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Association of mammographic density measures and breast cancer “intrinsic” molecular subtypes

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

We evaluated the association of percent mammographic density (PMD), absolute dense area (DA), and non-dense area (NDA) with risk of “intrinsic” molecular breast cancer (BC) subtypes.

Methods

We pooled 3492 invasive BC and 10,148 controls across six studies with density measures from prediagnostic, digitized film-screen mammograms. We classified BC tumors into subtypes [63% Luminal A, 21% Luminal B, 5% HER2 expressing, and 11% as triple negative (TN)] using information on estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and tumor grade. We used polytomous logistic regression to calculate odds ratio (OR) and 95% confidence intervals (CI) for density measures (per SD) across the subtypes compared to controls, adjusting for age, body mass index and study, and examined differences by age group.

Results

All density measures were similarly associated with BC risk across subtypes. Significant interaction of PMD by age (P = 0.001) was observed for Luminal A tumors, with stronger effect sizes seen for younger women < 45 years (OR = 1.69 per SD PMD) relative to women of older ages (OR = 1.53, ages 65–74, OR = 1.44 ages 75 +). Similar but opposite trends were seen for NDA by age for risk of Luminal A: risk for women: < 45 years (OR = 0.71 per SD NDA) was lower than older women (OR = 0.83 and OR = 0.84 for ages 65–74 and 75 + , respectively) (P < 0.001). Although not significant, similar patterns of associations were seen by age for TN cancers.

Conclusions

Mammographic density measures were associated with risk of all “intrinsic” molecular subtypes. However, findings of significant interactions between age and density measures may have implications for subtype-specific risk models.

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Abbreviations

HER2:

Human epidermal growth factor receptor 2

BC:

Breast cancer

ER:

Estrogen receptor

PMD:

Percent mammographic density

DA:

Dense area

NDA:

Non-dense area

IHC:

Immunohistochemical

OR:

Odds ratio

CI:

Confidence interval

MMHS:

Mayo Mammography Health Study

MCBCS:

Mayo Clinic Breast Cancer Study

NHSI:

Nurses’ Health Study I

NHSII:

Nurses’ Health Study II

MCMAM:

Mayo Clinic Mammography Study

SFMR:

San Francisco Bay Area Breast Cancer SPORE and San Francisco Mammography Registry

References

  1. McCormack VA, dos Santos SI (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15(6):1159–1169

    PubMed  Google Scholar 

  2. Eriksson L, Czene K, Rosenberg L, Humphreys K, Hall P (2012) The influence of mammographic density on breast tumor characteristics. Breast Cancer Res Treat 134(2):859–866

    PubMed  Google Scholar 

  3. Yaghjyan L, Colditz GA, Collins LC, Schnitt SJ, Rosner B, Vachon C, Tamimi RM (2011) Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to tumor characteristics. J Natl Cancer Inst 103(15):1179–1189

    PubMed  PubMed Central  Google Scholar 

  4. Chen JH, Hsu FT, Shih HN, Hsu CC, Chang D, Nie K, Nalcioglu O, Su MY (2009) Does breast density show difference in patients with estrogen receptor-positive and estrogen receptor-negative breast cancer measured on MRI? Ann Oncol 20(8):1447–1449

    PubMed  PubMed Central  Google Scholar 

  5. Eriksson L, Hall P, Czene K, dos Santos SI, McCormack V, Bergh J, Bjohle J, Ploner A (2012) Mammographic density and molecular subtypes of breast cancer. Br J Cancer 107(1):18–23

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Phipps AI, Buist DSM, Malone KE, Barlow WE, Porter PL, Kerlikowske K, O’Meara ES, Li CI (2012) Breast density, body mass index, and risk of tumor marker-defined subtypes of breast cancer. Ann Epidemiol 22(5):340–348

    PubMed  PubMed Central  Google Scholar 

  7. Yang W-T, Dryden M, Broglio K, Gilcrease M, Dawood S, Dempsey PJ, Valero V, Hortobagyi G, Atchley D, Arun B (2008) Mammographic features of triple receptor-negative primary breast cancers in young premenopausal women. Breast Cancer Res Treat 111(3):405–410

    PubMed  Google Scholar 

  8. Pizzato M, Carioli G, Rosso S, Zanetti R, La Vecchia C (2020) The impact of selected risk factors among breast cancer molecular subtypes: a case-only study. Breast Cancer Res Treat. https://doi.org/10.1007/s10549-020-05820-1

    Article  PubMed  Google Scholar 

  9. Shaikh AJ, Mullooly M, Sayed S, Ndumia R, Abayo I, Orwa J, Wasike R, Moloo Z, Gierach GL (2018) Mammographic breast density and breast cancer molecular subtypes: the Kenyan-African aspect. Biomed Res Int 2018:6026315

    PubMed  PubMed Central  Google Scholar 

  10. Antoni S, Sasco AJ, dos Santos SI, McCormack V (2013) Is mammographic density differentially associated with breast cancer according to receptor status? A meta-analysis. Breast Cancer Res Treat 137(2):337–347

    PubMed  Google Scholar 

  11. Holm J, Eriksson L, Ploner A, Eriksson M, Rantalainen M, Li J, Hall P, Czene K (2017) Assessment of breast cancer risk factors reveals subtype heterogeneity. Cancer Res 77(13):3708–3717

    CAS  PubMed  Google Scholar 

  12. Li E, Guida JL, Tian Y, Sung H, Koka H, Li M, Chan A, Zhang H, Tang E, Guo C et al (2019) Associations between mammographic density and tumor characteristics in Chinese women with breast cancer. Breast Cancer Res Treat 177(2):527–536

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Sartor H, Zackrisson S, Elebro K, Hartman L, Borgquist S (2015) Mammographic density in relation to tumor biomarkers, molecular subtypes, and mode of detection in breast cancer. Cancer Causes Control 26(6):931–939

    PubMed  Google Scholar 

  14. Shin J, Lee JE, Ko HY, Nguyen TL, Nam SJ, Hopper JL, Song Y-M (2018) Association between mammographic density and tumor marker-defined breast cancer subtypes. Eur J Cancer Prev 27(3):239–247

    CAS  PubMed  Google Scholar 

  15. Yaffe MJ (2008) Mammographic density. Measurement of mammographic density. Breast Cancer Res. 10(3):209–209

    PubMed  PubMed Central  Google Scholar 

  16. Bertrand KA, Tamimi RM, Scott CG, Jensen MR, Pankratz VS, Visscher D, Norman A, Couch F, Shepherd J, Fan B et al (2013) Mammographic density and risk of breast cancer by age and tumor characteristics. Breast Cancer Res 15(6):R104–R104

    PubMed  PubMed Central  Google Scholar 

  17. Velasquez Garcia HA, Gotay CC, Wilson CM, Lohrisch CA, Lai AS, Aronson KJ, Spinelli JJ (2019) Mammographic density parameters and breast cancer tumor characteristics among postmenopausal women. Breast Cancer (Dove Med Press) 11:261–271

    Google Scholar 

  18. Lokate M, Peeters PHM, Peelen LM, Haars G, Veldhuis WB, van Gils CH (2011) Mammographic density and breast cancer risk: the role of the fat surrounding the fibroglandular tissue. Breast Cancer Res 13(5):R103–R103

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Pettersson A, Graff RE, Ursin G, dos Santos SI, McCormack V, Baglietto L, Vachon C, Bakker MF, Giles GG, Chia KS et al (2014) Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst. https://doi.org/10.1093/jnci/dju078

    Article  PubMed  PubMed Central  Google Scholar 

  20. Pettersson A, Hankinson SE, Willett WC, Lagiou P, Trichopoulos D, Tamimi RM (2011) Nondense mammographic area and risk of breast cancer. Breast Cancer Res 13(5):R100–R100

    PubMed  PubMed Central  Google Scholar 

  21. Stone J, Ding J, Warren RML, Duffy SW, Hopper JL (2010) Using mammographic density to predict breast cancer risk: dense area or percentage dense area. Breast Cancer Res 12(6):R97–R97

    PubMed  PubMed Central  Google Scholar 

  22. Bertrand KA, Scott CG, Tamimi RM, Jensen MR, Pankratz VS, Norman AD, Visscher DW, Couch FJ, Shepherd J, Chen YY et al (2015) Dense and nondense mammographic area and risk of breast cancer by age and tumor characteristics. Cancer Epidemiol Biomarkers Prev 24(5):798–809

    PubMed  PubMed Central  Google Scholar 

  23. Inns J, James V (2015) Circulating microRNAs for the prediction of metastasis in breast cancer patients diagnosed with early stage disease. Breast 24(4):364–369

    PubMed  Google Scholar 

  24. Kahraman M, Röske A, Laufer T, Fehlmann T, Backes C, Kern F, Kohlhaas J, Schrörs H, Saiz A, Zabler C et al (2018) MicroRNA in diagnosis and therapy monitoring of early-stage triple-negative breast cancer. Sci Rep 8(1):11584–11584

    PubMed  PubMed Central  Google Scholar 

  25. van Schooneveld E, Wildiers H, Vergote I, Vermeulen PB, Dirix LY, Van Laere SJ (2015) Dysregulation of microRNAs in breast cancer and their potential role as prognostic and predictive biomarkers in patient management. Breast Cancer Res 17(1):21–21

    PubMed  PubMed Central  Google Scholar 

  26. Heine JJ, Scott CG, Sellers TA, Brandt KR, Serie DJ, Wu F-F, Morton MJ, Schueler BA, Couch FJ, Olson JE et al (2012) A novel automated mammographic density measure and breast cancer risk. J Natl Cancer Inst 104(13):1028–1037

    PubMed  PubMed Central  Google Scholar 

  27. Olson JE, Sellers TA, Scott CG, Schueler BA, Brandt KR, Serie DJ, Jensen MR, Wu F-F, Morton MJ, Heine JJ et al (2012) The influence of mammogram acquisition on the mammographic density and breast cancer association in the Mayo Mammography Health Study cohort. Breast Cancer Res 14(6):R147

    PubMed  PubMed Central  Google Scholar 

  28. Ghosh K, Brandt KR, Sellers TA, Reynolds C, Scott CG, Maloney SD, Carston MJ, Pankratz VS, Vachon CM (2008) Association of mammographic density with the pathology of subsequent breast cancer among postmenopausal women. Cancer Epidemiol Biomarkers Prev 17(4):872–879

    PubMed  PubMed Central  Google Scholar 

  29. Ma H, Luo J, Press MF, Wang Y, Bernstein L, Ursin G (2009) Is there a difference in the association between percent mammographic density and subtypes of breast cancer? Luminal A and triple-negative breast cancer. Cancer Epidemiol Biomarkers Prev 18(2):479–485

    CAS  PubMed  Google Scholar 

  30. Colditz GA, Hankinson SE (2005) The Nurses’ Health Study: lifestyle and health among women. Nat Rev Can 5(5):388–396

    CAS  Google Scholar 

  31. Tamimi RM, Hankinson SE, Colditz GA, Byrne C (2005) Endogenous sex hormone levels and mammographic density among postmenopausal women. Cancer Epidemiol Biomarkers Prev 14(11 Pt 1):2641–2647

    CAS  PubMed  Google Scholar 

  32. Tworoger SS, Sluss P, Hankinson SE (2006) Association between plasma prolactin concentrations and risk of breast cancer among predominately premenopausal women. Cancer Res 66(4):2476–2482

    CAS  PubMed  Google Scholar 

  33. Vachon CM, Brandt KR, Ghosh K, Scott CG, Maloney SD, Carston MJ, Pankratz VS, Sellers TA (2007) Mammographic breast density as a general marker of breast cancer risk. Cancer Epidemiol Biomarkers Prev 16(1):43–49

    PubMed  Google Scholar 

  34. Kerlikowske K, Carney PA, Geller B, Mandelson MT, Taplin SH, Malvin K, Ernster V, Urban N, Cutter G, Rosenberg R et al (2000) Performance of screening mammography among women with and without a first-degree relative with breast cancer. Ann intern Med 133(11):855–863

    CAS  PubMed  Google Scholar 

  35. Kerlikowske K, Shepherd J, Creasman J, Tice JA, Ziv E, Cummings SR (2005) Are breast density and bone mineral density independent risk factors for breast cancer? J Natl Cancer Inst 97(5):368–374

    PubMed  Google Scholar 

  36. Ziv E, Tice J, Smith-Bindman R, Shepherd J, Cummings S, Kerlikowske K (2004) Mammographic density and estrogen receptor status of breast cancer. Cancer Epidemiol Biomarkers Prev 13(12):2090–2095

    CAS  PubMed  Google Scholar 

  37. Boyd NF, Stone J, Martin LJ, Jong R, Fishell E, Yaffe M, Hammond G, Minkin S (2002) The association of breast mitogens with mammographic densities. Br J Cancer 87(8):876–882

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Shepherd JA, Kerlikowske K, Ma L, Duewer F, Fan B, Wang J, Malkov S, Vittinghoff E, Cummings SR (2011) Volume of mammographic density and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 20(7):1473–1482

    PubMed  PubMed Central  Google Scholar 

  39. Prevrhal S, Shepherd JA, Smith-Bindman R, Cummings SR, Kerlikowske K (2002) Accuracy of mammographic breast density analysis: results of formal operator training. Cancer Epidemiol Biomarkers Prev 11(11):1389–1393

    PubMed  Google Scholar 

  40. Edwards BL, Atkins KA, Stukenborg GJ, Novicoff WM, Larson KN, Cohn WF, Harvey JA, Schroen AT (2017) The association of mammographic density and molecular breast cancer subtype. Cancer Epidemiol Biomarkers Prev 26(10):1487–1492

    PubMed  Google Scholar 

  41. Krishnan K, Baglietto L, Stone J, McLean C, Southey MC, English DR, Giles GG, Hopper JL (2017) Mammographic density and risk of breast cancer by tumor characteristics: a case-control study. BMC Cancer 17(1):859–859

    PubMed  PubMed Central  Google Scholar 

  42. Razzaghi H, Troester MA, Gierach GL, Olshan AF, Yankaskas BC, Millikan RC (2013) Association between mammographic density and basal-like and luminal A breast cancer subtypes. Breast Cancer Res 15(5):R76–R76

    PubMed  PubMed Central  Google Scholar 

  43. Yaghjyan L, Tamimi RM, Bertrand KA, Scott CG, Jensen MR, Pankratz VS, Brandt K, Visscher D, Norman A, Couch F et al (2017) Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes. Breast Cancer Res Treat 165(2):421–431

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Haars G, van Noord PA, van Gils CH, Grobbee DE, Peeters PH (2005) Measurements of breast density: no ratio for a ratio. Cancer Epidemiol Biomarkers Prev 14(11 Pt 1):2634–2640

    PubMed  Google Scholar 

  45. Ghosh K, Hartmann LC, Reynolds C, Visscher DW, Brandt KR, Vierkant RA, Scott CG, Radisky DC, Sellers TA, Pankratz VS et al (2010) Association between mammographic density and age-related lobular involution of the breast. J Clin Oncol 28(13):2207–2212

    PubMed  PubMed Central  Google Scholar 

  46. Gierach GL, Patel DA, Pfeiffer RM, Figueroa JD, Linville L, Papathomas D, Johnson JM, Chicoine RE, Herschorn SD, Shepherd JA et al (2016) Relationship of terminal duct lobular unit involution of the breast with area and volume mammographic densities. Cancer Prev Res (Phila) 9(2):149–158

    CAS  Google Scholar 

  47. Kerlikowske K, Gard CC, Tice JA, Ziv E, Cummings SR, Miglioretti DL, Breast Cancer Surveillance Consortium (2017) Risk factors that increase risk of estrogen receptor-positive and -negative breast cancer. J Natl Cancer Inst. https://doi.org/10.7326/M14-1465

    Article  PubMed  Google Scholar 

  48. Kerlikowske K, Zhu W, Tosteson AN, Sprague BL, Tice JA, Lehman CD, Miglioretti DL, Breast Cancer Surveillance Consortium (2015) Identifying women with dense breasts at high risk for interval cancer: a cohort study. Ann Intern Med 162(10):673–681

    PubMed  PubMed Central  Google Scholar 

  49. Shieh Y, Scott CG, Jensen MR, Norman AD, Bertrand KA, Pankratz VS, Brandt KR, Visscher DW, Shepherd JA, Tamimi RM et al (2019) Body mass index, mammographic density, and breast cancer risk by estrogen receptor subtype. Breast Cancer Res 21(1):48

    PubMed  PubMed Central  Google Scholar 

  50. Brentnall AR, Cuzick J (2020) Risk models for breast cancer and their validation. Stat Sci 35(1):14–30

    PubMed  PubMed Central  Google Scholar 

  51. Tice JA, Miglioretti DL, Li CS, Vachon CM, Gard CC, Kerlikowske K (2015) Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer. J Clin Oncol 33(28):3137–3143

    PubMed  PubMed Central  Google Scholar 

  52. Brentnall AR, Cuzick J, Buist DSM, Bowles EJA (2018) Long-term accuracy of breast cancer risk assessment combining classic risk factors and breast density. JAMA Oncol 4(9):e180174

    PubMed  PubMed Central  Google Scholar 

  53. Lee A, Mavaddat N, Wilcox AN, Cunningham AP, Carver T, Hartley S, Babb de Villiers C, Izquierdo A, Simard J, Schmidt MK et al (2019) BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med 21(8):1708–1718

    PubMed  PubMed Central  Google Scholar 

  54. Jeffers AM, Sieh W, Lipson JA, Rothstein JH, McGuire V, Whittemore AS, Rubin DL (2017) Breast cancer risk and mammographic density assessed with semiautomated and fully automated methods and BI-RADS. Radiology 282(2):348–355

    PubMed  Google Scholar 

  55. Eng A, Gallant Z, Shepherd J, McCormack V, Li J, Dowsett M, Vinnicombe S, Allen S, dos-Santos-Silva I (2014) Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods. Breast Cancer Res 16(5):439

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank the participants and staff of all the studies for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, MN, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WI, WY. The authors assume full responsibility for analyses and interpretation of these data.

Funding

This work was supported in part by the National Institutes of Health, National Cancer Institute (R01 CA140286, R01 CA128931, P50 CA58207, P50 CA116201, R01 CA97396, R01 CA 122340, P01 CA087969, R01 CA050385, R01 CA124865, and R01 CA131332), the Breast Cancer Research Foundation and the Department of Defense (DAMD 17-00-1-033).

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Contributions

Conception and design: KK, CMV, GK, RT. Development of methodology: CGS, SW, VSP. Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): RMT, DWV, FJC, JAS, Y-YC, LM, KK, CMV, ADN, F-FW. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): CGS, GK, MRJ, VSP, DWV, JAS, Y-YC, BF, AH, KK, CMV, KB, KBr, DWV, SC. Writing, review, and/or revision of the manuscript: GK, KAB, CGS, RMT, VSP, ADN, DWV, FJC, JAS, Y-YC, SRC, KK, CMV. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): ADN, FJC, CMV. Study supervision: RMT, LM, KK, CMV. Other (IT support): F-FW, KK.

Corresponding author

Correspondence to Celine M. Vachon.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Kleinstern, G., Scott, C.G., Tamimi, R.M. et al. Association of mammographic density measures and breast cancer “intrinsic” molecular subtypes. Breast Cancer Res Treat 187, 215–224 (2021). https://doi.org/10.1007/s10549-020-06049-8

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