Dataset Information

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

ABSTRACT: Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.

SUBMITTER: Stone J

PROVIDER: S-EPMC4470785 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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

Stone Jennifer J Thompson Deborah J DJ Dos Santos Silva Isabel I Scott Christopher C Tamimi Rulla M RM Lindstrom Sara S Kraft Peter P Hazra Aditi A Li Jingmei J Eriksson Louise L Czene Kamila K Hall Per P Jensen Matt M Cunningham Julie J Olson Janet E JE Purrington Kristen K Couch Fergus J FJ Brown Judith J Leyland Jean J Warren Ruth M L RM Luben Robert N RN Khaw Kay-Tee KT Smith Paula P Wareham Nicholas J NJ Jud Sebastian M SM Heusinger Katharina K Beckmann Matthias W MW Douglas Julie A JA Shah Kaanan P KP Chan Heang-Ping HP Helvie Mark A MA Le Marchand Loic L Kolonel Laurence N LN Woolcott Christy C Maskarinec Gertraud G Haiman Christopher C Giles Graham G GG Baglietto Laura L Krishnan Kavitha K Southey Melissa C MC Apicella Carmel C Andrulis Irene L IL Knight Julia A JA Ursin Giske G Alnaes Grethe I Grenaker GI Kristensen Vessela N VN Borresen-Dale Anne-Lise AL Gram Inger Torhild IT Bolla Manjeet K MK Wang Qin Q Michailidou Kyriaki K Dennis Joe J Simard Jacques J Pharoah Paul P Dunning Alison M AM Easton Douglas F DF Fasching Peter A PA Pankratz V Shane VS Hopper John L JL Vachon Celine M CM

Cancer research 20150410 12

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support ...[more]

PMID: 25862352

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Project description:BackgroundHigher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD.MethodsIn this cohort of 611 invasive breast cancer patients (ages 23-91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype.ResultsWe found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08-1.51, ptrend = < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11-1.37, ptrend = < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09-1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors.ConclusionsOur findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.

Dataset Information

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

Publications

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

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