Skip to main content
SpringerLink
Log in

Understanding Tamoxifen Resistance of Breast Cancer Based on Integrative Bioinformatics Approaches

  • Chapter
  • First Online:
Breast Cancer Metastasis and Drug Resistance

Abstract

Global gene expression profiles on tumors are not only useful in developing prognosis signatures but also rich resource for the elucidation of underlying mechanisms related to poor clinical outcome and drug resistance. In this chapter we present a panel of bioinformatics strategies to derive biological insights based on gene expression profiles on estrogen receptor positive breast tumors that were collected prior to adjuvant tamoxifen treatment. The analyses reveal that the tamoxifen resistant tumors are highly proliferative and display a distinctive expression profile for genes related to inflammation and angiogenesis compared to tamoxifen sensitive tumors. The bioinformatics analysis also identifies a set of small molecules that may reverse the tamoxifen resistance in breast tumor.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98(19):10869–10874

    Article  PubMed  CAS  Google Scholar 

  2. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536

    Google Scholar 

  3. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B et al (2006) Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Nat Cancer Inst 98(4):262–272

    Article  PubMed  CAS  Google Scholar 

  4. Reis-Filho JS, Pusztai L (2011) Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 378(9805):1812–1823

    Article  PubMed  CAS  Google Scholar 

  5. Colombo P-E, Milanezi F, Weigelt B, Reis-Filho J (2011) Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification, prognostication, and prediction. Breast Cancer Res 13(3):212

    Google Scholar 

  6. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA et al (2000) Molecular portraits of human breast tumours. Nature 406(6797):747–752

    Article  PubMed  CAS  Google Scholar 

  7. Dai H, van’t Veer L, Lamb J, He YD, Mao M, Fine BM, Bernards R, van de Vijver M, Deutsch P, Sachs A et al (2005) A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res 65(10):4059–4066

    Google Scholar 

  8. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826

    Article  PubMed  CAS  Google Scholar 

  9. Ma X-J, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, Muir B, Mohapatra G, Salunga R, Tuggle JT et al (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5(6):607–616

    Article  PubMed  CAS  Google Scholar 

  10. Jansen MPHM, Foekens JA, van Staveren IL, Dirkzwager-Kiel MM, Ritstier K, Look MP, Meijer-van Gelder ME, Sieuwerts AM, Portengen H, Dorssers LCJ et al (2005) Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol 23(4):732–740

    Article  PubMed  CAS  Google Scholar 

  11. Wang Y, Klijn JGM, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J et al (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365(9460):671–679

    PubMed  CAS  Google Scholar 

  12. Loi S, Haibe-Kains B, Desmedt C, Lallemand F, Tutt AM, Gillet C, Ellis P, Harris A, Bergh J, Foekens JA et al (2007) Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol 25(10):1239–1246

    Article  PubMed  CAS  Google Scholar 

  13. Jansen MPHM, Sieuwerts AM, Look MP, Ritstier K, Meijer-van Gelder ME, van Staveren IL, Klijn JGM, Foekens JA, Berns EMJJ (2007) HOXB13-to-IL17BR expression ratio is related with tumor aggressiveness and response to tamoxifen of recurrent breast cancer: a retrospective study. J Clin Oncol 25(6):662–668

    Article  PubMed  CAS  Google Scholar 

  14. Loi S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, Gillet C, Ellis P, Ryder K, Reid JF et al (2008) Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC Genomics 9:239

    Article  PubMed  Google Scholar 

  15. Chanrion M, Negre V, Fontaine H, Salvetat N, Bibeau F, Grogan GM, Mauriac L, Katsaros D, Molina F, Theillet C et al (2008) A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer. Clin Cancer Res 14(6):1744–1752

    Article  PubMed  CAS  Google Scholar 

  16. Vendrell J, Robertson K, Ravel P, Bray S, Bajard A, Purdie C, Nguyen C, Hadad S, Bieche I, Chabaud S et al (2008) A candidate molecular signature associated with tamoxifen failure in primary breast cancer. Breast Cancer Res 10(5):R88

    Article  PubMed  Google Scholar 

  17. Kok M, Linn S, Van Laar R, Jansen M, van den Berg T, Delahaye L, Glas A, Peterse J, Hauptmann M, Foekens J et al (2009) Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen. Breast Cancer Res Treat 113(2):275

    Article  PubMed  CAS  Google Scholar 

  18. Loi S, Piccart M, Sotiriou C (2007) The use of gene-expression profiling to better understand the clinical heterogeneity of estrogen receptor positive breast cancers and tamoxifen response. Crit Rev Oncol Hematol 61(3):187–194

    Article  PubMed  Google Scholar 

  19. Loi S (2008) Molecular analysis of hormone receptor positive (luminal) breast cancers—what have we learnt? Eur J Cancer 44(18):2813–2818

    Article  PubMed  CAS  Google Scholar 

  20. Eroles P, Bosch A, Alejandro Pérez-Fidalgo J, Lluch A (2012) Molecular biology in breast cancer: intrinsic subtypes and signaling pathways. Cancer Tr Rev 38(6):698–707

    Google Scholar 

  21. Schiavon G, Smid M, Gupta GP, Redana S, Santini D, Martens JWM (2012) Heterogeneity of breast cancer gene signatures and beyond diagnostic, prognostic and therapeutic value of gene signatures. In: Russo A, Iacobelli S, Iovanna J (eds) Humana Press, New York, pp 13–25

    Google Scholar 

  22. Loi S, Sotiriou C, Haibe-Kains B, Lallemand F, Conus NM, Piccart MJ, Speed TP, McArthur GA (2009) Gene expression profiling identifies activated growth factor signaling in poor prognosis (Luminal-B) estrogen receptor positive breast cancer. BMC Med Genomics 2:37

    Article  PubMed  Google Scholar 

  23. Yu J, Sieuwerts A, Zhang Y, Martens J, Smid M, Klijn J, Wang Y, Foekens J (2007) Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer. BMC Cancer 7(1):182

    Article  PubMed  Google Scholar 

  24. Miller TW, Balko JM, Ghazoui Z, Dunbier A, Anderson H, Dowsett M, González-Angulo AM, Mills GB, Miller WR, Wu H et al (2011) A gene expression signature from human breast cancer cells with acquired hormone independence identifies MYC as a mediator of antiestrogen resistance. Clin Cancer Res 17(7):2024–2034

    Article  PubMed  CAS  Google Scholar 

  25. Huang L, Zhao S, Frasor JM, Dai Y (2011) An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors. PLoS One 6(7):e22274

    Article  PubMed  CAS  Google Scholar 

  26. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA KA et al (2009) Archive for high-throughput functional genomic data. Nucleic Acids Res 37:D885–D890

    Article  PubMed  CAS  Google Scholar 

  27. Huang DW, Sherman BT, Lempicki RA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44

    Article  Google Scholar 

  28. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550

    Article  PubMed  CAS  Google Scholar 

  29. Molecular Signatures Database (2010) Broad institute, Cambridge. http://www.broadinstitute.org/gsea/msigdb/index.jsp. Assessed 21 May 2012

  30. Wu CL, Zukerberg LR, Ngwu C, Harlow E, Lees JA (1995) In vivo association of E2F and DP family proteins. Mol Cell Biol 15(5):2536–2546

    PubMed  CAS  Google Scholar 

  31. Miller TW, Balko JM, Fox EM, Ghazoui Z, Dunbier A, Anderson H, Dowsett M, Jiang A, Smith RA, Maira S-M et al (2011) ERα-dependent E2F transcription can mediate resistance to estrogen deprivation in human breast cancer. Cancer Discov 1(4):338–351

    Article  PubMed  CAS  Google Scholar 

  32. Langfelder P, Horvath S (2008) WGCNA: An R package for weighted correlation network analysis. BMC Bioinform 9(1):559

    Google Scholar 

  33. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:Article17

    Google Scholar 

  34. Miller JA, Horvath S, Geschwind DH (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Nat Acad Sci 107(28):12698–12703

    Article  PubMed  CAS  Google Scholar 

  35. Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE, Drake TA, Lusis AJ et al (2006) Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet 2(8):e130

    Article  PubMed  Google Scholar 

  36. Baumgarten SC, Frasor J (2012) Minireview: inflammation: an instigator of more aggressive estrogen receptor (er) positive breast cancers. Mol Endocrinol 26(3):360–371

    Article  PubMed  CAS  Google Scholar 

  37. Asztalos S, Gann PH, Hayes MK, Nonn L, Beam CA, Dai Y, Wiley EL, Tonetti DA (2010) Gene expression patterns in the human breast after pregnancy. Cancer Prev Res 3(3):301–311

    Article  CAS  Google Scholar 

  38. Bianchini G, Qi Y, Alvarez RH, Iwamoto T, Coutant C, Ibrahim NK, Valero V, Cristofanilli M, Green MC, Radvanyi L et al (2010) Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. J Clin Oncol 28(28):4316–4323

    Article  PubMed  Google Scholar 

  39. Dedeurwaerder S, Desmedt C, Calonne E, Singhal SK, Haibe-Kains B, Defrance M, Michiels S, Volkmar M, Deplus R, Luciani J et al (2011) DNA methylation profiling reveals a predominant immune component in breast cancers. EMBO Mol Med 3(12):726–741

    Article  PubMed  CAS  Google Scholar 

  40. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795):1929–1935

    Article  PubMed  CAS  Google Scholar 

  41. Lamb J (2007) The connectivity map: a new tool for biomedical research. Nat Rev Cancer 7(1):54–60

    Article  PubMed  CAS  Google Scholar 

  42. Yde CW, Clausen MP, Bennetzen MV, Lykkesfeldt AE, Mouritsen OG, Guerra B (2009) The antipsychotic drug chlorpromazine enhances the cytotoxic effect of tamoxifen in tamoxifen-sensitive and tamoxifen-resistant human breast cancer cells. Anticancer Drugs 20(8):723–735. doi:10.1097/CAD.1090b1013e32832ec32041

    Google Scholar 

  43. Miller TE, Ghoshal K, Ramaswamy B, Roy S, Datta J, Shapiro CL, Jacob S, Majumder S (2008) MicroRNA-221/222 confers tamoxifen resistance in breast cancer by targeting p27Kip1. J Biol Chem 283(44):29897–29903

    Article  PubMed  CAS  Google Scholar 

  44. Zhao J–J, Lin J, Yang H, Kong W, He L, Ma X, Coppola D, Cheng JQ (2008) MicroRNA-221/222 negatively regulates estrogen receptor α and is associated with tamoxifen resistance in breast cancer. J Biol Chem 283(45):31079–31086

    Article  PubMed  CAS  Google Scholar 

  45. Stinson S, Lackner MR, Adai AT, Yu N, Kim H-J, O’Brien C, Spoerke J, Jhunjhunwala S, Boyd Z, Januario T et al (2011) TRPS1 targeting by miR-221/222 promotes the epithelial-to-mesenchymal transition in breast cancer. Sci Signal 4(177):ra41

    Google Scholar 

  46. Ward A, Balwierz A, Zhang JD, Kublbeck M, Pawitan Y, Hielscher T, Wiemann S, Sahin O (2012) Re-expression of microRNA-375 reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer. Oncogene 1–10

    Google Scholar 

  47. Natrajan R, Weigelt B, Mackay A, Geyer F, Grigoriadis A, Tan D, Jones C, Lord C, Vatcheva R, Rodriguez-Pinilla S et al (2010) An integrative genomic and transcriptomic analysis reveals molecular pathways and networks regulated by copy number aberrations in basal-like, HER2 and luminal cancers. Breast Cancer Res Tr 121(3):575–589

    Article  CAS  Google Scholar 

  48. Polyak K, Vogt PK (2012) Progress in breast cancer research. Proc Nat Acad Sci 109(8):2715–2717

    Article  PubMed  CAS  Google Scholar 

  49. Connectivity Map (2006) Broad institute, Cambridge. http://www.broadinstitute.org/cmap/. Assessed 17 Aug 2012

Download references

Author information

Authors and Affiliations

  1. Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA

    Y. Dai & L. Huang

Authors
  1. Y. Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. Dai .

Editor information

Editors and Affiliations

  1. School of Medicine, Karmanos Cancer Institute, Wayne State University, John R Street 4100, Detroit, 48201, Michigan, USA

    Aamir Ahmad

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Dai, Y., Huang, L. (2013). Understanding Tamoxifen Resistance of Breast Cancer Based on Integrative Bioinformatics Approaches. In: Ahmad, A. (eds) Breast Cancer Metastasis and Drug Resistance. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5647-6_14

Download citation

Publish with us

Policies and ethics

Search

Navigation