Introduction
Insulin-like growth factor 1 (IGF-1) and 2 (IGF-2), have been implicated in breast tumorigenesis due to their ability to stimulate mitogenesis, promote differentiation, and their key role in mammary gland cell proliferation and survival1–3. It has been reported that genetic variations in the gene encoding IGF-1 are associated with levels of the protein, and as a consequence, may alter breast cancer risk4,5. Results of recent studies investigating the role of IGF1 and IGF2 genetic polymorphisms in breast cancer risk have been inconsistent4–10. The majority of the previous studies, including one from our own group, have focused on the (CA)n repeat in the promoter of the IGF1 gene7–9,11, while fewer have characterized common variants across the IGF1 and IGF2 genes in relationship to breast cancer susceptibility4–6. To further assess the role of genetic variation in these genes, we evaluated the association between 23 single nucleotide polymorphisms (SNPs) in the IGF1 and IGF2 genes and breast cancer risk among participants of the Shanghai Breast Cancer Study, a population-based case-control study of incident breast cancer in urban Shanghai.
Materials and Methods
Study Population
Detailed study methods have been published previously12. Briefly, this study is a population-based case-control study of incident breast cancer in Chinese women aged 25–64 in urban Shanghai who were recruited from 1996–1998. Of 1,602 eligible cases identified by the Shanghai Cancer Registry and 1,724 age-frequency matched controls identified using the Shanghai Resident Registry, 1,459 cases (91.1%) and 1,556 controls (90.3%) participated in the study. Approximately, 82% of cases (1,193) and 84% of controls (1,310) provided blood sample. Genomic DNA was extracted from buffy coats using the Puregene® DNA Purification Kit (Gentra Systems, Minneapolis, MN) following the manufacturers protocol. There were no differences in the distribution of demographic and risk factors between individuals who did and did not have DNA available for genotyping13.
SNP Selection and Genotyping
In order to comprehensively evaluate the association between the IGF1 and IGF2 gene polymorphisms and breast cancer risk, we included haplotype tagging SNPs and potentially functional variants. Potentially functional and nonsynonymous SNPs were identified from literature reports and physical location (promoter or intron/exon boundary region) using the database SNPper (http://snpper.chip.org/bio/snpper-enter), or dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). Haplotype tagging SNPs (htSNP) were identified from the Han Chinese data in the HapMap project for each gene plus flanking 5 kb region with the pair-wise r2 ≥ 0.9 and MAF ≥ 0.05. The above mentioned potentially functional SNPs were forced into the htSNP list. A total of 20 IGF1 and three IGF2 SNPs were included in the present study. The SNPs were genotyped by running the 5’nuclease TaqMan allelic discrimination assay (Applied Biosystems, Foster City, CA) and with the Affymetrix MegAllele Targeted Genotyping System (Affymetrix, Santa Clara, CA). The concordance rate for the quality control samples were 97% and 99% for Taqman and Affymetrix methods respectively.
Statistical Analysis
The χ2 test was used to compare the distributions of IGF1 and IGF2 alleles and genotypes in the cases and controls. The exact χ2 goodness-of-fit test was used to evaluate whether genotype distribution were in Hardy-Weinberg equilibrium. Odds ratios and 95% confidence intervals were estimated using logistic regression. All analyses were adjusted for age, with additional adjustment for other confounding factors, including menopausal status, age at menarche, and age at first full term pregnancy. Haplotypes were generated using the Haploview program14 which employs an expectation-maximization algorithm to estimate haplotypes. Odds ratios and 95% CIs for the association between haplotypes and breast cancer risk were generated using the Haplostat program15. Associations between genotypes, haplotypes, and breast cancer risk were evaluated under additive, dominant, and recessive genetic modes.
Results
The distributions of selected demographic characteristics and major risk factors for breast cancer among the cases and controls have been presented elsewhere13. Briefly, the mean age was 47.7 ± 8.0 years among cases and 47.2 ± 8.7 years among controls. As compared to controls, cases were significantly more likely to have a history of fibroadenoma (9.8% vs 5.1%), a younger age at menarche (14.5 yrs. vs. 14.7 yrs.), an older age at menopause (48.2 yrs. vs. 47.5 yrs.) and a higher BMI.
Table 1 details the polymorphisms in the IGF1 and IGF2 genes and their association with breast cancer risk. Genotype frequencies were comparable to those for the Chinese Han population included in HapMap. With the exception of one SNP (rs2288377), all genotype frequencies were found to be consistent with Hardy-Weinberg equilibrium among controls. None of the 23 polymorphisms we investigated were significantly associated with breast cancer risk when evaluated under additive, dominant, and recessive models. Haplotype blocks were estimated for both IGF1 and IGF2 genes, and no association between any of the haplotypes and altered breast cancer risk was observed. Table 2 presents results under the additive model. Findings were similar under dominant and recessive models (data not shown). Potential modifying effects by traditional risk factors were investigated on the relationship of the single polymorphisms and haplotypes with breast cancer risk. No evidence was found for an interaction between any of the genetic variants or haplotypes with age, menopausal status, BMI, or waist-hip ratio (data not shown).
Table 1.
Association between genetic polymorphisms in the IGF-1 and IGF-2 genes and breast cancer risk
| Marker | Allele* | Location | MAF† | Genotype frequency ‡ | p-value§ | Odd ratios (95% CIs)∥ | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| AA | AB | BB | AA | AB | BB | |||||
| IGF-1 | ||||||||||
| rs9919733 | A/G | Promoter | 0.28 | 0.52 | 0.41 | 0.07 | 0.69 | 1.0 | 0.9 (0.7–1.0) | 1.1 (0.8–1.5) |
| rs35767 | C/T | Promoter | 0.34 | 0.42 | 0.47 | 0.11 | 0.20 | 1.0 | 0.8 (0.7–1.0) | 1.0 (0.8–1.4) |
| rs12579108 | A/T | Promoter | 0.28 | 0.49 | 0.43 | 0.07 | 0.50 | 1.0 | 0.8 (0.7–1.0) | 1.2 (0.8–1.6) |
| rs2288377 | C/A | Promoter | 0.29 | 0.50 | 0.42 | 0.08 | 0.04 | 1.0 | 0.8 (0.7–1.0) | 1.1 (0.8–1.5) |
| rs2162679 | A/G | intron | 0.35 | 0.41 | 0.48 | 0.11 | 0.19 | 1.0 | 0.8 (0.7–1.0) | 1.0 (0.8–1.4) |
| rs5742615 | C/A | intron | 0.27 | 0.52 | 0.41 | 0.07 | 0.24 | 1.0 | 1.1 (0.8–1.5) | 1.0 (0.8–1.4) |
| rs12821878 | G/A | intron | 0.05 | 0.91 | 0.08 | 0.01 | 0.23 | 1.0 | 1.0 (0.8–1.2) | 1.3 (0.4–4.7) |
| rs7956547 | T/C | intron | 0.16 | 0.71 | 0.26 | 0.03 | 0.68 | 1.0 | 0.8 (0.7–1.0) | 1.2 (0.7–2.0) |
| rs2195239 | G/C | intron | 0.43 | 0.32 | 0.50 | 0.18 | 0.57 | 1.0 | 1.0 (0.8–1.2) | 0.9 (0.7–1.2) |
| rs4764697 | C/T | intron | 0.16 | 0.71 | 0.26 | 0.03 | 0.94 | 1.0 | 0.9 (0.7–1.1) | 1.0 (0.6–1.7) |
| rs5742692 | T/C | intron | 0.26 | 0.53 | 0.41 | 0.06 | 0.18 | 1.0 | 0.9 (0.8–1.1) | 1.1 (0.8–1.6) |
| rs978458 | C/T | intron | 0.42 | 0.33 | 0.49 | 0.18 | 0.81 | 1.0 | 0.8 (0.7–1.0) | 1.0 (0.8–1.3) |
| rs6220 | T/C | 3’ UTR | 0.42 | 0.33 | 0.49 | 0.18 | 0.94 | 1.0 | 1.0 (0.8–1.2) | 1.0 (0.8–1.3) |
| Rs6218 | T/C | 3’ UTR | 0.25 | 0.55 | 0.39 | 0.06 | 0.59 | 1.0 | 1.0 (0.8–1.1) | 1.1 (0.8–1.7) |
| rs6214 | G/A | 3’ UTR | 0.48 | 0.27 | 0.50 | 0.23 | 0.88 | 1.0 | 1.0 (0.8–1.2) | 0.9 (0.7–1.1) |
| rs5742723 | C/A | 3’ UTR | 0.28 | 0.51 | 0.42 | 0.07 | 0.10 | 1.0 | 0.9 (0.8–1.1) | 1.2 (0.8–1.6) |
| rs2946834 | C/T | 3’ UTR | 0.46 | 0.29 | 0.50 | 0.21 | 0.54 | 1.0 | 1.0 (0.8–1.2) | 1.0 (0.8–1.3) |
| rs6219 | C/T | 3’ UTR | 0.16 | 0.70 | 0.27 | 0.03 | 0.49 | 1.0 | 1.0 (0.8–1.2) | 0.9 (0.6–1.5) |
| rs10860861 | T/C | 3’ UTR | 0.38 | 0.38 | 0.47 | 0.15 | 0.93 | 1.0 | 1.0 (0.8–1.2) | 1.1 (0.8–1.4) |
| rs10860862 | C/T | 3’ UTR | 0.16 | 0.70 | 0.28 | 0.02 | 0.47 | 1.0 | 0.9 (0.8–1.1) | 0.9 (0.5–1.6) |
| IGF-2 | ||||||||||
| rs734351 | C/T | intron | 0.22 | 0.56 | 0.38 | 0.06 | 0.70 | 1.0 | 1.1 (0.9–1.3) | 1.1 (0.8–1.6) |
| rs3802971 | C/T | 3’ UTR | 0.17 | 0.69 | 0.28 | 0.03 | 0.51 | 1.0 | 1.0 (0.8–1.2) | 1.4 (1.5–1.2) |
| rs2585 | T/C | 3’ UTR | 0.44 | 0.33 | 0.46 | 0.21 | 0.25 | 1.0 | 1.0 (0.8–1.2) | 1.2 (0.9–1.5) |
Major allele is in bold
Minor allele frequency (MAF) based on 1,110 cases and 1,203 controls
For SNP, AA, major allele homozygote, AB, heterozygote, BB, minor allele homozygote, among controls
P-value is the probability of the Chi-square test for Hardy Weinberg disequilibrium among controls
Logistic regression models conditioned on age, and adjusted for menopausal status, age at menarche, and age at first full term pregnancy.
Table 2.
Association between IGF-1 and IGF-2 haplotypes and breast cancer risk.
| Frequency | |||
|---|---|---|---|
| Haplotype | Cases (n=1,055) | Controls (n=1,059) | Odds ratio (95% CI)* |
| IGF-1 | |||
| block 1† | |||
| TCCA | 38.1 | 37.8 | 1.0 |
| CCCA | 15.8 | 16.1 | 1.0 (0.8–1.2) |
| CCTC | 28.1 | 27.9 | 1.0 (0.9–1.2) |
| CTTA | 16.5 | 16.7 | 0.9 (0.7–1.1) |
| block 2‡ | |||
| TT | 56.6 | 55.9 | 1.0 |
| TC | 18.0 | 18.0 | 1.0 (0.8–1.2) |
| CC | 25.2 | 25.6 | 1.0 (0.8–1.1) |
| block 3§ | |||
| CG | 26.2 | 27.4 | 1.0 |
| CC | 58.2 | 56.7 | 0.8 (0.5–1.4) |
| TG | 15.6 | 15.9 | 1.0 (0.5–2.1) |
| block 4∥ | |||
| AC | 66.1 | 64.3 | 1.0 |
| AT | 5.9 | 6.0 | 1.0 (0.7–1.3) |
| TT | 27.5 | 28.5 | 0.9 (0.8–1.1) |
| IGF-2 | |||
| Block 1** | |||
| TCC | 55.2 | 57.3 | 1.0 |
| CCT | 24.2 | 23.3 | 1.1 (0.8–1.7) |
| CTC | 16.7 | 15.8 | 1.1 (0.9–1.3) |
Additive model, adjusted for age, menopausal status, age at menarche, and age at first full term pregnancy
rs10860861, rs6219, rs2946834, and rs5742726
rs6218 and rs6220
rs4764697 and rs2195239
rs2288377 and rs35767
rs2558, rs3802971, and rs734351
Discussion
The results from this study suggest that common genetic variants in the IGF1 and IGF2 genes do not play a significant role in the breast cancer risk among Chinese women. One of the main strengths of this study is its comprehensive and systematic approach to characterizing variation in IGF1 and IGF2. We selected SNPs with known or potential function as well tagging SNPs to provide sufficient coverage across the gene. In addition, the large sample size provided sufficient power (≥80%) to detect a minimum OR of ≥ 1.25 (assuming minor allele frequency 10%, α=0.05 on the log-additive scale), and allowed evaluation of moderate or higher interactions between genetic polymorphisms and traditional breast cancer risk factors16.
Although a number of studies have investigated the association between the (CA)n repeat polymorphisms in the IGF1 promoter and breast cancer risk with inconsistent results7–9,11, only three evaluated the role of multiple common genetic variants across the IGF1 gene in breast cancer incidence4–6. Our results are consistent with those observed among four other ethnic groups in a multiethnic cohort which found no significant association between IGF1 variants or haplotypes and breast cancer risk5. In an investigation of nine IGF1 polymorphisms, Al-Zahrani et.al. found that the variant allele in rs1520220 (a SNP not evaluated in our study, but in high LD with rs6220), although significantly related with reduced plasma IGF-1 levels, was associated with an increased risk of breast cancer4. This finding is unexpected given the tumor-promoting effect of IGF-1. Results from the European Prospective Investigation into Cancer and Nutrition (EPIC) study conducted primarily in the Caucasian population found a borderline significant association with breast cancer risk for the rs2162679 polymorphism in the IGF1 gene (OR=0.57, 95% CI=0.34–0.97 for the homozygous variant genotype), but not the four other SNPs investigated (rs35765, rs35767, rs6220, rs6214). With respect to IGF2, ours is the first study to evaluate polymorphisms across the gene in relation to breast cancer susceptibility.
Our results indicate that common genetic variants in the IGF1 or IGF2 genes may not appreciably alter breast cancer risk among Chinese women. However, we cannot rule out the possibility that some genetic variants may exert their effect through interactions with genetic polymorphisms in the other genes or certain lifestyle factors. These interactions can be addressed in future studies with large sample size.
Acknowledgements
We thank Ms. Qing Wang and Ms. Regina Courtney for their excellent technical laboratory assistance and Brandy Venuti for technical support in manuscript preparation. This study would have not been possible without the support of all of the study participants and research staff of the Shanghai Breast Cancer Study.
This study is supported by USPHS Grants R01CA64277 and R01CA90899 from the National Cancer Institute
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