Tumor Biology

, Volume 36, Issue 4, pp 2473–2480 | Cite as

Diagnostic significance of alternative splice variants of REST and DOPEY1 in the peripheral blood of patients with breast cancer

  • Ave Kris Lend
  • Anna Kazantseva
  • Anri Kivil
  • Vahur Valvere
  • Kaia Palm
Research Article


Changes in alternative splicing have been linked to cancer development. We hypothesized that changes occurring in tumor tissue can also be detected in the peripheral blood of cancer patients leading to discovery of blood biomarkers of breast cancer. Alternative splicing profiles of 94 genes were examined in cancerous breast tissue. Discriminating splice variants were analyzed in the peripheral blood of early stage (BCI/II) (stage I–II; n = 26), neoadjuvant receiving locally advanced breast cancer patients (LABC) (stage IIb–IIIa, b; n = 10) and healthy volunteers (n = 26) using qRT-PCR analysis. Changes in marker expression during neoadjuvant therapy were analyzed at 15 timepoints. High expression of REST-N50, the alternatively spliced variant of REST, was detected in the blood of LABC patients but not in BCI/II and healthy controls (p = 0.0032 and p = 0.0029, respectively). Expression levels of DOPEY1v2, the alternative splice variant of DOPEY1, in the blood could differentiate cancer from healthy controls (p = 0.024) and discriminate between patient groups (BCI/II vs LABC, p = 0.002). Positive response to neoadjuvant therapy of REST-N50-positive LABC patients correlated with a decrease in REST-N50 levels (p < 0.0001). Assessment of REST-N50 and DOPEY1v2 may prove useful in diagnostic blood tests of breast cancer. REST-N50 shows a high potential as a blood biomarker for evaluating the effectiveness of therapy in the neoadjuvant setting.


Alternative splicing Locally advanced breast cancer Neoadjuvant therapy Blood REST DOPEY1 



This study was supported by Protobios’s grants from the Enterprise of Estonia and baseline financing from the Estonian Ministry of Education and Research. We are grateful to Dr. Jõeste and NEMC for the tissue samples. We thank K.J.M. Rand for the work with in silico database, primer design, and cloning; Dr. Pruunsild and Prof. Timmusk for the resources provided; and K. Taal and H. Verev for the technical help in sample handling.

Conflicts of interest


Supplementary material

13277_2014_2860_MOESM1_ESM.pdf (108 kb)
ESM 1 (PDF 107 kb)


  1. 1.
    Herschkowitz JI, Simin K, Weigman VJ, Mikaelian I, Usary J, Hu Z, et al. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol. 2007;8:R76. doi: 10.1186/gb-2007-8-5-r76.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006;7:96. doi: 10.1186/1471-2164-7-96.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–52. doi: 10.1038/35021093.CrossRefPubMedGoogle Scholar
  4. 4.
    Sharma P, Sahni NS, Tibshirani R, Skaane P, Urdal P, Berghagen H, et al. Early detection of breast cancer based on gene-expression patterns in peripheral blood cells. Breast Cancer Res. 2005;7:R634–44. doi: 10.1186/bcr1203.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Aarøe J, Lindahl T, Dumeaux V, Saebø S, Tobin D, Hagen N, et al. Gene expression profiling of peripheral blood cells for early detection of breast cancer. Breast Cancer Res. 2010;12:R7. doi: 10.1186/bcr2472.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Tobin D, Karlsson M, Hagen N, Børresen-Dale A, Sharma P. Use of the blood based, 96-assay set for breast cancer detection. Poster presented at the 21st meeting of the EACR 26–29 June 2010 Oslo, Norway. 2010.Google Scholar
  7. 7.
    Venables JP. Aberrant and alternative splicing in cancer. Cancer Res. 2004;64:7647–54. doi: 10.1158/0008-5472.CAN-04-1910.CrossRefPubMedGoogle Scholar
  8. 8.
    Faustino NA, Cooper TA. Pre-mRNA splicing and human disease. Genes Dev. 2003;17:419–37. doi: 10.1101/gad.1048803.CrossRefPubMedGoogle Scholar
  9. 9.
    Venables JP, Klinck R, Bramard A, Inkel L, Dufresne-Martin G, Koh C, et al. Identification of alternative splicing markers for breast cancer. Cancer Res. 2008;68:9525–31. doi: 10.1158/0008-5472.CAN-08-1769.CrossRefPubMedGoogle Scholar
  10. 10.
    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(−delta delta C(T)) method. Methods. 2001;25:402–8. doi: 10.1006/meth.2001.1262.CrossRefPubMedGoogle Scholar
  11. 11.
    Wagoner MP, Gunsalus KTW, Schoenike B, Richardson AL, Friedl A, Roopra A. The transcription factor REST is lost in aggressive breast cancer. PLoS Genet. 2010;6:e1000979. doi: 10.1371/journal.pgen.1000979.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Coulson JM, Edgson JL, Woll PJ, Quinn JP. A splice variant of the neuron-restrictive silencer factor repressor is expressed in small cell lung cancer: a potential role in derepression of neuroendocrine genes and a useful clinical marker. Cancer Res. 2000;60:1840–4.PubMedGoogle Scholar
  13. 13.
    Palm K, Metsis M, Timmusk T. Neuron-specific splicing of zinc finger transcription factor REST/NRSF/XBR is frequent in neuroblastomas and conserved in human, mouse and rat. Brain Res Mol Brain Res. 1999;72:30–9.CrossRefPubMedGoogle Scholar
  14. 14.
    Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004;101:6062–7. doi: 10.1073/pnas.0400782101.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Pachmann K, Dengler R, Lobodasch K, Fröhlich F, Kroll T, Rengsberger M, et al. An increase in cell number at completion of therapy may develop as an indicator of early relapse: quantification of circulating epithelial tumor cells (CETC) for monitoring of adjuvant therapy in breast cancer. J Cancer Res Clin Oncol. 2008;134:59–65. doi: 10.1007/s00432-007-0248-3.CrossRefPubMedGoogle Scholar
  16. 16.
    Liew C, Ma J, Tang H, Zheng R, Dempsey AA. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J Lab Clin Med. 2006;147:126–32. doi: 10.1016/j.lab.2005.10.005.CrossRefPubMedGoogle Scholar
  17. 17.
    Kowalewska M, Chechlińska M, Markowicz S, Kober P, Nowak R. The relevance of RT-PCR detection of disseminated tumour cells is hampered by the expression of markers regarded as tumour-specific in activated lymphocytes. Eur J Cancer. 2006;42:2671–4. doi: 10.1016/j.ejca.2006.05.036.CrossRefPubMedGoogle Scholar
  18. 18.
    Gunsalus KTW, Wagoner MP, Meyer K, Potter WB, Schoenike B, Kim S, et al. Induction of the RNA regulator LIN28A is required for the growth and pathogenesis of RESTless breast tumors. Cancer Res. 2012;72:3207–16. doi: 10.1158/0008-5472.CAN-11-1639.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2014

Authors and Affiliations

  • Ave Kris Lend
    • 1
    • 2
  • Anna Kazantseva
    • 1
  • Anri Kivil
    • 1
    • 3
  • Vahur Valvere
    • 2
    • 4
  • Kaia Palm
    • 1
    • 2
    • 3
  1. 1.Protobios LLCTallinnEstonia
  2. 2.Competence Center for Cancer ResearchTallinnEstonia
  3. 3.The Department of Gene TechnologyTallinn University of TechnologyTallinnEstonia
  4. 4.Clinic of Hematology and OncologyNorth Estonia Medical CenterTallinnEstonia

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