Skip to main content

Advertisement

Log in

Microarray analysis reveals potential mechanisms of BRMS1-mediated metastasis suppression

  • Research Paper
  • Published:
Clinical & Experimental Metastasis Aims and scope Submit manuscript

Abstract

We used Affymetrix microarrays to compare gene expression profiles of the metastatic parental breast cancer cell line MDA-MB-435 (435) and the non-metastatic daughter cell line created by the stable expression of the BReast cancer Metastasis Suppressor 1 (BRMS1) gene in 435 cells, MDA-MB-435-BRMS1 (435/BRMS1). Analysis of microarray data provided insight into some of the potential mechanisms by which BRMS1 inhibits tumor formation at secondary sites. Furthermore, due to the importance of the microenvironment, we also examined gene expression under different growth conditions (i.e., plus or minus serum). Expression of 565 genes was significantly (adjusted P-value <0.05) altered regardless of in vitro growth conditions. BRMS1 expression significantly increased multiple major histocompatability complex (MHC) genes and significantly decreased expression of several genes associated with protein localization and secretion. The pattern of gene expression associated with BRMS1 expression suggests that metastasis suppression may be mediated by enhanced immune recognition, altered transport, and/or secretion of metastasis-associated proteins.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

435:

MDA-MB-435 cells

435/BRMS1:

MDA-MB-435/BRMS1 cells

ABI:

Abl-interactor protein

ANOVA:

Analysis of variance

BP:

Biological process

BRCA:

Breast cancer gene

BRMS:

Breast cancer metastasis suppressor

CASP1:

Caspase 1

CC:

Cellular component

DAG:

Directed acyclic graph

DIAG:

Diacylglycerol

DMEM:

Dulbecco’s modified eagle’s medium

Ece:

Endothelin converting enzyme

EDTA:

Ethylene diamine tetraacetic acid

ENPP:

Ectonucleotide pyrophosphatase phosphodiesterase

ER:

Endoplasmic reticulum

FBS:

Fetal bovine serum

FDR:

False discovery rate

GO:

Gene ontology

Her:

Human epidermal growth factor receptor

HPRT:

Hypoxanthine guanine ribosyltransferase

MHC:

Major histocompatibility complex

NK:

Natural killer cell

NF-κ-B:

Nuclear factor-kappa in B-cells

PBS:

Phosphate buffered saline

PDGF:

Platelet derived growth factor

PKC:

Protein kinase C

pRb:

Retinoblastoma protein

qRT-PCR:

Quantitative real-time polymerase chain reaction

RMA:

Robust multi-array analysis

RNA:

Ribonucleic acid

Th:

Helper T cells

USU:

Utah State University

References

  1. Shevde LA, Welch DR (2003) Metastasis suppressor pathways–an evolving paradigm. Cancer Lett 198:1–20

    Article  PubMed  CAS  Google Scholar 

  2. Steeg PS (2003) Metastasis suppressors alter the signal transduction of cancer cells. Nat Rev Cancer 3:55–63

    Article  PubMed  CAS  Google Scholar 

  3. Rinker-Schaeffer CW, O’Keefe JP, Welch DR, Theodorescu D (2006) Metastasis suppressor proteins: discovery, molecular mechanisms, and clinical application. Clin Cancer Res 12:3882–3889

    Article  PubMed  CAS  Google Scholar 

  4. Samant RS, Seraj MJ, Saunders MM, Sakamaki TS, Shevde LA, Harms JF, Leonard TO, Goldberg SF, Budgeon L, Meehan WJ, Winter CR, Christensen ND, Verderame MF, Donahue HJ, Welch DR (2000) Analysis of mechanisms underlying BRMS1 suppression of metastasis. Clin Exp Metastasis 18:683–693

    Article  PubMed  CAS  Google Scholar 

  5. Saunders MM, Seraj MJ, Li Z, Zhou Z, Winter CR, Welch DR, Donahue HJ (2001) Breast cancer metastatic potential correlates with a breakdown in homospecific and heterospecific gap junctional intercellular communication. Cancer Res 61:1765–1767

    PubMed  CAS  Google Scholar 

  6. Cicek M, Fukuyama R, Welch DR, Sizemore N, Casey G (2005) Breast cancer metastasis suppressor 1 inhibits gene expression by targeting nuclear factor-kappaB activity. Cancer Res 65:3586–3595

    Article  PubMed  CAS  Google Scholar 

  7. DeWald DB, Torabinejad J, Samant RS, Johnston D, Erin N, Shope JC, Xie Y, Welch DR (2005) Metastasis suppression by breast cancer metastasis suppressor 1 involves reduction of phosphoinositide signaling in MDA-MB-435 breast carcinoma cells. Cancer Res 65:713–717

    PubMed  CAS  Google Scholar 

  8. Meehan WJ, Samant RS, Hopper JE, Carrozza MJ, Shevde LA, Workman JL, Eckert KA, Verderame MF, Welch DR (2004) Breast cancer metastasis suppressor 1 (BRMS1) forms complexes with retinoblastoma-binding protein 1 (RBP1) and the mSin3 histone deacetylase complex and represses transcription. J Biol Chem 279:1562–1569

    Article  PubMed  CAS  Google Scholar 

  9. Ohta S, Lai EW, Pang AL, Brouwers FM, Chan WY, Eisenhofer G, de Krijger R, Ksinantova L, Breza J, Blazicek P, Kvetnansky R, Wesley RA, Pacak K (2005) Downregulation of metastasis suppressor genes in malignant pheochromocytoma. Int J Cancer 114:139–143

    Article  PubMed  CAS  Google Scholar 

  10. Samant RS, Debies MT, Hurst DR, Moore BP, Shevde LA, Welch DR (2006) Suppression of murine mammary carcinoma metastasis by the murine ortholog of breast cancer metastasis suppressor 1 (Brms1). Cancer Lett 235:260–265

    Article  PubMed  CAS  Google Scholar 

  11. Marks P, Rifkind RA, Richon VM, Breslow R, Miller T, Kelly WK (2001) Histone deacetylases and cancer: causes and therapies. Nat Rev Cancer 1:194–202

    Article  PubMed  CAS  Google Scholar 

  12. R Development Core Team (2006) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

  13. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80

    Article  PubMed  Google Scholar 

  14. Irizarry RA, Gautier L, Bolstad BM, Miller C with contributions from Astrand M, Cope LM, Gentleman R, Gentry J, Halling C, Huber W, MacDonald J, Rubinstein BIP, Workman C, Zhang J (2005) affy: Methods for Affymetrix Oligonucleotide Arrays. In: R package

  15. Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (ed), Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York, pp 397–420

    Chapter  Google Scholar 

  16. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25:25–29

    Article  PubMed  CAS  Google Scholar 

  17. Maglott D, Ostell J, Pruitt KD, Tatusova T (2005) Entrez gene: gene-centered information at NCBI. Nucleic Acids Res 33:D54–58

    Article  PubMed  CAS  Google Scholar 

  18. Gentleman R (2004) Using GO for statistical analyses. In: Antoch J (ed) Proceedings in computational statistics. Physica Verlag, pp 171–180

  19. Lumley T, Warnes G, Bolker B (2006) Gplots: various R programming tools for plotting data. R package version 2.3.1

  20. Solana R, Romero J, Alonso C, Pena J (1992) MHC class I antigen expression is inversely related with tumor malignancy and ras oncogene product (p21ras) levels in human breast tumors. Invasion Metastasis 12:210–217

    PubMed  CAS  Google Scholar 

  21. Goepel JR, Rees RC, Rogers K, Stoddard CJ, Thomas WE, Shepherd L (1991) Loss of monomorphic and polymorphic HLA antigens in metastatic breast and colon carcinoma. Br J Cancer 64:880–883

    PubMed  CAS  Google Scholar 

  22. Palmisano GL, Pistillo MP, Fardin P, Capanni P, Nicolo G, Salvi S, Spina B, Pasciucco G, Ferrara GB (2002) Analysis of HLA-G expression in breast cancer tissues. Hum Immunol 63:969–976

    Article  PubMed  CAS  Google Scholar 

  23. Dadmarz R, Sgagias MK, Rosenberg SA, Schwartzentruber DJ (1995) CD4+ T lymphocytes infiltrating human breast cancer recognise autologous tumor in an MHC-class-II restricted fashion. Cancer Immunol Immunother 40:1–9

    PubMed  CAS  Google Scholar 

  24. Garrido F, Ruiz-Cabello F (1991) MHC expression on human tumors–its relevance for local tumor growth and metastasis. Semin Cancer Biol 2:3–10

    PubMed  CAS  Google Scholar 

  25. Maiorana A, Cesinaro AM, Fano RA, Collina G (1995) Expression of MHC class I and class II antigens in primary breast carcinomas and synchronous nodal metastases. Clin Exp Metastasis 13:43–48

    Article  PubMed  CAS  Google Scholar 

  26. Sotiriadou R, Perez SA, Gritzapis AD, Sotiropoulou PA, Echner H, Heinzel S, Mamalaki A, Pawelec G, Voelter W, Baxevanis CN, Papamichail M (2001) Peptide HER2(776–788) represents a naturally processed broad MHC class II-restricted T cell epitope. Br J Cancer 85:1527–1534

    Article  PubMed  CAS  Google Scholar 

  27. Foss FM (2002) Immunologic mechanisms of antitumor activity. Semin Oncol 29:5–11

    Article  PubMed  CAS  Google Scholar 

  28. Remedi MM, Bonacci G, Vides MA, Donadio AC (2003) Immune control of tumors by antigen presentation improvement. Tumour Biol 24:228–235

    Article  PubMed  CAS  Google Scholar 

  29. Fan P, Wang S, Liu X, Zhen L, Wu Z (2002) Major histocompatibility complex class II antigen and costimulatory molecule expression on the surface of breast cancer cells. Zhonghua Zhong Liu Za Zhi 24:327–330

    PubMed  CAS  Google Scholar 

  30. Shi B, Vinyals A, Alia P, Broceno C, Chen F, Adrover M, Gelpi C, Price JE, Fabra A (2006) Differential expression of MHC class II molecules in highly metastatic breast cancer cells is mediated by the regulation of the CIITA transcription Implication of CIITA in tumor and metastasis development. Int J Biochem Cell Biol 38:544–562

    Article  PubMed  CAS  Google Scholar 

  31. Gorelik E, Kim M, Duty L, Henion T, Galili U (1993) Control of metastatic properties of BL6 melanoma cells by H-2Kb gene: immunological and nonimmunological mechanisms. Clin Exp Metastasis 11:439–452

    Article  PubMed  CAS  Google Scholar 

  32. Walter W, Lingnau K, Schmitt E, Loos M, Maeurer MJ (2000) MHC class II antigen presentation pathway in murine tumours: tumour evasion from immunosurveillance? Br J Cancer 83:1192–1201

    Article  PubMed  CAS  Google Scholar 

  33. Jordens I, Marsman M, Kuijl C, Neefjes J (2005) Rab proteins, connecting transport and vesicle fusion. Traffic 6:1070–1077

    Article  PubMed  CAS  Google Scholar 

  34. Grosshans BL, Ortiz D, Novick P (2006) Rabs and their effectors: achieving specificity in membrane traffic. Proc Natl Acad Sci USA 103:11821–11827

    Article  PubMed  CAS  Google Scholar 

  35. Sannerud R, Marie M, Nizak C, Dale HA, Pernet-Gallay K, Perez F, Goud B, Saraste J (2006) Rab1 defines a novel pathway connecting the pre-Golgi intermediate compartment with the cell periphery. Mol Biol Cell 17:1514–1526

    Article  PubMed  CAS  Google Scholar 

  36. Kuroda TS, Fukuda M, Ariga H, Mikoshiba K (2002) The Slp homology domain of synaptotagmin-like proteins 1–4 and Slac2 functions as a novel Rab27A binding domain. J Biol Chem 277:9212–9218

    Article  PubMed  CAS  Google Scholar 

  37. Liu H, Liu ZQ, Chen CX, Magill S, Jiang Y, Liu YJ (2006) Inhibitory regulation of EGF receptor degradation by sorting nexin 5. Biochem Biophys Res Commun 342:537–546

    Article  PubMed  CAS  Google Scholar 

  38. Merino-Trigo A, Kerr MC, Houghton F, Lindberg A, Mitchell C, Teasdale RD, Gleeson PA (2004) Sorting nexin 5 is localized to a subdomain of the early endosomes and is recruited to the plasma membrane following EGF stimulation. J Cell Sci 117:6413–6424

    Article  PubMed  CAS  Google Scholar 

  39. Bruns JR, Ellis MA, Jeromin A, Weisz OA (2002) Multiple roles for phosphatidylinositol 4-kinase in biosynthetic transport in polarized Madin-Darby canine kidney cells. J Biol Chem 277:2012–2018

    Article  PubMed  CAS  Google Scholar 

  40. Wang YJ, Wang J, Sun HQ, Martinez M, Sun YX, Macia E, Kirchhausen T, Albanesi JP, Roth MG, Yin HL (2003) Phosphatidylinositol 4 phosphate regulates targeting of clathrin adaptor AP-1 complexes to the Golgi. Cell 114:299–310

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge the following organizations for funding: USDA/CSREES Special Research Grant # WY 2004/2005-06084, National Institutes of Health grant # CA87728, and the National Foundation for Cancer Research. We also wish to thank Ning Lin Yin, Ryan Black and Katie Grover of the USU Affymetrix Core Laboratory. We thank Graham Casey (Cleveland Clinic Foundation) and Kent Hunter (N.I.H.) for input. This is Utah Agricultural Experiment Station paper 7864.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daryll B. DeWald.

Electronic supplementary material

Appendix

Appendix

Table 1 Genes involved in immune response induced in 435/BRMS cells relative to 435 cells
Table 2 Genes involved in protein localization/secretion altered in 435/BRMS cells relative to 435 cells
Table 3 Genes involved in Golgi apparatus altered in 435/BRMS cells relative to 435 cells
Fig. A.1
figure 4

A flow chart of the analytical approach

Fig. A.2
figure 5

Heat map of significant genes within the biological process (BP) Gene Ontology (GO) term immune response. Color key is shown at top left and represents the log(2) expression intensity for the average of the probe sets for the specified gene determined to be significantly (adj. P-value <0.05) altered in our data. Cyan color indicates lower relative expression and magenta indicates higher relative expression. If more than one probe set for a given gene was determined to be significant, they were averaged together prior to heat mapping and the number of probe sets is indicated in parentheses to the right of the gene symbol on the map. The presence or absence of fetal bovine serum (FBS) in the samples from each cell line is indicated at the bottom of the map

Fig. A.3
figure 6

Heat map of significant genes within the biological process (BP) Gene Ontology (GO) term protein localization. Color key is shown at top left and represents the log(2) expression intensity for the average of the probe sets for the specified gene determined to be significantly altered in our data. Cyan color indicates lower relative expression and magenta indicates higher relative expression. If more than one probe set for a given gene was determined to be significant, they were averaged together prior to heat mapping and the number of probe sets is indicated in parentheses to the right of the gene symbol on the map. The presence or absence of fetal bovine serum (FBS) in the samples from each cell line is indicated at the bottom of the map

Fig. A.4
figure 7

Heat map of significant genes within the cellular component (CC) Gene Ontology (GO) term Golgi apparatus. Color key is shown at top left and represents the log(2) expression intensity for the average of the probe sets for the specified gene determined to be significantly altered in our data. Cyan color indicates lower relative expression and magenta indicates higher relative expression. If more than one probe set for a given gene was determined to be significant, they were averaged together prior to heat mapping and the number of probe sets is indicated in parentheses to the right of the gene symbol on the map. The presence or absence of fetal bovine serum (FBS) in the samples from each cell line is indicated at the bottom of the map

Fig. A.5
figure 8

Quantitive real-time expression analyses confirm microarray data. Panel A: Quantitative real-time polymerase chain reaction analysis of select gene expression in nanograms of RNA from 435 and 435/BRMS1 cells grown in serum containing media. Standard deviation of each of the three replicates is shown. Panel B: Quantitative real-time polymerase chain reaction analysis of select gene expression with standard deviation in nanograms of RNA from 435 and 435/BRMS1 cells grown in serum-free media. Standard deviation of each of the three replicates is shown. Gene expression was determined relative to standards from both cell lines using the hypoxanthine guanine phosphoribosyl transferase gene

Rights and permissions

Reprints and permissions

About this article

Cite this article

Champine, P.J., Michaelson, J., Weimer, B.C. et al. Microarray analysis reveals potential mechanisms of BRMS1-mediated metastasis suppression. Clin Exp Metastasis 24, 551–565 (2007). https://doi.org/10.1007/s10585-007-9092-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10585-007-9092-8

Keywords

Navigation