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Tran TXM, Chang Y, Kim S, Song H, Ryu S, Park B. Association of Breast Cancer Family History With Breast Density Over Time in Korean Women. JAMA Netw Open 2023; 6:e232420. [PMID: 36897591 DOI: 10.1001/jamanetworkopen.2023.2420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
IMPORTANCE Evidence suggests that women with a family history of breast cancer (FHBC) in first-degree relatives have a higher level of breast density; however, studies of premenopausal women remain limited. OBJECTIVE To investigate the association between FHBC and mammographic breast density and breast density changes among premenopausal women. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used population-based data obtained from the National Health Insurance Service-National Health Information Database of Korea. We included premenopausal women aged 40 to 55 years who underwent mammography for breast cancer screening once between January 1, 2015, and December 31, 2016 (n = 1 174 214), and women who underwent mammography twice (first in 2015-2016 and again between January 1, 2017 and December 31, 2018) (n = 838 855). EXPOSURES Family history of breast cancer was assessed using a self-reported questionnaire, which included information on FHBC in the mother and/or sister. MAIN OUTCOMES AND MEASURES Breast density, based on the Breast Imaging Reporting and Data System, was categorized as dense (heterogeneously or extremely dense) and nondense (almost entirely fat or scattered fibroglandular areas). Multivariate logistic regression was used to assess the association among FHBC, breast density, and changes in breast density from the first to second screening. Data analysis was performed from June 1 to September 31, 2022. RESULTS Of the 1 174 214 premenopausal women, 34 003 (2.4%; mean [SD] age, 46.3 [3.2] years) reported having FHBC among their first-degree relatives, and 1 140 211 (97.1%; mean [SD] age, 46.3 [3.2] years) reported no FHBC. Odds of having dense breasts was 22% higher (adjusted odds ratio [aOR], 1.22; 95% CI, 1.19-1.26) in women with FHBC than in women without FHBC, and the association varied by affected relatives: mother alone (aOR, 1.15; 95% CI, 1.10-1.21), sister alone (aOR, 1.26; 95% CI, 1.22-1.31), and both mother and sister (aOR, 1.64; 95% CI, 1.20-2.25). Among women with fatty breasts at baseline, the odds of developing dense breasts was higher in women with FHBC than in those without FHBC (aOR, 1.19; 95% CI, 1.11-1.26), whereas among women with dense breasts, higher odds of having persistently dense breasts were observed in women with FHBC (aOR, 1.11; 95% CI, 1.05-1.16) than in those without FHBC. CONCLUSIONS AND RELEVANCE In this cohort study of premenopausal Korean women, FHBC was positively associated with an increased incidence of having increased or persistently dense breasts over time. These findings suggest the need for a tailored breast cancer risk assessment for women with FHBC.
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Affiliation(s)
- Thi Xuan Mai Tran
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Soyeoun Kim
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Huiyeon Song
- Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea
| | - Seungho Ryu
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Boyoung Park
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
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Singh N, Joshi P, Gupta A, Marak JR, Singh DK. Evaluation of volumetric breast density as a risk factor for breast carcinoma in pre- and postmenopausal women, its association with hormone receptor status and breast carcinoma subtypes defined by histology and tumor markers. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00759-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Mammographic breast density is acknowledged as an independent risk factor for breast cancer. Its association with different pathological types and tumors markers is still under evaluation. This study aims to assess the associations of volumetric density grades (VDG) with breast cancer risk in premenopausal and postmenopausal age groups separately. We also aim to assess the association of VDG with hormone receptor status and breast cancer subtypes defined by histology and tumor markers (ER, PR, Her 2-neu and Ki 67).
Results
This retrospective study was done with inclusion of two comparable groups of 185 breast cancer cases and 244 healthy controls. These groups were further divided into pre‑ and postmenopausal subgroups. Mammograms of the cases and controls were evaluated by fully automated volumetric breast density software-VOLPARA and classified into four VDG. The hormone receptor status and breast cancer subtypes defined by histological features and tumor markers in the various VDG were also evaluated. The risk of developing carcinoma was significantly higher in women with high-density breasts (VDG-c + VDG-d) as compared with low-density breasts (VDG-a + VDG-b) in both premenopausal and postmenopausal subgroups. No significant difference was seen in the histopathological characteristics of breast cancer among various VDG.
Conclusions
Our study suggests positive association between high VDG and risk of cancer in both premenopausal and postmenopausal group of Indian women. The hormone receptor status and breast cancer subtypes defined by histology and tumor markers did not reveal any relation to the grades of breast density.
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Mai Tran TX, Kim S, Song H, Park B. Family history of breast cancer, mammographic breast density and breast cancer risk: Findings from a cohort study of Korean women. Breast 2022; 65:180-186. [PMID: 36049384 PMCID: PMC9441334 DOI: 10.1016/j.breast.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 07/22/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
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A Case Study in Breast Density Evaluation Using Bioimpedance Measurements. SENSORS 2022; 22:s22072747. [PMID: 35408360 PMCID: PMC9002785 DOI: 10.3390/s22072747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 11/16/2022]
Abstract
(1) Background: As breast cancer studies suggest, a high percentage of breast density (PBD) may be related to breast cancer incidence. Although PBD screening is one of the strongest predictors of breast cancer risk, X-ray-based mammography evaluation is subjective. Therefore, new objective PBD measuring techniques are of interest. A case study analyzing the PBD of thirteen female participants using a bioimpedance-based method, the anomalies tracking circle (ATC), is described in this paper. (2) Methods: In the first stage, the breast bioimpedance of each participant was measured. Then, the participant breast density was determined by applying a mammogram just after the breast bioimpedance measurement stage. In the third stage, the ATC algorithm was applied to the measured bioimpedance data for each participant, and a results analysis was done. (3) Results: An ATC variation according to the breast density was observed from the obtained data, this allowed the use of classification techniques to determine the PBD. (4) Conclusions: The described breast density method is a promising approach that might be applied as an auxiliary tool to the mammography in order to obtain precise and objective results for evaluation of breast density and with that determine potential breast cancer risk.
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Pubertal mammary gland development is a key determinant of adult mammographic density. Semin Cell Dev Biol 2020; 114:143-158. [PMID: 33309487 DOI: 10.1016/j.semcdb.2020.11.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/25/2020] [Accepted: 11/28/2020] [Indexed: 01/04/2023]
Abstract
Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.
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Sieh W, Rothstein JH, Klein RJ, Alexeeff SE, Sakoda LC, Jorgenson E, McBride RB, Graff RE, McGuire V, Achacoso N, Acton L, Liang RY, Lipson JA, Rubin DL, Yaffe MJ, Easton DF, Schaefer C, Risch N, Whittemore AS, Habel LA. Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk. Nat Commun 2020; 11:5116. [PMID: 33037222 PMCID: PMC7547012 DOI: 10.1038/s41467-020-18883-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 09/17/2020] [Indexed: 11/09/2022] Open
Abstract
Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10-8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk.
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Affiliation(s)
- Weiva Sieh
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Joseph H Rothstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Russell B McBride
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Valerie McGuire
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Luana Acton
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Rhea Y Liang
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jafi A Lipson
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Martin J Yaffe
- Departments of Medical Biophysics and Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care and Department of Oncology, University of Cambridge, Cambridge, UK
| | - Catherine Schaefer
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Neil Risch
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Alice S Whittemore
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
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Gabrielson M, Azam S, Hardell E, Holm M, Ubhayasekera KA, Eriksson M, Bäcklund M, Bergquist J, Czene K, Hall P. Hormonal determinants of mammographic density and density change. Breast Cancer Res 2020; 22:95. [PMID: 32847607 PMCID: PMC7449090 DOI: 10.1186/s13058-020-01332-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 08/13/2020] [Indexed: 11/12/2022] Open
Abstract
Background Mammographic density (MD) is a strong risk factor for breast cancer. We examined how endogenous plasma hormones are associated with average MD area (cm2) and annual MD change (cm2/year). Methods This study within the prospective KARMA cohort included analyses of plasma hormones of 1040 women. Hormones from the progestogen (n = 3), androgen (n = 7), oestrogen (n = 2) and corticoid (n = 5) pathways were analysed by ultra-performance supercritical fluid chromatography-tandem mass spectrometry (UPSFC-MS/MS), as well as peptide hormones and proteins (n = 2). MD was measured as a dense area using the STRATUS method (mean over the left and right breasts) and mean annual MD change over time. Results Greater baseline mean MD was associated with overall higher concentrations of progesterone (average + 1.29 cm2 per doubling of hormone concentration), 17OH-progesterone (+ 1.09 cm2), oesterone sulphate (+ 1.42 cm2), prolactin (+ 2.11 cm2) and SHBG (+ 4.18 cm2), and inversely associated with 11-deoxycortisol (− 1.33 cm2). The association between MD and progesterone was confined to the premenopausal women only. The overall annual MD change was − 0.8 cm2. Hormones from the androgen pathway were statistically significantly associated with MD change. The annual MD change was − 0.96 cm2 and − 1.16 cm2 lesser, for women in the highest quartile concentrations of testosterone and free testosterone, respectively, compared to those with the lowest concentrations. Conclusions Our results suggest that, whereas hormones from the progestogen, oestrogen and corticoid pathways drive baseline MD, MD change over time is mainly driven by androgens. This study emphasises the complexity of risk factors for breast cancer and their mechanisms of action.
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Affiliation(s)
- Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden.
| | - Shadi Azam
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden
| | - Elina Hardell
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden
| | - Madeleine Holm
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden
| | - Kumari A Ubhayasekera
- Analytical Chemistry and Neurochemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden
| | - Magnus Bäcklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden
| | - Jonas Bergquist
- Analytical Chemistry and Neurochemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, SE-171 77, Stockholm, Sweden.,Department of Oncology, South General Hospital, Stockholm, Sweden
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Han Y, Berkey CS, Herman CR, Appleton CM, Alimujiang A, Colditz GA, Toriola AT. Adiposity Change Over the Life Course and Mammographic Breast Density in Postmenopausal Women. Cancer Prev Res (Phila) 2020; 13:475-482. [PMID: 32102947 PMCID: PMC8210631 DOI: 10.1158/1940-6207.capr-19-0549] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 01/21/2020] [Accepted: 02/19/2020] [Indexed: 11/16/2022]
Abstract
Mammographic breast density is a strong risk factor for breast cancer. We comprehensively investigated the associations of body mass index (BMI) change from ages 10, 18, and 30 to age at mammogram with mammographic breast density in postmenopausal women. We used multivariable linear regression models, adjusted for confounders, to investigate the associations of BMI change with volumetric percent density, dense volume, and nondense volume, assessed using Volpara in 367 women. At the time of mammogram, the mean age was 57.9 years. Compared with women who had a BMI gain of 0.1-5 kg/m2 from age 10, women who had a BMI gain of 5.1-10 kg/m2 had a 24.4% decrease [95% confidence interval (CI), 6.0%-39.2%] in volumetric percent density; women who had a BMI gain of 10.1-15 kg/m2 had a 46.1% decrease (95% CI, 33.0%-56.7%) in volumetric percent density; and women who had a BMI gain of >15 kg/m2 had a 56.5% decrease (95% CI, 46.0%-65.0%) in volumetric percent density. Similar, but slightly attenuated associations were observed for BMI gain from ages 18 and 30 to age at mammogram and volumetric percent density. BMI gain over the life course was positively associated with nondense volume, but not dense volume. We observed strong associations between BMI change over the life course and mammographic breast density. The inverse associations between early-life adiposity change and volumetric percent density suggest that childhood adiposity may confer long-term protection against postmenopausal breast cancer via its effect of mammographic breast density.
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Affiliation(s)
- Yunan Han
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Breast Surgery, First Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Catherine S Berkey
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Cheryl R Herman
- Alvin J. Siteman Cancer Center, Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
| | | | - Aliya Alimujiang
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Graham A Colditz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri
- Alvin J. Siteman Cancer Center, Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
| | - Adetunji T Toriola
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri.
- Alvin J. Siteman Cancer Center, Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
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Skarping I, Förnvik D, Sartor H, Heide-Jørgensen U, Zackrisson S, Borgquist S. Mammographic density is a potential predictive marker of pathological response after neoadjuvant chemotherapy in breast cancer. BMC Cancer 2019; 19:1272. [PMID: 31888552 PMCID: PMC6937786 DOI: 10.1186/s12885-019-6485-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 12/20/2019] [Indexed: 12/12/2022] Open
Abstract
Background Our aim is to study if mammographic density (MD) prior to neoadjuvant chemotherapy is a predictive factor in accomplishing a pathological complete response (pCR) in neoadjuvant-treated breast cancer patients. Methods Data on all neoadjuvant treated breast cancer patients in Southern Sweden (2005–2016) were retrospectively identified, with patient and tumor characteristics retrieved from their medical charts. Diagnostic mammograms were used to evaluate and score MD as categorized by breast composition with the Breast Imaging-Reporting and Data System (BI-RADS) 5th edition. Logistic regression was used in complete cases to assess the odds ratios (OR) for pCR compared to BI-RADS categories (a vs b-d), adjusting for patient and pre-treatment tumor characteristics. Results A total of 302 patients were included in the study population, of which 57 (18.9%) patients accomplished pCR following neoadjuvant chemotherapy. The number of patients in the BI-RADS category a, b, c, and d were separately 16, 120, 140, and 26, respectively. In comparison to patients with BI-RADS breast composition a, patients with denser breasts had a lower OR of accomplishing pCR: BI-RADS b 0.32 (95%CI 0.07–0.1.5), BI-RADS c 0.30 (95%CI 0.06–1.45), and BI-RADS d 0.06 (95%CI 0.01–0.56). These associations were measured with lower point estimates, but wider confidence interval, in premenopausal patients; OR of accomplishing pCR for BI-RADS d in comparison to BI-RADS a: 0.03 (95%CI 0.00–0.76). Conclusions The likelihood of accomplishing pCR is indicated to be lower in breast cancer patients with higher MD, which need to be analysed in future studies for improved clinical decision-making regarding neoadjuvant treatment.
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Affiliation(s)
- Ida Skarping
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund, Sweden.
| | - Daniel Förnvik
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hanna Sartor
- Diagnostic Radiology, Department of Translational Medicine, Lund University, Skåne University Hospital, Lund and Malmö, Sweden
| | - Uffe Heide-Jørgensen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Sophia Zackrisson
- Diagnostic Radiology, Department of Translational Medicine, Lund University, Skåne University Hospital, Lund and Malmö, Sweden
| | - Signe Borgquist
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund, Sweden.,Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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Rebolj M, Blyuss O, Chia KS, Duffy SW. Long-term excess risk of breast cancer after a single breast density measurement. Eur J Cancer 2019; 117:41-47. [PMID: 31229948 PMCID: PMC6658627 DOI: 10.1016/j.ejca.2019.05.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/02/2019] [Accepted: 05/03/2019] [Indexed: 12/20/2022]
Abstract
AIM Breast density is a risk factor for breast cancer. As density changes across a woman's life span, we studied for how long a single density measurement taken in (post-)menopausal women remains informative. METHODS We used data from Singaporean women who underwent a single mammography screen at age 50-64 years. For each case with breast cancer diagnosed at screening or in the subsequent 10 years, whether screen detected or diagnosed following symptoms, two age-matched controls were selected. We studied the excess risk of breast cancer, calculated as an odds ratio (OR) with conditional logistic regression and adjusted for body mass index, associated with 26-50% and with 51-100% density compared with ≤25% density by time since screening. RESULTS In total, 490 women had breast cancer, of which 361 were diagnosed because of symptoms after screening. Women with 51-100% breast density had an excess risk of breast cancer that did not seem to attenuate with time. In 1-3 years after screening, the OR was 2.22 (95% confidence interval [CI]: 1.07-4.61); in 4-6 years after screening, the OR was 4.09 (95% CI: 2.21-7.58), and in 7-10 years after screening, the OR was 5.35 (95% CI: 2.57-11.15). Excess risk with a stable OR of about 2 was also observed for women with 26-50% breast density. These patterns were robust when the analyses were limited to post-menopausal women, non-users of hormonal replacement therapy and after stratification by age at density measurement. CONCLUSION A single breast density measurement identifies women with an excess risk of breast cancer during at least the subsequent 10 years.
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Affiliation(s)
- Matejka Rebolj
- Cancer Prevention Group, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London SE1 9RT, UK; Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
| | - Oleg Blyuss
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; Department of Paediatrics, Sechenov University, Moscow, Russia
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
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11
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Azam S, Sjölander A, Eriksson M, Gabrielson M, Czene K, Hall P. Determinants of Mammographic Density Change. JNCI Cancer Spectr 2019; 3:pkz004. [PMID: 31360892 PMCID: PMC6649843 DOI: 10.1093/jncics/pkz004] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 12/18/2018] [Accepted: 01/30/2019] [Indexed: 01/08/2023] Open
Abstract
Background Mammographic density (MD) is a strong risk factor for breast cancer. We examined how breast cancer risk factors are associated with MD area (cm2) change across age. Methods We conducted a cohort study of 31 782 Swedish women ages 40–70 years at time of baseline mammogram. Lifestyle and reproductive risk factors were assessed by a web-based questionnaire. MD was measured as dense area using the STRATUS method (mean over the left and right breast). Linear regression analyses with adjustments for age, body mass index (BMI), and menopausal status at baseline were performed to assess the association between breast cancer risk factors and mean baseline MD. To investigate mean MD change across age, linear regression analyses with adjustments for age, BMI, menopausal status, and age at last mammogram were performed. All tests of statistical significance were two-sided. Results Except for oral contraceptive use, established lifestyle and reproductive risk factors for breast cancer were associated with baseline mean MD. The overall average annual MD change was −1.0 cm2. BMI and physical activity were statistically significantly associated with MD change. Lean women (BMI <20 kg/m2) had a mean MD change of −1.13 cm2 per year (95% confidence interval = −1.25 to −1.02) compared with −0.46 cm2 per year (95% confidence interval = −0.57 to −0.35) for women with BMI 30 or higher. The annual MD change was −0.4 cm2 larger in women who were very physically active compared with less physically active women. Conclusions Our results indicate that all risk factors for breast cancer, except oral contraceptive use, are associated with baseline MD but that only age, BMI, and physical activity are determinants of MD change.
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Affiliation(s)
- Shadi Azam
- Correspondence to: Shadi Azam, PhD, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 171 77 Stockholm, Sweden ()
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Hjerkind KV, Ellingjord-Dale M, Johansson AL, Aase HS, Hoff SR, Hofvind S, Fagerheim S, dos-Santos-Silva I, Ursin G. Volumetric Mammographic Density, Age-Related Decline, and Breast Cancer Risk Factors in a National Breast Cancer Screening Program. Cancer Epidemiol Biomarkers Prev 2018; 27:1065-1074. [DOI: 10.1158/1055-9965.epi-18-0151] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 04/25/2018] [Accepted: 06/15/2018] [Indexed: 11/16/2022] Open
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An overview of mammographic density and its association with breast cancer. Breast Cancer 2018; 25:259-267. [PMID: 29651637 PMCID: PMC5906528 DOI: 10.1007/s12282-018-0857-5] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 03/30/2018] [Indexed: 12/24/2022]
Abstract
In 2017, breast cancer became the most commonly diagnosed cancer among women in the US. After lung cancer, breast cancer is the leading cause of cancer-related mortality in women. The breast consists of several components, including milk storage glands, milk ducts made of epithelial cells, adipose tissue, and stromal tissue. Mammographic density (MD) is based on the proportion of stromal, epithelial, and adipose tissue. Women with high MD have more stromal and epithelial cells and less fatty adipose tissue, and are more likely to develop breast cancer in their lifetime compared to women with low MD. Because of this correlation, high MD is an independent risk factor for breast cancer. Further, mammographic screening is less effective in detecting suspicious lesions in dense breast tissue, which can lead to late-stage diagnosis. Molecular differences between dense and non-dense breast tissues explain the underlying biological reasons for why women with dense breasts are at a higher risk for developing breast cancer. The goal of this review is to highlight the current molecular understanding of MD, its association with breast cancer risk, the demographics pertaining to MD, and the environmental factors that modulate MD. Finally, we will review the current legislation regarding the disclosure of MD on a traditional screening mammogram and the supplemental screening options available to women with dense breast tissue.
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14
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McLean KE, Stone J. Role of breast density measurement in screening for breast cancer. Climacteric 2018; 21:214-220. [DOI: 10.1080/13697137.2018.1424816] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- K. E. McLean
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia
| | - J. Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia
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15
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Byström S, Eklund M, Hong MG, Fredolini C, Eriksson M, Czene K, Hall P, Schwenk JM, Gabrielson M. Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density. Breast Cancer Res 2018; 20:14. [PMID: 29444691 PMCID: PMC5813412 DOI: 10.1186/s13058-018-0940-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 01/29/2018] [Indexed: 02/08/2023] Open
Abstract
Background Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density. Methods Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI). Results Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD. Conclusions Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women. Electronic supplementary material The online version of this article (10.1186/s13058-018-0940-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sanna Byström
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Claudia Fredolini
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden.,Department of Oncology, South General Hospital, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden.
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Mammographic Breast Density and Breast Cancer Molecular Subtypes: The Kenyan-African Aspect. BIOMED RESEARCH INTERNATIONAL 2018; 2018:6026315. [PMID: 29607324 PMCID: PMC5828539 DOI: 10.1155/2018/6026315] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 12/05/2017] [Indexed: 12/04/2022]
Abstract
Introduction Data examining mammographic breast density (MBD) among patients in Sub-Saharan Africa are sparse. We evaluated how MBD relates to breast cancer characteristics in Kenyan women undergoing diagnostic mammography. Methods This cross-sectional study included women with pathologically confirmed breast cancers (n = 123). Pretreatment mammograms of the unaffected breast were assessed to estimate absolute dense area (cm2), nondense area (cm2), and percent density (PD). Relationships between density measurements and clinical characteristics were evaluated using analysis of covariance. Results Median PD and dense area were 24.9% and 85.3 cm2. Higher PD and dense area were observed in younger women (P < 0.01). Higher dense and nondense areas were observed in obese women (P-trend < 0.01). Estrogen receptor (ER) positive patients (73%) had higher PD and dense area than ER-negative patients (P ≤ 0.02). Triple negative breast cancer (TNBC) patients (17%) had lower PD and dense area (P ≤ 0.01) compared with non-TNBCs. No associations were observed between MBD and tumor size and grade. Conclusions Our findings show discordant relationships between MBD and molecular tumor subtypes to those previously observed in Western populations. The relatively low breast density observed at diagnosis may have important implications for cancer prevention initiatives in Kenya. Subsequent larger studies are needed to confirm these findings.
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Ironside AJ, Jones JL. Stromal characteristics may hold the key to mammographic density: the evidence to date. Oncotarget 2017; 7:31550-62. [PMID: 26784251 PMCID: PMC5058777 DOI: 10.18632/oncotarget.6912] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 01/02/2016] [Indexed: 12/11/2022] Open
Abstract
There is strong epidemiological data indicating a role for increased mammographic density (MD) in predisposing to breast cancer, however, the biological mechanisms underlying this phenomenon are less well understood. Recently, studies of human breast tissues have started to characterise the features of mammographically dense breasts, and a number of in-vitro and in-vivo studies have explored the potential mechanisms through which dense breast tissue may exert this tumourigenic risk. This article aims to review both the pathological and biological evidence implicating a key role for the breast stromal compartment in MD, how this may be modified and the clinical significance of these findings. The epidemiological context will be briefly discussed but will not be covered in detail.
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Affiliation(s)
- Alastair J Ironside
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - J Louise Jones
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
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Lope V, García-Pérez J, Pérez-Gómez B, Pedraza-Flechas AM, Alguacil J, González-Galarzo MC, Alba MA, van der Haar R, Cortés-Barragán RA, Pedraz-Pingarrón C, Moreo P, Santamariña C, Ederra M, Vidal C, Salas-Trejo D, Sánchez-Contador C, Llobet R, Pollán M. Occupational exposures and mammographic density in Spanish women. Occup Environ Med 2017; 75:124-131. [PMID: 29074552 DOI: 10.1136/oemed-2017-104580] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 09/20/2017] [Accepted: 09/28/2017] [Indexed: 12/29/2022]
Abstract
OBJECTIVES The association between occupational exposures and mammographic density (MD), a marker of breast cancer risk, has not been previously explored. Our objective was to investigate the influence of occupational exposure to chemical, physical and microbiological agents on MD in adult women. METHODS This is a population-based cross-sectional study based on 1476 female workers aged 45-65 years from seven Spanish breast cancer screening programmes. Occupational history was surveyed by trained staff. Exposure to occupational agents was assessed using the Spanish job-exposure matrix MatEmESp. Percentage of MD was measured by two radiologists using a semiautomatic computer tool. The association was estimated using mixed log-linear regression models adjusting for age, education, body mass index, menopausal status, parity, smoking, alcohol intake, type of mammography, family history of breast cancer and hormonal therapy use, and including screening centre and professional reader as random effects terms. RESULTS Although no association was found with most of the agents, women occupationally exposed to perchloroethylene (eβ=1.51; 95% CI 1.04 to 2.19), ionising radiation (eβ=1.23; 95% CI 0.99 to 1.52) and mould spores (eβ=1.44; 95% CI 1.01 to 2.04) tended to have higher MD. The percentage of density increased 12% for every 5 years exposure to perchloroethylene or mould spores, 11% for every 5 years exposure to aliphatic/alicyclic hydrocarbon solvents and 3% for each 5 years exposure to ionising radiation. CONCLUSIONS Exposure to perchloroethylene, ionising radiation, mould spores or aliphatic/alicyclic hydrocarbon solvents in occupational settings could be associated with higher MD. Further studies are needed to clarify the accuracy and the reasons for these findings.
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Affiliation(s)
- Virginia Lope
- Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública - CIBERESP), Madrid, Spain
| | - Javier García-Pérez
- Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública - CIBERESP), Madrid, Spain
| | - Beatriz Pérez-Gómez
- Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública - CIBERESP), Madrid, Spain
| | - Ana María Pedraza-Flechas
- Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Juan Alguacil
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública - CIBERESP), Madrid, Spain.,Centro de Investigación en Salud y Medio Ambiente (CYSMA), Universidad de Huelva, Huelva, Spain
| | | | - Miguel Angel Alba
- Área de Higiene Industrial, PREMAP Seguridad y Salud S.L.U, Barcelona, Spain
| | | | | | | | - Pilar Moreo
- Aragon Breast Cancer Screening Program, Aragon Health Service, Zaragoza, Spain
| | - Carmen Santamariña
- Servicio de Alertas Epidemiolóxicas, Programa Galego Diagnostico Precoz Cancro de Mama, Unidade Central A Coruña, Conselleria de Sanidade, A Coruña, Spain
| | - María Ederra
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública - CIBERESP), Madrid, Spain.,Public Health Institute, Navarra Breast Cancer Screening Programme, Pamplona, Spain
| | - Carmen Vidal
- Prevention and Control Program, Catalan Institute of Oncology, Barcelona, Spain
| | - Dolores Salas-Trejo
- Valencia Breast Cancer Screening Program, General Directorate Public Health, Valencia, Spain
| | | | - Rafael Llobet
- Institute of Computer Technology, Universitat Politècnica de València, Valencia, Spain
| | - Marina Pollán
- Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública - CIBERESP), Madrid, Spain
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Alexeeff SE, Odo NU, Lipson JA, Achacoso N, Rothstein JH, Yaffe MJ, Liang RY, Acton L, McGuire V, Whittemore AS, Rubin DL, Sieh W, Habel LA. Age at Menarche and Late Adolescent Adiposity Associated with Mammographic Density on Processed Digital Mammograms in 24,840 Women. Cancer Epidemiol Biomarkers Prev 2017; 26:1450-1458. [PMID: 28698185 DOI: 10.1158/1055-9965.epi-17-0264] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 05/15/2017] [Accepted: 06/28/2017] [Indexed: 12/21/2022] Open
Abstract
Background: High mammographic density is strongly associated with increased breast cancer risk. Some, but not all, risk factors for breast cancer are also associated with higher mammographic density.Methods: The study cohort (N = 24,840) was drawn from the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California and included non-Hispanic white females ages 40 to 74 years with a full-field digital mammogram (FFDM). Percent density (PD) and dense area (DA) were measured by a radiological technologist using Cumulus. The association of age at menarche and late adolescent body mass index (BMI) with PD and DA were modeled using linear regression adjusted for confounders.Results: Age at menarche and late adolescent BMI were negatively correlated. Age at menarche was positively associated with PD (P value for trend <0.0001) and DA (P value for trend <0.0001) in fully adjusted models. Compared with the reference category of ages 12 to 13 years at menarche, menarche at age >16 years was associated with an increase in PD of 1.47% (95% CI, 0.69-2.25) and an increase in DA of 1.59 cm2 (95% CI, 0.48-2.70). Late adolescent BMI was inversely associated with PD (P < 0.0001) and DA (P < 0.0001) in fully adjusted models.Conclusions: Age at menarche and late adolescent BMI are both associated with Cumulus measures of mammographic density on processed FFDM images.Impact: Age at menarche and late adolescent BMI may act through different pathways. The long-term effects of age at menarche on cancer risk may be mediated through factors besides mammographic density. Cancer Epidemiol Biomarkers Prev; 26(9); 1450-8. ©2017 AACR.
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Affiliation(s)
- Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, California.
| | - Nnaemeka U Odo
- Data Mining & Analytics, Encounter Information Operations, Kaiser Permanente Northern California, Oakland, California.,Optum360, United Health Group, Las Vegas, Nevada
| | - Jafi A Lipson
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Joseph H Rothstein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Rhea Y Liang
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Luana Acton
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Valerie McGuire
- Department of Health Research and Policy, Division of Epidemiology, Stanford University School of Medicine, Stanford, California
| | - Alice S Whittemore
- Department of Health Research and Policy, Division of Epidemiology, Stanford University School of Medicine, Stanford, California.,Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, California.,Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Weiva Sieh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, California.,Department of Health Research and Policy, Division of Epidemiology, Stanford University School of Medicine, Stanford, California
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20
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Breast Density and Breast Cancer Incidence in the Lebanese Population: Results from a Retrospective Multicenter Study. BIOMED RESEARCH INTERNATIONAL 2017; 2017:7594953. [PMID: 28752096 PMCID: PMC5511666 DOI: 10.1155/2017/7594953] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 05/12/2017] [Accepted: 05/16/2017] [Indexed: 12/31/2022]
Abstract
Purpose To study the distribution of breast mammogram density in Lebanese women and correlate it with breast cancer (BC) incidence. Methods Data from 1,049 women who had screening or diagnostic mammography were retrospectively reviewed. Age, menopausal status, contraceptives or hormonal replacement therapy (HRT), parity, breastfeeding, history of BC, breast mammogram density, and final BI-RADS assessment were collected. Breast density was analyzed in each age category and compared according to factors that could influence breast density and BC incidence. Results 120 (11.4%) patients had BC personal history with radiation and/or chemotherapy; 66 patients were postmenopausal under HRT. Mean age was 52.58 ± 11.90 years. 76.4% of the patients (30–39 years) had dense breasts. Parity, age, and menopausal status were correlated to breast density whereas breastfeeding and personal/family history of BC and HRT were not. In multivariate analysis, it was shown that the risk of breast cancer significantly increases 3.3% with age (P = 0.005), 2.5 times in case of menopause (P = 0.004), and 1.4 times when breast density increases (P = 0.014). Conclusion Breast density distribution in Lebanon is similar to the western society. Similarly to other studies, it was shown that high breast density was statistically related to breast cancer, especially in older and menopausal women.
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Mammographic Breast Density and Breast Cancer Risk: Implications of the Breast Density Legislation for Health Care Practitioners. Clin Obstet Gynecol 2017; 59:419-38. [PMID: 26992182 DOI: 10.1097/grf.0000000000000192] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Breast density has emerged as a critical phenotypic marker of increased breast cancer risk. The breast density legislation, passed in multiple states, requires patient notification of the implications of the breast density on breast cancer risk and screening. Supplemental screening may be suggested in the state regulation; however, there are limited data to guide conversations with patients. This article will review the current state of supplemental screening in women with dense breasts and discuss theories of the mechanism of action. Guidance is provided to assist in shared decision making and appropriate patient counseling.
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Krishnan K, Baglietto L, Stone J, Simpson JA, Severi G, Evans CF, MacInnis RJ, Giles GG, Apicella C, Hopper JL. Longitudinal Study of Mammographic Density Measures That Predict Breast Cancer Risk. Cancer Epidemiol Biomarkers Prev 2017; 26:651-660. [PMID: 28062399 DOI: 10.1158/1055-9965.epi-16-0499] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 11/04/2016] [Accepted: 11/14/2016] [Indexed: 11/16/2022] Open
Abstract
Background: After adjusting for age and body mass index (BMI), mammographic measures-dense area (DA), percent dense area (PDA), and nondense area (NDA)-are associated with breast cancer risk. Our aim was to use longitudinal data to estimate the extent to which these risk-predicting measures track over time.Methods: We collected 4,320 mammograms (age range, 24-83 years) from 970 women in the Melbourne Collaborative Cohort Study and the Australian Breast Cancer Family Registry. Women had on average 4.5 mammograms (range, 1-14). DA, PDA, and NDA were measured using the Cumulus software and normalized using the Box-Cox method. Correlations in the normalized risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modeling of Gompertz curves.Results: Mean normalized DA and PDA were constant with age to the early 40s, decreased over the next two decades, and were almost constant from the mid-60s onward. Mean normalized NDA increased nonlinearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalized DA were 0.94, 0.93, 0.91, 0.91, and 0.91 for mammograms taken 2, 4, 6, 8, and 10 years apart, respectively. Similar correlations were estimated for the age- and BMI-adjusted normalized PDA and NDA.Conclusions: The mammographic measures that predict breast cancer risk are highly correlated over time.Impact: This has implications for etiologic research and clinical management whereby women at increased risk could be identified at a young age (e.g., early 40s or even younger) and recommended appropriate screening and prevention strategies. Cancer Epidemiol Biomarkers Prev; 26(4); 651-60. ©2017 AACR.
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Affiliation(s)
- Kavitha Krishnan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Laura Baglietto
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.,CESP, INSERM, Facultés de Medicine Université Paris-Sud, Villejuif, France.,Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia
| | - Julie A Simpson
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | | | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.,Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - Carmel Apicella
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. .,Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea.,Institute of Health and Environment, Seoul National University, Seoul, Korea
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Denholm R, De Stavola B, Hipwell JH, Doran SJ, Busana MC, Eng A, Jeffreys M, Leach MO, Hawkes D, dos Santos Silva I. Pre-natal exposures and breast tissue composition: findings from a British pre-birth cohort of young women and a systematic review. Breast Cancer Res 2016; 18:102. [PMID: 27729066 PMCID: PMC5059986 DOI: 10.1186/s13058-016-0751-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 08/23/2016] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Breast density, the amount of fibroglandular tissue in the adult breast for a women's age and body mass index, is a strong biomarker of susceptibility to breast cancer, which may, like breast cancer risk itself, be influenced by events early in life. In the present study, we investigated the association between pre-natal exposures and breast tissue composition. METHODS A sample of 500 young, nulliparous women (aged approximately 21 years) from a U.K. pre-birth cohort underwent a magnetic resonance imaging examination of their breasts to estimate percent water, a measure of the relative amount of fibroglandular tissue equivalent to mammographic percent density. Information on pre-natal exposures was collected throughout the mothers' pregnancy and shortly after delivery. Regression models were used to investigate associations between percent water and pre-natal exposures. Mediation analysis, and a systematic review and meta-analysis of the published literature, were also conducted. RESULTS Adjusted percent water in young women was positively associated with maternal height (p for linear trend [p t] = 0.005), maternal mammographic density in middle age (p t = 0.018) and the participant's birth size (p t < 0.001 for birthweight). A 1-SD increment in weight (473 g), length (2.3 cm), head circumference (1.2 cm) and Ponderal Index (4.1 g/cm3) at birth were associated with 3 % (95 % CI 2-5 %), 2 % (95 % CI 0-3 %), 3 % (95 % CI 1-4 %) and 1 % (95 % CI 0-3 %), respectively, increases in mean adjusted percent water. The effect of maternal height on the participants' percent water was partly mediated through birth size, but there was little evidence that the effect of birthweight was primarily mediated via adult body size. The meta-analysis supported the study findings, with breast density being positively associated with birth size. CONCLUSIONS These findings provide strong evidence of pre-natal influences on breast tissue composition. The positive association between birth size and relative amount of fibroglandular tissue indicates that breast density and breast cancer risk may share a common pre-natal origin.
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Affiliation(s)
- Rachel Denholm
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Bianca De Stavola
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - John H. Hipwell
- Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, UCL, London, UK
| | - Simon J. Doran
- Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research (ICH) and Royal Marsden NHS Foundation Trust (RHM), London, UK
| | - Marta C. Busana
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Amanda Eng
- Centre for Public Health Research, Massey University, Wellington, New Zealand
| | - Mona Jeffreys
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Martin O. Leach
- Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research (ICH) and Royal Marsden NHS Foundation Trust (RHM), London, UK
| | - David Hawkes
- Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, UCL, London, UK
| | - Isabel dos Santos Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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Fjeldheim FN, Frydenberg H, Flote VG, McTiernan A, Furberg AS, Ellison PT, Barrett ES, Wilsgaard T, Jasienska G, Ursin G, Wist EA, Thune I. Polymorphisms in the estrogen receptor alpha gene (ESR1), daily cycling estrogen and mammographic density phenotypes. BMC Cancer 2016; 16:776. [PMID: 27717337 PMCID: PMC5055696 DOI: 10.1186/s12885-016-2804-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 09/22/2016] [Indexed: 01/01/2023] Open
Abstract
Background Single nucleotide polymorphisms (SNPs) involved in the estrogen pathway and SNPs in the estrogen receptor alpha gene (ESR1 6q25) have been linked to breast cancer development, and mammographic density is an established breast cancer risk factor. Whether there is an association between daily estradiol levels, SNPs in ESR1 and premenopausal mammographic density phenotypes is unknown. Methods We assessed estradiol in daily saliva samples throughout an entire menstrual cycle in 202 healthy premenopausal women in the Norwegian Energy Balance and Breast Cancer Aspects I study. DNA was genotyped using the Illumina Golden Gate platform. Mammograms were taken between days 7 and 12 of the menstrual cycle, and digitized mammographic density was assessed using a computer-assisted method (Madena). Multivariable regression models were used to study the association between SNPs in ESR1, premenopausal mammographic density phenotypes and daily cycling estradiol. Results We observed inverse linear associations between the minor alleles of eight measured SNPs (rs3020364, rs2474148, rs12154178, rs2347867, rs6927072, rs2982712, rs3020407, rs9322335) and percent mammographic density (p-values: 0.002–0.026), these associations were strongest in lean women (BMI, ≤23.6 kg/m2.). The odds of above-median percent mammographic density (>28.5 %) among women with major homozygous genotypes were 3–6 times higher than those of women with minor homozygous genotypes in seven SNPs. Women with rs3020364 major homozygous genotype had an OR of 6.46 for above-median percent mammographic density (OR: 6.46; 95 % Confidence Interval 1.61, 25.94) when compared to women with the minor homozygous genotype. These associations were not observed in relation to absolute mammographic density. No associations between SNPs and daily cycling estradiol were observed. However, we suggest, based on results of borderline significance (p values: 0.025–0.079) that the level of 17β-estradiol for women with the minor genotype for rs3020364, rs24744148 and rs2982712 were lower throughout the cycle in women with low (<28.5 %) percent mammographic density and higher in women with high (>28.5 %) percent mammographic density, when compared to women with the major genotype. Conclusion Our results support an association between eight selected SNPs in the ESR1 gene and percent mammographic density. The results need to be confirmed in larger studies. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2804-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- F N Fjeldheim
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway. .,Institute of Clinical Medicine, University of Oslo, Oslo, N-0316, Norway.
| | - H Frydenberg
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway
| | - V G Flote
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway
| | - A McTiernan
- Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, WA, USA
| | - A-S Furberg
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037, Tromsø, Norway.,Department of Microbiology and Infection Control, University Hospital of North Norway, 9038, Tromsø, Norway
| | - P T Ellison
- Department of Anthropology, Harvard University, 11 Divinity Avenue, Cambridge, MA, 02138, USA
| | - E S Barrett
- Department of Obstetrics and Gynecology, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - T Wilsgaard
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037, Tromsø, Norway
| | - G Jasienska
- Department of Environmental Health, Institute of Public Health, Jagiellonian University Medical College, Grzegorzecka 20, Krakow, 31-351, Poland
| | - G Ursin
- Cancer Registry of Norway, PO Box 5313, Majorstuen, Oslo, N-0304, Norway
| | - E A Wist
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, N-0316, Norway
| | - I Thune
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway.,Department of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037, Tromsø, Norway
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Atkinson EJ, Eckel-Passow JE, Wang A, Greenberg AJ, Scott CG, Pankratz VS, Purrington KN, Sellers TA, Rider DN, Heit JA, de Andrade M, Cunningham JM, Couch FJ, Vachon CM. The association of copy number variation and percent mammographic density. BMC Res Notes 2015; 8:297. [PMID: 26152678 PMCID: PMC4494822 DOI: 10.1186/s13104-015-1212-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 05/22/2015] [Indexed: 11/10/2022] Open
Abstract
Background Percent mammographic density (PD) estimates the proportion of stromal, fat, and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer [1]. Inherited factors are hypothesized to explain between 30 and 60% of the variance in this trait [2–5]. However, previously identified common genetic variants account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained. We performed the first study to examine whether germline copy number variation (CNV) are associated with PD. Two genome-wide association studies (GWAS) of percent density conducted on the Illumina 660W-Quad were used to identify and replicate the association between candidate CNVs and PD: the Minnesota Breast Cancer Family Study (MBCFS) and controls from the Mayo Venous Thromboembolism (Mayo VTE) Case–Control Study, with 585 and 328 women, respectively. Linear models were utilized to examine the association of each probe with PD, adjusted for age, menopausal status and BMI. Segmentation was subsequently performed on the probe-level test statistics to identify candidate CNV regions that were associated with PD. Results Sixty-one probes from five chromosomal regions [3q26.1 (2 regions), 8q24.22, 11p15.3, and 17q22] were significantly associated with PD in MBCFS (p-values <0.0001). A CNV at 3q26.1 showed the greatest evidence for association with PD; a region without any known SNPs. Conversely, the CNV at 17q22 was largely due to the association between SNPs and PD in the region. SNPs in the 8q24.22 region have been shown to be associated with risk of many cancers; however, SNPs in this region were not responsible for the observed CNV association. While we were unable to replicate the associations with PD, two of the five CNVs (3q26.1 and 11p15.3) were also observed in the Mayo VTE controls. Conclusions CNVs may help to explain some of the variability in PD that is currently unexplained by SNPs. While we were able to replicate the existence of two CNVs across the two GWAS studies, we were unable to replicate the associations with PD. Even so, the proximity of the identified CNV regions to loci known to be associated with breast cancer risk suggests further investigation and potentially shared genetic mechanisms underlying the PD and breast cancer association. Electronic supplementary material The online version of this article (doi:10.1186/s13104-015-1212-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Elizabeth J Atkinson
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Jeanette E Eckel-Passow
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Alice Wang
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Alexandra J Greenberg
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Christopher G Scott
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - V Shane Pankratz
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Kristen N Purrington
- Wayne State University School of Medicine and Karmanos Cancer Institute, Detroit, MI, USA.
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA.
| | - David N Rider
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - John A Heit
- Division of Cardiovascular Disease, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Mariza de Andrade
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Julie M Cunningham
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
| | - Fergus J Couch
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
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Breast Tissue Composition and Immunophenotype and Its Relationship with Mammographic Density in Women at High Risk of Breast Cancer. PLoS One 2015; 10:e0128861. [PMID: 26110820 PMCID: PMC4481506 DOI: 10.1371/journal.pone.0128861] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 05/03/2015] [Indexed: 12/02/2022] Open
Abstract
Aim To investigate the cellular and immunophenotypic basis of mammographic density in women at high risk of breast cancer. Methods Mammograms and targeted breast biopsies were accrued from 24 women at high risk of breast cancer. Mammographic density was classified into Wolfe categories and ranked by increasing density. The histological composition and immunophenotypic profile were quantified from digitized haematoxylin and eosin-stained and immunohistochemically-stained (ERα, ERβ, PgR, HER2, Ki-67, and CD31) slides and correlated to mammographic density. Results Increasing mammographic density was significantly correlated with increased fibrous stroma proportion (rs (22) = 0.5226, p = 0.0088) and significantly inversely associated with adipose tissue proportion (rs (22) = -0.5409, p = 0.0064). Contrary to previous reports, stromal expression of ERα was common (19/20 cases, 95%). There was significantly higher stromal PgR expression in mammographically-dense breasts (p=0.026). Conclusions The proportion of stroma and fat underlies mammographic density in women at high risk of breast cancer. Increased expression of PgR in the stroma of mammographically dense breasts and frequent and unexpected presence of stromal ERα expression raises the possibility that hormone receptor expression in breast stroma may have a role in mediating the effects of exogenous hormonal therapy on mammographic density.
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Mammographic density and breast cancer risk by family history in women of white and Asian ancestry. Cancer Causes Control 2015; 26:621-6. [PMID: 25761408 DOI: 10.1007/s10552-015-0551-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 03/04/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE Mammographic density, i.e., the radiographic appearance of the breast, is a strong predictor of breast cancer risk. To determine whether the association of breast density with breast cancer is modified by a first-degree family history of breast cancer (FHBC) in women of white and Asian ancestry, we analyzed data from four case-control studies conducted in the USA and Japan. METHODS The study population included 1,699 breast cancer cases and 2,422 controls, of whom 45% reported white (N = 1,849) and 40% Asian (N = 1,633) ancestry. To standardize mammographic density assessment, a single observer re-read all mammograms using one type of interactive thresholding software. Logistic regression was applied to estimate odds ratios (OR) while adjusting for confounders. RESULTS Overall, 496 (12%) of participants reported a FHBC, which was significantly associated with breast cancer risk in the adjusted model (OR 1.51; 95% CI 1.23-1.84). There was a statistically significant interaction on a multiplicative scale between FHBC and continuous percent density (per 10 % density: p = 0.03). The OR per 10% increase in percent density was higher among women with a FHBC (OR 1.30; 95% CI 1.13-1.49) than among those without a FHBC (OR 1.14; 1.09-1.20). This pattern was apparent in whites and Asians. The respective ORs were 1.45 (95% CI 1.17-1.80) versus 1.22 (95% CI 1.14-1.32) in whites, whereas the values in Asians were only 1.24 (95% CI 0.97-1.58) versus 1.09 (95% CI 1.00-1.19). CONCLUSIONS These findings support the hypothesis that women with a FHBC appear to have a higher risk of breast cancer associated with percent mammographic density than women without a FHBC.
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Flote VG, Furberg AS, McTiernan A, Frydenberg H, Ursin G, Iversen A, Lofteroed T, Ellison PT, Wist EA, Egeland T, Wilsgaard T, Makar KW, Chang-Claude J, Thune I. Gene variations in oestrogen pathways, CYP19A1, daily 17β-estradiol and mammographic density phenotypes in premenopausal women. Breast Cancer Res 2014; 16:499. [PMID: 25522654 PMCID: PMC4303212 DOI: 10.1186/s13058-014-0499-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 12/08/2014] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION High mammographic density is an established breast cancer risk factor, and circulating oestrogen influences oestrogen-regulating gene expression in breast cancer development. However, less is known about the interrelationships of common variants in the CYP19A1 gene, daily levels of oestrogens, mammographic density phenotypes and body mass index (BMI) in premenopausal women. METHODS Based on plausible biological mechanisms related to the oestrogen pathway, we investigated the association of single nucleotide polymorphisms (SNPs) in CYP19A1, 17β-estradiol and mammographic density in 202 premenopausal women. DNA was genotyped using the Illumina Golden Gate platform. Daily salivary 17β-estradiol concentrations were measured throughout an entire menstrual cycle. Mammographic density phenotypes were assessed using a computer-assisted method (Madena). We determined associations using multivariable linear and logistic regression models. RESULTS The minor alleles of rs749292 were positively (P = 0.026), and the minor alleles of rs7172156 were inversely (P = 0.002) associated with daily 17β-estradiol. We observed an 87% lower level of daily 17β-estradiol throughout a menstrual cycle in heavier women (BMI >23.6 kg/m(2)) of rs7172156 with minor genotype aa compared with major genotype AA. Furthermore, the rs749292 minor alleles were inversely associated with absolute mammographic density (P = 0.032). Lean women with rs749292 minor alleles had 70 to 80% lower risk for high absolute mammographic density (>32.4 cm(2)); Aa: odds ratio (OR) = 0.23 (95% CI 0.07 to 0.75). Lean women with rs7172156 minor homozygous genotype had OR 5.45 for high absolute mammographic density (aa: OR = 5.45 (95% CI 1.13 to 26.3)). CONCLUSION Our findings suggest that two SNPs in CYP19A1, rs749292 and rs7172156, are associated with both daily oestrogen levels and mammographic density phenotypes. BMI may modify these associations, but larger studies are needed.
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Affiliation(s)
- Vidar G Flote
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway.
| | - Anne-Sofie Furberg
- Department of Community Medicine, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, N-9037, Norway.
| | - Anne McTiernan
- Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, WA, 98109-1024, USA.
| | - Hanne Frydenberg
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway.
| | - Giske Ursin
- Cancer Registry of Norway, PO Box 5313, Majorstuen, Oslo, N-0304, Norway.
| | - Anita Iversen
- Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, N-9037, Norway.
| | - Trygve Lofteroed
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway.
| | - Peter T Ellison
- Department of Anthropology, Harvard University, Cambridge, MA, 02138, USA.
| | - Erik A Wist
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway.
| | - Thore Egeland
- Department of Chemistry, Norwegian University of Life Sciences, Biotechnology and Food Science, Aas, N-1432, Norway.
| | - Tom Wilsgaard
- Department of Community Medicine, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, N-9037, Norway.
| | - Karen W Makar
- Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, WA, 98109-1024, USA.
| | - Jenny Chang-Claude
- Unit of Genetic Epidemiology, Division of Cancer Epidemiology, Deutches Krebsforschungszentrum, 69120, Heidelberg, Germany.
| | - Inger Thune
- The Cancer Centre, Oslo University Hospital, Oslo, N-0424, Norway. .,Department of Community Medicine, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, N-9037, Norway.
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Brand JS, Humphreys K, Thompson DJ, Li J, Eriksson M, Hall P, Czene K. Volumetric Mammographic Density: Heritability and Association With Breast Cancer Susceptibility Loci. J Natl Cancer Inst 2014; 106:dju334. [DOI: 10.1093/jnci/dju334] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
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Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, Prindiville SA, Eng-Wong J, Soballe PW, Giambartolomei C, Mai PL, Galbo CE, Nichols K, Calzone KA, Olopade OI, Gail MH, Giger ML. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res 2014. [PMID: 25159706 DOI: 10.1186/preaccept-1744229618121391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers. METHODS We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject's digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject's belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model's discriminatory capacity. RESULTS In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density. CONCLUSIONS Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.
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Affiliation(s)
- Gretchen L Gierach
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rm, 7-E108, Bethesda 20892-9774, MD, USA.
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Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, Prindiville SA, Eng-Wong J, Soballe PW, Giambartolomei C, Mai PL, Galbo CE, Nichols K, Calzone KA, Olopade OI, Gail MH, Giger ML. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res 2014; 16:424. [PMID: 25159706 PMCID: PMC4268674 DOI: 10.1186/s13058-014-0424-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Accepted: 07/31/2014] [Indexed: 12/24/2022] Open
Abstract
Introduction Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers. Methods We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject’s digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject’s belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model’s discriminatory capacity. Results In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density. Conclusions Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography. Electronic supplementary material The online version of this article (doi:10.1186/s13058-014-0424-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gretchen L Gierach
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rm, 7-E108, Bethesda 20892-9774, MD, USA.
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Ellingjord-Dale M, Grotmol T, Lee E, Van Den Berg DJ, Hofvind S, Couto E, Sovio U, Dos-Santos-Silva I, Ursin G. Breast cancer susceptibility variants and mammographic density phenotypes in norwegian postmenopausal women. Cancer Epidemiol Biomarkers Prev 2014; 23:1752-63. [PMID: 25002657 DOI: 10.1158/1055-9965.epi-13-1212] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Mammographic density (MD) is one of the strongest known breast cancer risk factors. Twin studies have suggested that a large part of the variation in MD is genetically determined. We hypothesized that breast cancer susceptibility variants may affect MD, and that their effects may be modified by nongenetic factors. METHODS We assessed MD, using a computer-assisted method, on 2,348 postmenopausal Caucasian women (50-69 years) who participated in the Norwegian Breast Cancer Screening Program (NBCSP) in 2004 or 2006-07. We used linear regression (additive models) to determine the association between each SNP and MD, adjusting for age, body mass index (BMI), and study. We evaluated MD associations with 17 established breast cancer SNPs, overall, and by strata defined by non-genetic factors. RESULTS Two variants, 6q25.1-rs9383938 and TXNRD2-rs8141691, were statistically significantly associated with percent MD (P = 0.019 and 0.03, respectively), with the 6q25.1-rs9383938 association being consistent with the SNP effect on breast cancer risk. The effect of 6q25.1-rs3734805 on percent MD varied between parous and nulliparous women (Pinteraction = 0.02), whereas the effects of 9q31.2-rs865686 and MRPS30:FGF10-rs4415084 differed across strata of BMI (Pinteraction = 0.01 and 0.005, respectively). There was no evidence of effect modification by estrogen and progestin therapy use or alcohol consumption. CONCLUSION This study provides novel evidence of shared genetic risk factors between MD and breast cancer and of possible MD genetic-environmental interactions. IMPACT Although the results may be chance findings, they nevertheless highlight the need to investigate interactions with nongenetic factors in studies on the genetics of MD.
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Affiliation(s)
| | | | - Eunjung Lee
- University of Southern California, Los Angeles, California
| | | | | | - Elisabeth Couto
- Norwegian Knowledge Centre for the Health Services, Health Economic and Drug Unit, Oslo, Norway
| | - Ulla Sovio
- University of Cambridge, Cambridge, United Kingdom
| | | | - Giske Ursin
- University of Oslo, Oslo, Norway. Cancer Registry of Norway, Oslo, Norway.
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Pettersson A, Tamimi RM. Breast Density and Breast Cancer Risk: Understanding of Biology and Risk. CURR EPIDEMIOL REP 2014. [DOI: 10.1007/s40471-014-0018-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Dai H, Yan Y, Wang P, Liu P, Cao Y, Xiong L, Luo Y, Pan T, Ma X, Wang J, Yang Z, Liu X, Chen C, Huang Y, Li Y, Wang Y, Hao X, Ye Z, Chen K. Distribution of mammographic density and its influential factors among Chinese women. Int J Epidemiol 2014; 43:1240-51. [PMID: 24639441 DOI: 10.1093/ije/dyu042] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Mammographic density (MD) has not been systematically investigated among Chinese women. Breast cancer screening programmes provided detailed information on MD in a large number of asymptomatic women. METHODS In the Multi-modality Independent Screening Trial (MIST), we estimated the association between MD and its influential factors using logistic regression, adjusting for age, body mass index (BMI) and study area. Differences between Chinese and other ethnic groups with respect to MD were also explored with adjustment for age and BMI. RESULTS A total of 28 388 women aged 45 to 65 years, who had been screened by mammography, were enrolled in the study. Of these, 49.2% were categorized as having dense breasts (BI-RADS density 3 and 4) and 50.8% as fatty breasts (BI-RADS density 1 and 2). Postmenopausal status [odds ratio (OR) = 0.66; 95% confidence interval (CI): 0.62-0.70] and higher number of live births (OR = 0.56; 95% CI: 0.46-0.68) were inversely associated with MD, whereas prior benign breast disease (OR = 1.48; 95% CI: 1.40-1.56) and later age at first birth (OR = 1.17; 95% CI: 1.08-1.27) were positively associated with MD. In comparison with the data from the Breast Cancer Surveillance Consortium, we found that women in MIST were more likely to have fatty breasts than Americans (from the Breast Cancer Surveillance Consortium) in the older age group (≥50 years) but more likely to have dense breasts in the younger age group (<50 years). CONCLUSIONS This study suggests that several risk factors for breast cancer were associated with breast density in Chinese women. Information on the determinants of mammographic density may provide valuable insights into breast cancer aetiology.
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Affiliation(s)
- Hongji Dai
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Ye Yan
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Peishan Wang
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Peifang Liu
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Yali Cao
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Li Xiong
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Yahong Luo
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Tie Pan
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Xiangjun Ma
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Jie Wang
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Zhenhua Yang
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Xueou Liu
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Chuan Chen
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Yubei Huang
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Yi Li
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Yaogang Wang
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Xishan Hao
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, ChinaDepartment of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tian
| | - Zhaoxiang Ye
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics and Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Prevention and Cure Center of Breast Disease, Third Hospital of Nanchang, Nanchang, China, Department of Medical Image and Department of Cancer Prevention and Control, Liaoning Cancer Institute and Hospital, Shenyang, China, Center for Breast Disease, Haidian Maternal and Child Health Hospital, Beijing, China, Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China and Chinese Anti-Cancer Association, Tianjin, China
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Nguyen TL, Schmidt DF, Makalic E, Dite GS, Stone J, Apicella C, Bui M, Macinnis RJ, Odefrey F, Cawson JN, Treloar SA, Southey MC, Giles GG, Hopper JL. Explaining variance in the cumulus mammographic measures that predict breast cancer risk: a twins and sisters study. Cancer Epidemiol Biomarkers Prev 2013; 22:2395-403. [PMID: 24130221 DOI: 10.1158/1055-9965.epi-13-0481] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Mammographic density, the area of the mammographic image that appears white or bright, predicts breast cancer risk. We estimated the proportions of variance explained by questionnaire-measured breast cancer risk factors and by unmeasured residual familial factors. METHODS For 544 MZ and 339 DZ twin pairs and 1,558 non-twin sisters from 1,564 families, mammographic density was measured using the computer-assisted method Cumulus. We estimated associations using multilevel mixed-effects linear regression and studied familial aspects using a multivariate normal model. RESULTS The proportions of variance explained by age, body mass index (BMI), and other risk factors, respectively, were 4%, 1%, and 4% for dense area; 7%, 14%, and 4% for percent dense area; and 7%, 40%, and 1% for nondense area. Associations with dense area and percent dense area were in opposite directions than for nondense area. After adjusting for measured factors, the correlations of dense area with percent dense area and nondense area were 0.84 and -0.46, respectively. The MZ, DZ, and sister pair correlations were 0.59, 0.28, and 0.29 for dense area; 0.57, 0.30, and 0.28 for percent dense area; and 0.56, 0.27, and 0.28 for nondense area (SE = 0.02, 0.04, and 0.03, respectively). CONCLUSIONS Under the classic twin model, 50% to 60% (SE = 5%) of the variance of mammographic density measures that predict breast cancer risk are due to undiscovered genetic factors, and the remainder to as yet unknown individual-specific, nongenetic factors. IMPACT Much remains to be learnt about the genetic and environmental determinants of mammographic density.
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Affiliation(s)
- Tuong L Nguyen
- Authors' Affiliations: Centre for Molecular, Environmental, Genetic, and Analytic Epidemiology, The University of Melbourne; Cancer Epidemiology Centre, Cancer Council Victoria, Carlton; Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville; Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy; and The University of Queensland, Centre for Military and Veterans' Health, Brisbane, Australia
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Aromatase inhibitor-induced modulation of breast density: clinical and genetic effects. Br J Cancer 2013; 109:2331-9. [PMID: 24084768 PMCID: PMC3817329 DOI: 10.1038/bjc.2013.587] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2013] [Revised: 09/01/2013] [Accepted: 09/04/2013] [Indexed: 11/08/2022] Open
Abstract
Background: Change in breast density may predict outcome of women receiving adjuvant hormone therapy for breast cancer. We performed a prospective clinical trial to evaluate the impact of inherited variants in genes involved in oestrogen metabolism and signalling on change in mammographic percent density (MPD) with aromatase inhibitor (AI) therapy. Methods: Postmenopausal women with breast cancer who were initiating adjuvant AI therapy were enrolled onto a multicentre, randomised clinical trial of exemestane vs letrozole, designed to identify associations between AI-induced change in MPD and single-nucleotide polymorphisms in candidate genes. Subjects underwent unilateral craniocaudal mammography before and following 24 months of treatment. Results: Of the 503 enrolled subjects, 259 had both paired mammograms at baseline and following 24 months of treatment and evaluable DNA. We observed a statistically significant decrease in mean MPD from 17.1 to 15.1% (P<0.001), more pronounced in women with baseline MPD ⩾20%. No AI-specific difference in change in MPD was identified. No significant associations between change in MPD and inherited genetic variants were observed. Conclusion: Subjects with higher baseline MPD had a greater average decrease in MPD with AI therapy. There does not appear to be a substantial effect of inherited variants in biologically selected candidate genes.
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Ozhand A, Lee E, Wu AH, Ellingjord-Dale M, Akslen LA, McKean-Cowdin R, Ursin G. Variation in inflammatory cytokine/growth-factor genes and mammographic density in premenopausal women aged 50-55. PLoS One 2013; 8:e65313. [PMID: 23762340 PMCID: PMC3676419 DOI: 10.1371/journal.pone.0065313] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Accepted: 04/28/2013] [Indexed: 11/24/2022] Open
Abstract
Background Mammographic density (MD) has been found to be an independent risk factor for breast cancer. Although data from twin studies suggest that MD has a strong genetic component, the exact genes involved remain to be identified. Alterations in stromal composition and the number of epithelial cells are the most predominant histopathological determinants of mammographic density. Interactions between the breast stroma and epithelium are critically important in the maturation and development of the mammary gland and the cross-talk between these cells are mediated by paracrine growth factors and cytokines. The potential impact of genetic variation in growth factors and cytokines on MD is largely unknown. Methods We investigated the association between 89 single nucleotide polymorphisms (SNPs) in 7 cytokine/growth-factor genes (FGFR2, IGFBP1, IGFBP3, TGFB1, TNF, VEGF, IL6) and percent MD in 301 premenopausal women (aged 50 to 55 years) participating in the Norwegian Breast Cancer Screening Program. We evaluated the suggestive associations in 216 premenopausal Singapore Chinese Women of the same age. Results We found statistically significant associations between 9 tagging SNPs in the IL6 gene and MD in Norwegian women; the effect ranged from 3–5% in MD per variant allele (p-values = 0.02 to 0.0002). One SNP in the IL6 (rs10242595) significantly influenced MD in Singapore Chinese women. Conclusion Genetic variations in IL6 may be associated with MD and therefore may be an indicator of breast cancer risk in premenopausal women.
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Affiliation(s)
- Ali Ozhand
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California, United States of America
| | - Eunjung Lee
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California, United States of America
| | - Anna H. Wu
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California, United States of America
| | | | - Lars A. Akslen
- Centre for Cancer Biomarkers, The Gade Laboratorium for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Roberta McKean-Cowdin
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California, United States of America
| | - Giske Ursin
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California, United States of America
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Cancer Registry of Norway, Oslo, Norway
- * E-mail:
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High mammographic density in women of Ashkenazi Jewish descent. Breast Cancer Res 2013; 15:R40. [PMID: 23668689 PMCID: PMC4053164 DOI: 10.1186/bcr3424] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2012] [Accepted: 05/13/2013] [Indexed: 11/21/2022] Open
Abstract
Introduction Percent mammographic density (PMD) adjusted for age and body mass index is one of the strongest risk factors for breast cancer and is known to be approximately 60% heritable. Here we report a finding of an association between genetic ancestry and adjusted PMD. Methods We selected self-identified Caucasian women in the California Pacific Medical Center Research Institute Cohort whose screening mammograms placed them in the top or bottom quintiles of age-adjusted and body mass index-adjusted PMD. Our final dataset included 474 women with the highest adjusted PMD and 469 with the lowest genotyped on the Illumina 1 M platform. Principal component analysis (PCA) and identity-by-descent analyses allowed us to infer the women's genetic ancestry and correlate it with adjusted PMD. Results Women of Ashkenazi Jewish ancestry, as defined by the first principal component of PCA and identity-by-descent analyses, represented approximately 15% of the sample. Ashkenazi Jewish ancestry, defined by the first principal component of PCA, was associated with higher adjusted PMD (P = 0.004). Using multivariate regression to adjust for epidemiologic factors associated with PMD, including age at parity and use of postmenopausal hormone therapy, did not attenuate the association. Conclusions Women of Ashkenazi Jewish ancestry, based on genetic analysis, are more likely to have high age-adjusted and body mass index-adjusted PMD. Ashkenazi Jews may have a unique set of genetic variants or environmental risk factors that increase mammographic density.
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Pollán M, Ascunce N, Ederra M, Murillo A, Erdozáin N, Alés-Martínez JE, Pastor-Barriuso R. Mammographic density and risk of breast cancer according to tumor characteristics and mode of detection: a Spanish population-based case-control study. Breast Cancer Res 2013; 15:R9. [PMID: 23360535 PMCID: PMC3672793 DOI: 10.1186/bcr3380] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2012] [Revised: 12/03/2012] [Accepted: 01/24/2013] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION It is not clear whether high mammographic density (MD) is equally associated with all subtypes of breast cancer (BC). We investigated the association between MD and subsequent BC, considering invasiveness, means of detection, pathologic subtype, and the time elapsed since mammographic exploration and BC diagnosis. METHODS BC cases occurring in the population of women who attended screening from 1997 through 2004 in Navarre, a Spanish region with a fully consolidated screening program, were identified via record linkage with the Navarre Cancer Registry (n = 1,172). Information was extracted from the records of their first attendance at screening in that period. For each case, we randomly selected four controls, matched by screening round, year of birth, and place of residence. Cases were classified according to invasiveness (ductal carcinoma in situ (DCIS) versus invasive tumors), pathologic subtype (considering hormonal receptors and HER2), and type of diagnosis (screen-detected versus interval cases). MD was evaluated by a single, experienced radiologist by using a semiquantitative scale. Data on BC risk factors were obtained by the screening program in the corresponding round. The association between MD and tumor subtype was assessed by using conditional logistic regression. RESULTS MD was clearly associated with subsequent BC. The odds ratio (OR) for the highest MD category (MD >75%) compared with the reference category (MD <10%) was similar for DCIS (OR = 3.47; 95% CI = 1.46 to 8.27) and invasive tumors (OR = 2.95; 95% CI = 2.01 to 4.35). The excess risk was particularly high for interval cases (OR = 7.72; 95% CI = 4.02 to 14.81) in comparison with screened detected tumors (OR = 2.17; 95% CI = 1.40 to 3.36). Sensitivity analyses excluding interval cases diagnosed in the first year after MD assessment or immediately after an early recall to screening yielded similar results. No differences were seen regarding pathologic subtypes. The excess risk associated with MD persisted for at least 7 to 8 years after mammographic exploration. CONCLUSIONS Our results confirm that MD is an important risk factor for all types of breast cancer. High breast density strongly increases the risk of developing an interval tumor, and this excess risk is not completely explained by a possible masking effect.
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Affiliation(s)
- Marina Pollán
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029 Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública-CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029, Spain
| | - Nieves Ascunce
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública-CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029, Spain
- Navarre Breast cancer Screening Program, Navarre Institute of Public Health, Leyre 15, Pamplona, 31003, Spain
| | - María Ederra
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública-CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029, Spain
- Navarre Breast cancer Screening Program, Navarre Institute of Public Health, Leyre 15, Pamplona, 31003, Spain
| | - Alberto Murillo
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública-CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029, Spain
- Navarre Breast cancer Screening Program, Navarre Institute of Public Health, Leyre 15, Pamplona, 31003, Spain
| | - Nieves Erdozáin
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública-CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029, Spain
- Navarre Breast cancer Screening Program, Navarre Institute of Public Health, Leyre 15, Pamplona, 31003, Spain
| | - Jose Enrique Alés-Martínez
- Medical Oncology Unit, Nuestra Señora de Sonsoles Hospital, Avenida Juan Carlos I s/n, Avila, 05004, Spain
| | - Roberto Pastor-Barriuso
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029 Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública-CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid, 28029, Spain
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Lee E, Van Den Berg D, Hsu C, Ursin G, Koh WP, Yuan JM, Stram DO, Yu MC, Wu AH. Genetic variation in transforming growth factor beta 1 and mammographic density in Singapore Chinese women. Cancer Res 2013; 73:1876-82. [PMID: 23333936 DOI: 10.1158/0008-5472.can-12-1870] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
TGF-β plays a critical role in normal mammary development and morphogenesis. Decreased TGF-β signaling has been associated with increased mammographic density. Percent mammographic density (PMD) adjusted for age and body mass index (BMI) is a strong risk factor and predictor of breast cancer risk. PMD is highly heritable, but few genetic determinants have been identified. We investigated the association between genetic variation in TGFB1 and PMD using a cross-sectional study of 2,038 women who were members of the population-based Singapore Chinese Health Study cohort. We assessed PMD using a computer-assisted method. We used linear regression to examine the association between nine tagging single-nucleotide polymorphisms (SNP) of TGFB1 and PMD and their interaction with parity, adjusting for age, BMI, and dialect group. We calculated P values adjusted for correlated tests (P(ACT)) to account for multiple testing. The strongest association was observed for rs2241716. Adjusted PMD was higher by 1.5% per minor allele (P(ACT) = 0.04). When stratifying by parity, this association was limited to nulliparous women. For nulliparous women, adjusted PMD was higher by 8.6% per minor allele (P(ACT) = 0.003; P for interaction with parity = 0.002). Three additional TGFB1 tagging SNPs, which were in linkage disequilibrium with rs2241716, were statistically significantly associated with adjusted PMD (P(ACT) < 0.05) for nulliparous women. However, none of these three SNPs showed statistically significant association after adjusting for rs2241716. Our data support that TGFB1 genetic variation may be an important genetic determinant of mammographic density measure that predicts breast cancer risk, particularly in nulliparous women.
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Affiliation(s)
- Eunjung Lee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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Abstract
The radiographic appearance of the breast on mammography varies among women, and reflects variations in breast tissue composition and the different X-ray attenuation characteristics of these tissues. Fat is radiologically lucent and appears dark on a mammogram. Connective and epithelial tissues are radiologically dense and appear light. These variations in appearance are commonly described as the percentage of the breast image that is radiologically dense, or as percent mammographic density (PMD). There is now extensive evidence that PMD is a risk factor for breast cancer, with a 4- to 6-fold gradient in risk between women with 75% or more PMD compared with those with 10% or less. However, the accuracy of risk prediction in individual women is modest. The extent of PMD is associated inversely with greater age, parity, and weight, and is reduced by the menopause and by tamoxifen. PMD is positively associated with greater height, a family history of breast cancer, and is increased by combined hormone therapy. The relative risk associated with density is substantially larger than the relative risk of breast cancer associated with a family history of the disease or any of the menstrual and reproductive risk factors. It is estimated that the risks of breast cancer attributable to density of 50% or more may be 16% for all breast cancers. Although combined hormone therapy and tamoxifen respectively increase a decrease both PMD and breast cancer risk, there is as yet insufficient evidence to use PMD as a surrogate marker for breast cancer.
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Affiliation(s)
- Norman F Boyd
- From the Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute, Toronto, Ontario, Canada
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The USC Adult Twin Cohorts: International Twin Study and California Twin Program. Twin Res Hum Genet 2012; 16:366-70. [PMID: 23218448 DOI: 10.1017/thg.2012.134] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The study of twin subjects permits the documentation of crude heritability and may promote the identification of specific causal alleles. We believe that at the current time, the chief research advantage of twins as subjects, especially monozygotic twins, is that the commonality of their genetic and cultural identity simplifies the interpretation of biological associations. In order to study genetic and environmental determinants of cancer and chronic diseases, we developed two twin registries, maintained at the University of Southern California: The International Twin Study (ITS) and the California Twin Program (CTP). The ITS is a volunteer registry of twins with cancer and chronic disease consisting of 17,245 twin pairs affected by cancer and chronic disease, respectively, ascertained by advertising in periodicals from 1980-1991. The CTP is a population-based registry of California-born twin pairs ascertained by linking the California birth records to the State Department of Motor Vehicles. Over 51,000 individual California twins representing 36,965 pairs completed and returned 16-page questionnaires. Cancer diagnoses in the California twins are updated by regular linkage to the California Cancer Registry. Over 5,000 cancer patients are represented in the CTP. Twins from both registries have participated extensively in studies of breast cancer, melanoma, lymphoma, multiple sclerosis, systemic lupus erythematosus, diabetes mellitus type 1, mammographic density, smoking, and other traits and conditions.
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Ellingjord-Dale M, Lee E, Couto E, Ozhand A, Qureshi S, Hofvind S, Van Den Berg DJ, Akslen LA, Grotmol T, Ursin G. Polymorphisms in hormone metabolism and growth factor genes and mammographic density in Norwegian postmenopausal hormone therapy users and non-users. Breast Cancer Res 2012; 14:R135. [PMID: 23095343 PMCID: PMC4053113 DOI: 10.1186/bcr3337] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2012] [Accepted: 08/30/2012] [Indexed: 01/10/2023] Open
Abstract
INTRODUCTION Mammographic density (MD) is one of the strongest known breast cancer risk factors. Estrogen and progestin therapy (EPT) has been associated with increases in MD. Dense breast tissue is characterized by increased stromal tissue and (to a lesser degree) increased numbers of breast epithelial cells. It is possible that genetic factors modify the association between EPT and MD, and that certain genetic variants are particularly important in determining MD in hormone users. We evaluated the association between MD and 340 tagging single nucleotide polymorphisms (SNPs) from about 30 candidate genes in hormone metabolism/growth factor pathways among women who participated in the Norwegian Breast Cancer Screening Program (NBCSP) in 2004. METHODS We assessed MD on 2,036 postmenopausal women aged 50 to 69 years using a computer-assisted method (Madena, University of Southern California) in a cross-sectional study. We used linear regression to determine the association between each SNP and MD, adjusting for potential confounders. The postmenopausal women were stratified into HT users (EPT and estrogen-only) and non-users (never HT). RESULTS For current EPT users, there was an association between a variant in the prolactin gene (PRL; rs10946545) and MD (dominant model, Bonferroni-adjusted P (Pb) = 0.0144). This association remained statistically significant among current users of norethisterone acetate (NETA)-based EPT, a regimen common in Nordic countries. Among current estrogen-only users (ET), there was an association between rs4670813 in the cytochrome P450 gene (CYP1B1) and MD (dominant model, Pb = 0.0396). In never HT users, rs769177 in the tumor necrosis factor (TNF) gene and rs1968752 in the region of the sulfotransferase gene (SULT1A1/SULT1A2), were significantly associated with MD (Pb = 0.0202; Pb = 0.0349). CONCLUSIONS We found some evidence that variants in the PRL gene were associated with MD in current EPT and NETA users. In never HT users, variants in the TNF and SULT1A1/SULT1A2 genes were significantly associated with MD. These findings may suggest that several genes in the hormone metabolism and growth factor pathways are implicated in determining MD.
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Varghese JS, Smith PL, Folkerd E, Brown J, Leyland J, Audley T, Warren RML, Dowsett M, Easton DF, Thompson DJ. The heritability of mammographic breast density and circulating sex-hormone levels: two independent breast cancer risk factors. Cancer Epidemiol Biomarkers Prev 2012; 21:2167-75. [PMID: 23074290 DOI: 10.1158/1055-9965.epi-12-0789] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Mammographic breast density and endogenous sex-hormone levels are both strong risk factors for breast cancer. This study investigated whether there is evidence for a shared genetic basis between these risk factors. METHODS Using data on 1,286 women from 617 families, we estimated the heritabilities of serum estradiol, testosterone, and sex-hormone binding globulin (SHBG) levels and of three measures of breast density (dense area, nondense area, and percentage density). We tested for associations between hormone levels and density measures and estimated the genetic and environmental correlations between pairs of traits using variance and covariance components models and pedigree-based maximum likelihood methods. RESULTS We found no significant associations between estradiol, testosterone, or SHBG levels and any of the three density measures, after adjusting for body mass index (BMI). The estimated heritabilities were 63%, 66%, and 65% for square root-transformed adjusted percentage density, dense area, and nondense area, respectively, and 40%, 25%, and 58% for log-transformed-adjusted estradiol, testosterone, and SHBG. We found no evidence of a shared genetic basis between any hormone levels and any measure of density, after adjusting for BMI. The negative genetic correlation between dense and nondense areas remained significant even after adjustment for BMI and other covariates (ρ = -0.34; SE = 0.08; P = 0.0005). CONCLUSIONS Breast density and sex hormones can be considered as independent sets of traits. IMPACT Breast density and sex hormones can be used as intermediate phenotypes in the search for breast cancer susceptibility loci.
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Affiliation(s)
- Jajini S Varghese
- Department of Public Heath and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
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DeFilippis RA, Chang H, Dumont N, Rabban JT, Chen YY, Fontenay GV, Berman HK, Gauthier ML, Zhao J, Hu D, Marx JJ, Tjoe JA, Ziv E, Febbraio M, Kerlikowske K, Parvin B, Tlsty TD. CD36 repression activates a multicellular stromal program shared by high mammographic density and tumor tissues. Cancer Discov 2012; 2:826-39. [PMID: 22777768 DOI: 10.1158/2159-8290.cd-12-0107] [Citation(s) in RCA: 143] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
UNLABELLED Although high mammographic density is considered one of the strongest risk factors for invasive breast cancer, the genes involved in modulating this clinical feature are unknown. Tissues of high mammographic density share key histologic features with stromal components within malignant lesions of tumor tissues, specifically low adipocyte and high extracellular matrix (ECM) content. We show that CD36, a transmembrane receptor that coordinately modulates multiple protumorigenic phenotypes, including adipocyte differentiation, angiogenesis, cell-ECM interactions, and immune signaling, is greatly repressed in multiple cell types of disease-free stroma associated with high mammographic density and tumor stroma. Using both in vitro and in vivo assays, we show that CD36 repression is necessary and sufficient to recapitulate the above-mentioned phenotypes observed in high mammographic density and tumor tissues. Consistent with a functional role for this coordinated program in tumorigenesis, we observe that clinical outcomes are strongly associated with CD36 expression. SIGNIFICANCE CD36 simultaneously controls adipocyte content and matrix accumulation and is coordinately repressed in multiple cell types within tumor and high mammographic density stroma, suggesting that activation of this stromal program is an early event in tumorigenesis. Levels of CD36 and extent of mammographic density are both modifiable factors that provide potential for intervention.
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Affiliation(s)
- Rosa Anna DeFilippis
- Department of Pathology, Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94143, USA
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Vachon CM, Scott CG, Fasching PA, Hall P, Tamimi RM, Li J, Stone J, Apicella C, Odefrey F, Gierach GL, Jud SM, Heusinger K, Beckmann MW, Pollan M, Fernández-Navarro P, Gonzalez-Neira A, Benitez J, van Gils CH, Lokate M, Onland-Moret NC, Peeters PHM, Brown J, Leyland J, Varghese JS, Easton DF, Thompson DJ, Luben RN, Warren RML, Wareham NJ, Loos RJF, Khaw KT, Ursin G, Lee E, Gayther SA, Ramus SJ, Eeles RA, Leach MO, Kwan-Lim G, Couch FJ, Giles GG, Baglietto L, Krishnan K, Southey MC, Le Marchand L, Kolonel LN, Woolcott C, Maskarinec G, Haiman CA, Walker K, Johnson N, McCormack VA, Biong M, Alnaes GIG, Gram IT, Kristensen VN, Børresen-Dale AL, Lindström S, Hankinson SE, Hunter DJ, Andrulis IL, Knight JA, Boyd NF, Figuero JD, Lissowska J, Wesolowska E, Peplonska B, Bukowska A, Reszka E, Liu J, Eriksson L, Czene K, Audley T, Wu AH, Pankratz VS, Hopper JL, dos-Santos-Silva I. Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev 2012; 21:1156-66. [PMID: 22454379 PMCID: PMC3569092 DOI: 10.1158/1055-9965.epi-12-0066] [Citation(s) in RCA: 83] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures. METHODS We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status. RESULTS Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07). CONCLUSION We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland. IMPACT We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association.
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Affiliation(s)
- Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
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Pollán M, Lope V, Miranda-García J, García M, Casanova F, Sánchez-Contador C, Santamariña C, Moreo P, Vidal C, Peris M, Moreno MP, Vázquez-Carrete JA, Collado F, Pedraz-Pingarrón C, Ascunce N, Salas-Trejo D, Aragonés N, Pérez-Gómez B, Ruiz-Perales F. Adult weight gain, fat distribution and mammographic density in Spanish pre- and post-menopausal women (DDM-Spain). Breast Cancer Res Treat 2012; 134:823-38. [PMID: 22689088 PMCID: PMC3401511 DOI: 10.1007/s10549-012-2108-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2012] [Accepted: 05/19/2012] [Indexed: 02/04/2023]
Abstract
High mammographic density (MD) is a phenotype risk marker for breast cancer. Body mass index (BMI) is inversely associated with MD, with the breast being a fat storage site. We investigated the influence of abdominal fat distribution and adult weight gain on MD, taking age, BMI and other confounders into account. Because visceral adiposity and BMI are associated with breast cancer only after menopause, differences in pre- and post-menopausal women were also explored. We recruited 3,584 women aged 45–68 years within the Spanish breast cancer screening network. Demographic, reproductive, family and personal history data were collected by purpose-trained staff, who measured current weight, height, waist and hip circumferences under the same protocol and with the same tools. MD was assessed in the left craniocaudal view using Boyd’s Semiquantitative Scale. Association between waist-to-hip ratio, adult weight gain (difference between current weight and self-reported weight at 18 years) and MD was quantified by ordinal logistic regression, with random center-specific intercepts. Models were adjusted for age, BMI, breast size, time since menopause, parity, family history of breast cancer and hormonal replacement therapy use. Natural splines were used to describe the shape of the relationship between these two variables and MD. Waist-to-hip ratio was inversely associated with MD, and the effect was more pronounced in pre-menopausal (OR = 0.53 per 0.1 units; 95 % CI = 0.42–0.66) than in post-menopausal women (OR = 0.73; 95 % CI = 0.65–0.82) (P of heterogeneity = 0.010). In contrast, adult weight gain displayed a positive association with MD, which was similar in both groups (OR = 1.17 per 6 kg; 95 % CI = 1.11–1.23). Women who had gained more than 24 kg displayed higher MD (OR = 2.05; 95 % CI = 1.53–2.73). MD was also evaluated using Wolfe’s and Tabár’s classifications, with similar results being obtained. Once BMI, fat distribution and other confounders were considered, our results showed a clear dose–response gradient between the number of kg gained during adulthood and the proportion of dense tissue in the breast.
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Affiliation(s)
- Marina Pollán
- Cancer Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos 5, 28029 Madrid, Spain.
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Greenwood CMT, Paterson AD, Linton L, Andrulis IL, Apicella C, Dimitromanolakis A, Kriukov V, Martin LJ, Salleh A, Samiltchuk E, Parekh RV, Southey MC, John EM, Hopper JL, Boyd NF, Rommens JM. A genome-wide linkage study of mammographic density, a risk factor for breast cancer. Breast Cancer Res 2011; 13:R132. [PMID: 22188651 PMCID: PMC3326574 DOI: 10.1186/bcr3078] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2011] [Revised: 10/16/2011] [Accepted: 12/21/2011] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION Mammographic breast density is a highly heritable (h2 > 0.6) and strong risk factor for breast cancer. We conducted a genome-wide linkage study to identify loci influencing mammographic breast density (MD). METHODS Epidemiological data were assembled on 1,415 families from the Australia, Northern California and Ontario sites of the Breast Cancer Family Registry, and additional families recruited in Australia and Ontario. Families consisted of sister pairs with age-matched mammograms and data on factors known to influence MD. Single nucleotide polymorphism (SNP) genotyping was performed on 3,952 individuals using the Illumina Infinium 6K linkage panel. RESULTS Using a variance components method, genome-wide linkage analysis was performed using quantitative traits obtained by adjusting MD measurements for known covariates. Our primary trait was formed by fitting a linear model to the square root of the percentage of the breast area that was dense (PMD), adjusting for age at mammogram, number of live births, menopausal status, weight, height, weight squared, and menopausal hormone therapy. The maximum logarithm of odds (LOD) score from the genome-wide scan was on chromosome 7p14.1-p13 (LOD = 2.69; 63.5 cM) for covariate-adjusted PMD, with a 1-LOD interval spanning 8.6 cM. A similar signal was seen for the covariate adjusted area of the breast that was dense (DA) phenotype. Simulations showed that the complete sample had adequate power to detect LOD scores of 3 or 3.5 for a locus accounting for 20% of phenotypic variance. A modest peak initially seen on chromosome 7q32.3-q34 increased in strength when only the 513 families with at least two sisters below 50 years of age were included in the analysis (LOD 3.2; 140.7 cM, 1-LOD interval spanning 9.6 cM). In a subgroup analysis, we also found a LOD score of 3.3 for DA phenotype on chromosome 12.11.22-q13.11 (60.8 cM, 1-LOD interval spanning 9.3 cM), overlapping a region identified in a previous study. CONCLUSIONS The suggestive peaks and the larger linkage signal seen in the subset of pedigrees with younger participants highlight regions of interest for further study to identify genes that determine MD, with the goal of understanding mammographic density and its involvement in susceptibility to breast cancer.
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Affiliation(s)
- Celia MT Greenwood
- Department of Oncology (Division of Cancer Epidemiology), and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC; Lady Davis Research Institute, Centre for Clinical Epidemiology and Community Studies, Jewish General Hospital, 3755 Côte Ste-Catherine, Montreal, QC H3T 1E2 Canada
| | - Andrew D Paterson
- Program in Genetics & Genome Biology, The Hospital for Sick Children, 101 College Street, East Tower, Toronto, ON M5G 1L7 Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7 Canada
| | - Linda Linton
- The Campbell Family Cancer Research Institute, Toronto, ON M5G 2M9 Canada
| | - Irene L Andrulis
- Ontario Genetics Network, Ontario Cancer Care, Toronto; Samuel Lunenfeld Research Institute and Department of Pathology & Laboratory Medicine, Mount Sinai Hospital, Toronto, ON M5G 1X5 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8 Canada
| | - Carmel Apicella
- Center for Molecular, Environmental, Genetic and Analytical Epidemiology, School of Public Health, The University of Melbourne, Melbourne, Melbourne, Victoria 3053, Australia
| | - Apostolos Dimitromanolakis
- Program in Genetics & Genome Biology, The Hospital for Sick Children, 101 College Street, East Tower, Toronto, ON M5G 1L7 Canada
| | - Valentina Kriukov
- The Campbell Family Cancer Research Institute, Toronto, ON M5G 2M9 Canada
| | - Lisa J Martin
- The Campbell Family Cancer Research Institute, Toronto, ON M5G 2M9 Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9 Canada
| | - Ayesha Salleh
- The Campbell Family Cancer Research Institute, Toronto, ON M5G 2M9 Canada
| | - Elena Samiltchuk
- Program in Genetics & Genome Biology, The Hospital for Sick Children, 101 College Street, East Tower, Toronto, ON M5G 1L7 Canada
| | - Rashmi V Parekh
- Program in Genetics & Genome Biology, The Hospital for Sick Children, 101 College Street, East Tower, Toronto, ON M5G 1L7 Canada
| | - Melissa C Southey
- Department of Pathology, The University of Melbourne, Melbourne, Melbourne, Victoria 3053, Australia
| | - Esther M John
- Department of Health Research and Policy, Stanford University School of Medicine and Stanford Cancer Center, Stanford; Cancer Prevention Institute of California, Fremont, CA 94538, USA
| | - John L Hopper
- Center for Molecular, Environmental, Genetic and Analytical Epidemiology, School of Public Health, The University of Melbourne, Melbourne, Melbourne, Victoria 3053, Australia
| | - Norman F Boyd
- The Campbell Family Cancer Research Institute, Toronto, ON M5G 2M9 Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9 Canada
| | - Johanna M Rommens
- Program in Genetics & Genome Biology, The Hospital for Sick Children, 101 College Street, East Tower, Toronto, ON M5G 1L7 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8 Canada
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