Europe PMC

This website requires cookies, and the limited processing of your personal data in order to function. By using the site you are agreeing to this as outlined in our privacy notice and cookie policy.

Abstract 


Mammographic density (MD) reflects variations in fat, stromal and epithelial tissues that are thought to be regulated by several genes. High MD is an established risk factor for breast cancer; therefore, genes that regulate MD may indirectly influence breast cancer. These genes might also be fewer in number and easier to identify than those for breast cancer risk outside of inherited predisposition syndromes. In this Perspective, we review the limited genetic studies of MD and propose future directions.

Free full text 


Logo of nihpaLink to Publisher's site
Nat Rev Cancer. Author manuscript; available in PMC 2010 Feb 9.
Published in final edited form as:
PMCID: PMC2818036
NIHMSID: NIHMS129400
PMID: 18772892

Can genes for mammographic density inform cancer etiology?

Preface

Mammographic density (MD) reflects variation in fat, stromal and epithelial tissues that is thought to be regulated by several genes. High MD is an established risk factor for breast cancer; therefore genes that regulate MD may indirectly influence breast cancer. These genes may also be fewer in number and easier to identify than those for breast cancer risk outside of inherited predisposition syndromes. In this Perspective, we review the limited genetic studies of MD and propose future directions.

Unraveling the genetics of complex diseases can be enhanced by focusing on underlying risk factors. For example, studies on lipoproteins and blood pressure have been shown to correlate well with the risk of vascular disease1, 2. Insights into the pathogenesis of breast cancer may be informed in a similar way using MD because both may be determined by similar genetic variants. In fact, the number of genetic and environmental factors influencing an intermediate such as MD is likely smaller than that influencing the endpoint, namely breast cancer3. Consequently, studies to identify genes for MD could be more powerful and efficient than those for breast cancer.

MD reflects the proportion of stromal and epithelial tissue relative to fat on a mammogram. Dense breast tissue appears light or lucent on radiographs of breast images; in contrast, fat appears dark. Early measures of MD were qualitative, relying on visual scales of morphologic features4, 5. Contemporary methods seek to provide a quantitative and continuous measure of the amount of dense tissue in the mammographic breast image69 (Box 2). Results from both measures show that MD is strongly associated with breast cancer risk10. Indeed, the ~30%11 of women who have dense breast tissue in >50% of total breast area have a 3–5 fold increased risk of breast cancer compared to women with no measurably dense breast tissue10, 11. Although the [0]inclusion of MD in breast cancer risk prediction models has minimally improved their overall discrimination among women of mammogram age,1214 the identification of genes that regulate MD might further enhance the ability to identify women at risk of developing breast cancer. Improved risk prediction will be particularly important for young women, not yet of recommended mammogram screening age for whom prevention efforts will be most beneficial.

Textbox 2 Breast density classification methods

QualitativeQuantitative
Wolfe 4BI-RADS 5, 8Planimeter (outlining tool) 6Boyd 6-category scale7, 94Computer-assisted threshold method 95
Visual assessment of the relative percentage of fat (appears dark) and epithelial/stromal (appear white) tissue obtained from mammographic x-rays. Four categorical patterns of breast density are described: N1 – “normal”, almost completely fatty breast; P1 – mainly fatty breast with prominent ducts, up to 25% density; P2 – prominent ducts, more than 25% density; and DY – no visible ducts, diffuse and extensive nodular density.Standardized reporting of visual assessment of mammographic findings by the American College of Radiology Breast Imaging Reporting and Data Systems (BI-RADS). Four categorical patterns of breast density are described: Category 1 – entirely fatty breast; Category 2 – breast containing scattered stromal tissue; Category 3 – heterogeneously dense breast; Category 4 – extremely dense breast.An acetate overlay is placed over radiographic image of the breast to outline the breast and to trace dysplastic elements. The total breast and dense areas are measured with an outlining tool. Agreement (kappa) between Wolfe score and planimeter = 0.91. Results can be reported as percent density distribution by quartiles or quintiles.Radiologist assessment of the relative percentage of fat and epithelial/stromal tissue is obtained from mammographic x-rays. Individual scores for a given density estimate are assigned the midpoint of six potential categories of breast density: 0%; 5% for 0–10%; 17.5% for 10–25%; 37.5% for 25–50%; 62.5% for 50–75%; and 87.5% for 75–100%.Mammographic films are digitized (Figure) and 2 different threshold values selected: one separates the image of the breast from background (red line) and the other identifies the edges of the regions representing radiographically dense tissue (green line). Percent density is the proportion of total breast area calculated from the histogram of pixel values in the 2 different threshold areas. Results can be reported as mean percent density by genotype.

Evidence for a Genetic Influence

Evidence for a genetic influence on MD has come from studies on family history, familial aggregation, twin and segregation analyses.

Family History

If the genetic factors that underlie MD increase breast cancer risk, then MD should be associated with family history of breast cancer. Early investigations focused on whether unrelated women with a family history of breast cancer are more likely to have a high MD pattern than women without such a history. Both positive1517 and negative1820 results were found. More recently, two large studies of 35,01921 and 6,14622 women from population-based mammography registries have illustrated positive associations between family history of breast cancer and higher MD as determined by BI-RADS21, 22. The odds of a woman having dense breast tissue is 17% greater for a first degree relative with a family history of breast cancer and 22% greater for women with breast cancer that was diagnosed at <50 years of age21.

Familial Aggregation and Twin Studies

Data on related individuals, although based on small sample sizes, show similar mammographic patterns among mother-daughter and sister pairs compared to unrelated pairs23. Familial correlation of percent MD was estimated to be 22% among 275 sister pairs but attenuated to 16% after adjustment for body weight.

Twin studies demonstrated greater concordance of MD patterns among monozygotic compared to dizygotic twin pairs24, 25. A large multi-centered study of 571 monozygotic and 380 dizygotic twin pairs estimated that 60–67% of the variation in MD is attributable to common genetic factors26, 27. Estimates of heritability are stronger when twin pairs are of similar BMI (77% versus 28% when dissimilar on BMI)28. This underscores the importance of accounting for BMI and possibly other covariates when searching for genetic influences on MD.

Segregation Analysis

The above analyses implicate a genetic influence on MD but not the possible mode (Mendelian pattern of inheritance, for example) of transmission of a putative gene(s). In the only reported segregation analysis, the distribution of MD among 1,370 females in 258 independent families was compatible with Mendelian dominant or codominant transmission of a major gene. This was only apparent after adjustment for covariates, including BMI and postmenopausal hormone (PMH) use29. Under the dominant model, 12% of the female population could carry an allele for higher MD, and women carrying at least one dominant allele would have a mean MD that is 27% higher than the rest of the female population29.

The Search for Mammographic Density Genes

Two approaches have been used to identify genes that influence MD: a hypothesis-dependent approach in which knowledge of breast biology guides the selection of candidate genes, and a hypothesis-free approach, in which markers positioned across the genome are evaluated with MD using linkage or genome wide association methodology.

Candidate Gene Analysis

This approach estimates the extent that polymorphisms in a biologically plausible candidate gene influence MD, usually in unrelated individuals. However, our limited understanding of the biology of MD makes selection of candidate genes challenging. Furthermore, MD is a dynamic trait, decreasing with older age and menopause, and even varying transiently during the menstrual cycle. Higher densities are observed among women who are premenopausal, nulliparous, of low BMI or taking combination PMH, and lower densities among those who are postmenopausal, multiparous, of high BMI or on tamoxifen therapy. Because of these hormonal influences the majority of candidate gene studies have focused on evaluation of the pathways that regulate steroid hormone synthesis and metabolism, hormone receptors and proliferative pathways including the insulin-like growth factor (IGF) pathway. However, some studies have focused on genes previously noted to be strongly (BRCA1/BRCA2) or hypothetically (XPD, XRCC3, and ERBB2) associated with breast cancer risk. The results from all of these candidate gene association studies are reviewed in the online Table and some are highlighted below. It should be noted that the size of these studies vary greatly, as do the aims of the studies, the characteristics of the women analyzed and the method of assessing MD, which makes interpretation challenging.

Characteristics of Studies

The majority of studies compared a difference in MD among women with 1 or 2 copies of a polymorphism to women with 0 copies and also estimated tests for trend of a dose-response of 0, 1 or 2 copies. Of 25 identified publications (Table 1), three studies examined several polymorphisms representing genetic variation throughout the entire gene and used haplotype approaches for analyses3032 while the remainder largely evaluated candidate polymorphisms with putative functional effects that were previously examined with breast cancer. Six studies (24%) had fewer than 300 women3338, 14 (56%) had between 300–650 women31, 3951 and five (20%) had between 1,100–2,000 women30, 32, 5254. All studies except four investigations of BRCA1/2 mutation carriers3336 and one cross-sectional study 53 estimated MD using the computer-assisted thresholding method.

Table 1

Characteristics of genetic association studies of mammographic density*.

Author, yearStudy/ SettingDesignSubject selectionParticipantsDensity
Assessment
Genes studiedCovariates
Helvie, 199733Michigan, U.S.Case-seriesClinic-based9 mutation + casesBI-RADSBRCA1/2None; visual assessment only
Chang, 199934Singapore/ Hong KongCase-seriesClinic-based9 mutation + cases;
19 mutation – cases
Visual assessment, 5-point scaleBRCA1/2A
Huo, 200035Chicago, U.S.RetrospectiveScreening mammogram clinic & cancer risk clinic15 mutation + cases;
30 “low risk” women
Computer based, 7-point scaleBRCA1/2A
Tilanus-Linthorst, 200236NetherlandsCross-sectionalClinic breast cancer cases, mammogram at time of diagnosis34 mutation + cases;
34 sproadic cancers
BI-RADSBRCA1/2A, U
Haiman, 200239Los Angeles, U.S.Cross-sectionalCancer registry cases with pre-diagnosis mammogram176 Premenopausal
171 Postmenopausal
Computer based, USCCYP17A1, COMT, HSD17β1, HSD3β1A–H
Lillie, 200440191 Premenopausal
195 Post menopausal
Computer based, USCARA–C
Haiman, 200341U.S.Cross-sectionalControl subjects in a case-control study of breast cancer94 Premenopausal
392 Postmenopausal
Computer based, UTCYP17A1, CYP19A1, COMT, CYP1A1, CYP1B1, UGT1A1, AR, AIB1A, C–F, I
Tamimi, 200730204 Premenopausal
917 Postmenopausal
Computer based, UTIGF-1, IGFBP-1, IGFBP-3A, C–F, H, I, X
Hong, 200342Toronto, CanadaCross-sectionalScreening mammogram clinic181 Premenopausal
171 Postmenopausal
Computer based, UTCOMTA, B
Hong, 200443181 Premenopausal
173 Postmenopausal
Computer based, UTCYP17A1A–C, E, G, H, J
Mulhall, 200544177 Premenopausal
171 Postmenopausal
Computer based, UTGH1, GHRHRA–D
Lai, 200445206 Premenopausal
206 Postmenopausal
Computer based, UTIGF-1, IGFBP-3A, C, E, I, S
Maskarinec, 200446Hawaii, U.S.Cross-sectional and 2 dietary intervention trialsScreening mammogram clinic268 Premenopausal
60 Postmenopausal
Computer based, UTCYP17A1, COMT, CYP1A1, CYP1A2, CYP1B1A–F
Lord, 200537Los Angeles, U.S.MD changes over timeSubjects in a randomized control trial of PMH & atherosclerosis232 PostmenopausalComputer based, USCCOMT, CYP1B1, UGT1A1, AKR1C4A–C, L–P
Van Duijnhoven, 200547NetherlandsCross-sectionalLarge prospective cohort participants181 Premenopausal
432 Postmenopausal
Computer based, UTESR1A, C, D–H, K, I, Q
Van Duijnhoven, 200652Netherlands and U.K.MD changes over time781 non PMH users
795 PMH users
Computer based, UTESR1, PGRA, C–E, G–I, K, N, P–R
Verheus, 200732NetherlandsCross-sectional1,928 PremenopausalComputer based, UTIGF-1C (no observed effect from A,C–G, K, Q, R)
MD changes over time656 Premenopausal women who became Postmenopausal during an average 4.4 yearsComputer based, UTIGF-1None (no observed effect from A, D–G, K, M, N, Q, R)
Warren, 200653U.K.Cross-sectionalLarge prospective cohort participants1,260 Postmenopausal non PMH users6-category Boyd scaleCYP17A1, CYP19A1, COMT, CYP1B1, ESR1, PGR, SHBGB, E, G, K, L
dos Santos Silva, 200648U.K.Cross-sectionalLarge prospective cohort participants215 Premenopausal
238 Postmenopausal
Computer based, UKIGFBP-3A, C, F, J, K, L, Q, T
Mitchell, 200649U.K.Cross-sectionalLarge prospective cohort participants206 mutation + carriers;
136 mutation – relatives
Computer based, UTBRCA1/2A, C–E
Takata, 200738Hawaii and Los Angeles, U.S.Average of repeated mammogram assessments at time of breast cancer diagnosisNested case-control study, pre-treatment mammograms for casesCases:
92 Premenopausal
233 Postmenopausal
Controls:
97 Premenopausal
153 Postmenopausal
Computer based, UTCOMT, CYP1A2B–H, V, W
Olson, 200731Upper mid-west, U.S.Cross-sectionalControl subjects in a case-control study of breast cancer550 womenComputer based, UTCYP19A1A, C–F,Y
Olson, 200855402 Postmenopausal
136 Premenopausal
Computer based, UTCOMTA, C–F,Y
Stone, 200750AustraliaCross-sectional and within-sister pair analysisTwin and sister-pairs (104 MZ, 182 DZ and 171 singletons)150 Premenopausal
Computer based, UTCOMT, HSD3β1, IGFBP-3, HER2, XPD, XRCC3A, C–E, K
Lee, 200851Los Angeles, U.S.Cross-sectionalCancer registry cases with mammogram516 womenComputer based, USCrs889312 – chr5q C>A
rs3803662 – chr16q T>C
rs3817198 – chr11p LSP1 T>C
rs13281615 – chr8q A>G
rs13387042 – chr2q A>G
rs2981582 – chr10q FGFR2 T>C
A–D
Diorio, 2008 54Quebec, CanadaCross-sectionalScreening mammogram clinic741 PremenopausalComputer based, UTIGF-1, IGFBP-3, IGF1R, IRS1, PIK3CBC, E, J, K, V, Z (no observed effect from F–I, O, Q, R, AA–EE
*Abbreviations: BI-RADS, Breast Imaging Reporting and Data Systems; MD, Mammographic Density; USC, University of Southern California; UT, University of Toronto.
Covariates: Aage, Brace or ethnicity, CBMI, Dmenopausal status/PMH use, Eparity, Fage at first birth, Gage at menarche, Hfamily history of breast cancer, Ialcohol intake, Jwaist circumference or waist to hip ratio, Ksmoking, Lyears since menopause, Mchange in BMI during study period, Ndensity at baseline, Opast use of PMH, Pstudy arm/population, Qoral contraceptive use, Rphysical activity, Scoffee intake, Ttime since blood draw, Uyear of breast cancer diagnosis, Vage at mammogram, wcase-control status, Xhistory of benign breast disease, Ygeographic region of residence, Z previous breast biopsies, AA height, BB total energy intake, CC lactation, DD education, EE season of recruitment.

Most studies adjusted statistically for age and BMI, and also adjusted or stratified by menopausal status30, 31, 38, 39, 4148, 50, 55 or PMH use31, 3848, 55 or restricted analyses to women who did37, 52 or did not47, 53 take PMH, or women who were premenopausal32, 54. Although stratification produces homogeneous subsets of women to examine associations between genetic factors and MD, the resulting sample sizes can be too small to produce any meaningful conclusions. The populations also varied and included women from screening mammogram populations who were4244 or were not45, 46, 54 selected for extreme MD phenotypes, control subjects in case-control studies of breast cancer30, 31, 41, 55, healthy women participating in cohort studies32, 47, 48, 52, 53, breast cancer cases with pre-diagnosis mammograms39, 40, 51, and a twin study50. The highly-selected sample from a cardiovascular-focused clinical trial37 will not be discussed in the remainder of this Perspective.

Below, we focus our discussion on those polymorphisms evaluated in >1 study of percent MD with similar sample characteristics, and we seek to explain similar or disparate findings. The differences in absolute dense and non-dense area in relation to the polymorphisms will not be discussed.

CYP17A1 +27T>C

This polymorphism is located in the cytochrome P450 17A1 promoter region but its effect on steroid hormone biosynthesis is inconclusive56 (Figure 1). Inappreciable differences in percent MD across genotypes were observed in four studies restricted to 94 to 268 premenopausal women and regardless of age (< 50 years versus > 50 years), study design (mammograms from prospectively enrolled healthy women versus pre-diagnosis mammograms from breast cancer cases), and ethnicity39, 41, 42, 46. Non-significant associations were also observed among samples of 171 to 1,260 postmenopausal women irrespective of current39, 41 or past or never39, 53 PMH use. The data, therefore, provide little support for a role of this polymorphism in MD or in breast cancer57.

An external file that holds a picture, illustration, etc.
Object name is nihms129400f1.jpg
Pathways of Steroid Hormone Synthesis, Metabolism and Sensitivity of Tissues

Steroid hormone biosynthesis. The cytochrome p450 (CYP) 17A1 enzyme catalyzes the conversion of pregnenolone and progesterone to the hormones, dehydroepidandrosterone (DHEA) and androstenedione, respectively, which are further metabolized to estrone (E1) and 17β-estradiol (E2) by the CYP19A1 (aromatase) enzyme. Androgen conversion to estrogen in adipose tissue by CYP19A1 is an important source of bioactive endogenous estrogens among postmenopausal women.The hydroxysteroid dehydrogenase (HSD) enzyme HSD3β1 catalyzes the interconversion of pregnenolone and progesterone and of DHEA and androstenedione, while HSD17β1 catalyzes the interconversions of androstenedione and testosterone and of E1 and E2. Testosterone and E2 bind to the serum sex hormone binding globulin (SHBG), which regulates the bioavailability of these hormones in target cells. A. Estrogen catabolism. The CYP1 family of enzymes are phase I enzymes that function in the oxidative metabolic activation and deactivation of compounds including several steroid hormones. E1 and E2 preferentially undergo 2-hydroxylation (2-OH-E1 or -E2) by the CYP1A1 and 1A2 enzymes, whereas E1 and E2 preferentially undergo 4-hydroxylation (4-OH-E1 or -E2) by CYP1B1. Catecholestrogens are deactivated by catechol-O-methyltransferase (COMT). UDP-glucuronosyltransferase (UGT) 1A1 is a detoxifying enzyme, which converts endogenous substrates including E1 or E2 and their metabolites to inactive hydrophilic glucuronides. B. Progesterone catabolism. The aldoketo reductase family 1, member C4 (AKR1C4) enzyme catalyzes the conversion of progesterone and androstenedione to their corresponding alcohols. C. Gene transcription. Progesterone, testosterone and E2 bind to their respective nuclear receptor proteins, progesterone receptor (PR), androgen receptor (AR) and estrogen receptor alpha (ERα), and activate genes with corresponding responsive elements. The steroid receptor coactivator protein, amplified in breast-1 (AIB1) interacts with ERαin a ligand-dependent manner to increase estrogen-dependent transcription.

COMT Val158Met

The COMT enzyme inactivates the bioactive 2- and 4- hydroxy catechol estrogens by methylation58 (Figure 1). The Val158Met polymorphism decreases enzyme activity58 and is the most widely studied polymorphism with MD. Among healthy premenopausal women41, 42, 46, carriers of two copies of the Met allele had higher MD in one study of Caucasians participating in a case-control study of breast cancer41, and lower MD in two studies of women enrolled from a screening mammography clinic42, 46. The latter two studies comprised mixed ethnicities including Asians and, although adjustment was made for ethnicity, this and the younger age distribution compared to the study by Haiman et al41 may account for the differences among relatively healthy premenopausal women. The remaining studies of COMT Val158Met among premenopausal women included breast cancer patients whose screening mammograms were retrieved within the five years preceding diagnosis39 or whose mammograms were performed after breast cancer diagnosis but prior to treatment38 showed only minor differences in MD across COMT genotypes, and the study by Takata et al38 did not evaluate healthy controls separately from cases by menopausal status.

Two studies which enrolled 6739 and 1,26053 postmenopausal women who never used PMH or used PMH in the past showed no particular pattern of percent MD across COMT genotypes. Investigations of postmenopausal women that did not differentiate among hormonal regimens but which adjusted for PMH use reported either no significant differences of MD across COMT genotypes42, 50 or significantly lower MD among carriers of both Met alleles41. The two studies41, 53 closest in comparability due to similar age distributions and study populations (healthy women participating in a nested case-control or prospective cohort study) had disparate findings, which could be attributable to the three fold difference in sample sizes, the statistical adjustment for PMH use41 versus sample restriction to past or never PMH use53, or to method of mammogram assessments (computer assisted39, 41 versus visual assessment by three radiologists using the Boyd six-category scale53). Statistical adjustment for PMH use or menopausal status without evaluation of the assumption that the effect is constant across strata of these variables results in pooled estimates that can conceal important stratum-specific effects. Also, although both MD assessment methods are shown to predict risk of breast cancer10, classification of MD using the Boyd categorical 6-point scale can overestimate risk of breast cancer among women aged >50 years compared to the computer-assisted threshold assessment method 7. Thus, an ideal comparison of results by genotype necessarily relies on similar methodology. In summary, although the COMT Val158Met polymorphism does not appear to be a strong risk factor for breast cancer59 regardless of menopausal status57, the data suggest it may influence premenopausal MD.

ESR1 XbaI and PvuII

Estrogen receptor alpha is a nuclear receptor that mediates estrogen or other steroid hormone action by regulating gene transcription (Figure 1). The two polymorphisms in intron 1 of ESR1 (known as XbaI and PvuII) have no known functional effect on gene expression57, 60, although intronic polymorphisms may affect the post transcriptional processing of gene products and affect receptor binding affinity.

Three studies examined the XbaI polymorphism. The European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts analyzed cross-sectional estimates of MD across XbaI genotypes from 620 postmenopausal women in the Netherlands (EPIC-Prospect) who never used PMH47 and 1,286 postmenopausal women in the United Kingdom (EPIC-Norfolk) who were past or never users of PMH53. While the former study47 reported a significantly higher MD with increasing copies of the x variant allele, the latter study53 reported no appreciable difference in percent MD across genotypes. The greater sample size in the latter study53 is a strength, although MD assessments were subjective and differed from the computer-assisted method used in the EPIC-Prospect study. In a third study52, the computer-assisted method was used for all MD readings among mostly (~70%) postmenopausal women participating in the combined EPIC Prospect and Norfolk cohorts. In this study, the densities of 781 women who never used PMH were compared at baseline and at 5 years of follow-up across XbaI genotypes, thereby reflecting a natural change in MD during this time period. In addition, mammographic densities were compared across XbaI genotypes among 795 women who never used PMH at baseline but who subsequently initiated PMH use during the 5 years of follow-up, thereby reflecting the influence of PMH use during this period. The results suggested that the women who carried at least one x allele had significantly attenuated declines in MD if they took PMH52. The PvuII polymorphism, which is in linkage disequilibrium with the XbaI polymorphism, was also studied in the two aforementioned investigations and shows similar results47, 52. Confirmation of these findings by independent investigators would suggest that PMH use interacts with the XbaI and PvuII polymorphisms to moderate the natural decline in MD associated with the postmenopausal years, possibly to influence breast cancer risk, but this remains to be determined. The PvuII p allele and the XbaI x allele have each been associated with breast cancer6163.

PGR PROGINS complex

Two (Val660Leu and His770His) of three polymorphisms in the progesterone receptor (PGR) gene are in complete linkage disequilibrium and together all three are called the PROGINS complex64 (Figure 1): they may affect ligand binding and gene transcription65. In the EPIC-Prospect47 and EPIC-Norfolk53 studies, neither Val660Leu or His770His were associated with cross-sectional estimates of MD among postmenopausal women who had not used or who were past users of PMH53, nor with longitudinal change in MD among women who used PMH52. Each copy of the Val660Leu allele was reported to increase breast cancer risk by a modest 8% (P = 0.05)66.

IGFBP-3 −202C>A

This polymorphism is located in the promoter region of the gene and may increase serum IGFBP-3 levels67. It has been investigated by five groups. Examination of 215 healthy premenopausal women participating in a prospective study of breast cancer in the United Kingdom48 and 741 healthy premenopausal women attending a mammography clinic in Quebec, Canada54 showed no influence of the genotype on MD. A third study among 206 healthy premenopausal women attending an outpatient clinic in Toronto, Canada showed a significantly higher MD among women with two copies of the A allele45. Each study used a computer-assisted method to assess MD. However, none of the studies defined the ethnic composition of their sample of women or adjusted for ethnicity. The potential for population stratification among a potentially multi-ethnic sample residing in Toronto45 with the UK48 and French Canadian54 participants warrants further exploration. In addition, postmenopausal women who never used PMH or used PMH in the past45, 48 showed no appreciable difference in percent MD across genotypes of this polymorphism. Also, two additional studies, the Australian twin study of 307 postmenopausal women50 and the Nurses’ Health Study of 917 postmenopausal controls that participated in a case-control study of breast cancer30 found no difference in MD across genotypes following adjustment for PMH use. In summary, the data suggest this polymorphism is not associated with MD regardless of menopausal status, although the C allele has been associated with lower circulating levels of IGFBP-368 and marginally significant decreased breast cancer risk66.

IGF-I rs1520220 C>G and rs6220 A>G

The rs1520220 C>G polymorphism is located in intron 3, while rs6220 A>G is located in the 3′ UTR; neither polymorphism’s function is known. Both were evaluated in two studies of premenopausal women comprising large samples of healthy women attending a mammography clinic (746 in Quebec, Canada54 and 1,928 in the Netherlands32). Only the Canadian study observed a significantly higher percent MD among women who carried two copies of the rs1520220 G allele54 and both studies observed a marginally significantly higher percent MD with two copies of the rs6220 G allele32, 54. Both studies32, 54 also showed higher serum IGF-I with each polymorphism, although significance was attained with two copies of the rs6220 only32. Rs6220 A>G has also been associated with increased breast cancer risk69.

A third study among healthy (204 premenopausal and 917 postmenopausal) women in the Nurses’ Health Study observed significantly lower percent MD among those with both copies of the rs1520220 G allele, but no apparent difference was noted by menopausal status30. The evidence thus far does not support a consistent relation of rs1520220 C>G with MD or with breast cancer risk70, 71, although rs6220 A>G may be a potential candidate for premenopausal MD.

HSD3β1 Asn367Thr

One seemingly consistent finding among genes in the steroid hormone biosynthesis pathway (Figure 1) is the lower level of MD among Caucasian carriers of the Asn367Thr variant of hydroxysteroid dehydrogenase 3β1 (HSD3β1). This polymorphism has no known function. A cross-sectional study of 242 (~50% postmenopausal) Caucasian breast cancer patients with pre-diagnosis mammograms found a significantly lower percent MD among women with one or more copies of the Thr allele compared to no copies39. This finding was confirmed in a study of 457 healthy Caucasian women (~67% postmenopausal) from 207 sisterhoods that also reported a significantly lower MD from one or more copies of the Thr allele50. Analyses restricted to 176 premenopausal women with two copies of the Thr allele displayed a non-significantly higher MD in the former study39, but a marginally significantly lower MD in the latter study of 150 premenopausal women50. Potential reasons for the discrepancy could be variability from small sample sizes within subgroup analyses, the comparison of mammographic densities obtained retrospectively from women who were recruited as breast cancer cases with those of a selected group of twin-pairs, and the slightly lower age among the breast cancer cases compared to the twin-pairs, suggesting an enrichment of younger, higher-risk women in this39 study. Although lower MD is seen for the Thr allele among Caucasian women after adjusting for menopausal status, the associations are inconsistent among premenopausal women, and not significant among postmenopausal women. The relevance of this polymorphism to MD, therefore, remains unclear. The association between the Asn367Thr variant and breast cancer risk has not been studied.

CYP19A1 (TTTA)n

The cytochrome P450 19A1 gene, also known as aromatase, contains a microsatellite repeat polymorphism that may alter the splice site during post-translational protein modification72; however no functional effect of the polymorphism on estrogen biosynthesis is known73 (Figure 1). Two studies of 48641 and 55031 Caucasian women found no statistical association with MD among carriers of the 10 repeat allele compared to carriers without this allele in models that adjusted for age, BMI and menopausal status31, 41. In the same studies, carriers of the 12 repeat allele had marginally significantly lower MD levels than women without this allele in one31 but not in the other41 investigation. Both were case-control studies of breast cancer and used the computer-assisted thresholding method of MD assessment among controls. Differences in SNP associations with MD may be from chance or from subtle ancestral differences among the Caucasian samples. Additional analyses of haplotypes that captured variation across the entire CYP19A1 gene with MD were not statistically significant31. Thus, the association of the CYP19A1 12 repeat allele with MD is inconclusive, and does not appear to be associated with breast cancer57, 74, 75.

CYP19A1 +27(TCT)+/- and +268T>C

CYP19A1 +27(TCT)+/- is an insertion/deletion polymorphism in intron 4 and is in linkage disequilibrium with CYP19A1 (TTTA)n. CYP19A1 +268T>C resides in exon 10 of the 3′ untranslated region (UTR) of the gene, but has no known function (Figure 1). Neither polymorphism was significantly associated with percent MD among 550 Caucasian women participating as controls in a case-control study of breast cancer in the United States regardless of menopausal status or PMH use among postmenopausal women31 or among 1,286 postmenopausal women of unreported ethnicity participating in a prospective cohort study in the United Kingdom who were past or never users of PMH53. A third study among 486 Caucasian controls in the United States also did not observe a significant association of the +268T>C polymorphism following adjustment for age and BMI and irrespective of menopausal status or use of PMH among postmenopausal women41. In the absence of additional data, these polymorphisms do not appear to be associated with MD nor appear to be breast cancer risk factors74, 75.

AR CAG repeat polymorphism

Two studies investigated this polymorphism in the androgen receptor gene (Figure 1). Longer transcripts may produce less active receptor76, 77. MD did not vary by number of AR CAG repeats (≥ 22) among 538 (~73% postmenopausal) healthy Caucasian women participating as controls in a study of breast cancer41 or by ≥ 21 AR CAG repeats among 246 (~50% postmenopausal) Caucasian women with breast cancer whose screening mammograms were retrieved within the five years preceding their cancer diagnosis40. The former study41 did not observe an effect on percent MD from PMH use, although women in the latter study40 with the longer less active AR allele had higher MD if they also took a combination formulation of estrogen plus progesterone PMH. The associations with MD may reflect a large number of comparisons in the data of relatively small numbers of women40. Moreover, the AR polymorphism is inconsistently associated with breast cancer risk78, 79.

Tumor suppressor genes

Over 750 protein-truncating mutations exist in BRCA1 and BRCA280, 81. Findings from early studies were mixed3336 and relied on varied methods of qualitative assessments of MD in addition to very small sample sizes of 9 to 34 mutation carriers (Table 1). A recent well-designed study using the computer-assisted thresholding assessment found no difference in MD between 206 mutation carriers and 136 noncarriers after adjustment for age at mammography and BMI49 (online Table). Although strong risk factors for heritable breast cancer, these mutations are not associated with MD.

CYP1A2 −154A>C (CYP1A1*1F)

This polymorphism may induce enzyme activity82 to lower bioactive estrogen concentrations and, hypothetically, MD. A marginally significantly lower MD was observed overall among 268 pre- and 60 postmenopausal women screened in a mammography clinic, and particularly among premenopausal women46, whereas a statistically significantly higher MD was reported among control subjects from a case control study of breast cancer in 97 pre- and 153 postmenopausal women38. Interestingly, both studies sampled from among female residents in Hawaii. The former study46 composed healthy women with an entry (baseline) mammogram, while the latter study38 incorporated healthy women participating in two nested case-control studies nested within the Multiethnic Cohort Study who each had an average of 2.5 mammograms in order to reduce intra-individual variability in MD measurements. Given similarities in computerized mammographic assessments and adjustment with similar covariates, the findings from the latter study38 may be more reliable due to increased precision of the measure. With only two studies, the influence of the polymorphism with MD is inconclusive, as is its association with breast cancer in the Shanghai Breast Cancer Study83, although decreased risk has been observed among predominantly postmenopausal women in the Multiethnic Cohort Study59.

CYP1A1 Ile462Val and CYP1B1 Val432Leu

Higher CYP1A1 enzyme expression82 or activity84 and CYP1B1 enzyme activity85, 86 may lower bioactive estrogen concentrations (Figure 1). Neither polymorphism is associated with MD among predominantly premenopausal46 or predominantly postmenopausal41 healthy women. Also, CYP1B1 Val432Leu was not associated with MD among 1,260 cohort members who were past or never users of PMH53. MD among 140 current users of PMH with both copies of the CYP1B1 Leu allele, however, was lower than Val/Val carriers, although of borderline significance only41. This relation among current users was not evaluated in the other studies and requires confirmation. Neither polymorphism is strongly associated with breast cancer 57, 59, 84, 87.

Summary

The hypothesis-dependent approach focusing on candidate genes that may influence breast cancer has produced mostly conflicting results regardless of whether the polymorphisms have known functional effects (Table 2). Indeed, the accumulated evidence indicates that the COMT Val158Met and IGF-I rs6220 3′ UTR polymorphisms (or variants in strong linkage disequilibrium with these polymorphisms) influence percent MD in premenopausal women, and the ESR1 intronic Xba I and Pvu II variants may lead to higher percent MD among women taking PMH. The remaining studies are inconclusive, mostly from too few independent investigations or from between-study heterogeneity in sample characteristics.

Table 2

Summary of association studies between polymorphisms and mammographic density.

No significant effect in >1 studyMixed results*Significantly lower density observed in the only study to examine itSignificantly lower density in >1 studySignificantly higher density in the only study to examine itSignificantly higher density in >1 study
CYP17A1+27T>C39, 41, 43, 46, 53CYP19A1 +268T>C41, 53COMT +701A>G 55HSD3β1 Asn367Thr among Caucasians adjusted for menopausal status39, 50HSD3β1 Asn367Thr among African Americans 39ESR1 Xba I47,52Pvu II47,52 among postmenopausal women taking PMH
CYP1A1 M2A>G 41, 46CYP1A2*1FA>C38, 46IGF-1 −26989G>A30COMT Val158Met among premenopausal women adjusted for ethnicity 42, 46AKR1C4 Leu311Val 37IGF-1 +1830T>C 31,53 among premenopausal women
PROGINS complex 51,52CYP1B1 Val432Leu 37, 41, 46, 53IGF-1 T>C rs7136446 (change in density from pre to post menopause)32PGR +331G>A52
IGF-1 −17526T>C30, 32, +4996C>A29,31, C>A rs154959330,54COMT Val158Met3739, 41, 50, 53 among postmenopausal womenPIK3CB T>C rs36107254AIB1 (CAG)n repeat 41
BRCA1/BRCA232,35,48UGT1A1 (TA)n TAA repeat 37, 41IGF-1 (CA)n repeat 45
AR (CAG)n repeat40, 41IGF-1 A>G rs795654732
IGF-1 3′ UTR G>A30, 32, −178C>G30, 32, 54IGFBP-1 −574G>A30
IGFBP-3 −202C>A45, 48, 50, 54GH1 Val26Val 44, Gly30Gly44
*No consistent association in >1 study (e.g., studies that vary in significant findings or significant studies that vary in direction of effect).
Examined the change in density phenotype due to PMH.

Linkage Analysis

A major limitation of candidate gene studies is the low prior probability that an association is true88. Selecting candidate genes in a region with prior evidence of genetic linkage directly addresses this weakness. Because the regions identified by linkage analysis are hypothesis-independent, they can identify a gene or genes that are not anticipated. In multifactorial complex traits like MD this is particularly important.

Two linkage analyses of MD have been conducted. Both used a large breast cancer family study as the sampling frame. The first was a small sibpair study of 71 sisterhoods in 22 families and used 147 microsatellite markers placed across the autosomes at a spacing of 30 centiMorgans (cM). The results indicated a possible linkage to chromosome 689. The second larger study included 889 members of 89 multigenerational families and 403 markers spaced an average of 9 cM apart90. The strongest evidence for linkage was identified on chromosome 5p with a maximum log odds for linkage (or LOD score) of 2.9. Finer mapping of this region with a dense set of markers strengthened the evidence for linkage (LOD=4.2). The 1-LOD region which contains 45 genes has not been previously implicated in breast cancer and is currently being pursued. Further, although this locus was not linked to BMI, it was only apparent after adjustment for BMI, and could explain up to 22% of the total variability in MD among women. Two other suggestive regions for linkage were also identified on chromosome 12, one containing the IGF-1 gene. Linkage analysis, then, can result in identification of novel genes that may influence both MD and breast cancer risk.

Genome Wide Association Studies

Genome wide association studies (GWAS) utilize a large number of polymorphisms positioned across the genome. Polymorphisms are not confined to coding or regulatory regions and the function of the majority of these polymorphisms is unknown. No GWAS has been reported for MD, but six polymorphisms identified from three GWASs of breast cancer9193 have been evaluated by one investigative team in relation to MD (online Table). In this study of 516 Caucasian breast cancer cases, Lee et al.51 observed no association of these variants with MD using the mammogram of the contralateral (non-cancerous) breast, although a positive association with MD of rs3817198 T>C within the lymphocyte-specific protein 1 (LSP1) gene on chromosome 11p was seen specifically in women with ER+/PR+ tumors. One of the LSP1 protein functions is to regulate adhesion to fibrinogen matrix proteins, which may have implications for MD.

Summary and Future Directions

In summary, there is strong evidence for a genetic component to MD. Both hypothesis-dependent and hypothesis-free approaches have provided clues to relevant genes and/or loci. Candidate gene approaches yielded replicated associations between COMT Val158Met and IGF-I rs6220 A>G and premenopausal MD and ESR1 (Xba I and Pvu II) polymorphisms and postmenopausal MD, which possibly translate to breast cancer risk. However, many polymorphisms have only been examined in a few studies, limiting the conclusions that can be drawn (Table 2). Technological advances make genome-wide scans feasible and may reveal novel candidate genes, while haplotype block or tagSNP approaches are advantageous to study all common variation within a gene. Linkage analyses identified a candidate region on chromosome 5p encompassing 45 genes that are also under consideration for their influence on MD.

Future investigations should incorporate well-characterized study populations including specification of age, race and ethnicity, quantify MD and its changes in the most objective manner possible, stratify subjects by design (rather than post-hoc) by menopause and specific type of hormone regimen with mammographic densities reflecting the timing of PMH exposure, and consider assessment of earlier periods in life when MD changes are most dramatic and the inter-individual variation is greatest. Several protocols reviewed in this Perspective drive these recommendations and are strong studies for future comparisons and synthesis32, 48, 52. Also, the reviewed studies underscore the importance of measuring and accounting for non-heritable factors to allow better detection of genetic influences on MD. Several of the polymorphisms examined thus far have inconclusive functional significance, requiring research to understand tissue response to genetic polymorphisms that alter local biosynthesis, metabolism or catabolism of hormones or growth factors. Ultimately, identification of genetic risk factors for early markers of disease may permit better risk prediction and targeted early preventive or therapeutic strategies for breast cancer.

Textbox 3 Polymorphisms in the IGF-1 axis

Components of the IGF axis may influence the tissue composition of the breast (Figure 2). High circulating levels of IGF-1, or higher IGF-1 in relation to IGFBP-3, correlate with higher premenopausal mammographic density47,8891 similar to their association with breast cancer risk96. Six studies reported 29 polymorphisms in IGF-1 and 16 polymorphisms in IGFBP-3 or IGFBP-1 with mammographic density. Most polymorphisms were examined in only one study and are described in the online Table. In addition to the three SNPs discussed in the text, five polymorphisms have each been evaluated by two investigative teams:

An external file that holds a picture, illustration, etc.
Object name is nihms129400f2.jpg
The IGF Axis

The insulin-like growth factor (IGF)-1 plays a role in regulating cellular growth, differentiation and apoptosis in normal breast and in mammary carcinogenesis and works in conjunction with growth hormone (GH), sex steroids and other hormones. Growth hormone-releasing hormone (GHRH) and its receptor (GHRHR) are both involved in the regulation of pituitary synthesis and release of GH. Although most IGF-1 is synthesized in the liver in response to pituitary GH secretion, local production occurs in tissues including the breast. This autocrine and paracrine production of IGF-1 within IGF-1-responsive tissues is of major physiological importance and supports a role for IGF-1 as a potent mitogen in the development of some cancers including breast cancer118. IGF-1 production is also influenced by estradiol, which directly stimulates IGF-1 production in estrogen-responsive tissues through the association of estrogen receptor α (ERα) with IGF1R 119 and indirectly stimulates IGF-1 production in the liver through pituitary release of GH. In circulation, IGF-1 binds to one of six different IGFBP, the majority of which is bound to IGFBP-3. IGFBP-3 is largely dependent on hepatic production and can prolong the half-life of IGF-1 and prevent it from interacting with cell surface receptors (IGFR1), where it is capable of modulating IGF-1’s biological response 118 by activating downstream signaling molecules including insulin receptor substrate 1 (IRS1) and PI3K 118. IGFBP-3 production is negatively regulated by GH to limit apoptosis and antiproliferation.

IGF-1 polymorphisms. Four polymorphisms (rs2946834 G>A, rs7965399 T>C, rs1019731 C>A, and rs1549593 G>T) have either been evaluated among ~700 premenopausal women32, 54 or among 204 premenopausal and 917 postmenopausal women combined30. Of these, rs2946834 G>A in the 3′ UTR, was inversely related to percent density among pre- and postmenopausal women combined who carry both copies of the A allele30, but not among premenopausal women only32. Different sample compositions make interpretation difficult. Polymorphisms rs7965399 T>C, rs1019731 C>A, and rs1549593 G>T were each evaluated in premenopausal women and in pre- and postmenopausal women combined with no significant differences observed in density across genotypes30, 32, 54.

IGFBP-3 rs3110697C>T. This polymorphism was investigated in two studies, one among 741 premenopausal women 54 and another among 204 premenopausal and 917 postmenopausal women evaluated separately and combined 30. Neither showed appreciable differences in density across genotypes.

IGF-1 and IGFBP-3 haplotypes. The Nurses’ Health Study30 and EPIC-Prospect 32 explored haplotypes with mammographic density. None were significantly associated with percent MD29,31.

The associations of the aforementioned individual polymorphisms and haplotypes with density are too few to interpret definitively. Furthermore, associations with breast cancer risk have not been found for selected polymorphisms and haplotypes 70, 71, 97

Supplementary Material

01

Acknowledgement

National Cancer Institute Grants R01 CA97396, R01 CA128931 and P50 CA116201

Textbox 1 Glossary terms

StromaThe supportive framework of a biologic tissue with an extensive extracellular matrix that serves to support cells, separate tissues and regulate intercellular communication.
Familial aggregationA tendency for the trait under study to occur or cluster in multiple family members more often than would be expected by chance.
Parent-offspring/twin studiesInvestigates the proportion of variation in the trait under study that is explained by unmeasured additive genetic factors.
Segregation analysisTests hypotheses about whether the existence of major genes account for the observed pattern of familial aggregation of the trait and provides evidence for the mode of inheritance of the genes using pedigree data. Statistical models compare Mendelian inheritance patterns of a trait to a model in which there are no restrictions on mode of inheritance or other model parameters.
Linkage analysisThe process of determining the approximate chromosomal location of a gene associated with the trait being studied by looking for evidence of cosegregation with other marker genes whose locations are already known.
Genetic association studiesTests whether a polymorphism in a specific candidate gene explains the inter-individual variation in the trait being studied.
Linkage disequilibrium (LD)The nonrandom association of alleles at two or more loci on chromosomes in a population more than would be expected by chance.
Haplotype blocksConfigurations of alleles within regions on the chromosome which tend to be inherited together (that is, in high LD). Little genetic variability is observed within this region among individuals in a population.
tagSNPsA reduced set of single nucleotide polymorphisms that identify or “tag” other SNPs with which it is in high LD.

References

1. Turner ST, et al. Genomic loci with pleiotropic effects on coronary artery calcification. Atherosclerosis. 2006;185:340–346. [Abstract] [Google Scholar]
2. Kullo IJ, Ding K, Boerwinkle E, Turner ST, de Andrade M. Quantitative trait loci influencing low density lipoprotein particle size in African Americans. J Lipid Res. 2006;47:1457–1462. [Abstract] [Google Scholar]
3. Carlson CS, et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet. 2004;74:106–120. [Europe PMC free article] [Abstract] [Google Scholar]
4. Wolfe JN. Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol. 1976;126:1130–1137. [Abstract] [Google Scholar]
5. Lehman C, Holt S, Peacock S, White E, Urban N. Use of the American College of Radiology BI-RADS guidelines by community radiologists: concordance of assessments and recommendations assigned to screening mammograms. AJR Am J Roentgenol. 2002;179:15–20. [Abstract] [Google Scholar]
6. Wolfe JN, Saftlas AF, Salane M. Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study. AJR Am J Roentgenol. 1987;148:1087–1092. [Abstract] [Google Scholar]
7. Boyd NF, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst. 1995;87:670–675. [Abstract] [Google Scholar]
8. Greendale GA, et al. Postmenopausal hormone therapy and change in mammographic density. J Natl Cancer Inst. 2003;95:30–37. [Abstract] [Google Scholar]
9. Boyd NF, et al. Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol. 2005;6:798–808. [Abstract] [Google Scholar]
10. McCormack VA, Dos Santos Silva I. Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15:1159–1169. [Abstract] [Google Scholar]
11. Byrne C, et al. Mammographic features and breast cancer risk: effects with time, age, and menopause status. J Natl Cancer Inst. 1995;87:1622–1629. [Abstract] [Google Scholar]
12. Tice JA, Cummings SR, Ziv E, Kerlikowske K. Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat. 2005;94:115–122. [Abstract] [Google Scholar]
13. Chen J, et al. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006;98:1215–1226. [Abstract] [Google Scholar]
14. Barlow WE, et al. Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst. 2006;98:1204–1214. [Abstract] [Google Scholar]
15. Wilkinson E, et al. Mammographic parenchymal pattern and the risk of breast cancer. J Natl Cancer Inst. 1977;59:1397–1400. [Abstract] [Google Scholar]
16. Hainline S, et al. Mammographic patterns and risk of breast cancer. AJR Am J Roentgenol. 1978;130:1157–1158. [Abstract] [Google Scholar]
17. Saftlas AF, et al. Mammographic parenchymal patterns as indicators of breast cancer risk. Am J Epidemiol. 1989;129:518–526. [Abstract] [Google Scholar]
18. Saftlas AF, et al. Mammographic densities and risk of breast cancer. Cancer. 1991;67:2833–2838. [Abstract] [Google Scholar]
19. Kaufman Z, Garstin WI, Hayes R, Michell MJ, Baum M. The mammographic parenchymal patterns of nulliparous women and women with a family history of breast cancer. Clin Radiol. 1991;43:385–388. [Abstract] [Google Scholar]
20. Brisson J, et al. The relation of mammographic features of the breast to breast cancer risk factors. Am J Epidemiol. 1982;115:438–443. [Abstract] [Google Scholar]
21. Crest AB, Aiello EJ, Anderson ML, Buist DS. Varying levels of family history of breast cancer in relation to mammographic breast density (United States) Cancer Causes Control. 2006;17:843–850. [Abstract] [Google Scholar]
22. Ziv E, Shepherd J, Smith-Bindman R, Kerlikowske K. Mammographic breast density and family history of breast cancer. J Natl Cancer Inst. 2003;95:556–558. [Abstract] [Google Scholar]
23. Wolfe JN, Albert S, Belle S, Salane M. Familial influences on breast parenchymal patterns. Cancer. 1980;46:2433–2437. [Abstract] [Google Scholar]
24. Kaprio J, Alanko A, Kivisaari L, Standertskjold-Nordenstam CG. Mammographic patterns in twin pairs discordant for breast cancer. Br J Radiol. 1987;60:459–462. [Abstract] [Google Scholar]
25. Haars G, van Noord PA, van Gils CH, Peeters PH, Grobbee DE. Heritable aspects of dysplastic breast glandular tissue (DY) Breast Cancer Res Treat. 2004;87:149–156. [Abstract] [Google Scholar]
26. Boyd NF, et al. Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med. 2002;347:886–894. [Abstract] [Google Scholar]
27. Stone J, et al. The heritability of mammographically dense and nondense breast tissue. Cancer Epidemiol Biomarkers Prev. 2006;15:612–617. [Abstract] [Google Scholar]
28. Ursin G, et al. A revised heritability estimate of mammographic density. Proc Am Assoc Cancer Res. 2007 [Google Scholar]
29. Pankow JS, et al. Genetic analysis of mammographic breast density in adult women: evidence of a gene effect. J Natl Cancer Inst. 1997;89:549–556. [Abstract] [Google Scholar]
30. Tamimi RM, et al. Common genetic variation in IGF1,pIGFBP-1, and IGFBP-3 in relation to mammographic density: a cross-sectional study. Breast Cancer Res. 2007;9:R18. [Europe PMC free article] [Abstract] [Google Scholar]
31. Olson JE, et al. A comprehensive examination of CYP19 variation and breast density. Cancer Epidemiol Biomarkers Prev. 2007;16:623–625. [Abstract] [Google Scholar]
32. Verheus M, et al. Common genetic variation in the IGF-1 gene, serum IGF-I levels and breast density. Breast Cancer Res Treat. 2007 [Abstract] [Google Scholar]
33. Helvie MA, Roubidoux MA, Weber BL, Merajver SD. Mammography of breast carcinoma in women who have mutations of the breast cancer gene BRCA1: initial experience. AJR Am J Roentgenol. 1997;168:1599–1602. [Abstract] [Google Scholar]
34. Chang J, Yang WT, Choo HF. Mammography in Asian patients with BRCA1 mutations. Lancet. 1999;353:2070–2071. [Abstract] [Google Scholar]
35. Huo Z, et al. Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. Med Phys. 2000;27:4–12. [Abstract] [Google Scholar]
36. Tilanus-Linthorst M, et al. A BRCA1/2 mutation, high breast density and prominent pushing margins of a tumor independently contribute to a frequent false-negative mammography. Int J Cancer. 2002;102:91–95. [Abstract] [Google Scholar]
37. Lord SJ, et al. Polymorphisms in genes involved in estrogen and progesterone metabolism and mammographic density changes in women randomized to postmenopausal hormone therapy: results from a pilot study. Breast Cancer Res. 2005;7:R336–R344. [Europe PMC free article] [Abstract] [Google Scholar]
38. Takata Y, Maskarinec G, Le Marchand L. Breast density and polymorphisms in genes coding for CYP1A2 and COMT: the Multiethnic Cohort. BMC Cancer. 2007;7:30. [Europe PMC free article] [Abstract] [Google Scholar]
39. Haiman CA, et al. Genetic determinants of mammographic density. Breast Cancer Res. 2002;4:R5. [Europe PMC free article] [Abstract] [Google Scholar]
40. Lillie EO, et al. Polymorphism in the androgen receptor and mammographic density in women taking and not taking estrogen and progestin therapy. Cancer Res. 2004;64:1237–1241. [Abstract] [Google Scholar]
41. Haiman CA, et al. Polymorphisms in steroid hormone pathway genes and mammographic density. Breast Cancer Res Treat. 2003;77:27–36. [Abstract] [Google Scholar]
42. Hong CC, et al. Val158Met Polymorphism in catechol-O-methyltransferase gene associated with risk factors for breast cancer. Cancer Epidemiol Biomarkers Prev. 2003;12:838–847. [Abstract] [Google Scholar]
43. Hong CC, et al. Association between the T27C polymorphism in the cytochrome P450 c17alpha (CYP17) gene and risk factors for breast cancer. Breast Cancer Res Treat. 2004;88:217–230. [Abstract] [Google Scholar]
44. Mulhall C, et al. Pituitary growth hormone and growth hormone-releasing hormone receptor genes and associations with mammographic measures and serum growth hormone. Cancer Epidemiol Biomarkers Prev. 2005;14:2648–2654. [Abstract] [Google Scholar]
45. Lai JH, et al. A polymorphic locus in the promoter region of the IGFBP3 gene is related to mammographic breast density. Cancer Epidemiol Biomarkers Prev. 2004;13:573–582. [Abstract] [Google Scholar]
46. Maskarinec G, Lurie G, Williams AE, Le Marchand L. An investigation of mammographic density and gene variants in healthy women. Int J Cancer. 2004;112:683–688. [Abstract] [Google Scholar]
47. van Duijnhoven FJ, et al. Polymorphisms in the estrogen receptor alpha gene and mammographic density. Cancer Epidemiol Biomarkers Prev. 2005;14:2655–2660. [Abstract] [Google Scholar]
48. dos Santos Silva I, et al. The insulin-like growth factor system and mammographic features in premenopausal and postmenopausal women. Cancer Epidemiol Biomarkers Prev. 2006;15:449–455. [Abstract] [Google Scholar]
49. Mitchell G, et al. Mammographic density and breast cancer risk in BRCA1 and BRCA2 mutation carriers. Cancer Res. 2006;66:1866–1872. [Abstract] [Google Scholar]
50. Stone J, et al. Mammographic density and candidate gene variants: a twins and sisters study. Cancer Epidemiol Biomarkers Prev. 2007;16:1479–1484. [Abstract] [Google Scholar]
51. Lee E, et al. The role of established breast cancer susceptibility loci in mammographic density in young women. Cancer Epidemiol Biomarkers Prev. 2008;17:258–260. [Abstract] [Google Scholar]
52. van Duijnhoven FJ, et al. Influence of estrogen receptor alpha and progesterone receptor polymorphisms on the effects of hormone therapy on mammographic density. Cancer Epidemiol Biomarkers Prev. 2006;15:462–467. [Abstract] [Google Scholar]
53. Warren R, Skinner J, Sala E, Denton E, Dowsett M, Folkerd E, Healey CS, Dunning A, Doody D, Ponder B, Luben RN, Day NE, Easton D. Associations among mammographic density, circulating sex hormones, and polymorphisms in sex hormone metabolism genes in postmenopausal women. Cancer Epidemiol Biomarkers Prev. 2006;15:1502–1508. [Abstract] [Google Scholar]
54. Diorio C, Brisson J, Berube S, Pollak M. Genetic polymorphisms involved in insulin-like growth factor (IGF) pathway in relation to mammographic breast density and IGF levels. Cancer Epidemiol Biomarkers Prev. 2008;17:880–888. [Abstract] [Google Scholar]
55. Olson JE, et al. Mammographic breast density and membrane-bound catechol O-methyltransferase (COMT) genetic polymorphisms. Proc Am Assoc Cancer Res. 2008 in press. [Google Scholar]
56. Nedelcheva Kristensen V, et al. CYP17 and breast cancer risk: the polymorphism in the 5’ flanking area of the gene does not influence binding to Sp-1. Cancer Res. 1999;59:2825–2828. [Abstract] [Google Scholar]
57. Dunning AM, et al. A systematic review of genetic polymorphisms and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 1999;8:843–854. [Abstract] [Google Scholar]
58. Lachman HM, et al. Human catechol-O-methyltransferase pharmacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders. Pharmacogenetics. 1996;6:243–250. [Abstract] [Google Scholar]
59. Le Marchand L, Donlon T, Kolonel LN, Henderson BE, Wilkens LR. Estrogen metabolism-related genes and breast cancer risk: the multiethnic cohort study. Cancer Epidemiol Biomarkers Prev. 2005;14:1998–2003. [Abstract] [Google Scholar]
60. Yaich L, Dupont WD, Cavener DR, Parl FF. Analysis of the PvuII restriction fragment-length polymorphism and exon structure of the estrogen receptor gene in breast cancer and peripheral blood. Cancer Res. 1992;52:77–83. [Abstract] [Google Scholar]
61. Cai Q, et al. Genetic polymorphisms in the estrogen receptor alpha gene and risk of breast cancer: results from the Shanghai Breast Cancer Study. Cancer Epidemiol Biomarkers Prev. 2003;12:853–859. [Abstract] [Google Scholar]
62. Onland-Moret NC, van Gils CH, Roest M, Grobbee DE, Peeters PH. The estrogen receptor alpha gene and breast cancer risk (The Netherlands) Cancer Causes Control. 2005;16:1195–1202. [Abstract] [Google Scholar]
63. Wedren S, et al. Oestrogen receptor alpha gene haplotype and postmenopausal breast cancer risk: a case control study. Breast Cancer Res. 2004;6:R437–R449. [Europe PMC free article] [Abstract] [Google Scholar]
64. De Vivo I, et al. A functional polymorphism in the promoter of the progesterone receptor gene associated with endometrial cancer risk. Proc Natl Acad Sci U S A. 2002;99:12263–12268. [Europe PMC free article] [Abstract] [Google Scholar]
65. Modugno F. Ovarian cancer and polymorphisms in the androgen and progesterone receptor genes: a HuGE review. Am J Epidemiol. 2004;159:319–335. [Abstract] [Google Scholar]
66. Breast Cancer Association Consortium. Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium. J Natl Cancer Inst. 2006;98:1382–1396. [Abstract] [Google Scholar]
67. Deal C, et al. Novel promoter polymorphism in insulin-like growth factor-binding protein-3: correlation with serum levels and interaction with known regulators. J Clin Endocrinol Metab. 2001;86:1274–1280. [Abstract] [Google Scholar]
68. Schernhammer ES, Hankinson SE, Hunter DJ, Blouin MJ, Pollak MN. Polymorphic variation at the −202 locus in IGFBP3: Influence on serum levels of insulin-like growth factors, interaction with plasma retinol and vitamin D and breast cancer risk. Int J Cancer. 2003;107:60–64. [Abstract] [Google Scholar]
69. Al-Zahrani A, et al. IGF1 and IGFBP3 tagging polymorphisms are associated with circulating levels of IGF1, IGFBP3 and risk of breast cancer. Hum Mol Genet. 2006;15:1–10. [Abstract] [Google Scholar]
70. Setiawan VW, et al. Igf-I genetic variation and breast cancer: the multiethnic cohort. Cancer Epidemiol Biomarkers Prev. 2006;15:172–174. [Abstract] [Google Scholar]
71. Canzian F, et al. Polymorphisms of genes coding for insulin-like growth factor 1 and its major binding proteins, circulating levels of IGF-I and IGFBP-3 and breast cancer risk: results from the EPIC study. Br J Cancer. 2006;94:299–307. [Europe PMC free article] [Abstract] [Google Scholar]
72. Mitrunen K, Hirvonen A. Molecular epidemiology of sporadic breast cancer. The role of polymorphic genes involved in oestrogen biosynthesis and metabolism. Mutat Res. 2003;544:9–41. [Abstract] [Google Scholar]
73. Puranen TJ, Poutanen MH, Peltoketo HE, Vihko PT, Vihko RK. Site-directed mutagenesis of the putative active site of human 17 beta-hydroxysteroid dehydrogenase type 1. Biochem J 304 ( Pt 1) 1994:289–293. [Europe PMC free article] [Abstract] [Google Scholar]
74. Cai Q, et al. Haplotype analyses of CYP19A1 gene variants and breast cancer risk: results from the Shanghai Breast Cancer Study. Cancer Epidemiol Biomarkers Prev. 2008;17:27–32. [Europe PMC free article] [Abstract] [Google Scholar]
75. Healey CS, et al. Polymorphisms in the human aromatase cytochrome P450 gene (CYP19) and breast cancer risk. Carcinogenesis. 2000;21:189–193. [Abstract] [Google Scholar]
76. Tut TG, Ghadessy FJ, Trifiro MA, Pinsky L, Yong EL. Long polyglutamine tracts in the androgen receptor are associated with reduced trans-activation, impaired sperm production, and male infertility. J Clin Endocrinol Metab. 1997;82:3777–3782. [Abstract] [Google Scholar]
77. Grierson AJ, Mootoosamy RC, Miller CC. Polyglutamine repeat length influences human androgen receptor/c-Jun mediated transcription. Neurosci Lett. 1999;277:9–12. [Abstract] [Google Scholar]
78. Lillie EO, Bernstein L, Ursin G. The role of androgens and polymorphisms in the androgen receptor in the epidemiology of breast cancer. Breast Cancer Res. 2003;5:164–173. [Europe PMC free article] [Abstract] [Google Scholar]
79. Rebbeck TR, et al. Modification of BRCA1-associated breast cancer risk by the polymorphic androgen-receptor CAG repeat. Am J Hum Genet. 1999;64:1371–1377. [Europe PMC free article] [Abstract] [Google Scholar]
80. Antoniou A, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003;72:1117–1130. [Europe PMC free article] [Abstract] [Google Scholar]
81. Breast Cancer Information Core. ( http://research.nhgri.nih.gov/bic/) [Google Scholar]
82. Brockstedt U, et al. Analyses of bulky DNA adduct levels in human breast tissue and genetic polymorphisms of cytochromes P450 (CYPs), myeloperoxidase (MPO), quinone oxidoreductase (NQO1), and glutathione S-transferases (GSTs) Mutat Res. 2002;516:41–47. [Abstract] [Google Scholar]
83. Long JR, et al. Population-based case-control study of AhR (aryl hydrocarbon receptor) and CYP1A2 polymorphisms and breast cancer risk. Pharmacogenet Genomics. 2006;16:237–243. [Abstract] [Google Scholar]
84. Masson LF, Sharp L, Cotton SC, Little J. Cytochrome P-450 1A1 gene polymorphisms and risk of breast cancer: a HuGE review. Am J Epidemiol. 2005;161:901–915. [Abstract] [Google Scholar]
85. Shimada T, et al. Catalytic properties of polymorphic human cytochrome P450 1B1 variants. Carcinogenesis. 1999;20:1607–1613. [Abstract] [Google Scholar]
86. Lee AJ, Cai MX, Thomas PE, Conney AH, Zhu BT. Characterization of the oxidative metabolites of 17beta-estradiol and estrone formed by 15 selectively expressed human cytochrome p450 isoforms. Endocrinology. 2003;144:3382–3398. [Abstract] [Google Scholar]
87. Gaudet MM, et al. Genetic variation of Cytochrome P450 1B1 (CYP1B1) and risk of breast cancer among Polish women. Pharmacogenet Genomics. 2006;16:547–553. [Abstract] [Google Scholar]
88. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96:434–442. [Abstract] [Google Scholar]
89. Vachon CM, King RA, Atwood LD, Kuni CC, Sellers TA. Preliminary sibpair linkage analysis of percent mammographic density. J Natl Cancer Inst. 1999;91:1778–1779. [Abstract] [Google Scholar]
90. Vachon CM, et al. Strong evidence of a genetic determinant for mammographic density, a major risk factor for breast cancer. Cancer Res. 2007;67:8412–8418. [Abstract] [Google Scholar]
91. Easton DF, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 2007;447:1087–1093. [Europe PMC free article] [Abstract] [Google Scholar]
92. Hunter DJ, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. 2007;39:870–874. [Europe PMC free article] [Abstract] [Google Scholar]
93. Stacey SN, et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2007;39:865–869. [Abstract] [Google Scholar]
94. Boyd NF, Jensen HM, Cooke G, Han HL. Relationship between mammographic and histological risk factors for breast cancer. J Natl Cancer Inst. 1992;84:1170–1179. [Abstract] [Google Scholar]
95. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Phys Med Biol. 1994;39:1629–1638. [Abstract] [Google Scholar]
96. Fletcher O, et al. Polymorphisms and circulating levels in the insulin-like growth factor system and risk of breast cancer: a systematic review. Cancer Epidemiol Biomarkers Prev. 2005;14:2–19. [Abstract] [Google Scholar]
97. Cheng I, et al. Haplotype-based association studies of IGFBP1 and IGFBP3 with prostate and breast cancer risk: the multiethnic cohort. Cancer Epidemiol Biomarkers Prev. 2006;15:1993–1997. [Abstract] [Google Scholar]
98. Sharp L, Cardy AH, Cotton SC, Little J. CYP17 gene polymorphisms: prevalence and associations with hormone levels related factors a HuGE review. Am J Epidemiol. 2004;160:729–740. [Abstract] [Google Scholar]
99. Power SG, et al. Molecular analyses of a human sex hormone-binding globulin variant: evidence for an additional carbohydrate chain. J Clin Endocrinol Metab. 1992;75:1066–1070. [Abstract] [Google Scholar]
100. Cousin P, Dechaud H, Grenot C, Lejeune H, Pugeat M. Human variant sex hormone-binding globulin (SHBG) with an additional carbohydrate chain has a reduced clearance rate in rabbit. J Clin Endocrinol Metab. 1998;83:235–240. [Abstract] [Google Scholar]
101. Sissung TM, Price DK, Sparreboom A, Figg WD. Pharmacogenetics and regulation of human cytochrome P450 1B1: implications in hormone-mediated tumor metabolism and a novel target for therapeutic intervention. Mol Cancer Res. 2006;4:135–150. [Abstract] [Google Scholar]
102. Raftogianis R, Creveling C, Weinshilboum R, Weisz J. Estrogen metabolism by conjugation. J Natl Cancer Inst Monogr. 2000:113–124. [Abstract] [Google Scholar]
103. Kume T, et al. Characterization of a novel variant (S145C/L311V) of 3alpha-hydroxysteroid/dihydrodiol dehydrogenase in human liver. Pharmacogenetics. 1999;9:763–771. [Abstract] [Google Scholar]
104. Penning TM, et al. Human 3alpha-hydroxysteroid dehydrogenase isoforms (AKR1C1-AKR1C4) of the aldo-keto reductase superfamily: functional plasticity and tissue distribution reveals roles in the inactivation and formation of male and female sex hormones. Biochem J. 2000;351:67–77. [Europe PMC free article] [Abstract] [Google Scholar]
105. Yager JD, Davidson NE. Estrogen carcinogenesis in breast cancer. N Engl J Med. 2006;354:270–282. [Abstract] [Google Scholar]
106. Planas-Silva MD, Shang Y, Donaher JL, Brown M, Weinberg RA. AIB1 enhances estrogen-dependent induction of cyclin D1 expression. Cancer Res. 2001;61:3858–3862. [Abstract] [Google Scholar]
107. Singh RR, Kumar R. Steroid hormone receptor signaling in tumorigenesis. J Cell Biochem. 2005;96:490–505. [Abstract] [Google Scholar]
108. Hayashi Y, et al. Polymorphism of homopolymeric glutamines in coactivators for nuclear hormone receptors. Endocr J. 1999;46:279–284. [Abstract] [Google Scholar]
109. Rosen CJ, et al. Association between serum insulin growth factor-I (IGF-I) and a simple sequence repeat in IGF-I gene: implications for genetic studies of bone mineral density. J Clin Endocrinol Metab. 1998;83:2286–2290. [Abstract] [Google Scholar]
110. Rasmussen SK, et al. Studies of the variability of the genes encoding the insulin-like growth factor I receptor and its ligand in relation to type 2 diabetes mellitus. J Clin Endocrinol Metab. 2000;85:1606–1610. [Abstract] [Google Scholar]
111. Almind K, Inoue G, Pedersen O, Kahn CRA. common amino acid polymorphism in insulin receptor substrate-1 causes impaired insulin signaling. Evidence from transfection studies. J Clin Invest. 1996;97:2569–2575. [Europe PMC free article] [Abstract] [Google Scholar]
112. Le Stunff C, et al. Association analysis indicates that a variant GATA-binding site in the PIK3CB promoter is a Cis-acting expression quantitative trait locus for this gene and attenuates insulin resistance in obese children. Diabetes. 2008;57:494–502. [Abstract] [Google Scholar]
113. Canzian F, et al. Genetic variation in the growth hormone synthesis pathway in relation to circulating insulin-like growth factor-I, insulin-like growth factor binding protein-3, and breast cancer risk: results from the European prospective investigation into cancer and nutrition study. Cancer Epidemiol Biomarkers Prev. 2005;14:2316–2325. [Abstract] [Google Scholar]
114. Hasegawa Y, et al. Identification of novel human GH-1 gene polymorphisms that are associated with growth hormone secretion and height. J Clin Endocrinol Metab. 2000;85:1290–1295. [Abstract] [Google Scholar]
115. Adams EF, Symowski H, Buchfelder M, Poyner DR. A polymorphism in the growth hormone (GH)-releasing hormone (GHRH) receptor gene is associated with elevated response to GHRH by human pituitary somatotrophinomas in vitro. Biochem Biophys Res Commun. 2000;275:33–36. [Abstract] [Google Scholar]
116. Lunn RM, et al. XPD polymorphisms: effects on DNA repair proficiency. Carcinogenesis. 2000;21:551–555. [Abstract] [Google Scholar]
117. Savas S, Ozcelik H. Phosphorylation states of cell cycle and DNA repair proteins can be altered by the nsSNPs. BMC Cancer. 2005;5:107. [Europe PMC free article] [Abstract] [Google Scholar]
118. Pollak MN, Schernhammer ES, Hankinson SE. Insulin-like growth factors and neoplasia. Nat Rev Cancer. 2004;4:505–518. [Abstract] [Google Scholar]
119. Mendez P, Azcoitia I, Garcia-Segura LM. Interdependence of oestrogen and insulin-like growth factor-I in the brain: potential for analysing neuroprotective mechanisms. J Endocrinol. 2005;185:11–17. [Abstract] [Google Scholar]

Citations & impact 


Impact metrics

Jump to Citations

Citations of article over time

Smart citations by scite.ai
Smart citations by scite.ai include citation statements extracted from the full text of the citing article. The number of the statements may be higher than the number of citations provided by EuropePMC if one paper cites another multiple times or lower if scite has not yet processed some of the citing articles.
Explore citation contexts and check if this article has been supported or disputed.
https://scite.ai/reports/10.1038/nrc2466

Supporting
Mentioning
Contrasting
1
42
0

Article citations


Go to all (32) article citations

Data 


Data behind the article

This data has been text mined from the article, or deposited into data resources.

Funding 


Funders who supported this work.

NCI NIH HHS (7)