Common genetic variation in IGF 1 , IGFBP-1 , and IGFBP-3 in relation to mammographic density : a cross-sectional study

Introduction Mammographic density is one of the strongest risk factors for breast cancer and is believed to represent epithelial and stromal proliferation. Because of the high heritability of breast density, and the role of the insulin-like growth factor (IGF) pathway in cellular proliferation and breast development, we examined the association between common genetic variation in this pathway and mammographic density. Methods We conducted a cross-sectional analysis among controls (n = 1,121) who were between the ages of 42 and 78 years at mammography, from a breast cancer case-control study nested within the Nurses' Health Study cohort. At the time of mammography, 204 women were premenopausal and 917 were postmenopausal. We genotyped 29 haplotype-tagging SNPs demonstrated to capture common genetic variation in IGF1, IGF binding protein (IGFBP)-1, and IGFBP-3. Results Common haplotype patterns in three of the four haplotype blocks spanning the gene encoding IGF1 were associated with mammographic density. Haplotype patterns in block 1 (p = 0.03), block 3 (p = 0.009), and block 4 (p = 0.007) were associated with mammographic density, whereas those in block 2 were not. None of the common haplotypes in the three haplotype blocks spanning the genes encoding IGFBP-1/ IGFBP-3 were significantly associated with mammographic density. Two haplotype-tagging SNPs in IGF1, rs1520220 and rs2946834, showed a strong association with mammographic density. Those with the homozygous variant genotype for rs1520220 had a mean percentage mammographic density of 19.6% compared with those with the homozygous wild-type genotype, who had a mean percentage mammographic density of 27.9% (p for trend < 0.0001). Those that were homozygous variant for rs2946834 had a mean percentage mammographic density of 23.2% compared with those who were homozygous wild-type with a mean percentage mammographic density of 28.2% (p for trend = 0.0004). Permutation testing demonstrated that results as strong as these are unlikely to occur by chance (p = 0.0005). Conclusion Common genetic variation in IGF1 is strongly associated with percentage mammographic density. BMI = body mass index; CI = confidence interval; IGF = insulin-like growth factor; IGFBP = insulin-like growth factor binding protein; SNP = single nucleotide polymorphism. Breast Cancer Research Vol 9 No 1 Tamimi et al. Page 2 of 13 (page number not for citation purposes) Introduction Mammographic density is one of the strongest risk factors for breast cancer. Women with 75% or more breast density are at a fourfold to sixfold greater risk of breast cancer than women with no density [1,2]. It has been hypothesized that mammographic density reflects cumulative exposure to estrogens [3]; however, accumulating evidence suggests that the mechanism by which mammographic density influences breast cancer may be independent of circulating estrogen levels [4-6]. The insulin-like growth factor (IGF) pathway has a critical role in cell proliferation, as well as in the growth and development of the breast [7]. Because mammographic density is associated with epithelial and stromal proliferation [8,9], and circulating IGF1 and IGF binding protein (IGFBP)-3 are associated with premenopausal breast cancer in some studies [10,11], but not all [12-14], the IGF pathway is a compelling candidate for examination with respect to mammographic density. Circulating IGF1 levels have been positively associated with mammographic density [15,16], and IGFBP-3 levels inversely with mammographic density in premenopausal women [15-17], although no association has been observed in postmenopausal women [15,16]. It is estimated from twin studies that genetics accounts for 60 to 67% of the variation in mammographic density [18]. Given the high degree of heritability [18,19], identifying the genes involved is important for understanding the biology of breast density and how it influences breast cancer risk. Several studies have addressed the role of polymorphisms in estrogen synthesis and metabolizing genes and mammographic density, with inconclusive results [20-26]. Evidence relating the -202 promoter SNP (rs2854744) in IGFBP-3 to mammographic density has also been mixed [27,28]. No studies so far have examined the association between polymorphisms in IGF1 or IGFBP-1 and mammographic density. Genetic variation in genes involved in the IGF pathway may reflect long-term or lifetime exposure of circulating levels of IGF1, IGFBP-1, and IGFBP-3. We conducted a cross-sectional study in the Nurses' Health Study (n = 1,121) to assess the relation between common genetic variation in these genes and mammographic density. So far, no study has comprehensively examined the relation between common genetic variation in these genes and mammographic density. Materials and methods Study design and population The Nurses' Health Study was initiated in 1976, when 121,700 US registered nurses aged 30 to 55 years returned an initial questionnaire [29]. Information on body mass index (BMI), reproductive history, age at menopause, and postmenopausal hormone use as well as diagnosis of cancer and other diseases are updated every 2 years through questionnaires. During 1989 and 1990, blood samples were collected from 32,826 women. Detailed information regarding blood collection methods has been published [30]. In general, blood samples were returned within 26 hours of blood draw, then immediately centrifuged, separated into plasma, red blood cells, and buffy coat fractions, and stored in freezers under liquid nitrogen. The follow-up rate among women who provided blood samples was 99% up to and including 1998. We conducted a cross-sectional analysis among controls from a breast cancer case-control study nested within the Nurses' Health Study cohort. This nested case-control study, examining plasma markers and genetic variation with respect to breast cancer risk, included breast cancer cases diagnosed after blood collection but before 1 June 1998, and matched controls [31]. Controls were matched to cases on year of birth, menopausal status, postmenopausal hormone use, time of day, month, and fasting status at time of blood draw. Mammography collection was targeted to 1,329 breast cancer controls, with DNA samples, through the 1998 follow-up cycle. At the time of mammography collection, 1,297 of these participants were alive and eligible to receive letters for participation in this study. Of these women, 1,189 (92%) gave permission to obtain mammograms; 5% did not give permission, and 3% reported not having had a mammogram. For all consenting women, we attempted to obtain the mammograms taken as close to the date of blood collection as possible. We successfully obtained mammograms from 1,142 controls (96% of those consenting); 21 of these were excluded for not having usable film mammograms. Women for whom we obtained usable mammograms (n = 1,121) were very similar to those whom we were unable to obtain mammograms with respect to age, BMI, and circulating hormone levels [4]. This study was approved by the Committee on the Use of Human Subjects in Research at Brigham and Women's Hospital. Mammographic density measurements To assess mammographic density, the craniocaudal views of both breasts were digitized at 261 μm per pixel with a Lumysis 85 laser film scanner, which covers a range of 0 to 4.0 optical density. The software for computer-assisted thresholding was developed at the University of Toronto [32]. The film screen images were digitized and viewed on the computer screen. For each image, the observer set one threshold level to define the edge of the breast and a second threshold delineating the dense area of the breast, within the original threshold region. The Cumulus software calculated the total number of pixels within the entire region of interest and within the area identified as dense. Using these values, the software program calculated the percentage of the breast area that was dense. This measure of mammographic breast density was highly reproducible within this study. The within-person intraclass correlation coefficient was 0.93 [33]. We used the average percentage density of both breasts for this analysis. Previous studies have shown similar results when the breast density of a random side (right or left) or the average of the two are used [16]. We also evaluated the association of IGF1, IGFBP-1, Available online http://breast-cancer-research.com/content/9/1/R18 Page 3 of 13 (page number not for citation purposes) and IGFBP-3 genotypes and haplotypes with the absolute area of mammographic density, but because results were similar and percentage breast density has consistently been a stronger predictor of breast cancer risk, we present the results for percentage mammographic density only. SNP selection and genotyping SNP discovery and haplotype-tagging SNP selection was conducted in the Multiethnic Cohort [34,35]. Novel SNPs were identified by resequencing of the exons in IGF1, IGFBP1, and IGFBP-3 in 95 cases of advanced prostate cancer and 95 advanced breast cancer from equal numbers of US Caucasians, Latinos, Japanese, native Hawaiians, and African Americans [36]. To identify regions of strong linkage disequilibrium, 64 SNPs in the gene encoding IGF1 and 36 SNPs in the genes encoding IGFBP-1 and IGFBP-3 were genotyped in a panel of 349 cancer-free women from the Multiethnic Cohort [37,38]. Pairwise linkage disequilibrium between SNPs was determined with the D' statistic [39]. Regions of strong linkage disequilibrium (that is, haplotype blocks) were defined with the use of criteria from Gabriel and colleagues [40]. Haplotype-tagging SNPs were selected for a Caucasian population using the program TagSNPs [41]. In brief, the selection of haplotype-tagging SNPs is based on RH, a measure of the correlation between observed haplotypes and those predicted by the tagging SNP genotypes [42]. Fourteen SNPs tag the common haplotypes in four haplotype blocks