Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Breast Cancer Research
Published in: Breast Cancer Research 1/2018

Open Access 01-12-2018 | Research Article

Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density

Authors: Sanna Byström, Martin Eklund, Mun-Gwan Hong, Claudia Fredolini, Mikael Eriksson, Kamila Czene, Per Hall, Jochen M. Schwenk, Marike Gabrielson

Published in: Breast Cancer Research | Issue 1/2018

Login to get access

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.
Appendix
Available only for authorised users
Literature
1.
Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, Lockwood GA, Tritchler DL, Yaffe MJ. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst. 1995;87(9):670–5.CrossRefPubMed
2.
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):227–36.CrossRefPubMed
3.
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):1622–9.CrossRefPubMed
4.
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.CrossRefPubMed
5.
Boyd NF, Lockwood GA, Martin LJ, Knight JA, Byng JW, Yaffe MJ, Tritchler DL. Mammographic densities and breast cancer risk. Breast Dis. 1998;10(3-4):113–26.CrossRefPubMed
6.
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.CrossRefPubMedPubMedCentral
7.
Ursin G, Lillie EO, Lee E, Cockburn M, Schork NJ, Cozen W, Parisky YR, Hamilton AS, Astrahan MA, Mack T. The relative importance of genetics and environment on mammographic density. Cancer Epidemiol Biomarkers Prev. 2009;18(1):102–12.CrossRefPubMed
8.
Boyd NF, Dite GS, Stone J, Gunasekara A, English DR, McCredie MR, 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):886–94.CrossRefPubMed
9.
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;106:12.
10.
Chung L, Baxter RC. Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays. Expert Rev Proteomics. 2012;9(6):599–614.CrossRefPubMed
11.
Bystrom S, Ayoglu B, Haggmark A, Mitsios N, Hong MG, Drobin K, Forsstrom B, Fredolini C, Khademi M, Amor S, et al. Affinity proteomic profiling of plasma, cerebrospinal fluid, and brain tissue within multiple sclerosis. J Proteome Res. 2014;13(11):4607–19.CrossRefPubMed
12.
Drobin K, Nilsson P, Schwenk JM. Highly multiplexed antibody suspension bead arrays for plasma protein profiling. Methods Mol Biol. 2013;1023:137–45.CrossRefPubMed
13.
Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.CrossRefPubMed
14.
15.
Gabrielson M, Eriksson M, Hammarstrom M, Borgquist S, Leifland K, Czene K, Hall P. Cohort profile: the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA). Int J Epidemiol. 2017;46(6):1740–1g. doi:10.1093/ije/dyw357.
16.
Eriksson M, Czene K, Pawitan Y, Leifland K, Darabi H, Hall P. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res. 2017;19(1):29.CrossRefPubMedPubMedCentral
17.
Gabrielson M, Chiesa F, Paulsson J, Strell C, Behmer C, Ronnow K, Czene K, Ostman A, Hall P. Amount of stroma is associated with mammographic density and stromal expression of oestrogen receptor in normal breast tissues. Breast Cancer Res Treat. 2016;158(2):253–61. https://​doi.​org/​10.​1007/​s10549-016-3877-x. Epub 2016 Jun 27.
18.
19.
Ayoglu B, Chaouch A, Lochmuller H, Politano L, Bertini E, Spitali P, Hiller M, Niks EH, Gualandi F, Ponten F, et al. Affinity proteomics within rare diseases: a BIO-NMD study for blood biomarkers of muscular dystrophies. EMBO Mol Med. 2014;6(7):918–36.CrossRefPubMedPubMedCentral
20.
Hubert M, Rousseeuw PJ, Branden KV. ROBPCA: A new approach to robust principal component analysis. Technometrics. 2005;47(1):64–79.CrossRef
21.
Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006;78(13):4281–90.CrossRefPubMed
22.
Hong MG, Lee W, Nilsson P, Pawitan Y, Schwenk JM. Multidimensional normalization to minimize plate effects of suspension bead array data. J Proteome Res. 2016;15(10):3473–80.CrossRefPubMed
23.
Nguyen TL, Aung YK, Evans CF, Yoon-Ho C, Jenkins MA, Sung J, Hopper JL, Song YM. Mammographic density defined by higher than conventional brightness threshold better predicts breast cancer risk for full-field digital mammograms. Breast Cancer Res. 2015;17:142.CrossRefPubMedPubMedCentral
24.
25.
Uhlen M, Hallstrom BM, Lindskog C, Mardinoglu A, Ponten F, Nielsen J. Transcriptomics resources of human tissues and organs. Mol Syst Biol. 2016;12(4):862.CrossRefPubMedPubMedCentral
26.
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):267–75.CrossRefPubMed
27.
Lin SJ, Cawson J, Hill P, Haviv I, Jenkins M, Hopper JL, Southey MC, Campbell IG, Thompson EW. Image-guided sampling reveals increased stroma and lower glandular complexity in mammographically dense breast tissue. Breast Cancer Res Treat. 2011;128(2):505–16.CrossRefPubMed
28.
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;6:11.CrossRefPubMedPubMedCentral
29.
Roman-Perez E, Casbas-Hernandez P, Pirone JR, Rein J, Carey LA, Lubet RA, Mani SA, Amos KD, Troester MA. Gene expression in extratumoral microenvironment predicts clinical outcome in breast cancer patients. Breast Cancer Res. 2012;14(2):R51.CrossRefPubMedPubMedCentral
30.
Sun X, Gierach GL, Sandhu R, Williams T, Midkiff BR, Lissowska J, Wesolowska E, Boyd NF, Johnson NB, Figueroa JD, et al. Relationship of mammographic density and gene expression: analysis of normal breast tissue surrounding breast cancer. Clin Cancer Res. 2013;19(18):4972–82.CrossRefPubMedPubMedCentral
31.
Paszek MJ, Zahir N, Johnson KR, Lakins JN, Rozenberg GI, Gefen A, Reinhart-King CA, Margulies SS, Dembo M, Boettiger D, et al. Tensional homeostasis and the malignant phenotype. Cancer Cell. 2005;8(3):241–54.CrossRefPubMed
32.
Provenzano PP, Inman DR, Eliceiri KW, Keely PJ. Matrix density-induced mechanoregulation of breast cell phenotype, signaling and gene expression through a FAK-ERK linkage. Oncogene. 2009;28(49):4326–43.CrossRefPubMedPubMedCentral
33.
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.CrossRefPubMedPubMedCentral
34.
35.
36.
DeFilippis RA, Fordyce C, Patten K, Chang H, Zhao J, Fontenay GV, Kerlikowske K, Parvin B, Tlsty TD. Stress signaling from human mammary epithelial cells contributes to phenotypes of mammographic density. Cancer Res. 2014;74(18):5032–44.CrossRefPubMedPubMedCentral
37.
Aitken Z, MC VA, Highnam RP, Martin L, Gunasekara A, Melnichouk O, Mawdsley G, Peressotti C, Yaffe M, Boyd NF, et al. Screen-film mammographic density and breast cancer risk: a comparison of the volumetric standard mammogram form and the interactive threshold measurement methods. Cancer Epidemiol Biomarkers Prev. 2010;19(2):418–28.CrossRefPubMedPubMedCentral
38.
Boyd N, Martin L, Gunasekara A, Melnichouk O, Maudsley G, Peressotti C, Yaffe M, Minkin S. Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes. Cancer Epidemiol Biomarkers Prev. 2009;18(6):1754–62.CrossRefPubMed
39.
Jeffers AM, Sieh W, Lipson JA, Rothstein JH, McGuire V, Whittemore AS, Rubin DL. Breast cancer risk and mammographic density assessed with semiautomated and fully automated methods and BI-RADS. Radiology. 2017;282(2):348–55.CrossRefPubMed
40.
Keller BM, Chen J, Daye D, Conant EF, Kontos D. Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography. Breast Cancer Res. 2015;17:117.CrossRefPubMedPubMedCentral
41.
Keller BM, McCarthy AM, Chen J, Armstrong K, Conant EF, Domchek SM, Kontos D. Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography. BMC Cancer. 2015;15:143.CrossRefPubMedPubMedCentral
Metadata
Title
Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density
Authors
Sanna Byström
Martin Eklund
Mun-Gwan Hong
Claudia Fredolini
Mikael Eriksson
Kamila Czene
Per Hall
Jochen M. Schwenk
Marike Gabrielson
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2018
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-018-0940-z

Other articles of this Issue 1/2018

Breast Cancer Research 1/2018 Go to the issue

Research article

Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts

Research article

Integrin-Rac signalling for mammary epithelial stem cell self-renewal

Research article

Real-world evidence analysis of palbociclib prescribing patterns for patients with advanced/metastatic breast cancer treated in community oncology practice in the USA one year post approval

Research article

Loss of amphiregulin reduces myoepithelial cell coverage of mammary ducts and alters breast tumor growth

Research article

Tumor-derived granulocyte colony-stimulating factor diminishes efficacy of breast tumor cell vaccines

Research article

Thyroid hormones and breast cancer association according to menopausal status and body mass index
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
Image Credits