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Associations between mammographic density and tumor characteristics in Chinese women with breast cancer

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

Abstract

Purpose

Mammographic density (MD) is a strong risk factor for breast cancer, yet its relationship with tumor characteristics is not well established, particularly in Asian populations.

Methods

MD was assessed from a total of 2001 Chinese breast cancer patients using Breast Imaging Reporting and Data System (BI-RADS) categories. Molecular subtypes were defined using immunohistochemical status on ER, PR, HER2, and Ki-67, as well as tumor grade. Multinomial logistic regression was used to test associations between MD and molecular subtype (luminal A = reference) adjusting for age, body mass index (BMI), menopausal status, parity, and nodal status.

Results

The mean age at diagnosis was 51.7 years (SD = 10.7) and the average BMI was 24.7 kg/m2 (SD = 3.8). The distribution of BI-RADS categories was 7.4% A = almost entirely fat, 24.2% B = scattered fibroglandular dense, 49.4% C = heterogeneously dense, and 19.0% D = extremely dense. Compared to women with BI-RADS = A/B, women with BI-RADS = D were more likely to have HER2-enriched tumors (OR = 1.81, 95% CI 1.08–3.06, p = 0.03), regardless of menopausal status. The association was only observed in women with normal (< 25 kg/m2) BMI (OR = 2.43, 95% CI 1.24–4.76, p < 0.01), but not among overweight/obese women (OR: 0.98, 95% CI 0.38–2.52, p = 0.96).

Conclusions

Among Chinese women with normal BMI, higher breast density was associated with HER2-enriched tumors. The results may partially explain the higher proportion of HER2+ tumors previously reported in Asian women.

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Abbreviations

MD:

Mammographic density

ER+:

Estrogen receptor-positive

ER−:

Estrogen receptor-negative

PR+:

Progesterone receptor-positive

PR−:

Progesterone receptor-negative

CHCAMS:

Chinese Academy of Medical Sciences and Peking Union Medical College

BI-RADS:

Breast Imaging Reporting and Data System

IHC:

Immunohistochemical

FISH:

Fluorescence in situ hybridization

BMI:

Body mass index

ANOVA:

Analysis of variance

OR:

Odds ratio

CI:

Confidence interval

SD:

Standard deviation

DAS:

Density analysis software

SEER:

Surveillance, Epidemiology, and End Results

TDLU:

Terminal duct lobular involution

References

  1. Boyd NF, Guo H, Martin LJ et al (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–236

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  3. Yaghjyan L, Colditz GA, Collins LC et al (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. https://doi.org/10.1093/jnci/djr225

    Article  PubMed  PubMed Central  Google Scholar 

  4. Eriksson L, Czene K, Rosenberg L et al (2012) The influence of mammographic density on breast tumor characteristics. Breast Cancer Res Treat 134:859–866. https://doi.org/10.1007/s10549-012-2127-0

    Article  PubMed  Google Scholar 

  5. Phipps AI, Buist DS, Malone KE et al (2012) Breast density, body mass index, and risk of tumor marker-defined subtypes of breast cancer. Ann Epidemiol 22:340–348

    Article  PubMed  PubMed Central  Google Scholar 

  6. Eriksson L, Hall P, Czene K et al (2012) Mammographic density and molecular subtypes of breast cancer. Br J Cancer 107:18–23

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang W-T, Dryden M, Broglio K et al (2008) Mammographic features of triple receptor-negative primary breast cancers in young premenopausal women. Breast Cancer Res Treat 111:405–410

    Article  PubMed  Google Scholar 

  8. Chen J-H, Hsu F-T, Shih H-N et al (2009) Does breast density show difference in patients with estrogen receptor-positive and estrogen receptor-negative breast cancer measured on MRI? Ann Oncol 20:1447–1449

    Article  PubMed  PubMed Central  Google Scholar 

  9. Holm J, Eriksson L, Ploner A et al (2017) Assessment of breast cancer risk factors reveals subtype heterogeneity. Cancer Res 77:3708–3717. https://doi.org/10.1158/0008-5472.CAN-16-2574

    Article  CAS  PubMed  Google Scholar 

  10. Shin J, Lee JE, Ko HY et al (2018) Association between mammographic density and tumor marker-defined breast cancer subtypes: a case-control study. Eur J Cancer Prev 27:239–247. https://doi.org/10.1097/CEJ.0000000000000353

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  12. Sartor H, Zackrisson S, Elebro K et al (2015) Mammographic density in relation to tumor biomarkers, molecular subtypes, and mode of detection in breast cancer. Cancer Causes Control 26:931–939. https://doi.org/10.1007/s10552-015-0576-6

    Article  PubMed  Google Scholar 

  13. Carey LA, Perou CM, Livasy CA et al (2006) Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295:2492–2502

    Article  CAS  Google Scholar 

  14. Clarke CA, Keegan THM, Yang J et al (2012) Age-specific incidence of breast cancer subtypes: understanding the black-white crossover. J Natl Cancer Inst 104:1094–1101. https://doi.org/10.1093/jnci/djs264

    Article  PubMed  PubMed Central  Google Scholar 

  15. del Carmen MG, Halpern EF, Kopans DB et al (2007) Mammographic breast density and race. Am J Roentgenol 188:1147–1150

    Article  Google Scholar 

  16. Rajaram N, Mariapun S, Eriksson M et al (2017) Differences in mammographic density between Asian and Caucasian populations: a comparative analysis. Breast Cancer Res Treat 161:353–362. https://doi.org/10.1007/s10549-016-4054-y

    Article  PubMed  Google Scholar 

  17. Nie K, Su M-Y, Chau M-K et al (2010) Age- and race-dependence of the fibroglandular breast density analyzed on 3D MRI. Med Phys 37:2770–2776. https://doi.org/10.1118/1.3426317

    Article  PubMed  PubMed Central  Google Scholar 

  18. Sung H, Ren J, Li J et al (2018) Breast cancer risk factors and mammographic density among high-risk women in urban China. NPJ Breast Cancer 4:3. https://doi.org/10.1038/s41523-018-0055-9

    Article  PubMed  PubMed Central  Google Scholar 

  19. Nazari SS, Mukherjee P (2018) An overview of mammographic density and its association with breast cancer. Breast Cancer 25:259–267. https://doi.org/10.1007/s12282-018-0857-5

    Article  PubMed  PubMed Central  Google Scholar 

  20. Heller SL, Hudson S, Wilkinson LS (2015) Breast density across a regional screening population: effects of age, ethnicity and deprivation. Br J Radiol 88:20150242. https://doi.org/10.1259/bjr.20150242

    Article  PubMed  PubMed Central  Google Scholar 

  21. Horne HN, Beena Devi CR, Sung H et al (2015) Greater absolute risk for all subtypes of breast cancer in the US than Malaysia. Breast Cancer Res Treat 149:285–291. https://doi.org/10.1007/s10549-014-3243-9

    Article  PubMed  Google Scholar 

  22. D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA et al (2013) ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. American College of Radiology, Reston

    Google Scholar 

  23. Sisti JS, Collins LC, Beck AH et al (2016) Reproductive risk factors in relation to molecular subtypes of breast cancer: results from the nurses’ health studies. Int J Cancer 138:2346–2356. https://doi.org/10.1002/ijc.29968

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Edwards BL, Atkins KA, Stukenborg GJ et al (2017) The association of mammographic density and molecular breast cancer subtype. Cancer Epidemiol Biomark Prev 26:1487–1492. https://doi.org/10.1158/1055-9965.EPI-16-0881

    Article  Google Scholar 

  25. Vachon C, Tamimi R, Chen Y-Y et al (2016) Abstract IA22: mammographic density: a risk factor for all breast cancers or only specific subtypes? Cancer Epidemiol Prev Biomark 25:IA22. https://doi.org/10.1158/1538-7755.DISP15-IA22

    Article  Google Scholar 

  26. Razzaghi H, Troester MA, Gierach GL et al (2013) Association between mammographic density and basal-like and luminal A breast cancer subtypes. Breast Cancer Res BCR 15:R76. https://doi.org/10.1186/bcr3470

    Article  PubMed  Google Scholar 

  27. Yaghjyan L, Tamimi RM, Bertrand KA 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 165:421–431. https://doi.org/10.1007/s10549-017-4341-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Krishnan K, Baglietto L, Stone J et al (2017) Mammographic density and risk of breast cancer by tumor characteristics: a case–control study. BMC Cancer 17:859. https://doi.org/10.1186/s12885-017-3871-7

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ji Y, Shao Z, Liu J et al (2018) The correlation between mammographic densities and molecular pathology in breast cancer. Cancer Biomark. https://doi.org/10.3233/CBM-181185

    Article  PubMed  Google Scholar 

  30. Neuhouser ML, Aragaki AK, Prentice RL et al (2015) Overweight, obesity, and postmenopausal invasive breast cancer risk: a secondary analysis of the women’s health initiative randomized clinical trials. JAMA Oncol 1:611–621. https://doi.org/10.1001/jamaoncol.2015.1546

    Article  PubMed  PubMed Central  Google Scholar 

  31. Iyengar NM, Gucalp A, Dannenberg AJ, Hudis CA (2016) Obesity and cancer mechanisms: tumor microenvironment and inflammation. J Clin Oncol 34:4270–4276. https://doi.org/10.1200/JCO.2016.67.4283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lengyel E, Makowski L, DiGiovanni J, Kolonin MG (2018) Cancer as a matter of fat: the crosstalk between adipose tissue and tumors. Trends Cancer 4:374–384. https://doi.org/10.1016/j.trecan.2018.03.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Radenkovic S, Konjevic G, Gavrilovic D et al (2019) pSTAT3 expression associated with survival and mammographic density of breast cancer patients. Pathol Res Pract 215:366–372. https://doi.org/10.1016/j.prp.2018.12.023

    Article  CAS  PubMed  Google Scholar 

  34. Ginsburg OM, Martin LJ, Boyd NF (2008) Mammographic density, lobular involution, and risk of breast cancer. Br J Cancer 99:1369–1374. https://doi.org/10.1038/sj.bjc.6604635

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ghosh K, Hartmann LC, Reynolds C et al (2010) Association between mammographic density and age-related lobular involution of the breast. J Clin Oncol 28:2207–2212. https://doi.org/10.1200/JCO.2009.23.4120

    Article  PubMed  PubMed Central  Google Scholar 

  36. Gierach GL, Patel DA, Pfeiffer RM et al (2016) Relationship of terminal duct lobular unit involution of the breast with area and volume mammographic densities. Cancer Prev Res 9:149–158. https://doi.org/10.1158/1940-6207.CAPR-15-0282

    Article  CAS  Google Scholar 

  37. Ghosh K, Vierkant RA, Frank RD et al (2017) Association between mammographic breast density and histologic features of benign breast disease. Breast Cancer Res 19:134. https://doi.org/10.1186/s13058-017-0922-6

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sherratt MJ, McConnell JC, Streuli CH (2016) Raised mammographic density: causative mechanisms and biological consequences. Breast Cancer Res 18:45. https://doi.org/10.1186/s13058-016-0701-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sawada Y, Tamada M, Dubin-Thaler BJ et al (2006) Force sensing by mechanical extension of the Src family kinase substrate p130Cas. Cell 127:1015–1026. https://doi.org/10.1016/j.cell.2006.09.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Mouw JK, Yui Y, Damiano L et al (2014) Tissue mechanics modulate microRNA-dependent PTEN expression to regulate malignant progression. Nat Med 20:360–367. https://doi.org/10.1038/nm.3497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Triulzi T, Forte L, Regondi V et al (2019) HER2 signaling regulates the tumor immune microenvironment and trastuzumab efficacy. Oncoimmunology 8:e1512942. https://doi.org/10.1080/2162402X.2018.1512942

    Article  PubMed  Google Scholar 

  42. Huo CW, Hill P, Chew G et al (2018) High mammographic density in women is associated with protumor inflammation. Breast Cancer Res 20:92. https://doi.org/10.1186/s13058-018-1010-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sun X, Gierach GL, Sandhu R et al (2013) Relationship of mammographic density and gene expression: analysis of normal breast tissue surrounding breast cancer. Clin Cancer Res 19:4972–4982. https://doi.org/10.1158/1078-0432.CCR-13-0029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gabrielson M, Chiesa F, Paulsson J et al (2016) Amount of stroma is associated with mammographic density and stromal expression of oestrogen receptor in normal breast tissues. Breast Cancer Res Treat 158:253–261. https://doi.org/10.1007/s10549-016-3877-x

    Article  CAS  PubMed  Google Scholar 

  45. Fasching PA, Heusinger K, Loehberg CR et al (2006) Influence of mammographic density on the diagnostic accuracy of tumor size assessment and association with breast cancer tumor characteristics. Eur J Radiol 60:398–404. https://doi.org/10.1016/j.ejrad.2006.08.002

    Article  PubMed  Google Scholar 

  46. Lee HN, Sohn Y-M, Han KH (2015) Comparison of mammographic density estimation by Volpara software with radiologists’ visual assessment: analysis of clinical-radiologic factors affecting discrepancy between them. Acta Radiol 56:1061–1068. https://doi.org/10.1177/0284185114554674

    Article  PubMed  Google Scholar 

  47. Singh T, Sharma M, Singla V, Khandelwal N (2016) Breast density estimation with fully automated volumetric method: comparison to radiologists’ assessment by BI-RADS categories. Acad Radiol 23:78–83. https://doi.org/10.1016/j.acra.2015.09.012

    Article  PubMed  Google Scholar 

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Acknowledgement

This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics.

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Correspondence to Jing Li or Xiaohong R. Yang.

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Li, E., Guida, J.L., Tian, Y. et al. Associations between mammographic density and tumor characteristics in Chinese women with breast cancer. Breast Cancer Res Treat 177, 527–536 (2019). https://doi.org/10.1007/s10549-019-05325-6

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