Skip to main content

NRF1 motif sequence-enriched genes involved in ER/PR −ve HER2 +ve breast cancer signaling pathways

Breast Cancer Research and Treatment volume 172, pages 469–485 (2018)Cite this article

Abstract

Nuclear respiratory factor 1 (NRF1) transcription factor has recently been shown to control breast cancer progression. However, mechanistic aspects by which NRF1 may contribute to susceptibility to different breast tumor subtypes are still not fully understood. Since transcriptional control of NRF1 seems to be dependent on epidermal growth factor receptor signaling, herein, we investigated the role of NRF1 in estrogen receptor/progesterone receptor negative, but human epidermal growth factor receptor 2-positive (ER/PR −ve HER2 +ve) breast cancer. We found that both mRNA and protein levels of NRF1 and its transcriptional activity were significantly higher in ER/PR −ve HER2 +ve breast cancer samples compared to normal breast tissues. This was consistent with our observation of higher NRF1 protein expression in the experimental model of HER2+ breast cancer brain metastasis. To identify network-based pathways involved in the susceptibility to the ER/PR −ve HER2 +ve breast cancer subtype, the NRF1 transcriptional regulatory genome-wide landscape was analyzed using the approach consisting of a systematic integration of ChIP DNA-seq, RNA-Microarray, NRF1 protein-DNA motif binding, signal pathway analysis, and Bayesian machine learning. Our findings show that a high percentage of known HER2+ breast cancer susceptibility genes, including EGFR, IGFR, and E2F1, are under transcriptional control of NRF1. Promoters of several genes from the KEGG HER2+ breast cancer pathway and 11 signaling pathways linked to 6 hallmarks of cancer contain the NRF1 motif. By pathway analysis, key breast cancer hallmark genes of epithelial-mesenchymal transition, stemness, cell apoptosis, cell cycle regulation, chromosomal integrity, and DNA damage/repair were highly enriched with NRF1 motifs. In addition, we found using Bayesian network-based machine learning that 30 NRF1 motif-enriched genes including growth factor receptors—FGFR1, IGF1R; E2Fs transcription factor family—E2F1, E2F3; MAPK pathway—SHC2, GRB2, MAPK1; PI3K-AKT-mTOR signaling pathway—PIK3CD, PIK3R1, PIK3R3, RPS6KB2; WNT signaling pathway—WNT7B, DLV1, DLV2, GSK3B, NRF1, and DDB2, known for its role in DNA repair and involvement in early events associated with metastatic progression of breast cancer cells, were associated with HER2-amplified breast cancer. Machine learning search further revealed that the likelihood of HER2-positive breast cancer is almost 100% in a patient with the high NRF1 expression combined with expression patterns of high E2F3, GSK3B, and MAPK1, low or no change in E2F1 and FGFR1, and high or no change in PIK3R3. In summary, our findings suggest novel roles of NRF1 and its regulatory networks in susceptibility to the ER/PR −ve HER2 +ve aggressive breast cancer subtype. Clinical confirmation of our machine learned Bayesian networks will have significant impact on our understanding of the role of NRF1 as a valuable biomarker for breast cancer diagnosis and prognosis as well as provide strong rationale for future studies to develop NRF1 signaling-based therapeutics to target HER2+ breast cancer.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Andrechek ER (2015) HER2/Neu tumorigenesis and metastasis is regulated by E2F activator transcription factors. Oncogene 34(2):217–225

    CAS  Article  Google Scholar 

  2. Barbieux C, Bacharouche J, Soussen C, Hupont S, Razafitianamaharavo A, Klotz R et al (2016) DDB2 (damaged-DNA binding 2) protein: A new modulator of nanomechanical properties and cell adhesion of breast cancer cells. Nanoscale 8(9):5268–5279

    CAS  Article  Google Scholar 

  3. Benner C, Konovalov S, Mackintosh C, Hutt KR, Stunnenberg R, Garcia-Bassets I (2013) Decoding a signature-based model of transcription cofactor recruitment dictated by cardinal cis-regulatory elements in proximal promoter regions. PLoS Genetics, 9(11), e1003906

    Article  Google Scholar 

  4. Campoy EM, Laurito SR, Branham MT, Urrutia G, Mathison A, Gago F et al (2016) Asymmetric cancer hallmarks in breast tumors on different sides of the body. PLoS ONE, 11(7), e0157416

    Article  Google Scholar 

  5. Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490(7418):61–70

    Article  Google Scholar 

  6. Domcke S, Bardet AF, Adrian Ginno P, Hartl D, Burger L, Schubeler D (2015) Competition between DNA methylation and transcription factors determines binding of NRF1. Nature 528(7583):575–579

    CAS  Article  Google Scholar 

  7. ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57–74

    Article  Google Scholar 

  8. Ertel A, Tsirigos A, Whitaker-Menezes D, Birbe RC, Pavlides S, Martinez-Outschoorn UE et al (2012) Is cancer a metabolic rebellion against host aging? in the quest for immortality, tumor cells try to save themselves by boosting mitochondrial metabolism. Cell Cycle 11(2):253–263

    CAS  Article  Google Scholar 

  9. Falco MM, Bleda M, Carbonell-Caballero J, Dopazo J (2016) The pan-cancer pathological regulatory landscape. Sci Rep 6:39709

    CAS  Article  Google Scholar 

  10. Friedman N, Linial M, Nachman I, Pe’er D (2000) Using bayesian networks to analyze expression data. J Comput Biol 7(3–4):601–620

    CAS  Article  Google Scholar 

  11. Gao W, Wu M, Wang N, Zhang Y, Hua J, Tang G et al (2018) Increased expression of mitochondrial transcription factor A and nuclear respiratory factor-1 predicts a poor clinical outcome of breast cancer. Oncol Lett 15(2):1449–1458

    PubMed  Google Scholar 

  12. Gril B, Palmieri D, Bronder JL, Herring JM, Vega-Valle E, Feigenbaum L et al (2008) Effect of lapatinib on the outgrowth of metastatic breast cancer cells to the brain. J Natl Cancer Inst 100(15):1092–1103

    CAS  Article  Google Scholar 

  13. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674

    CAS  Article  Google Scholar 

  14. Henjes F, Bender C, von der Heyde S, Braun L, Mannsperger HA, Schmidt C et al (2012) Strong EGFR signaling in cell line models of ERBB2-amplified breast cancer attenuates response towards ERBB2-targeting drugs. Oncogenesis, 1:e16

    CAS  Article  Google Scholar 

  15. Kataoka Y, Mukohara T, Shimada H, Saijo N, Hirai M, Minami H (2010) Association between gain-of-function mutations in PIK3CA and resistance to HER2-targeted agents in HER2-amplified breast cancer cell lines. Ann Oncol 21(2):255–262

    CAS  Article  Google Scholar 

  16. Kunkle BW, Yoo C, Roy D (2013) Reverse engineering of modified genes by bayesian network analysis defines molecular determinants critical to the development of glioblastoma. PLoS ONE 8(5):e64140

    CAS  Article  Google Scholar 

  17. Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S et al (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22(9):1813–1831

    CAS  Article  Google Scholar 

  18. Lavigne MD, Vatsellas G, Polyzos A, Mantouvalou E, Sianidis G, Maraziotis I et al (2015) Composite macroH2A/NRF-1 nucleosomes suppress noise and generate robustness in gene expression. Cell Rep 11(7):1090–1101

    CAS  Article  Google Scholar 

  19. Le TD, Liu L, Liu B, Tsykin A, Goodall GJ, Satou K et al (2013) Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinform 14:92

    CAS  Article  Google Scholar 

  20. Lee M, Oprea-Ilies G, Saavedra HI (2015) Silencing of E2F3 suppresses tumor growth of HER2+ breast cancer cells by restricting mitosis. Oncotarget 6(35):37316–37334

    PubMed  PubMed Central  Google Scholar 

  21. Lee MY, Moreno CS, Saavedra HI (2014) E2F activators signal and maintain centrosome amplification in breast cancer cells. Mol Cell Biol 34(14):2581–2599

    Article  Google Scholar 

  22. Murrell DH, Hamilton AM, Mallett CL, van Gorkum R, Chambers AF, Foster PJ (2015) Understanding heterogeneity and permeability of brain metastases in murine models of HER2-positive breast cancer through magnetic resonance imaging: Implications for detection and therapy. Transl Oncol 8(3):176–184

    Article  Google Scholar 

  23. Niida A, Smith AD, Imoto S, Tsutsumi S, Aburatani H, Zhang MQ et al (2008) Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells. BMC Bioinform 9:404

    Article  Google Scholar 

  24. Okoh V, Deoraj A, Roy D (2011) Estrogen-induced reactive oxygen species-mediated signalings contribute to breast cancer. Biochim Biophysica Acta 1815(1):115–133

    CAS  Google Scholar 

  25. Okoh VO, Garba NA, Penney RB, Das J, Deoraj A, Singh KP et al (2015) Redox signalling to nuclear regulatory proteins by reactive oxygen species contributes to oestrogen-induced growth of breast cancer cells. Br J Cancer 112(10):1687–1702

    CAS  Article  Google Scholar 

  26. Piantadosi CA, Suliman HB (2006) Mitochondrial transcription factor A induction by redox activation of nuclear respiratory factor 1. J Biol Chem 281(1):324–333

    CAS  Article  Google Scholar 

  27. Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG et al (2016) The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database. https://doi.org/10.1093/database/baw100

    Article  PubMed  PubMed Central  Google Scholar 

  28. Roy D, Tamuli R (2008) NRF1 (nuclear respiratory factor 1) atlas of genetics and cytogenetics in oncology and haematolology, 02/13/2018

  29. Scarpulla RC (2006) Nuclear control of respiratory gene expression in mammalian cells. J Cell Biochem 97(4):673–683

    CAS  Article  Google Scholar 

  30. Scarpulla RC (2008) Transcriptional paradigms in mammalian mitochondrial biogenesis and function. Physiol Rev 88(2):611–638

    CAS  Article  Google Scholar 

  31. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    CAS  Article  Google Scholar 

  32. Sherwood RI, Hashimoto T, O’Donnell CW, Lewis S, Barkal AA, van Hoff JP et al (2014) Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nature Biotechnol 32(2):171–178

    CAS  Article  Google Scholar 

  33. SMILE: Structural Modeling, Inference, and Learning Engine, BayesFusion, LLC (2018) https://www.bayesfusion.com/smile-engine

  34. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98(19):10869–10874

    CAS  Article  Google Scholar 

  35. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J et al (2015) STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucl Acids Res 43(Database issue):D447–D452

    CAS  Article  Google Scholar 

  36. von der Heyde S, Wagner S, Czerny A, Nietert M, Ludewig F, Salinas-Riester G et al (2015) mRNA profiling reveals determinants of trastuzumab efficiency in HER2-positive breast cancer. PLoS ONE, 10(2):e0117818

    Article  Google Scholar 

  37. Weaver Z, Difilippantonio S, Carretero J, Martin PL, El Meskini R, Iacovelli AJ et al (2012) Temporal molecular and biological assessment of an erlotinib-resistant lung adenocarcinoma model reveals markers of tumor progression and treatment response. Cancer Res 72(22):5921–5933

    CAS  Article  Google Scholar 

  38. Wu X, Baig A, Kasymjanova G, Kafi K, Holcroft C, Mekouar H et al (2016) Pattern of local recurrence and distant metastasis in breast cancer by molecular subtype. Cureus 8(12):e924

    PubMed  PubMed Central  Google Scholar 

  39. Yeo EJ, Cassetta L, Qian BZ, Lewkowich I, Li JF, Stefater JA, 3rd et al (2014) Myeloid WNT7b mediates the angiogenic switch and metastasis in breast cancer. Cancer Res 74(11):2962–2973

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This work was, in part, supported by a VA MERIT Review (VA BX001463) grant to DR.

Author information

Affiliations

  1. Department of Environmental Health Sciences, Florida International University, Miami, FL, 33199, USA

    Jairo Ramos, Jayanta Das, Quentin Felty & Deodutta Roy

  2. Department of Biostatistics, Florida International University, Miami, FL, 33199, USA

    Changwon Yoo

  3. Department of Pathology, Florida International University, Miami, FL, USA

    Robert Poppiti

  4. Robarts Research Institute, Western University, 1151 Richmond Street North, London, ON, N6A 5B7, Canada

    Donna Murrell & Paula J. Foster

Authors
  1. Jairo Ramos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jayanta Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quentin Felty
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changwon Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert Poppiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Donna Murrell
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paula J. Foster
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Deodutta Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Experiments were conceived and designed by JR and DR. Experiments were performed by the JR and JD. DM and PJM provided sections of experimental model of breast cancer brain metastasis. JR, JD, DR, and CY analyzed data. JR and DR drafted the first version of the manuscript. JR, JD, DR, CY, QF, and RP contributed to the writing of the manuscript. All authors have read, and confirm that they meet, the ICMJE criteria for authorship.

Corresponding author

Correspondence to Deodutta Roy.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Sections of experimental model of breast cancer brain metastasis from MDA-MB-231-BR-HER2 cells were kindly provided by Dr. Donna Murrel [22] and MDA-MB-231-BR-vector cells by Dr. Brunilde Gril [12] injected intracardially into mice. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed involving human breast cancer tissue arrays purchased from US Biomax, Rockville, MD in the study described were in accordance with the ethical standards of the institutional and/or national research committee.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ramos, J., Das, J., Felty, Q. et al. NRF1 motif sequence-enriched genes involved in ER/PR −ve HER2 +ve breast cancer signaling pathways. Breast Cancer Res Treat 172, 469–485 (2018). https://doi.org/10.1007/s10549-018-4905-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-018-4905-9

Keywords

  • Nuclear respiratory factor 1
  • NRF1
  • ER/PR −ve HER2 +ve breast cancer