Top

Published in:

01-12-2021 | Breast Cancer | Research article

Biomarkers of mammographic density in premenopausal women

Authors: Mathilde His, Martin Lajous, Liliana Gómez-Flores-Ramos, Adriana Monge, Laure Dossus, Vivian Viallon, Audrey Gicquiau, Carine Biessy, Marc J. Gunter, Sabina Rinaldi

Published in: Breast Cancer Research | Issue 1/2021

Abstract

Background

While mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal women. Then, we aimed to identify potential correlates of these biomarkers to guide future research on potential modifiable determinants of mammographic density.

Methods

A total of 132 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, hexose) were measured by tandem liquid chromatography/mass spectrometry in plasma samples from 573 premenopausal participants in the Mexican Teachers’ Cohort. Associations between metabolites and percent mammographic density were assessed using linear regression models, adjusting for breast cancer risk factors and accounting for multiple tests. Mean concentrations of metabolites associated with percent mammographic density were estimated across levels of several lifestyle and metabolic factors.

Results

Sphingomyelin (SM) C16:1 and phosphatidylcholine (PC) ae C30:2 were inversely associated with percent mammographic density after correction for multiple tests. Linear trends with percent mammographic density were observed for SM C16:1 only in women with body mass index (BMI) below the median (27.4) and for PC ae C30:2 in women with a BMI over the median. SM C16:1 and PC ae C30:2 concentrations were positively associated with cholesterol (total and HDL) and inversely associated with number of metabolic syndrome components.

Conclusions

We identified new biomarkers associated with mammographic density in young women. The association of these biomarkers with mammographic density and metabolic parameters may provide new perspectives to support future preventive actions for breast cancer.

Available only for authorised users

Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227–36. https://doi.org/10.1056/NEJMoa062790.CrossRef

Holowko N, Eriksson M, Kuja-Halkola R, Azam S, He W, Hall P, et al. Heritability of mammographic breast density, density change, microcalcifications, and masses. Cancer Res. 2020;80(7):1590–600. https://doi.org/10.1158/0008-5472.CAN-19-2455.CrossRefPubMed

Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, et al. Mammographic density and ageing: a collaborative pooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med. 2017;14(6):e1002335. https://doi.org/10.1371/journal.pmed.1002335.CrossRefPubMedPubMedCentral

Alexeeff SE, Odo NU, McBride R, McGuire V, Achacoso N, Rothstein JH, et al. Reproductive factors and mammographic density: associations among 24,840 women and comparison of studies using digitized film-screen mammography and full-field digital mammography. Am J Epidemiol. 2019;188(6):1144–54. https://doi.org/10.1093/aje/kwz033.CrossRefPubMedPubMedCentral

Azam S, Jacobsen KK, Aro AR, Lynge E, Andersen ZJ. Hormone replacement therapy and mammographic density: a systematic literature review. Breast Cancer Res Treat. 2020;182(3):555–79. https://doi.org/10.1007/s10549-020-05744-w.CrossRefPubMedPubMedCentral

Boyd NF, Martin LJ, Sun L, Guo H, Chiarelli A, Hislop G, et al. Body size, mammographic density, and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2006;15(11):2086–92. https://doi.org/10.1158/1055-9965.EPI-06-0345.CrossRefPubMed

Hudson S, Vik Hjerkind K, Vinnicombe S, Allen S, Trewin C, Ursin G, et al. Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk. Breast Cancer Res. 2018;20(1):156. https://doi.org/10.1186/s13058-018-1078-8.CrossRefPubMedPubMedCentral

Ziembicki S, Zhu J, Tse E, Martin LJ, Minkin S, Boyd NF. The association between alcohol consumption and breast density: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev. 2017;26(2):170–8. https://doi.org/10.1158/1055-9965.EPI-16-0522.CrossRefPubMed

McBride RB, Fei K, Rothstein JH, Alexeeff SE, Song X, Sakoda LC, et al. Alcohol and tobacco use in relation to mammographic density in 23,456 women. Cancer Epidemiol Biomarkers Prev. 2020;29(5):1039–48. https://doi.org/10.1158/1055-9965.EPI-19-0348.CrossRefPubMedPubMedCentral

10.

McCormack VA, dos Santos SI. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1159–69. https://doi.org/10.1158/1055-9965.EPI-06-0034.CrossRefPubMed

11.

Pettersson A, Graff RE, Ursin G, Santos Silva ID, McCormack V, Baglietto L, et al. Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst. 2014;106(5):dju078. https://doi.org/10.1093/jnci/dju078.

12.

Martin LJ, Boyd NF. Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence. Breast Cancer Res. 2008;10(1):201.CrossRef

13.

Rinaldi S, Biessy C, Hernandez M. Lesueur F, dos-Santos-Silva I, Rice MS, Lajous M, Lopez-Ridaura R, Torres-Mejia G, Romieu I: Circulating concentrations of insulin-like growth factor-I, insulin-like growth factor-binding protein-3, genetic polymorphisms and mammographic density in premenopausal Mexican women: results from the ESMaestras cohort. Int J Cancer. 2014;134(6):1436–44.CrossRef

14.

Rice MS, Tworoger SS, Rosner BA, Pollak MN, Hankinson SE, Tamimi RM. Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, growth hormone, and mammographic density in the Nurses' Health Studies. Breast Cancer Res Treat. 2012;136(3):805–12. https://doi.org/10.1007/s10549-012-2303-2.CrossRefPubMedPubMedCentral

15.

Dossus L, Rinaldi S, Biessy C, Hernandez M, Lajous M, Monge A, et al. Circulating leptin and adiponectin, and breast density in premenopausal Mexican women: the Mexican Teachers' Cohort. Cancer Causes Control. 2017;28(9):939–46. https://doi.org/10.1007/s10552-017-0917-8.CrossRefPubMed

16.

Bertrand KA, Eliassen AH, Hankinson SE, Rosner BA, Tamimi RM. Circulating hormones and mammographic density in premenopausal women. Horm Cancer. 2018;9(2):117–27. https://doi.org/10.1007/s12672-017-0321-6.CrossRefPubMedPubMedCentral

17.

His M, Viallon V, Dossus L, Gicquiau A, Achaintre D, Scalbert A, et al. Prospective analysis of circulating metabolites and breast cancer in EPIC. BMC Med. 2019;17(1):178. https://doi.org/10.1186/s12916-019-1408-4.CrossRefPubMedPubMedCentral

18.

Kuhn T, Floegel A, Sookthai D, Johnson T, Rolle-Kampczyk U, Otto W, et al. Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Med. 2016;14(1):13. https://doi.org/10.1186/s12916-016-0552-3.CrossRefPubMedPubMedCentral

19.

Playdon MC, Ziegler RG, Sampson JN, Stolzenberg-Solomon R, Thompson HJ, Irwin ML, et al. Nutritional metabolomics and breast cancer risk in a prospective study. Am J Clin Nutr. 2017;106(2):637–49. https://doi.org/10.3945/ajcn.116.150912.CrossRefPubMedPubMedCentral

20.

Lecuyer L, Victor Bala A, Deschasaux M, Bouchemal N, Nawfal Triba M, Vasson MP, et al. NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer. Int J Epidemiol. 2018;47(2):484–94. https://doi.org/10.1093/ije/dyx271.CrossRefPubMed

21.

Moore SC, Playdon MC, Sampson JN, Hoover RN, Trabert B, Matthews CE, et al. A metabolomics analysis of body mass index and postmenopausal breast cancer risk. J Natl Cancer Inst. 2018;110(6):588–97. https://doi.org/10.1093/jnci/djx244.CrossRefPubMedPubMedCentral

22.

Lajous M, Ortiz-Panozo E, Monge A, Santoyo-Vistrain R, Garcia-Anaya A, Yunes-Diaz E, et al. Cohort profile: the Mexican teachers’ cohort (MTC). Int J Epidemiol. 2017;46(2):e10. https://doi.org/10.1093/ije/dyv123.CrossRefPubMed

23.

Rice MS, Biessy C, Lajous M, Bertrand KA, Tamimi RM, Torres-Mejia G, et al. Metabolic syndrome and mammographic density in Mexican women. Cancer Prev Res (Phila). 2013;6(7):701–10. https://doi.org/10.1158/1940-6207.CAPR-12-0475.CrossRef

24.

Angeles-Llerenas A, Ortega-Olvera C, Perez-Rodriguez E, Esparza-Cano JP, Lazcano-Ponce E, Romieu I, et al. Moderate physical activity and breast cancer risk: the effect of menopausal status. Cancer Causes Control. 2010;21(4):577–86. https://doi.org/10.1007/s10552-009-9487-8.CrossRefPubMed

25.

26.

Torres-Mejia G, De Stavola B, Allen DS, Perez-Gavilan JJ, Ferreira JM, Fentiman IS, et al. Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies. Cancer Epidemiol Biomarkers Prev. 2005;14(5):1052–9. https://doi.org/10.1158/1055-9965.EPI-04-0717.CrossRefPubMed

27.

Rice MS, Bertrand KA, Lajous M, Tamimi RM, Torres-Mejia G, Biessy C, et al. Body size throughout the life course and mammographic density in Mexican women. Breast Cancer Res Treat. 2013;138(2):601–10. https://doi.org/10.1007/s10549-013-2463-8.CrossRefPubMedPubMedCentral

28.

Monge A, Lajous M, Ortiz-Panozo E, Rodriguez BL, Gongora JJ, Lopez-Ridaura R. Western and Modern Mexican dietary patterns are directly associated with incident hypertension in Mexican women: a prospective follow-up study. Nutr J. 2018;17(1):21. https://doi.org/10.1186/s12937-018-0332-3.CrossRefPubMedPubMedCentral

29.

Reedy J, Lerman JL, Krebs-Smith SM, Kirkpatrick SI, Pannucci TE, Wilson MM, et al. Evaluation of the Healthy Eating Index-2015. J Acad Nutr Diet. 2018;118(9):1622–33. https://doi.org/10.1016/j.jand.2018.05.019.CrossRefPubMedPubMedCentral

30.

Rinaldi S, Biessy C, de la Luz HM, Lajous M, Ortiz-Panozo E, Yunes E, et al. Endogenous hormones, inflammation, and body size in premenopausal Mexican women: results from the Mexican Teachers' Cohort (MTC, ESMaestras). Cancer Causes Control. 2015;26(3):475–86. https://doi.org/10.1007/s10552-015-0527-2.CrossRefPubMed

31.

Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. https://doi.org/10.1161/CIRCULATIONAHA.109.192644.CrossRefPubMed

32.

Westfall PH, Young SS. Resampling-based multiple testing: examples and methods for p-value adjustment. New York: John Wiley & Sons; 1993.

33.

Bach F: Model-consistent sparse estimation through the bootstrap. arXiv preprint arXiv:0901.3202. 2009. https://arxiv.org/abs/0901.3202v1.

34.

Tibshirani R. Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met. 1996;58(1):267–88.

35.

Caughey D. NPC: Nonparametric Combination of Hypothesis Tests. R package version 1.1.0. 2016. https://CRAN.R-project.org/package=NPC. Accessed 08 Feb 2021.

36.

Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software. 2010;33(1):1–22.CrossRef

37.

Iqbal J, Walsh MT, Hammad SM, Hussain MM. Sphingolipids and lipoproteins in health and metabolic disorders. Trends Endocrinol Metab. 2017;28(7):506–18. https://doi.org/10.1016/j.tem.2017.03.005.CrossRefPubMedPubMedCentral

38.

Martinez-Beamonte R, Lou-Bonafonte JM, Martinez-Gracia MV, Osada J. Sphingomyelin in high-density lipoproteins: structural role and biological function. Int J Mol Sci. 2013;14(4):7716–41. https://doi.org/10.3390/ijms14047716.CrossRefPubMedPubMedCentral

39.

Lemaitre RN, Jensen PN, Hoofnagle A, McKnight B, Fretts AM, King IB, et al. Plasma ceramides and sphingomyelins in relation to heart failure risk. Circ Heart Fail. 2019;12(7):e005708. https://doi.org/10.1161/CIRCHEARTFAILURE.118.005708.CrossRefPubMedPubMedCentral

40.

Hanamatsu H, Ohnishi S, Sakai S, Yuyama K, Mitsutake S, Takeda H, et al. Altered levels of serum sphingomyelin and ceramide containing distinct acyl chains in young obese adults. Nutr Diabetes. 2014;4(10):e141. https://doi.org/10.1038/nutd.2014.38.CrossRefPubMedPubMedCentral

41.

Zeleznik OA, Clish CB, Kraft P, Avila-Pacheco J, Eliassen AH, Tworoger SS. Circulating lysophosphatidylcholines, phosphatidylcholines, ceramides, and sphingomyelins and ovarian cancer risk: a 23-year prospective study. J Natl Cancer Inst. 2020;112(6):628–36. https://doi.org/10.1093/jnci/djz195.CrossRefPubMed

42.

Adams CD. Null effect of circulating sphingomyelins on risk for breast cancer: a Mendelian randomization report using Breast Cancer Association Consortium (BCAC) data. [version 1; peer review: 2 approved with reservations]. F1000Research. 2019;2019(8):2119.CrossRef

43.

Adams CD. Circulating sphingomyelins on estrogen receptor-positive and estrogen receptor-negative breast cancer-specific survival. Breast Cancer Manag. 2020;9(3):BMT42. https://doi.org/10.2217/bmt-2020-0002

44.

Ogretmen B. Sphingolipid metabolism in cancer signalling and therapy. Nat Rev Cancer. 2018;18(1):33–50. https://doi.org/10.1038/nrc.2017.96.CrossRefPubMed

45.

Zheng K, Chen Z, Feng H, Chen Y, Zhang C, Yu J, et al. Sphingomyelin synthase 2 promotes an aggressive breast cancer phenotype by disrupting the homoeostasis of ceramide and sphingomyelin. Cell Death Dis. 2019;10(3):157. https://doi.org/10.1038/s41419-019-1303-0.CrossRefPubMedPubMedCentral

46.

Nganga R, Oleinik N, Ogretmen B. Mechanisms of ceramide-dependent cancer cell death. Adv Cancer Res. 2018;140:1–25. https://doi.org/10.1016/bs.acr.2018.04.007.CrossRefPubMed

47.

Field BC, Gordillo R, Scherer PE. The role of ceramides in diabetes and cardiovascular disease regulation of ceramides by adipokines. Front Endocrinol (Lausanne). 2020;11:569250. https://doi.org/10.3389/fendo.2020.569250.CrossRef

48.

Floegel A, Stefan N, Yu Z, Muhlenbruch K, Drogan D, Joost HG, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639–48. https://doi.org/10.2337/db12-0495.CrossRefPubMedPubMedCentral

49.

Yang SJ, Kwak SY, Jo G, Song TJ, Shin MJ. Serum metabolite profile associated with incident type 2 diabetes in Koreans: findings from the Korean Genome and Epidemiology Study. Sci Rep. 2018;8(1):8207. https://doi.org/10.1038/s41598-018-26320-9.CrossRefPubMedPubMedCentral

50.

Zhang W, Randell EW, Sun G, Likhodii S, Liu M, Furey A, et al. Hyperglycemia-related advanced glycation end-products is associated with the altered phosphatidylcholine metabolism in osteoarthritis patients with diabetes. PLoS One. 2017;12(9):e0184105. https://doi.org/10.1371/journal.pone.0184105.CrossRefPubMedPubMedCentral

51.

van der Veen JN, Kennelly JP, Wan S, Vance JE, Vance DE, Jacobs RL. The critical role of phosphatidylcholine and phosphatidylethanolamine metabolism in health and disease. Biochim Biophys Acta Biomembr. 2017;1859(9 Pt B):1558–72.CrossRef

52.

Brites P, Waterham HR, Wanders RJ. Functions and biosynthesis of plasmalogens in health and disease. Biochim Biophys Acta. 2004;1636(2-3):219–31. https://doi.org/10.1016/j.bbalip.2003.12.010.CrossRefPubMed

53.

Gibellini F, Smith TK. The Kennedy pathway--de novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life. 2010;62(6):414–28. https://doi.org/10.1002/iub.337.CrossRefPubMed

54.

Chester DN, Goldman JD, Ahuja JK, Moshfegh AJ. Dietary intakes of choline: What We Eat In America, NHANES 2007-2008. Worldwide Web Site: Food Surv Res Group. Available: www.ars.usda.gov/Services/docs.html?docid=19476. Accessed 08 Feb 2021.

55.

Carayol M, Licaj I, Achaintre D, Sacerdote C, Vineis P, Key TJ, et al. Reliability of serum metabolites over a two-year period: a targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS One. 2015;10(8):e0135437. https://doi.org/10.1371/journal.pone.0135437.CrossRefPubMedPubMedCentral

56.

Floegel A, Drogan D, Wang-Sattler R, Prehn C, Illig T, Adamski J, et al. Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS One. 2011;6(6):e21103. https://doi.org/10.1371/journal.pone.0021103.CrossRefPubMedPubMedCentral

57.

Townsend MK, Clish CB, Kraft P, Wu C, Souza AL, Deik AA, et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem. 2013;59(11):1657–67. https://doi.org/10.1373/clinchem.2012.199133.CrossRefPubMed

Title: Biomarkers of mammographic density in premenopausal women
Authors: Mathilde His
Martin Lajous
Liliana Gómez-Flores-Ramos
Adriana Monge
Laure Dossus
Vivian Viallon
Audrey Gicquiau
Carine Biessy
Marc J. Gunter
Sabina Rinaldi
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Breast Cancer
Breast Cancer
Biomarkers
Published in: Breast Cancer Research / Issue 1/2021
Electronic ISSN: 1465-542X
DOI: https://doi.org/10.1186/s13058-021-01454-3

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Evaluation of FGFR targeting in breast cancer through interrogation of patient-derived models

Acquired mutations and transcriptional remodeling in long-term estrogen-deprived locoregional breast cancer recurrences

PDGFRβ is an essential therapeutic target for BRCA1-deficient mammary tumors

Correction to: AKT-mediated phosphorylation of Sox9 induces Sox10 transcription in a murine model of HER2-positive breast cancer

Oestrogen deprivation induces chemokine production and immune cell recruitment in in vitro and in vivo models of oestrogen receptor-positive breast cancer

Optical tissue measurements of invasive carcinoma and ductal carcinoma in situ for surgical guidance

Keynote webinar | Spotlight on antibody–drug conjugates in cancer