The origins of breast cancer associated with mammographic density: a testable biological hypothesis

Norman Boyd, Hal Berman, Jie Zhu, Lisa J Martin, Martin J Yaffe, Sofia Chavez, Greg Stanisz, Greg Hislop, Anna M Chiarelli, Salomon Minkin, Andrew D Paterson, Norman Boyd, Hal Berman, Jie Zhu, Lisa J Martin, Martin J Yaffe, Sofia Chavez, Greg Stanisz, Greg Hislop, Anna M Chiarelli, Salomon Minkin, Andrew D Paterson

Abstract

Background: Our purpose is to develop a testable biological hypothesis to explain the known increased risk of breast cancer associated with extensive percent mammographic density (PMD), and to reconcile the apparent paradox that although PMD decreases with increasing age, breast cancer incidence increases.

Methods: We used the Moolgavkar model of carcinogenesis as a framework to examine the known biological properties of the breast tissue components associated with PMD that includes epithelium and stroma, in relation to the development of breast cancer. In this model, normal epithelial cells undergo a mutation to become intermediate cells, which, after further mutation, become malignant cells. A clone of such cells grows to become a tumor. The model also incorporates changes with age in the number of susceptible epithelial cells associated with menarche, parity, and menopause. We used measurements of the radiological properties of breast tissue in 4454 healthy subjects aged from 15 to 80+ years to estimate cumulative exposure to PMD (CBD) in the population, and we examined the association of CBD with the age-incidence curve of breast cancer in the population.

Results: Extensive PMD is associated with a greater number of breast epithelial cells, lobules, and fibroblasts, and greater amounts of collagen and extracellular matrix. The known biological properties of these tissue components may, singly or in combination, promote the acquisition of mutations by breast epithelial cells specified by the Moolgavkar model, and the subsequent growth of a clone of malignant cells to form a tumor. We also show that estimated CBD in the population from ages 15 to 80+ years is closely associated with the age-incidence curve of breast cancer in the population.

Conclusions: These findings are consistent with the hypothesis that the biological properties of the breast tissue components associated with PMD increase the probability of the transition of normal epithelium to malignant cells, and that the accumulation of mutations with CBD may influence the age-incidence curve of breast cancer. This hypothesis gives rise to several testable predictions.

Keywords: Breast cancer; Carcinogenesis; Mammographic density; Two-stage model.

Conflict of interest statement

Ethics approval and consent to participate

The data in the section of the manuscript concerned with CBD and the age-specific incidence of breast cancer were obtained in a number of separate studies for which ethics approval was obtained from the University Health Network, Sunnybrook Hospital, and Women’s College Hospital (all in Toronto), and from the Toronto District School Board, the Toronto Catholic District School Board, the York Region District School Board, the York Catholic District School Board, and the mammography screening programs of Ontario and British Columbia.

Consent for publication

All subjects provided signed inform consent.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Two-stage model of carcinogenesis of Moolgavkar et al. [17]. In this model, normal stem cells, with a birth rate (α1) and rate of death (β1), can be transformed into cells of an intermediate form at a stochastic event rate (μ1) (the first mutation rate). These intermediate cells can divide into two further intermediate cells at a stochastic rate (α2), then die or differentiate at rate β2. In addition, intermediate cells can divide into one intermediate and one transformed (malignant) cell with a second stochastic event rate (μ2). The malignant cells are assumed to develop into a tumor after a deterministic lag time
Fig. 2
Fig. 2
Breast tissue components associated with percent mammographic density (PMD). PMD was assessed in the BioVision (Faxitron Bioptics) image of the enucleated breast from which the section had been taken (a) [Li T, et al. Cancer Epidemiol Biomarkers Prev. 2005;14(2):343–9]. We used quantitative microscopy in randomly selected areas of the tissue section (b) to measure the total, epithelial, and nonepithelial nuclear areas (H&E stain in c) as an index of the number of cells (outlined in green in d), the area of collagen (H&E stain in f and Masson’s trichrome in g), and the glandular area. PMD was associated inversely with age, and, after age adjustment, positively with the nuclear area (e) of epithelial and nonepithelial cells, glandular area, and the area of collagen (h). Box plots in e and h show the associations of total nuclear area (e) and collagen (h) with PMD. The median values are shown as horizontal lines, and the boxes show the 25th and 75th percentiles of the distributions. Age, parity, and menopausal status were also associated with variations in one or more of these tissue components. Similar associations of PMD with these breast tissue components have been found in prophylactic mastectomies [13]. Original magnification ×10 (c, d, f, and g)
Fig. 3
Fig. 3
Proposed model of the two-stage model of carcinogenesis with risk of breast cancer in percent mammographic density. (The third column represents effects on the growth of a clone of malignant cells.) CAF Cancer-associated fibroblast, ECM Extracellular matrix, HGF Hepatocyte growth factor, IGF-1 Insulin-like growth factor 1, MMP-3 Matrix metalloproteinase 3, TGF-β Transforming growth factor-β
Fig. 4
Fig. 4
Cumulative breast density: observed and predicted breast cancer incidence. Left: Breast density according to age. Values derived from mammogram in open circles; values from calibrated measures derived from magnetic resonance in closed circles. Right: Log breast cancer incidence in closed circles, log cumulative breast density in open circles. Incidence data for age-specific incidence of invasive breast cancer for Canada were obtained from Curado MP et al. Cancer incidence in five continents. Vol. IX. IARC Scientific Publication no. 160. Lyon, France: IARC Press; 2007

References

    1. Boyd NF, Martin LJ, Yaffe MJ, Minkin S. Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res. 2011;13(6):223. doi: 10.1186/bcr2942.
    1. Johns PC, Yaffe MJ. X-ray characterisation of normal and neoplastic breast tissues. Phys Med Biol. 1987;32(6):675–695. doi: 10.1088/0031-9155/32/6/002.
    1. Pettersson A, Graff RE, Ursin G, Santos Silva ID, McCormack V, Baglietto L, Vachon C, Bakker MF, Giles GG, Chia KS, et al. Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst. 2014:106(5).
    1. Bertrand KA, Scott CG, Tamimi RM, Jensen MR, Pankratz VS, Norman AD, Visscher DW, Couch FJ, Shepherd J, Chen YY, et al. Dense and nondense mammographic area and risk of breast cancer by age and tumor characteristics. Cancer Epidemiol Biomarkers Prevent. 2015;24(5):798–809. doi: 10.1158/1055-9965.EPI-14-1136.
    1. Byrne C, Schairer C, Wolfe J, Parekh N, Salane M, Brinton LA, Hoover R, Haile R. Mammographic features and breast cancer risk: effects with time, age, and menopause status. J Natl Cancer Inst. 1995;87(21):1622–1629. doi: 10.1093/jnci/87.21.1622.
    1. Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin S, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227–236. doi: 10.1056/NEJMoa062790.
    1. McCormack VA, dos Santos SI. Breast density and parenchymal patterns as markers of breast cancer risk: A meta-analysis. Cancer Epidemiol Biomarkers Prevent. 2006;15(6):1159–1169. doi: 10.1158/1055-9965.EPI-06-0034.
    1. Boyd NF, Jensen HM, Cooke G, Han HL. Relationship between mammographic and histological risk factors for breast cancer. J Natl Cancer Inst. 1992;84(15):1170–1179. doi: 10.1093/jnci/84.15.1170.
    1. Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, Lopez-Ridaura R, Rice M, Pereira A, Garmendia ML, et al. Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med. 2017;14(6):e1002335. doi: 10.1371/journal.pmed.1002335.
    1. Maskarinec G, Pagano I, Lurie G, Kolonel LN. A longitudinal investigation of mammographic density: the multiethnic cohort. Cancer Epidemiol Biomarkers Prev. 2006;15(4):732–739. doi: 10.1158/1055-9965.EPI-05-0798.
    1. Boyd N, Martin L, Stone J, Little L, Minkin S, Yaffe M. A longitudinal study of the effects of menopause on mammographic features. Cancer Epidemiol Biomarkers Prev. 2002;11(10 Pt 1):1048–1053.
    1. Antoni S, Sasco AJ, dos Santos SI, McCormack V. Is mammographic density differentially associated with breast cancer according to receptor status? A meta-analysis. Breast Cancer Res Treat. 2013;137(2):337–347. doi: 10.1007/s10549-012-2362-4.
    1. Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, Benichou J, Gail MH. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006;98(17):1215–1226. doi: 10.1093/jnci/djj332.
    1. Sherratt MJ, McConnell JC, Streuli CH. Raised mammographic density: causative mechanisms and biological consequences. Breast Cancer Res. 2016;18(1):45. doi: 10.1186/s13058-016-0701-9.
    1. Boyd NF, Lockwood GA, Martin LJ, Byng JW, Yaffe MJ, Tritchler DL. Mammographic density as a marker of susceptibility to breast cancer: a hypothesis. IARC Sci Publ. 2001;154:163–169.
    1. Pike MC, Krailo MD, Henderson BE, Casagrande JT, Hoel DG. ‘Hormonal’ risk factors, 'breast tissue age’ and the age-incidence of breast cancer. Nature. 1983;303(5920):767–770. doi: 10.1038/303767a0.
    1. Moolgavkar SH, Day NE, Stevens RG. Two-stage model for carcinogenesis: Epidemiology of breast cancer in females. J Natl Cancer Inst. 1980;65(3):559–569.
    1. Newburger DE, Kashef-Haghighi D, Weng Z, Salari R, Sweeney RT, Brunner AL, Zhu SX, Guo X, Varma S, Troxell ML, et al. Genome evolution during progression to breast cancer. Genome Res. 2013;23(7):1097–1108. doi: 10.1101/gr.151670.112.
    1. Nik-Zainal S, Van Loo P, Wedge DC, Alexandrov LB, Greenman CD, Lau KW, Raine K, Jones D, Marshall J, Ramakrishna M, et al. The life history of 21 breast cancers. Cell. 2012;149(5):994–1007. doi: 10.1016/j.cell.2012.04.023.
    1. Martincorena I, Raine KM, Gerstung M, Dawson KJ, Haase K, Van Loo P, Davies H, Stratton MR, Campbell PJ. Universal Patterns of Selection in Cancer and Somatic Tissues. Cell. 2017;171(5):1029–1041. doi: 10.1016/j.cell.2017.09.042.
    1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–674. doi: 10.1016/j.cell.2011.02.013.
    1. Li T, Sun L, Miller N, Nicklee T, Woo J, Hulse-Smith L, Tsao MS, Khokha R, Martin L, Boyd N. The association of measured breast tissue characteristics with mammographic density and other risk factors for breast cancer. Cancer Epidemiol Biomarkers Prev. 2005;14(2):343–349. doi: 10.1158/1055-9965.EPI-04-0490.
    1. Huo CW, Chew G, Hill P, Huang D, Ingman W, Hodson L, Brown KA, Magenau A, Allam AH, McGhee E, et al. High mammographic density is associated with an increase in stromal collagen and immune cells within the mammary epithelium. Breast Cancer Res. 2015;17:79. doi: 10.1186/s13058-015-0592-1.
    1. Bland KI, Kuhns JG, Buchanan JB, Dwyer PA, Heuser LF, O'Connor CA, Gray LA, Sr, Polk HC., Jr A clinicopathologic correlation of mammographic parenchymal patterns and associated risk factors for human mammary carcinoma. Ann Surg. 1982;195(5):582–594. doi: 10.1097/00000658-198205000-00007.
    1. Ghosh K, Brandt KR, Reynolds C, Scott CG, Pankratz VS, Riehle DL, Lingle WL, Odogwu T, Radisky DC, Visscher DW, et al. Tissue composition of mammographically dense and non-dense breast tissue. Breast Cancer Res Treat. 2012;131(1):267–275. doi: 10.1007/s10549-011-1727-4.
    1. Urbanski S, Jensen HM, Cooke G, McFarlane D, Shannon P, Kruikov V, Boyd NF. The association of histological and radiological indicators of breast cancer risk. Br J Cancer. 1988;58(4):474–479. doi: 10.1038/bjc.1988.244.
    1. Bartow SA, Mettler FA, Jr, Black Iii WC. Correlations between radiographic patterns and morphology of the female breast. Rad Patterns Morph. 1997;13:263–275.
    1. Simpson ER, Clyne C, Rubin G, Boon WC, Robertson K, Britt K, Speed C, Jones M. Aromatase--a brief overview. Annu Rev Physiol. 2002;64:93–127. doi: 10.1146/annurev.physiol.64.081601.142703.
    1. Wellings SR, Jensen HM. On the origin and progression of ductal carcinoma in the human breast. J Natl Cancer Inst. 1973;50(5):1111–1118. doi: 10.1093/jnci/50.5.1111.
    1. Wellings SR, Jensen HM, Marcum RG. An atlas of subgross pathology of the human breast with special reference to possible precancerous lesions. J Natl Cancer Inst. 1975;55(2):231–273.
    1. Renehan AG, Harvie M, Howell A. Insulin-like growth factor (IGF)-I, IGF binding protein-3, and breast cancer risk: eight years on. EndocrRelat Cancer. 2006;13(2):273–278. doi: 10.1677/erc.1.01219.
    1. Tworoger SS, Eliassen AH, Sluss P, Hankinson SE. A prospective study of plasma prolactin concentrations and risk of premenopausal and postmenopausal breast cancer. J Clin Oncol. 2007;25(12):1482–1488. doi: 10.1200/JCO.2006.07.6356.
    1. Horne HN, Sherman ME, Pfeiffer RM, Figueroa JD, Khodr ZG, Falk RT, Pollak M, Patel DA, Palakal MM, Linville L, et al. Circulating insulin-like growth factor-I, insulin-like growth factor binding protein-3 and terminal duct lobular unit involution of the breast: a cross-sectional study of women with benign breast disease. Breast Cancer Res. 2016;18(1):24. doi: 10.1186/s13058-016-0678-4.
    1. Huh SJ, Oh H, Peterson MA, Almendro V, Hu R, Bowden M, Lis RL, Cotter MB, Loda M, Barry WT, et al. The Proliferative Activity of Mammary Epithelial Cells in Normal Tissue Predicts Breast Cancer Risk in Premenopausal Women. Cancer Res. 2016;76(7):1926–1934. doi: 10.1158/0008-5472.CAN-15-1927.
    1. Hawes D, Downey S, Pearce CL, Bartow S, Wan P, Pike MC, Wu AH. Dense breast stromal tissue shows greatly increased concentration of breast epithelium but no increase in its proliferative activity. Breast Cancer Res. 2006;8(2):R24. doi: 10.1186/bcr1408.
    1. Colditz GA, Frazier LA. Models of breast cancer show that risk is set by events of early life: prevention efforts much shift focus (review) Cancer Epidemiol Biomarkers Prevent. 1995;4(5):567–571.
    1. Sun X, Glynn DJ, Hodson LJ, Huo C, Britt K, Thompson EW, Woolford L, Evdokiou A, Pollard JW, Robertson SA, et al. CCL2-driven inflammation increases mammary gland stromal density and cancer susceptibility in a transgenic mouse model. Breast Cancer Res. 2017;19(1):4. doi: 10.1186/s13058-016-0796-z.
    1. Provenzano PP, Inman DR, Eliceiri KW, Knittel JG, Yan L, Rueden CT, White JG, Keely PJ. Collagen density promotes mammary tumor initiation and progression. BMC Med. 2008;6(0):11. doi: 10.1186/1741-7015-6-11.
    1. McConnell JC, O'Connell OV, Brennan K, Weiping L, Howe M, Joseph L, Knight D, O'Cualain R, Lim Y, Leek A, et al. Increased peri-ductal collagen micro-organization may contribute to raised mammographic density. Breast Cancer Res. 2016;18(1):5. doi: 10.1186/s13058-015-0664-2.
    1. Ray A, Slama ZM, Morford RK, Madden SA, Provenzano PP. Enhanced Directional Migration of Cancer Stem Cells in 3D Aligned Collagen Matrices. Biophys J. 2017;112(5):1023–1036. doi: 10.1016/j.bpj.2017.01.007.
    1. Kuperwasser C, Chavarria T, Wu M, Magrane G, Gray JW, Carey L, Richardson A, Weinberg RA. Reconstruction of functionally normal and malignant human breast tissues in mice. Proc Natl Acad Sc U S A. 2004;101(14):4966–4971. doi: 10.1073/pnas.0401064101.
    1. Medina D. Stromal fibroblasts influence human mammary epithelial cell morphogenesis. Proc Natl Acad Sci U S A. 2004;101(14):4723–4724. doi: 10.1073/pnas.0401376101.
    1. Kalluri R. The biology and function of fibroblasts in cancer. Nat Rev Cancer. 2016;16(9):582–598. doi: 10.1038/nrc.2016.73.
    1. O'Connor JW, Gomez EW. Biomechanics of TGFbeta-induced epithelial-mesenchymal transition: implications for fibrosis and cancer. Clin Transl Med. 2014;3:23. doi: 10.1186/2001-1326-3-23.
    1. Pickup M, Novitskiy S, Moses HL. The roles of TGFbeta in the tumour microenvironment. Nat Rev Cancer. 2013;13(11):788–799. doi: 10.1038/nrc3603.
    1. DeFilippis RA, Chang H, Dumont N, Rabban JT, Chen YY, Fontenay GV, Berman HK, Gauthier ML, Zhao J, Hu D, et al. CD36 repression activates a multicellular stromal program shared by high mammographic density and tumor tissues. Cancer Discov. 2012;2(9):826–839. doi: 10.1158/-12-0107.
    1. Folgueira MA, Maistro S, Katayama ML, Roela RA, Mundim FG, Nanogaki S, de Bock GH, Brentani MM. Markers of breast cancer stromal fibroblasts in the primary tumour site associated with lymph node metastasis: a systematic review including our case series. Biosci Rep. 2013;33(6)
    1. Hasebe T, Sasaki S, Imoto S, Mukai K, Yokose T, Ochiai A. Prognostic significance of fibrotic focus in invasive ductal carcinoma of the breast: a prospective observational study. Mod Pathol. 2002;15(5):502–516. doi: 10.1038/modpathol.3880555.
    1. Mujtaba SS, Ni YB, Tsang JY, Chan SK, Yamaguchi R, Tanaka M, Tan PH, Tse GM. Fibrotic focus in breast carcinomas: relationship with prognostic parameters and biomarkers. Ann Surg Oncol. 2013;20(9):2842–2849. doi: 10.1245/s10434-013-2955-0.
    1. Vachon CM, Sasano H, Ghosh K, Brandt KR, Watson DA, Reynolds C, Lingle WL, Goss PE, Li R, Aiyar SE, et al. Aromatase immunoreactivity is increased in mammographically dense regions of the breast. Breast Cancer Res Treat. 2011;125(1):243–252. doi: 10.1007/s10549-010-0944-6.
    1. Simpson ER, Clyne CD, Rubin G, Boon WC, Robertson K, Britt K, Speed C, Jones M. Aromatase--a brief overview. Annu Rev Physiol. 2002;64(0):93–127. doi: 10.1146/annurev.physiol.64.081601.142703.
    1. Simpson ER, McInnes KJ, Brown KA, Knower KC, Chand AL, Clyne CD, Simpson ER. Characterisation of aromatase expression in the human adipocyte cell line SGBS. Breast Cancer Res Treat. 2008;112(3):429–435. doi: 10.1007/s10549-007-9883-2.
    1. Bulun SE, Mahendroo MS, Simpson ER. Aromatase gene expression in adipose tissue: relationship to breast cancer. J Steriod Biochem Molec Biol. 1994;49(4-6):319–326. doi: 10.1016/0960-0760(94)90274-7.
    1. Bulun SE, Sharda G, Rink J, Sharma S, Simpson ER. Distribution of aromatase P450 transcripts and adipose fibroblasts in the human breast. J Clin Endocrinol Metabol. 1996;81(3):1273–1277.
    1. Simpson ER. Biology of aromatase in the mammary gland. J Mammary Gland Biol Neoplasia. 2000;5(3):251–258. doi: 10.1023/A:1009590626450.
    1. Lisanti MP, Tsirigos A, Pavlides S, Reeves KJ, Peiris-Pages M, Chadwick AL, Sanchez-Alvarez R, Lamb R, Howell A, Martinez-Outschoorn UE, et al. JNK1 stress signaling is hyper-activated in high breast density and the tumor stroma: connecting fibrosis, inflammation, and stemness for cancer prevention. Cell Cycle. 2014;13(4):580–599. doi: 10.4161/cc.27379.
    1. Butcher DT, Alliston T, Weaver VM. A tense situation: forcing tumour progression. Nat Rev Cancer. 2009;9(2):108–122. doi: 10.1038/nrc2544.
    1. Paszek MJ, Weaver VM. The tension mounts: mechanics meets morphogenesis and malignancy. J Mammary Gland Biol Neoplasia. 2004;9(4):325–342. doi: 10.1007/s10911-004-1404-x.
    1. Alowami S, Troup S, Al-Haddad S, Kirkpatrick I, Watson PH. Mammographic density is related to stroma and stromal proteoglycan expression. Breast Cancer Res. 2003;5(5):R129–R135. doi: 10.1186/bcr622.
    1. Guo YP, Martin LJ, Hanna W, Banerjee D, Miller N, Fishell E, Khokha R, Boyd NF. Growth factors and stromal matrix proteins associated with mammographic densities. Cancer Epidemiol Biomarkers Prev. 2001;10(3):243–248.
    1. Hojilla CV, Mohammed FF, Khokha R. Matrix metalloproteinases and their tissue inhibitors direct cell fate during cancer development. Br J Cancer. 2003;89(10):1817–1821. doi: 10.1038/sj.bjc.6601327.
    1. Huo CW, Waltham M, Khoo C, Fox SB, Hill P, Chen S, Chew GL, Price JT, Nguyen CH, Williams ED, et al. Mammographically dense human breast tissue stimulates progression to invasive lesions and metastasis. Breast Cancer Res. 2016;18(1):106. doi: 10.1186/s13058-016-0767-4.
    1. Boyd NF, Dite GS, Stone J, Gunasekara A, English DR, McCredie MRE, Giles GG, Tritchler D, Chiarelli A, Yaffe MJ, et al. Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med. 2002;347(12):886–894. doi: 10.1056/NEJMoa013390.
    1. Varghese JS, Thompson DJ, Michailidou K, Lindstrom S, Turnbull C, Brown J, Leyland J, Warren RM, Luben RN, Loos RJ, et al. Mammographic breast density and breast cancer: evidence of a shared genetic basis. Cancer Res. 2012;72(6):1478–1484. doi: 10.1158/0008-5472.CAN-11-3295.
    1. Lindstrom S, Thompson DJ, Paterson AD, Li J, Gierach GL, Scott C, Stone J, Douglas JA, dos-Santos-Silva I, Fernandez-Navarro P, et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun. 2014;5:5303. doi: 10.1038/ncomms6303.
    1. Lindstrom S, Vachon CM, Li J, Varghese J, Thompson D, Warren R, Brown J, Leyland J, Audley T, Wareham NJ, et al. Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nat Genet. 2011;43(3):185–187. doi: 10.1038/ng.760.
    1. Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, Li X, Li H, Kuperwasser N, Ruda VM, et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature. 2010;466(7307):714–719. doi: 10.1038/nature09266.
    1. Smemo S, Tena JJ, Kim KH, Gamazon ER, Sakabe NJ, Gomez-Marin C, Aneas I, Credidio FL, Sobreira DR, Wasserman NF, et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature. 2014;507(7492):371–375. doi: 10.1038/nature13138.
    1. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42(Database issue):D1001–D1006. doi: 10.1093/nar/gkt1229.
    1. Berasain C, Avila MA. Amphiregulin. Semin Cell Dev Biol. 2014;28:31–41. doi: 10.1016/j.semcdb.2014.01.005.
    1. Zhou Y, Lee JY, Lee CM, Cho WK, Kang MJ, Koff JL, Yoon PO, Chae J, Park HO, Elias JA, et al. Amphiregulin, an epidermal growth factor receptor ligand, plays an essential role in the pathogenesis of transforming growth factor-beta-induced pulmonary fibrosis. J Biol Chem. 2012;287(50):41991–42000. doi: 10.1074/jbc.M112.356824.
    1. Herrington DM, Howard TD, Hawkins GA, Reboussin DM, Xu J, Zheng SL, Brosnihan KB, Meyers DA, Bleecker ER. Estrogen-receptor polymorphisms and effects of estrogen replacement on high-density lipoprotein cholesterol in women with coronary disease. N Engl J Med. 2002;346(13):967–974. doi: 10.1056/NEJMoa012952.
    1. Wiedemann E, Schwartz E, Frantz AG. Acute and chronic estrogen effects upon serum somatomedin activity, growth hormone, and prolactin in man. J Clin Endocrinol Metab. 1976;42(5):942–952. doi: 10.1210/jcem-42-5-942.
    1. Hankinson SE, Willett WC, Michaud DS, Manson JE, Colditz GA, Longcope C, Rosner B, Speizer FE. Plasma prolactin levels and subsequent risk of breast cancer in postmenopausal women. J Natl Cancer Inst. 1999;91(7):629–634. doi: 10.1093/jnci/91.7.629.
    1. Boyd N, Martin L, Chavez S, Gunasekara A, Salleh A, Melnichouk O, Yaffe M, Friedenreich C, Minkin S, Bronskill M. Breast-tissue composition and other risk factors for breast cancer in young women: a cross-sectional study. Lancet Oncol. 2009;10(6):569–580. doi: 10.1016/S1470-2045(09)70078-6.
    1. Franco L, Williams FM, Trofimov S, Malkin I, Surdulescu G, Spector T, Livshits G. Assessment of age-related changes in heritability and IGF-1 gene effect on circulating IGF-1 levels. Age (Dordr) 2014;36(3):9622. doi: 10.1007/s11357-014-9622-7.
    1. Pollak M. The insulin and insulin-like growth factor receptor family in neoplasia: an update. Nat Rev Cancer. 2012;12(3):159–169. doi: 10.1038/nrc3215.
    1. Zhang B, Shu XO, Delahanty RJ, Zeng C, Michailidou K, Bolla MK, Wang Q, Dennis J, Wen W, Long J, et al. Height and Breast Cancer Risk: Evidence From Prospective Studies and Mendelian Randomization. J Natl Cancer Inst. 2015;107(11)
    1. Boyd NF, Lockwood GA, Byng JW, Little LE, Yaffe MJ, Tritchler DL. The relationship of anthropometric measures to radiological features of the breast in premenopausal women. Br J Cancer. 1998;78(9):1233–1238. doi: 10.1038/bjc.1998.660.
    1. Johansson A, Marroni F, Hayward C, Franklin CS, Kirichenko AV, Jonasson I, Hicks AA, Vitart V, Isaacs A, Axenovich T, et al. Linkage and genome-wide association analysis of obesity-related phenotypes: association of weight with the MGAT1 gene. Obesity. 2010;18(4):803–808. doi: 10.1038/oby.2009.359.
    1. Okada Y, Kamatani Y, Takahashi A, Matsuda K, Hosono N, Ohmiya H, Daigo Y, Yamamoto K, Kubo M, Nakamura Y, et al. A genome-wide association study in 19 633 Japanese subjects identified LHX3-QSOX2 and IGF1 as adult height loci. Hum mol Genet. 2010;19(11):2303–2312. doi: 10.1093/hmg/ddq091.
    1. Sasazuki T, Sawada T, Sakon S, Kitamura T, Kishi T, Okazaki T, Katano M, Tanaka M, Watanabe M, Yagita H, et al. Identification of a novel transcriptional activator, BSAC, by a functional cloning to inhibit tumor necrosis factor-induced cell death. J Biol Chem. 2002;277(32):28853–28860. doi: 10.1074/jbc.M203190200.
    1. Hossain M, Qadri SM, Su Y, Liu L. ICAM-1-mediated leukocyte adhesion is critical for the activation of endothelial LSP1. Am J Physiol Cell Physiol. 2013;304(9):C895–C904. doi: 10.1152/ajpcell.00297.2012.
    1. Moolgavkar SH, Day NE, Stevens RG. Two-stage model for carcinogenesis: epidemiology of breast cancer in females. J Natl Cancer Inst. 1980;65(3):559–569.
    1. Vogelstein B, Kinzler KW. The multistep nature of cancer. Trends Genet. 1993;9(4):138–141. doi: 10.1016/0168-9525(93)90209-Z.
    1. Boyd NF, Martin LJ, Bronskill M, Yaffe MJ, Duric N, Minkin S. Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst. 2010;102(16):1224–1237. doi: 10.1093/jnci/djq239.
    1. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Phys Med Biol. 1994;39(10):1629–1638. doi: 10.1088/0031-9155/39/10/008.
    1. Graham SJ, Ness S, Hamilton BS, Bronskill MJ. Magnetic resonance properties of ex vivo breast tissue at 1.5 T. Magn Reson Med. 1997;38(4):669–677. doi: 10.1002/mrm.1910380422.
    1. Boyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ. Mammographic densities and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 1998;7(12):1133–1144.
    1. Bernstein L. Epidemiology of endocrine-related risk factors for breast cancer. J Mammary Gland Biol Neoplasia. 2002;7(1):3–15. doi: 10.1023/A:1015714305420.
    1. Rice MS, Bertrand KA, VanderWeele TJ, Rosner BA, Liao X, Adami HO, Tamimi RM. Mammographic density and breast cancer risk: a mediation analysis. Breast Cancer Res. 2016;18(1):94. doi: 10.1186/s13058-016-0750-0.
    1. Cuzick J. Breast density predicts endocrine treatment outcome in the adjuvant setting. Breast Cancer Res. 2012;14(4):109. doi: 10.1186/bcr3235.
    1. Byrne C, Ursin G, Martin CF, Peck JD, Cole EB, Zeng D, Kim E, Yaffe MD, Boyd NF, Heiss G, et al. Mammographic Density Change With Estrogen and Progestin Therapy and Breast Cancer Risk. J Natl Cancer Inst. 2017;109(9)
    1. Toriola AT, Dang HX, Hagemann IS, Appleton CM, Colditz GA, Luo J, Maher CA. Increased breast tissue receptor activator of nuclear factor-kappaB ligand (RANKL) gene expression is associated with higher mammographic density in premenopausal women. Oncotarget. 2017;8(43):73787–73792. doi: 10.18632/oncotarget.17909.

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever