Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Stone, Jennifer; Thompson, Deborah J.; dos‐Santos‐Silva, Isabel; Scott, Christopher G.; Tamimi, Rulla M.; Lindström, Sara; Kraft, Peter; Hazra, Aditi; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Hall, Per; Jensen, Matthew B.; Cunningham, Julie M.; Olson, Janet E.; Purrington, Kristen; Couch, Fergus J.; Brown, Judith; Leyland, Jean; Warren, Ruth; Luben, Robert; Khaw, Kay‐Tee; Smith, Paula; Wareham, Nicholas J.; Jud, Sebastian M.; Heusinger, Katharina; Beckmann, Matthias W.; Douglas, Julie A.; Shah, Kaanan P.; Chan, Heang Ping; Helvie, Mark A.; Marchand, Loı̈c Le; Kolonel, Laurence N.; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher A.; Giles, Graham G.; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C.; Apicella, Carmel; Andrulis, Irene L.; Knight, Julia A.; Ursin, Giske; Alnæs, Grethe I.G.; Kristensen, Vessela N.; Børresen‐Dale, Anne‐Lise; Gram, Inger Torhild; Bolla, Manjeet K.; Wang, Qin; Michailidou, Kyriaki; Dennis, Joe; Simard, Jacques; Pharoah, Paul D.P.; Dunning, Alison M.; Easton, Douglas F.; Fasching, Peter A.; Pankratz, V. Shane; Hopper, John L.; Vachon, Celine M.

doi:10.1158/0008-5472.can-14-2012

Cited by 56 publications

(49 citation statements)

References 47 publications

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“…Associations between ESR1 SNPs and mammographic density have previously been reported 25–27 , but, in this detailed analysis, only signal 2 was significantly associated with mammographic dense area ( P = 1.7 × 10 −5 ), although signal 1 also showed some evidence of an effect in the conditional analysis ( P = 0.017). Although adjusting the breast cancer analysis of signal 2 for mammographic dense area produced some attenuation of the associated effect, the lead SNP remained significantly associated with breast cancer risk (unconditional OR = 1.30, 95% CI = 1.13–1.49; P = 0.00024; OR conditional on dense area = 1.24, 95% CI = 1.08–1.43; P = 0.0025), suggesting either that the mechanism by which the signal 2 candidate causal variant affects breast cancer risk is not mediated through mammographic density or, alternatively, that dense area, as measured here, is unable to capture the association with breast composition that is most relevant to risk.…”

Section: Discussionmentioning

confidence: 48%

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Dunning¹,

Michailidou²,

et al. 2016

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We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER+ or ER−) and human ERBB2 (HER2+ or HER2−) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER− tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

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Section: Discussionmentioning

confidence: 48%

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Dunning¹,

Michailidou²,

et al. 2016

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“…(56–58) Improving outcomes for women with dense breasts may require dual consideration of risk and density,(58) and/or use of technology that employs alternative approaches to tissue visualization, such as tomosynthesis (59,60) or breast-specific gamma imaging,(61,62) or identification of genetic risk markers. (63–65)…”

Section: Discussionmentioning

confidence: 99%

Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies

et al. 2016

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Background Controversy persists about optimal mammography screening strategies. Objective To evaluate mammography strategies considering screening and treatment advances. Design Collaboration of six simulation models. Data Sources National data on incidence, risk, breast density, digital mammography performance, treatment effects, and other-cause mortality. Target Population An average-risk cohort. Time Horizon Lifetime. Perspective Societal. Interventions Mammograms from age 40, 45 or 50 to 74 at annual or biennial intervals, or annually from 40 or 45 to 49 then biennially to 74, assuming 100% screening and treatment adherence. Outcome Measures Screening benefits (vs. no screening) include percent breast cancer mortality reduction, deaths averted, and life-years gained. Harms include number of mammograms, false-positives, benign biopsies, and overdiagnosis. Results for Average-Risk Women Biennial strategies maintain 79.8%-81.3% (range across strategies and models: 68.3–98.9%) of annual screening benefits with almost half the false-positives and fewer overdiagnoses. Screening biennially from ages 50–74 achieves a median 25.8% (range: 24.1%-31.8%) breast cancer mortality reduction; annual screening from ages 40–74 years reduces mortality an additional 12.0% (range: 5.7%-17.2%) vs. no screening, but yields 1988 more false-positives and 7 more overdiagnoses per 1000 women screened. Annual screening from ages 50–74 had similar benefits as other strategies but more harms, so would not be recommended. Sub-population Results Annual screening starting at age 40 for women who have a two- to four-fold increase in risk has a similar balance of harms and benefits as biennial screening of average-risk women from 50–74. Limitations We do not consider other imaging technologies, polygenic risk, or non-adherence. Conclusion These results suggest that biennial screening is efficient for average-risk groups, but decisions on strategies depend on the weight given to the balance of harms and benefits. Primary Funding Source National Institutes of Health

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“…Age, BMI and other breast cancer risk factors explain about 30% of the variation in mammographic density, and over 60% of the residual variation appears to be accounted for by additive genetic factors . Genome‐wide association studies (GWAS) have identified variants at specific genetic loci associated with mammographic density, and about 15% of the genetic variants known to be associated with breast cancer risk are also associated with mammographic density measures, although they explain only a small fraction of the latter. Despite these known determinants, the molecular mechanisms underlying variation in mammographic density are not well understood, nor is how mammographic density translates into breast cancer risk at the biological level …”

Section: Introductionmentioning

confidence: 99%

Genome‐wide association study of peripheral blood DNA methylation and conventional mammographic density measures

Dugué

Baglietto

et al. 2019

Intl Journal of Cancer

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Age‐ and body mass index (BMI)‐adjusted mammographic density is one of the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie inter‐individual variation in mammographic density. We aimed to investigate the association between breast cancer risk‐predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, nondense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed‐effect meta‐analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome‐wide significant (p < 10−7) association for any mammographic density measure from the meta‐analysis, or from the cohort‐specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all p > 0.05/299 = 1.7 × 10−4). In summary, our study did not find evidence for associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.

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Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Cited by 56 publications

References 47 publications

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies

Genome‐wide association study of peripheral blood DNA methylation and conventional mammographic density measures

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