, 81–85].

Conclusion

Our study had a sound epidemiological design and a comprehensive coverage of SNPs. The results indicate that common variation in the ATM, CHEK2 or ERBB2 genes does not have a role in breast cancer aetiology or progression.

Abbreviations

A-T: 

ataxia-telangiectasia

ATM: 

ataxia-telangiectasia mutated

CHEK2: 

checkpoint kinase 2

CI: 

confidence interval

dbSNP: 

Single Nucleotide Polymorphism database

ERBB2: 

v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2

HWE: 

Hardy–Weinberg equilibrium

kb: 

kilobase

LD: 

linkage disequilibrium

OR: 

odds ratio

SNP: 

single nucleotide polymorphism

tagSNP: 

haplotype-tagging SNP.

Declarations

Acknowledgements

We are indebted to Christer Halldén at Swegene laboratories, Malmö, Sweden, for overseeing the isolation of DNA from the blood samples, Meah Wee Yang and Ong Eng Hu Jason for genotyping, Lim Siew Lan and Irene Chen for isolating DNA from paraffin-embedded tissue, Marie Vivian Wong Tzu Yen for retrieving the SNP information, Frans Verhoeff for processing the genotyping data, and Mattias Hammarström and Eija Flygare for reviewing the medical records. We are also grateful to all the women who took the time and effort to participate in this study and Anna Christensson and Boel Bissmarck, who obtained consent and co-ordinated the collecting of samples from the study participants. The Wallenberg Consortium North and the Swedish Foundation for Strategic Research also deserve our gratitude for financially supporting KH. This study was supported by funding from the Agency for Science and Technology and Research of Singapore (A*STAR), Susan G Komen Breast Cancer Foundation and National Institute of Health (grant number, R01 CA 104021).

Authors’ Affiliations

(1)
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Solna, Sweden
(2)
Population Genetics, Genome Institute of Singapore, Genome, Singapore
(3)
Department of Mathematics, Stockholm University, Stockholm, Sweden
(4)
Information and Mathematical Sciences, Genome Institute of Singapore, Genome, Singapore
(5)
Centre for Molecular Epidemiology, Department of Community, Occupational and Family Medicine, National University of Singapore, Singapore

References

  1. Thompson D, Easton D: The genetic epidemiology of breast cancer genes. J Mammary Gland Biol Neoplasia. 2004, 9: 221-236. 10.1023/B:JOMG.0000048770.90334.3b.View ArticlePubMedGoogle Scholar
  2. Antoniou AC, Pharoah PD, McMullan G, Day NE, Stratton MR, Peto J, Ponder BJ, Easton DF: A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. Br J Cancer. 2002, 86: 76-83. 10.1038/sj.bjc.6600008.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Hunter K, Welch DR, Liu ET: Genetic background is an important determinant of metastatic potential. Nat Genet. 2003, 34: 23-24; 25. 10.1038/ng0503-23b. [author reply]View ArticlePubMedGoogle Scholar
  4. Qiu TH, Chandramouli GV, Hunter KW, Alkharouf NW, Green JE, Liu ET: Global expression profiling identifies signatures of tumor virulence in MMTV-PyMT-transgenic mice: correlation to human disease. Cancer Res. 2004, 64: 5973-5981. 10.1158/0008-5472.CAN-04-0242.View ArticlePubMedGoogle Scholar
  5. Cozma D, Lukes L, Rouse J, Qiu TH, Liu ET, Hunter KW: A bioinformatics-based strategy identifies c-Myc and Cdc25A as candidates for the Apmt mammary tumor latency modifiers. Genome Res. 2002, 12: 969-975. 10.1101/gr.210502.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Matsuoka S, Rotman G, Ogawa A, Shiloh Y, Tamai K, Elledge SJ: Ataxia telangiectasia-mutated phosphorylates Chk2 in vivo and in vitro. Proc Natl Acad Sci USA. 2000, 97: 10389-10394. 10.1073/pnas.190030497.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Shieh SY, Ahn J, Tamai K, Taya Y, Prives C: The human homologs of checkpoint kinases Chk1 and Cds1 (Chk2) phosphorylate p53 at multiple DNA damage-inducible sites. Genes Dev. 2000, 14: 289-300.PubMedPubMed CentralGoogle Scholar
  8. Zeng Y, Forbes KC, Wu Z, Moreno S, Piwnica-Worms H, Enoch T: Replication checkpoint requires phosphorylation of the phosphatase Cdc25 by Cds1 or Chk1. Nature. 1998, 395: 507-510. 10.1038/26766.View ArticlePubMedGoogle Scholar
  9. Lee JS, Collins KM, Brown AL, Lee CH, Chung JH: hCds1-mediated phosphorylation of BRCA1 regulates the DNA damage response. Nature. 2000, 404: 201-204. 10.1038/35004614.View ArticlePubMedGoogle Scholar
  10. Savitsky K, Bar-Shira A, Gilad S, Rotman G, Ziv Y, Vanagaite L, Tagle DA, Smith S, Uziel T, Sfez S, et al: A single ataxia telangiectasia gene with a product similar to PI-3 kinase. Science. 1995, 268: 1749-1753. 10.1126/science.7792600.View ArticlePubMedGoogle Scholar
  11. Cortez D, Wang Y, Qin J, Elledge SJ: Requirement of ATM-dependent phosphorylation of brca1 in the DNA damage response to double-strand breaks. Science. 1999, 286: 1162-1166. 10.1126/science.286.5442.1162.View ArticlePubMedGoogle Scholar
  12. Khosravi R, Maya R, Gottlieb T, Oren M, Shiloh Y, Shkedy D: Rapid ATM-dependent phosphorylation of MDM2 precedes p53 accumulation in response to DNA damage. Proc Natl Acad Sci USA. 1999, 96: 14973-14977. 10.1073/pnas.96.26.14973.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Olsen JH, Hahnemann JM, Borresen-Dale AL, Brondum-Nielsen K, Hammarstrom L, Kleinerman R, Kaariainen H, Lonnqvist T, Sankila R, Seersholm N, et al: Cancer in patients with ataxia-telangiectasia and in their relatives in the nordic countries. J Natl Cancer Inst. 2001, 93: 121-127. 10.1093/jnci/93.2.121.View ArticlePubMedGoogle Scholar
  14. Swift M, Morrell D, Massey RB, Chase CL: Incidence of cancer in 161 families affected by ataxia-telangiectasia. N Engl J Med. 1991, 325: 1831-1836.View ArticlePubMedGoogle Scholar
  15. Thompson D, Duedal S, Kirner J, McGuffog L, Last J, Reiman A, Byrd P, Taylor M, Easton DF: Cancer risks and mortality in heterozygous ATM mutation carriers. J Natl Cancer Inst. 2005, 97: 813-822.View ArticlePubMedGoogle Scholar
  16. Inskip HM, Kinlen LJ, Taylor AM, Woods CG, Arlett CF: Risk of breast cancer and other cancers in heterozygotes for ataxia-telangiectasia. Br J Cancer. 1999, 79: 1304-1307. 10.1038/sj.bjc.6690209.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Bell DW, Varley JM, Szydlo TE, Kang DH, Wahrer DC, Shannon KE, Lubratovich M, Verselis SJ, Isselbacher KJ, Fraumeni JF, et al: Heterozygous germ line hCHK2 mutations in Li-Fraumeni syndrome. Science. 1999, 286: 2528-2531. 10.1126/science.286.5449.2528.View ArticlePubMedGoogle Scholar
  18. CHEK2 Breast Cancer Case-Control Consortium: CHEK2*1100delC and susceptibility to breast cancer: a collaborative analysis involving 10,860 breast cancer cases and 9,065 controls from 10 studies. Am J Hum Genet. 2004, 74: 1175-1182. 10.1086/421251.View ArticleGoogle Scholar
  19. Meijers-Heijboer H, van den Ouweland A, Klijn J, Wasielewski M, de Snoo A, Oldenburg R, Hollestelle A, Houben M, Crepin E, van Veghel-Plandsoen M, et al: Low-penetrance susceptibility to breast cancer due to CHEK2(*)1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat Genet. 2002, 31: 55-59. 10.1038/ng879.View ArticlePubMedGoogle Scholar
  20. Vahteristo P, Bartkova J, Eerola H, Syrjakoski K, Ojala S, Kilpivaara O, Tamminen A, Kononen J, Aittomaki K, Heikkila P, et al: A CHEK2 genetic variant contributing to a substantial fraction of familial breast cancer. Am J Hum Genet. 2002, 71: 432-438. 10.1086/341943.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Schechter AL, Stern DF, Vaidyanathan L, Decker SJ, Drebin JA, Greene MI, Weinberg RA: The neu oncogene: an erb-B-related gene encoding a 185,000-Mr tumour antigen. Nature. 1984, 312: 513-516. 10.1038/312513a0.View ArticlePubMedGoogle Scholar
  22. King CR, Kraus MH, Aaronson SA: Amplification of a novel v-erbB-related gene in a human mammary carcinoma. Science. 1985, 229: 974-976. 10.1126/science.2992089.View ArticlePubMedGoogle Scholar
  23. Coussens L, Yang-Feng TL, Liao YC, Chen E, Gray A, McGrath J, Seeburg PH, Libermann TA, Schlessinger J, Francke U, et al: Tyrosine kinase receptor with extensive homology to EGF receptor shares chromosomal location with neu oncogene. Science. 1985, 230: 1132-1139. 10.1126/science.2999974.View ArticlePubMedGoogle Scholar
  24. Bargmann CI, Hung MC, Weinberg RA: The neu oncogene encodes an epidermal growth factor receptor-related protein. Nature. 1986, 319: 226-230. 10.1038/319226a0.View ArticlePubMedGoogle Scholar
  25. Akiyama T, Sudo C, Ogawara H, Toyoshima K, Yamamoto T: The product of the human c-erbB-2 gene: a 185-kilodalton glycoprotein with tyrosine kinase activity. Science. 1986, 232: 1644-1646. 10.1126/science.3012781.View ArticlePubMedGoogle Scholar
  26. Yarden Y, Sliwkowski MX: Untangling the ErbB signalling network. Nat Rev Mol Cell Biol. 2001, 2: 127-137. 10.1038/35052073.View ArticlePubMedGoogle Scholar
  27. Threadgill DW, Dlugosz AA, Hansen LA, Tennenbaum T, Lichti U, Yee D, LaMantia C, Mourton T, Herrup K, Harris RC, et al: Targeted disruption of mouse EGF receptor: effect of genetic background on mutant phenotype. Science. 1995, 269: 230-234. 10.1126/science.7618084.View ArticlePubMedGoogle Scholar
  28. Lee KF, Simon H, Chen H, Bates B, Hung MC, Hauser C: Requirement for neuregulin receptor erbB2 in neural and cardiac development. Nature. 1995, 378: 394-398. 10.1038/378394a0.View ArticlePubMedGoogle Scholar
  29. Revillion F, Bonneterre J, Peyrat JP: ERBB2 oncogene in human breast cancer and its clinical significance. Eur J Cancer. 1998, 34: 791-808. 10.1016/S0959-8049(97)10157-5.View ArticlePubMedGoogle Scholar
  30. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL: Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 1987, 235: 177-182. 10.1126/science.3798106.View ArticlePubMedGoogle Scholar
  31. Slamon DJ, Godolphin W, Jones LA, Holt JA, Wong SG, Keith DE, Levin WJ, Stuart SG, Udove J, Ullrich A, et al: Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science. 1989, 244: 707-712. 10.1126/science.2470152.View ArticlePubMedGoogle Scholar
  32. Yu Q, La Rose J, Zhang H, Takemura H, Kohn KW, Pommier Y: UCN-01 inhibits p53 up-regulation and abrogates gamma-radiation-induced G(2)-M checkpoint independently of p53 by targeting both of the checkpoint kinases, Chk2 and Chk1. Cancer Res. 2002, 62: 5743-5748.PubMedGoogle Scholar
  33. Barlow C, Eckhaus MA, Schaffer AA, Wynshaw-Boris A: Atm haploinsufficiency results in increased sensitivity to sublethal doses of ionizing radiation in mice. Nat Genet. 1999, 21: 359-360. 10.1038/7684.View ArticlePubMedGoogle Scholar
  34. Pietras RJ, Poen JC, Gallardo D, Wongvipat PN, Lee HJ, Slamon DJ: Monoclonal antibody to HER-2/neureceptor modulates repair of radiation-induced DNA damage and enhances radiosensitivity of human breast cancer cells overexpressing this oncogene. Cancer Res. 1999, 59: 1347-1355.PubMedGoogle Scholar
  35. Han W, Kang D, Lee JE, Park IA, Choi JY, Lee KM, Bae JY, Kim S, Shin ES, Shin HJ, et al: A haplotype analysis of HER-2 gene polymorphisms: association with breast cancer risk, HER-2 protein expression in the tumor, and disease recurrence in Korea. Clin Cancer Res. 2005, 11: 4775-4778. 10.1158/1078-0432.CCR-04-2208.View ArticlePubMedGoogle Scholar
  36. Centre for Epidemiology: Cancer Incidence in Sweden 2003: Official Statistics of Sweden. 2005, Stockholm: The National Board of Health and WelfareGoogle Scholar
  37. Magnusson C, Baron JA, Correia N, Bergstrom R, Adami HO, Persson I: Breast-cancer risk following long-term oestrogen- and oestrogen-progestin-replacement therapy. Int J Cancer. 1999, 81: 339-344. 10.1002/(SICI)1097-0215(19990505)81:3<339::AID-IJC5>3.0.CO;2-6.View ArticlePubMedGoogle Scholar
  38. Magnusson C, Colditz G, Rosner B, Bergstrom R, Persson I: Association of family history and other risk factors with breast cancer risk (Sweden). Cancer Causes Control. 1998, 9: 259-267. 10.1023/A:1008817018942.View ArticlePubMedGoogle Scholar
  39. Magnusson CM, Persson IR, Baron JA, Ekbom A, Bergstrom R, Adami HO: The role of reproductive factors and use of oral contraceptives in the aetiology of breast cancer in women aged 50 to 74 years. Int J Cancer. 1999, 80: 231-236. 10.1002/(SICI)1097-0215(19990118)80:2<231::AID-IJC11>3.0.CO;2-R.View ArticlePubMedGoogle Scholar
  40. Moradi T, Nyren O, Zack M, Magnusson C, Persson I, Adami HO: Breast cancer risk and lifetime leisure-time and occupational physical activity (Sweden). Cancer Causes Control. 2000, 11: 523-531. 10.1023/A:1008900512471.View ArticlePubMedGoogle Scholar
  41. Terry P, Wolk A, Persson I, Magnusson C: Brassica vegetables and breast cancer risk. Jama. 2001, 285: 2975-2977. 10.1001/jama.285.23.2975.View ArticlePubMedGoogle Scholar
  42. Isola J, DeVries S, Chu L, Ghazvini S, Waldman F: Analysis of changes in DNA sequence copy number by comparative genomic hybridization in archival paraffin-embedded tumor samples. Am J Pathol. 1994, 145: 1301-1308.PubMedPubMed CentralGoogle Scholar
  43. Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.View ArticlePubMedGoogle Scholar
  44. Qin ZS, Niu T, Liu JS: Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms. Am J Hum Genet. 2002, 71: 1242-1247. 10.1086/344207.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Stram DO, Haiman CA, Hirschhorn JN, Altshuler D, Kolonel LN, Henderson BE, Pike MC: Choosing haplotype-tagging SNPS based on unphased genotype data using a preliminary sample of unrelated subjects with an example from the Multiethnic Cohort Study. Hum Hered. 2003, 55: 27-36. 10.1159/000071807.View ArticlePubMedGoogle Scholar
  46. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, et al: The structure of haplotype blocks in the human genome. Science. 2002, 296: 2225-2229. 10.1126/science.1069424.View ArticlePubMedGoogle Scholar
  47. Weale ME, Depondt C, Macdonald SJ, Smith A, Lai PS, Shorvon SD, Wood NW, Goldstein DB: Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: implications for linkage-disequilibrium gene mapping. Am J Hum Genet. 2003, 73: 551-565. 10.1086/378098.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Iles MM: Obtaining unbiased estimates of tagging SNP performance. Ann Hum Genet. 2006, 70: 254-261. 10.1111/j.1529-8817.2005.00212.x.View ArticlePubMedGoogle Scholar
  49. Nystrom L, Larsson LG, Rutqvist LE, Lindgren A, Lindqvist M, Ryden S, Andersson I, Bjurstam N, Fagerberg G, Frisell J, et al: Determination of cause of death among breast cancer cases in the Swedish randomized mammography screening trials. A comparison between official statistics and validation by an endpoint committee. Acta Oncol. 1995, 34: 145-152.View ArticlePubMedGoogle Scholar
  50. Brouillet JP, Dujardin MA, Chalbos D, Rey JM, Grenier J, Lamy PJ, Maudelonde T, Pujol P: Analysis of the potential contribution of estrogen receptor (ER) beta in ER cytosolic assay of breast cancer. Int J Cancer. 2001, 95: 205-208. 10.1002/1097-0215(20010720)95:4<205::AID-IJC1035>3.0.CO;2-Y.View ArticlePubMedGoogle Scholar
  51. Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA: Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet. 2002, 70: 425-434. 10.1086/338688.View ArticlePubMedGoogle Scholar
  52. Chapman JM, Cooper JD, Todd JA, Clayton DG: Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power. Hum Hered. 2003, 56: 18-31. 10.1159/000073729.View ArticlePubMedGoogle Scholar
  53. Gauderman WJ: Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med. 2002, 21: 35-50. 10.1002/sim.973.View ArticlePubMedGoogle Scholar
  54. Manolio TA, Bailey-Wilson JE, Collins FS: Genes, environment and the value of prospective cohort studies. Nat Rev Genet. 2006, 7: 812-820. 10.1038/nrg1919.View ArticlePubMedGoogle Scholar
  55. Hernan MA, Hernandez-Diaz S, Werler MM, Mitchell AA: Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002, 155: 176-184. 10.1093/aje/155.2.176.View ArticlePubMedGoogle Scholar
  56. Einarsdottir K, Humphreys K, Bonnard C, Palmgren J, Iles MM, Sjolander A, Li Y, Chia KS, Liu ET, Hall P, et al: Linkage Disequilibrium Mapping of CHEK2: Common Variation and Breast Cancer Risk. PLoS Med. 2006, 3: e168-10.1371/journal.pmed.0030168.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Nilsson Å: Immigration and emigration in the postwar period. 2004, Statistics Sweden: Demographic ReportsGoogle Scholar
  58. Pritchard JK, Przeworski M: Linkage disequilibrium in humans: models and data. Am J Hum Genet. 2001, 69: 1-14. 10.1086/321275.View ArticlePubMedPubMed CentralGoogle Scholar
  59. Terwilliger JD, Hiekkalinna T: An utter refutation of the 'Fundamental Theorem of the HapMap'. Eur J Hum Genet. 2006, 14: 426-437. 10.1038/sj.ejhg.5201583.View ArticlePubMedGoogle Scholar
  60. Goode EL, Dunning AM, Kuschel B, Healey CS, Day NE, Ponder BA, Easton DF, Pharoah PP: Effect of germ-line genetic variation on breast cancer survival in a population-based study. Cancer Res. 2002, 62: 3052-3057.PubMedGoogle Scholar
  61. Kilpivaara O, Bartkova J, Eerola H, Syrjakoski K, Vahteristo P, Lukas J, Blomqvist C, Holli K, Heikkila P, Sauter G, et al: Correlation of CHEK2 protein expression and c.1100delC mutation status with tumor characteristics among unselected breast cancer patients. Int J Cancer. 2005, 113: 575-580. 10.1002/ijc.20638.View ArticlePubMedGoogle Scholar
  62. de Bock GH, Schutte M, Krol-Warmerdam EM, Seynaeve C, Blom J, Brekelmans CT, Meijers-Heijboer H, van Asperen CJ, Cornelisse CJ, Devilee P, et al: Tumour characteristics and prognosis of breast cancer patients carrying the germline CHEK2*1100delC variant. J Med Genet. 2004, 41: 731-735. 10.1136/jmg.2004.019737.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Kuschel B, Auranen A, Gregory CS, Day NE, Easton DF, Ponder BA, Dunning AM, Pharoah PD: Common polymorphisms in checkpoint kinase 2 are not associated with breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2003, 12: 809-812.PubMedGoogle Scholar
  64. Tamimi RM, Hankinson SE, Spiegelman D, Kraft P, Colditz GA, Hunter DJ: Common ataxia telangiectasia mutated haplotypes and risk of breast cancer: a nested case-control study. Breast Cancer Res. 2004, 6: R416-422. 10.1186/bcr809.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Benusiglio PR, Lesueur F, Luccarini C, Conroy DM, Shah M, Easton DF, Day NE, Dunning AM, Pharoah PD, Ponder BA: Common ERBB2 polymorphisms and risk of breast cancer in a white British population: a case-control study. Breast Cancer Res. 2005, 7: R204-209. 10.1186/bcr982.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Cox DG, Hankinson SE, Hunter DJ: The erbB2/HER2/neu receptor polymorphism Ile655Val and breast cancer risk. Pharmacogenet Genomics. 2005, 15: 447-450.View ArticlePubMedGoogle Scholar
  67. Millikan RC, Hummer AJ, Wolff MS, Hishida A, Begg CB: HER2 codon 655 polymorphism and breast cancer: results from kin-cohort and case-control analyses. Breast Cancer Res Treat. 2005, 89: 309-312. 10.1007/s10549-004-2171-5.View ArticlePubMedGoogle Scholar
  68. Koren M, Kimmel G, Ben-Asher E, Gal I, Papa MZ, Beckmann JS, Lancet D, Shamir R, Friedman E: ATM haplotypes and breast cancer risk in Jewish high-risk women. Br J Cancer. 2006, 94: 1537-1543. 10.1038/sj.bjc.6603062.View ArticlePubMedPubMed CentralGoogle Scholar
  69. Angele S, Romestaing P, Moullan N, Vuillaume M, Chapot B, Friesen M, Jongmans W, Cox DG, Pisani P, Gerard JP, et al: ATM haplotypes and cellular response to DNA damage: association with breast cancer risk and clinical radiosensitivity. Cancer Res. 2003, 63: 8717-8725.PubMedGoogle Scholar
  70. Lee KM, Choi JY, Park SK, Chung HW, Ahn B, Yoo KY, Han W, Noh DY, Ahn SH, Kim H, et al: Genetic polymorphisms of ataxia telangiectasia mutated and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2005, 14: 821-825. 10.1158/1055-9965.EPI-04-0330.View ArticlePubMedGoogle Scholar
  71. Bretsky P, Haiman CA, Gilad S, Yahalom J, Grossman A, Paglin S, Van Den Berg D, Kolonel LN, Skaliter R, Henderson BE: The relationship between twenty missense ATM variants and breast cancer risk: the Multiethnic Cohort. Cancer Epidemiol Biomarkers Prev. 2003, 12: 733-738.PubMedGoogle Scholar
  72. Dork T, Bendix R, Bremer M, Rades D, Klopper K, Nicke M, Skawran B, Hector A, Yamini P, Steinmann D, et al: Spectrum of ATM gene mutations in a hospital-based series of unselected breast cancer patients. Cancer Res. 2001, 61: 7608-7615.PubMedGoogle Scholar
  73. Stredrick DL, Garcia-Closas M, Pineda MA, Bhatti P, Alexander BH, Doody MM, Lissowska J, Peplonska B, Brinton LA, Chanock SJ, et al: The ATM missense mutation p.Ser49Cys (c.146C>G) and the risk of breast cancer. Hum Mutat. 2006, 27: 538-544. 10.1002/humu.20323.View ArticlePubMedPubMed CentralGoogle Scholar
  74. Scott SP, Bendix R, Chen P, Clark R, Dork T, Lavin MF: Missense mutations but not allelic variants alter the function of ATM by dominant interference in patients with breast cancer. Proc Natl Acad Sci USA. 2002, 99: 925-930. 10.1073/pnas.012329699.View ArticlePubMedPubMed CentralGoogle Scholar
  75. Teraoka SN, Malone KE, Doody DR, Suter NM, Ostrander EA, Daling JR, Concannon P: Increased frequency of ATM mutations in breast carcinoma patients with early onset disease and positive family history. Cancer. 2001, 92: 479-487. 10.1002/1097-0142(20010801)92:3<479::AID-CNCR1346>3.0.CO;2-G.View ArticlePubMedGoogle Scholar
  76. Sommer SS, Jiang Z, Feng J, Buzin CH, Zheng J, Longmate J, Jung M, Moulds J, Dritschilo A: ATM missense mutations are frequent in patients with breast cancer. Cancer Genet Cytogenet. 2003, 145: 115-120. 10.1016/S0165-4608(03)00119-5.View ArticlePubMedGoogle Scholar
  77. FitzGerald MG, Bean JM, Hegde SR, Unsal H, MacDonald DJ, Harkin DP, Finkelstein DM, Isselbacher KJ, Haber DA: Heterozygous ATM mutations do not contribute to early onset of breast cancer. Nat Genet. 1997, 15: 307-310. 10.1038/ng0397-307.View ArticlePubMedGoogle Scholar
  78. Chen J, Birkholtz GG, Lindblom P, Rubio C, Lindblom A: The role of ataxia-telangiectasia heterozygotes in familial breast cancer. Cancer Res. 1998, 58: 1376-1379.PubMedGoogle Scholar
  79. Concannon P: ATM heterozygosity and cancer risk. Nat Genet. 2002, 32: 89-90. 10.1038/ng0902-89.View ArticlePubMedGoogle Scholar
  80. Renwick A, Thompson D, Seal S, Kelly P, Chagtai T, Ahmed M, North B, Jayatilake H, Barfoot R, Spanova K, et al: ATM mutations that cause ataxia-telangiectasia are breast cancer susceptibility alleles. Nat Genet. 2006, 38: 873-875. 10.1038/ng1837.View ArticlePubMedGoogle Scholar
  81. Chenevix-Trench G, Spurdle AB, Gatei M, Kelly H, Marsh A, Chen X, Donn K, Cummings M, Nyholt D, Jenkins MA, et al: Dominant negative ATM mutations in breast cancer families. J Natl Cancer Inst. 2002, 94: 205-215.View ArticlePubMedGoogle Scholar
  82. Stankovic T, Kidd AM, Sutcliffe A, McGuire GM, Robinson P, Weber P, Bedenham T, Bradwell AR, Easton DF, Lennox GG, et al: ATM mutations and phenotypes in ataxia-telangiectasia families in the British Isles: expression of mutant ATM and the risk of leukemia, lymphoma, and breast cancer. Am J Hum Genet. 1998, 62: 334-345. 10.1086/301706.View ArticlePubMedPubMed CentralGoogle Scholar
  83. Larson GP, Zhang G, Ding S, Foldenauer K, Udar N, Gatti RA, Neuberg D, Lunetta KL, Ruckdeschel JC, Longmate J, et al: An allelic variant at the ATM locus is implicated in breast cancer susceptibility. Genet Test. 1997, 1: 165-170.View ArticlePubMedGoogle Scholar
  84. Szabo CI, Schutte M, Broeks A, Houwing-Duistermaat JJ, Thorstenson YR, Durocher F, Oldenburg RA, Wasielewski M, Odefrey F, Thompson D, et al: Are ATM mutations 7271T-->G and IVS10-6T-->G really high-risk breast cancer-susceptibility alleles?. Cancer Res. 2004, 64: 840-843. 10.1158/0008-5472.CAN-03-2678.View ArticlePubMedGoogle Scholar
  85. Buchholz TA, Weil MM, Ashorn CL, Strom EA, Sigurdson A, Bondy M, Chakraborty R, Cox JD, McNeese MD, Story MD: A Ser49Cys variant in the ataxia telangiectasia, mutated, gene that is more common in patients with breast carcinoma compared with population controls. Cancer. 2004, 100: 1345-1351. 10.1002/cncr.20133.View ArticlePubMedGoogle Scholar

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© Einarsdóttir et al.; licensee BioMed Central Ltd. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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