Breast Cancer Research and Treatment

, Volume 128, Issue 2, pp 315–326 | Cite as

RERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in breast cancer: a marker of ER-positive luminal-like subtype

  • Hany Onsy Habashy
  • Desmond G. Powe
  • Enrico Glaab
  • Graham Ball
  • Inmaculada Spiteri
  • Natalio Krasnogor
  • Jonathan M. Garibaldi
  • Emad A. Rakha
  • Andrew R. Green
  • C. Caldas
  • Ian O. Ellis
Preclinical study


Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER− non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups.


Breast carcinoma RERG Gene expression Luminal Oestrogen receptor Immunohistochemistry 



We thank the ministry of higher education (Egypt) for funding HO Habashy and E Rakha. Funding (salaries and infrastructure) was provided by the University of Nottingham and Nottingham University Hospitals Trust. N Krasnogor and E Glaab would like to acknowledge funding by Marie Curie Early Stage Training Programme (MEST-CT-2004-007597).

Conflict of interest



  1. 1.
    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 406:747–752PubMedCrossRefGoogle Scholar
  2. 2.
    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. PNAS 98(19):10869–10874PubMedCrossRefGoogle Scholar
  3. 3.
    Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100(18):10393–10398PubMedCrossRefGoogle Scholar
  4. 4.
    Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S et al (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100(14):8418–8423PubMedCrossRefGoogle Scholar
  5. 5.
    Habashy H, Powe D, Rakha E, Ball G, Macmillan R, Green A, Ellis I (2010) The prognostic significance of PELP1 expression in invasive breast cancer with emphasis on the ER-positive luminal-like subtype. Breast Cancer Res Treat 120(3):603–612PubMedCrossRefGoogle Scholar
  6. 6.
    Habashy H, Powe D, Staka C, Rakha E, Ball G, Green A, Aleskandarany M, Paish E, Douglas Macmillan R, Nicholson R et al (2010) Transferrin receptor (CD71) is a marker of poor prognosis in breast cancer and can predict response to tamoxifen. Breast Cancer Res Treat 119(2):283–293PubMedCrossRefGoogle Scholar
  7. 7.
    Habashy HO, Powe DG, Rakha EA, Ball G, Paish C, Gee J, Nicholson RI, Ellis IO (2008) Forkhead-box A1 (FOXA1) expression in breast cancer and its prognostic significance. Eur J Cancer 44(11):1541–1551PubMedCrossRefGoogle Scholar
  8. 8.
    Lancashire LJ, Rees RC, Ball GR (2008) Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach. Artif Intell Med 43(2):99–111PubMedCrossRefGoogle Scholar
  9. 9.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536CrossRefGoogle Scholar
  10. 10.
    Lancashire L, Powe D, Reis-Filho J, Rakha E, Lemetre C, Weigelt B, Abdel-Fatah T, Green A, Mukta R, Blamey R et al (2010) A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks. Breast Cancer Res Treat 120(1):83–93PubMedCrossRefGoogle Scholar
  11. 11.
    Finlin BS, Gau CL, Murphy GA, Shao HP, Kimel T, Seitz RS, Chiu YF, Botstein D, Brown PO, Tamanoi F et al (2001) RERG is a novel ras-related, estrogen-regulated and growth-inhibitory gene in breast cancer. J Biol Chem 276(45):42259–42267PubMedCrossRefGoogle Scholar
  12. 12.
    Chin S, Teschendorff A, Marioni J, Wang Y, Barbosa-Morais N, Thorne N, Costa J, Pinder S, van de Wiel M, Green A et al (2007) High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer. Genome Biol 8(10):R215PubMedCrossRefGoogle Scholar
  13. 13.
    Blenkiron C, Goldstein L, Thorne N, Spiteri I, Chin S-F, Dunning M, Barbosa-Morais N, Teschendorff A, Green A, Ellis I et al (2007) MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 8(10):R214PubMedCrossRefGoogle Scholar
  14. 14.
    Zhang H, Rakha E, Ball G, Spiteri I, Aleskandarany M, Paish E, Powe D, Macmillan R, Caldas C, Ellis I et al (2010) The proteins FABP7 and OATP2 are associated with the basal phenotype and patient outcome in human breast cancer. Breast Cancer Res Treat 121(1):41–51PubMedCrossRefGoogle Scholar
  15. 15.
    Abd El-Rehim DM, Ball G, Pinder SE, Rakha E, Paish C, Robertson JFR, Macmillan D, Blamey RW, Ellis IO (2005) High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. Int J Cancer 116(3):340–350PubMedCrossRefGoogle Scholar
  16. 16.
    Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3Google Scholar
  17. 17.
    Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99(10):6567–6572PubMedCrossRefGoogle Scholar
  18. 18.
    Glaab E, Garibaldi J, Krasnogor N (2009) ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization. BMC Bioinformatics 10(1):358PubMedCrossRefGoogle Scholar
  19. 19.
    Ellis IO, Galea M, Broughton N, Locker A, Blamey RW, Elston CW (1992) Pathological prognostic factors in breast-cancer. 2. Histological type—relationship with survival in a large study with long-term follow-up. Histopathology 20(6):479–489PubMedCrossRefGoogle Scholar
  20. 20.
    Elston CW, Ellis IO (1991) Pathological prognostic factors in breast-cancer. 1. The value of histological grade in breast-cancer—experience from a large study with long-term follow-up. Histopathology 19(5):403–410PubMedCrossRefGoogle Scholar
  21. 21.
    Galea MH, Blamey RW, Elston CE, Ellis IO (1992) The Nottingham prognostic index in primary breast-cancer. Breast Cancer Res Treat 22(3):207–219PubMedCrossRefGoogle Scholar
  22. 22.
    Madjd Z, Pinder SE, Paish C, Ellis IO, Carmichael J, Durrant LG (2003) Loss of CD59 expression in breast tumours correlates with poor survival. J Pathol 200(5):633–639PubMedCrossRefGoogle Scholar
  23. 23.
    Rakha EA, El-Sayed ME, Lee AH, Elston CW, Grainge MJ, Hodi Z, Blamey RW, Ellis IO (2008) Prognostic significance of Nottingham histologic grade in invasive breast carcinoma. J Clin Oncol 26(19):3153–3158PubMedCrossRefGoogle Scholar
  24. 24.
    Galea MH, Blamey RW, Elston CE, Ellis IO (1992) The Nottingham prognostic index in primary breast cancer. Breast Cancer Res Treat 22(3):207–219PubMedCrossRefGoogle Scholar
  25. 25.
    Rakha EA, Elsheikh SE, Aleskandarany MA, Habashi HO, Green AR, Powe DG, El-Sayed ME, Benhasouna A, Brunet JS, Akslen LA et al (2009) Triple-negative breast cancer: distinguishing between basal and nonbasal subtypes. Clin Cancer Res 15(7):2302–2310PubMedCrossRefGoogle Scholar
  26. 26.
    Aleskandarany MA, Green AR, Rakha EA, Mohammed RA, Elsheikh SE, Powe DG, Paish EC, Macmillan RD, Chan S, Ahmed SI et al (2010) Growth fraction as a predictor of response to chemotherapy in node-negative breast cancer. Int J Cancer 126(7):1761–1769PubMedGoogle Scholar
  27. 27.
    Abd El-Rehim DM, Pinder SE, Paish CE, Bell JA, Rampaul RS, Blamey RW, Robertson JFR, Nicholson RI, Ellis IO (2004) Expression and co-expression of the members of the epidermal growth factor receptor (EGFR) family in invasive breast carcinoma. Br J Cancer 91(8):1532–1542PubMedCrossRefGoogle Scholar
  28. 28.
    McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM (2006) REporting recommendations for tumor MARKer prognostic studies (REMARK). Breast Cancer Res Treat 100(2):229–235PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Hany Onsy Habashy
    • 1
    • 2
  • Desmond G. Powe
    • 3
  • Enrico Glaab
    • 4
  • Graham Ball
    • 5
  • Inmaculada Spiteri
    • 6
  • Natalio Krasnogor
    • 4
  • Jonathan M. Garibaldi
    • 4
  • Emad A. Rakha
    • 1
  • Andrew R. Green
    • 1
  • C. Caldas
    • 6
  • Ian O. Ellis
    • 1
    • 7
  1. 1.Department of Pathology, School of Molecular Medical SciencesNottingham University Hospitals NHS Trust, University of NottinghamNottinghamUK
  2. 2.Department of Histopathology, Faculty of MedicineMansoura UniversityMansouraEgypt
  3. 3.Department of Cellular Pathology, Queen’s Medical CentreNottingham University Hospitals NHS TrustNottinghamUK
  4. 4.School of Computer ScienceUniversity of NottinghamNottinghamUK
  5. 5.John Van Geest Cancer Research Centre, School of Science and TechnologyNottingham Trent UniversityNottinghamUK
  6. 6.Department of OncologyUniversity of Cambridge, Cancer Research UK Cambridge Research InstituteCambridgeUK
  7. 7.Department of Histopathology, Molecular Medical SciencesNottingham City Hospital NHS Trust, University of NottinghamNottinghamUK

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