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 1/2019

Open Access 01-12-2019 | Breast Cancer | Research article

Genomic signature of parity in the breast of premenopausal women

Authors: Julia Santucci-Pereira, Anne Zeleniuch-Jacquotte, Yelena Afanasyeva, Hua Zhong, Michael Slifker, Suraj Peri, Eric A. Ross, Ricardo López de Cicco, Yubo Zhai, Theresa Nguyen, Fathima Sheriff, Irma H. Russo, Yanrong Su, Alan A. Arslan, Pal Bordas, Per Lenner, Janet Åhman, Anna Stina Landström Eriksson, Robert Johansson, Göran Hallmans, Paolo Toniolo, Jose Russo

Published in: Breast Cancer Research | Issue 1/2019

Login to get access

Abstract

Background

Full-term pregnancy (FTP) at an early age confers long-term protection against breast cancer. Previously, we reported that a FTP imprints a specific gene expression profile in the breast of postmenopausal women. Herein, we evaluated gene expression changes induced by parity in the breast of premenopausal women.

Methods

Gene expression profiling of normal breast tissue from 30 nulliparous (NP) and 79 parous (P) premenopausal volunteers was performed using Affymetrix microarrays. In addition to a discovery/validation analysis, we conducted an analysis of gene expression differences in P vs. NP women as a function of time since last FTP. Finally, a laser capture microdissection substudy was performed to compare the gene expression profile in the whole breast biopsy with that in the epithelial and stromal tissues.

Results

Discovery/validation analysis identified 43 differentially expressed genes in P vs. NP breast. Analysis of expression as a function of time since FTP revealed 286 differentially expressed genes (238 up- and 48 downregulated) comparing all P vs. all NP, and/or P women whose last FTP was less than 5 years before biopsy vs. all NP women. The upregulated genes showed three expression patterns: (1) transient: genes upregulated after FTP but whose expression levels returned to NP levels. These genes were mainly related to immune response, specifically activation of T cells. (2) Long-term changing: genes upregulated following FTP, whose expression levels decreased with increasing time since FTP but did not return to NP levels. These were related to immune response and development. (3) Long-term constant: genes that remained upregulated in parous compared to nulliparous breast, independently of time since FTP. These were mainly involved in development/cell differentiation processes, and also chromatin remodeling. Lastly, we found that the gene expression in whole tissue was a weighted average of the expression in epithelial and stromal tissues.

Conclusions

Genes transiently activated by FTP may have a role in protecting the mammary gland against neoplastically transformed cells through activation of T cells. Furthermore, chromatin remodeling and cell differentiation, represented by the genes that are maintained upregulated long after the FTP, may be responsible for the lasting preventive effect against breast cancer.

Literature
  1. Russo J, Moral R, Balogh GA, Mailo D, Russo IH. The protective role of pregnancy in breast cancer. Breast Cancer Res. 2005;7:131–42.PubMedPubMed CentralView Article
  2. Cancer CGoHFiB. Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet. 2002;360:187–95.View Article
  3. Russo J, Tay LK, Russo IH. Differentiation of the mammary gland and susceptibility to carcinogenesis. Breast Cancer Res Treat. 1982;2:5–73.PubMedView Article
  4. Russo IH, Koszalka M, Russo J. Comparative study of the influence of pregnancy and hormonal treatment on mammary carcinogenesis. Br J Cancer. 1991;64:481–4.PubMedPubMed CentralView Article
  5. Albrektsen G, Heuch I, Hansen S, Kvale G. Breast cancer risk by age at birth, time since birth and time intervals between births: exploring interaction effects. Br J Cancer. 2005;92:167–75.PubMedView Article
  6. Belitskaya-Levy I, Zeleniuch-Jacquotte A, Russo J, Russo IH, Bordas P, Ahman J, et al. Characterization of a genomic signature of pregnancy identified in the breast. Cancer Prev Res (Phila). 2011;4:1457–64.View Article
  7. Peri S, de Cicco RL, Santucci-Pereira J, Slifker M, Ross EA, Russo IH, et al. Defining the genomic signature of the parous breast. BMC Med Genet. 2012;5:46.
  8. Russo J, Santucci-Pereira J, de Cicco RL, Sheriff F, Russo PA, Peri S, et al. Pregnancy-induced chromatin remodeling in the breast of postmenopausal women. Int J Cancer. 2012;131:1059–70.PubMedPubMed CentralView Article
  9. Bolstad BM, Collin F, Brettschneider J, Simpson K, Cope L, Irizarry RA, et al. Quality assessment of Affymetrix GeneChip Data. In: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S, editors. Bioinformatics and computational biology solutions using R and bioconductor. New York: Springer; 2005. p. 33–47.View Article
  10. R: a language and environment for statistical computing. http://​www.​R-project.​org.
  11. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80.PubMedPubMed CentralView Article
  12. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.PubMedView Article
  13. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–3.PubMedPubMed CentralView Article
  14. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100:9440–5.PubMedPubMed CentralView Article
  15. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003;34:374–8.PubMedView Article
  16. Falcon S, Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics. 2007;23:257–8.PubMedView Article
  17. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.PubMedPubMed CentralView Article
  18. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1.PubMedPubMed CentralView Article
  19. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401–4.View ArticlePubMed
  20. Coudry RA, Meireles SI, Stoyanova R, Cooper HS, Carpino A, Wang X, et al. Successful application of microarray technology to microdissected formalin-fixed, paraffin-embedded tissue. J Mol Diagn. 2007;9:70–9.PubMedPubMed CentralView Article
  21. North AJ, Chidgey MA, Clarke JP, Bardsley WG, Garrod DR. Distinct desmocollin isoforms occur in the same desmosomes and show reciprocally graded distributions in bovine nasal epidermis. Proc Natl Acad Sci U S A. 1996;93:7701–5.PubMedPubMed CentralView Article
  22. KRT5 keratin 5. Homo sapiens (human). http://​www.​ncbi.​nlm.​nih.​gov/​gene/​3852. Accessed Feb 2018.
  23. Cui T, Chen Y, Yang L, Knosel T, Huber O, Pacyna-Gengelbach M, et al. The p53 target gene desmocollin 3 acts as a novel tumor suppressor through inhibiting EGFR/ERK pathway in human lung cancer. Carcinogenesis. 2012;33:2326–33.PubMedView Article
  24. Chen J, O'Shea C, Fitzpatrick JE, Koster MI, Koch PJ. Loss of Desmocollin 3 in skin tumor development and progression. Mol Carcinog. 2012;51:535–45.PubMedView Article
  25. Knosel T, Chen Y, Hotovy S, Settmacher U, Altendorf-Hofmann A, Petersen I. Loss of desmocollin 1-3 and homeobox genes PITX1 and CDX2 are associated with tumor progression and survival in colorectal carcinoma. Int J Color Dis. 2012;27:1391–9.View Article
  26. Oshiro MM, Kim CJ, Wozniak RJ, Junk DJ, Munoz-Rodriguez JL, Burr JA, et al. Epigenetic silencing of DSC3 is a common event in human breast cancer. Breast Cancer Res. 2005;7:R669–80.PubMedPubMed CentralView Article
  27. Vasiliu D, Clamons S, McDonough M, Rabe B, Saha M. A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size. PLoS One. 2015;10:e0118198.PubMedPubMed CentralView Article
  28. Braga-Neto UM, Dougherty ER. Is cross-validation valid for small-sample microarray classification? Bioinformatics. 2004;20:374–80.PubMedView Article
  29. Huang H, Jin T, Wang L, Wang F, Zhang R, Pan Y, et al. The RAS guanyl nucleotide-releasing protein RasGRP1 is involved in lymphatic development in zebrafish. J Biol Chem. 2013;288:2355–64.PubMedView Article
  30. Kortum RL, Rouquette-Jazdanian AK, Samelson LE. Ras and extracellular signal-regulated kinase signaling in thymocytes and T cells. Trends Immunol. 2013;34:259–68.PubMedView Article
  31. To SQ, Knower KC, Clyne CD. NFkappaB and MAPK signalling pathways mediate TNFalpha-induced early growth response gene transcription leading to aromatase expression. Biochem Biophys Res Commun. 2013;433:96–101.PubMedView Article
  32. Dugas JC, Ibrahim A, Barres BA. The T3-induced gene KLF9 regulates oligodendrocyte differentiation and myelin regeneration. Mol Cell Neurosci. 2012;50:45–57.PubMedPubMed CentralView Article
  33. Wang HQ, Xu ML, Ma J, Zhang Y, Xie CH. Frizzled-8 as a putative therapeutic target in human lung cancer. Biochem Biophys Res Commun. 2012;417:62–6.PubMedView Article
  34. Katoh M. WNT signaling in stem cell biology and regenerative medicine. Curr Drug Targets. 2008;9:565–70.PubMedView Article
  35. Meier-Abt F, Milani E, Roloff T, Brinkhaus H, Duss S, Meyer DS, et al. Parity induces differentiation and reduces Wnt/Notch signaling ratio and proliferation potential of basal stem/progenitor cells isolated from mouse mammary epithelium. Breast Cancer Res. 2013;15:R36.PubMedPubMed CentralView Article
  36. Russo J, Santucci-Pereira J, Russo IH. The genomic signature of breast cancer prevention. Genes. 2014;5:65–83.PubMedPubMed CentralView Article
  37. Wan X, Ji W, Mei X, Zhou J, Liu JX, Fang C, et al. Negative feedback regulation of Wnt4 signaling by EAF1 and EAF2/U19. PLoS One. 2010;5:e9118.PubMedPubMed CentralView Article
  38. Su F, Pascal LE, Xiao W, Wang Z. Tumor suppressor U19/EAF2 regulates thrombospondin-1 expression via p53. Oncogene. 2010;29:421–31.PubMedView Article
  39. Wissmann C, Wild PJ, Kaiser S, Roepcke S, Stoehr R, Woenckhaus M, et al. WIF1, a component of the Wnt pathway, is down-regulated in prostate, breast, lung, and bladder cancer. J Pathol. 2003;201:204–12.PubMedView Article
  40. Ai L, Tao Q, Zhong S, Fields CR, Kim WJ, Lee MW, et al. Inactivation of Wnt inhibitory factor-1 (WIF1) expression by epigenetic silencing is a common event in breast cancer. Carcinogenesis. 2006;27:1341–8.PubMedView Article
  41. Russo J, Balogh GA, Chen J, Fernandez SV, Fernbaugh R, Heulings R, et al. The concept of stem cell in the mammary gland and its implication in morphogenesis, cancer and prevention. Front Biosci. 2006;11:151–72.PubMedView Article
  42. Christensen J, Bentz S, Sengstag T, Shastri VP, Anderle P. FOXQ1, a novel target of the Wnt pathway and a new marker for activation of Wnt signaling in solid tumors. PLoS One. 2013;8:e60051.PubMedPubMed CentralView Article
  43. Sehrawat A, Kim SH, Vogt A, Singh SV. Suppression of FOXQ1 in benzyl isothiocyanate-mediated inhibition of epithelial-mesenchymal transition in human breast cancer cells. Carcinogenesis. 2013;34:864–73.PubMedView Article
  44. Qiao Y, Jiang X, Lee ST, Karuturi RK, Hooi SC, Yu Q. FOXQ1 regulates epithelial-mesenchymal transition in human cancers. Cancer Res. 2011;71:3076–86.PubMedView Article
  45. Gao M, Shih Ie M, Wang TL. The role of forkhead box Q1 transcription factor in ovarian epithelial carcinomas. Int J Mol Sci. 2012;13:13881–93.PubMedPubMed CentralView Article
  46. Tessema M, Yingling CM, Grimes MJ, Thomas CL, Liu Y, Leng S, et al. Differential epigenetic regulation of TOX subfamily high mobility group box genes in lung and breast cancers. PLoS One. 2012;7:e34850.PubMedPubMed CentralView Article
  47. Aliahmad P, de la Torre B, Kaye J. Shared dependence on the DNA-binding factor TOX for the development of lymphoid tissue-inducer cell and NK cell lineages. Nat Immunol. 2010;11:945–52.PubMedPubMed CentralView Article
  48. Aliahmad P, Kadavallore A, de la Torre B, Kappes D, Kaye J. TOX is required for development of the CD4 T cell lineage gene program. J Immunol. 2011;187:5931–40.PubMedView Article
  49. Unoki M, Nishidate T, Nakamura Y. ICBP90, an E2F-1 target, recruits HDAC1 and binds to methyl-CpG through its SRA domain. Oncogene. 2004;23:7601–10.PubMedView Article
  50. Jin W, Chen L, Chen Y, Xu SG, Di GH, Yin WJ, et al. UHRF1 is associated with epigenetic silencing of BRCA1 in sporadic breast cancer. Breast Cancer Res Treat. 2010;123:359–73.PubMedView Article
  51. Attia M, Forster A, Rachez C, Freemont P, Avner P, Rogner UC. Interaction between nucleosome assembly protein 1-like family members. J Mol Biol. 2011;407:647–60.PubMedView Article
  52. Attia M, Rachez C, De Pauw A, Avner P, Rogner UC. Nap1l2 promotes histone acetylation activity during neuronal differentiation. Mol Cell Biol. 2007;27:6093–102.PubMedPubMed CentralView Article
  53. Lackey L, Law EK, Brown WL, Harris RS. Subcellular localization of the APOBEC3 proteins during mitosis and implications for genomic DNA deamination. Cell Cycle. 2013;12:762–72.PubMedPubMed CentralView Article
  54. Long J, Delahanty RJ, Li G, Gao YT, Lu W, Cai Q, et al. A common deletion in the APOBEC3 genes and breast cancer risk. J Natl Cancer Inst. 2013;105:573–9.PubMedPubMed CentralView Article
  55. Monks J, Geske FJ, Lehman L, Fadok VA. Do inflammatory cells participate in mammary gland involution? J Mammary Gland Biol Neoplasia. 2002;7:163–76.PubMedView Article
  56. Csanaky K, Doppler W, Tamas A, Kovacs K, Toth G, Reglodi D. Influence of terminal differentiation and PACAP on the cytokine, chemokine, and growth factor secretion of mammary epithelial cells. J Mol Neurosci. 2014;52:28–36.PubMedView Article
  57. Martinson HA, Jindal S, Durand-Rougely C, Borges VF, Schedin P. Wound healing-like immune program facilitates postpartum mammary gland involution and tumor progression. Int J Cancer. 2015;136:1803–13.PubMedView Article
  58. Plaks V, Boldajipour B, Linnemann JR, Nguyen NH, Kersten K, Wolf Y, et al. Adaptive immune regulation of mammary postnatal organogenesis. Dev Cell. 2015;34:493–504.PubMedPubMed CentralView Article
  59. Asztalos S, Gann PH, Hayes MK, Nonn L, Beam CA, Dai Y, et al. Gene expression patterns in the human breast after pregnancy. Cancer Prev Res (Phila). 2010;3:301–11.View Article
  60. Rotunno M, Sun X, Figueroa J, Sherman ME, Garcia-Closas M, Meltzer P, et al. Parity-related molecular signatures and breast cancer subtypes by estrogen receptor status. Breast Cancer Res. 2014;16:R74.PubMedPubMed CentralView Article
  61. Clarkson RW, Wayland MT, Lee J, Freeman T, Watson CJ. Gene expression profiling of mammary gland development reveals putative roles for death receptors and immune mediators in post-lactational regression. Breast Cancer Res. 2004;6:R92–109.PubMedView Article
  62. Stein T, Morris JS, Davies CR, Weber-Hall SJ, Duffy MA, Heath VJ, et al. Involution of the mouse mammary gland is associated with an immune cascade and an acute-phase response, involving LBP, CD14 and STAT3. Breast Cancer Res. 2004;6:R75–91.PubMedView Article
  63. Liu Q, Wuu J, Lambe M, Hsieh SF, Ekbom A, Hsieh CC. Transient increase in breast cancer risk after giving birth: postpartum period with the highest risk (Sweden). Cancer Causes Control. 2002;13:299–305.PubMedView Article
  64. Schumacher A, Heinze K, Witte J, Poloski E, Linzke N, Woidacki K, et al. Human chorionic gonadotropin as a central regulator of pregnancy immune tolerance. J Immunol. 2013;190:2650–8.PubMedView Article
  65. Gadi VK. Fetal microchimerism in breast from women with and without breast cancer. Breast Cancer Res Treat. 2010;121:241–4.PubMedView Article
  66. Gadi VK. Fetal microchimerism and cancer. Cancer Lett. 2009;276:8–13.PubMedView Article
  67. Gadi VK, Nelson JL. Fetal microchimerism in women with breast cancer. Cancer Res. 2007;67:9035–8.PubMedView Article
  68. Eun JK, Guthrie KA, Zirpoli G, Gadi VK. In situ breast cancer and microchimerism. Sci Rep. 2013;3:2192.PubMedPubMed CentralView Article
  69. Boyon C, Collinet P, Boulanger L, Rubod C, Lucot JP, Vinatier D. Fetal microchimerism: benevolence or malevolence for the mother? Eur J Obstet Gynecol Reprod Biol. 2011;158:148–52.PubMedView Article
  70. Kallenbach LR, Johnson KL, Bianchi DW. Fetal cell microchimerism and cancer: a nexus of reproduction, immunology, and tumor biology. Cancer Res. 2011;71:8–12.PubMedPubMed CentralView Article
  71. Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008;14:518–27.PubMedView Article
  72. Winslow S, Leandersson K, Edsjo A, Larsson C. Prognostic stromal gene signatures in breast cancer. Breast Cancer Res. 2015;17:23.PubMedPubMed CentralView Article
  73. Nagalla S, Chou JW, Willingham MC, Ruiz J, Vaughn JP, Dubey P, et al. Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis. Genome Biol. 2013;14:R34.PubMedPubMed CentralView Article
  74. Farmer P, Bonnefoi H, Anderle P, Cameron D, Wirapati P, Becette V, et al. A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer. Nat Med. 2009;15:68–74.PubMedView Article
  75. Mahmoud SM, Paish EC, Powe DG, Macmillan RD, Grainge MJ, Lee AH, et al. Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. J Clin Oncol. 2011;29:1949–55.View ArticlePubMed
  76. Huang H, Hara A, Homma T, Yonekawa Y, Ohgaki H. Altered expression of immune defense genes in pilocytic astrocytomas. J Neuropathol Exp Neurol. 2005;64:891–901.PubMedView Article
Metadata
Title
Genomic signature of parity in the breast of premenopausal women
Authors
Julia Santucci-Pereira
Anne Zeleniuch-Jacquotte
Yelena Afanasyeva
Hua Zhong
Michael Slifker
Suraj Peri
Eric A. Ross
Ricardo López de Cicco
Yubo Zhai
Theresa Nguyen
Fathima Sheriff
Irma H. Russo
Yanrong Su
Alan A. Arslan
Pal Bordas
Per Lenner
Janet Åhman
Anna Stina Landström Eriksson
Robert Johansson
Göran Hallmans
Paolo Toniolo
Jose Russo
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2019
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-019-1128-x

Other articles of this Issue 1/2019

Breast Cancer Research 1/2019 Go to the issue

Research article

Disease trajectories and mortality among women diagnosed with breast cancer

Research article

Development of a prediction model for breast cancer based on the national cancer registry in Taiwan

Research article

Blood DNA methylation and breast cancer risk: a meta-analysis of four prospective cohort studies

Research article

Absence of integrin α3β1 promotes the progression of HER2-driven breast cancer in vivo

Research article

The lncRNA MIR2052HG regulates ERα levels and aromatase inhibitor resistance through LMTK3 by recruiting EGR1

Research article

Regular use of aspirin and other non-steroidal anti-inflammatory drugs and breast cancer risk for women at familial or genetic risk: a cohort study
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