Cluster Analytic Strategy for Identification of Metagenes Relevant for Prognosis of Node Negative Breast Cancer

  • Evgenia Freis
  • Silvia Selinski
  • Jan G. Hengstler
  • Katja Ickstadt
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Worldwide, breast cancer is the second leading cause of cancer deaths in women. To gain insight into the processes related to the course of the disease, human genetic data can be used to identify associations between gene expression and prognosis. Moreover, the expression data of groups of genes may be aggregated to metagenes that may be used for investigating complex diseases like breast cancer. Here we introduce a cluster analytic approach for identification of potentially relevant metagenes. In a first step of our approach we used gene expression patterns over time of erbB2 breast cancer MCF7 cell lines to obtain promising sets of genes for a metagene calculation. For this purpose, two cluster analytic approaches for short time-series of gene expression data – DIB-C and STEM – were applied to identify gene clusters with similar expression patterns. Among these we next focussed on groups of genes with transcription factor (TF) binding site enrichment or associated with a GO group. These gene clusters were then used to calculate metagenes of the gene expression data of 766 breast cancer patients from three breast cancer studies. In the last step of our approach Cox models were applied to determine the effect of the metagenes on the prognosis. Using this strategy we identified new metagenes that were associated with metastasis-free survival patients.


Gene Ontology Node Negative Breast Cancer Model Profile Node Negative Breast Cancer Patient Gene Expression Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank Ulrike Krahn, Marcus Schmidt, Mathias Gehrmann, Matthias Hermes, Lindsey Maccoux, Jonathan West, and Holger Schwender for collaboration and numerous helpful discussions.


  1. Dortet-Bernadet JL, Wicker N (2008) Model-based clustering on the unit sphere with an illustration using gene expression profiles. Biostatistics 9(1):66–80zbMATHCrossRefGoogle Scholar
  2. Elkon R, Linhart C, Sharan R, Shamir R, Shiloh Y (2003) Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome Res 13:773–780CrossRefGoogle Scholar
  3. Ernst J, Nau GJ, Bar-Joseph Z (2005) Clustering short time series gene expression data. Bioinformatics 21(1):159–168CrossRefGoogle Scholar
  4. Freis E, Selinski S, Weibert B, Krahn U, Schmidt M, Gehrmann M, Hermes M, Maccoux L, West J, Schwender H, Rahnenfhrer J, Hengstler J, Ickstadt K (2009) Effects of metagene calculation on survival: An integrative approach using cluster and promoter analysis. In: Sixth International Workshop on Computational Systems Biology, Tampere, Finland, TICSP series 48, pp 47–50Google Scholar
  5. Glahn F, Schmidt-Heck W, Zellmer S, Guthke R, Wiese J, Golka K, Hergenroder R, Degen GH, Lehmann T, Hermes M, Schormann W, Brulport M, Bauer A, Bedawy E, Gebhardt R, Hengstler JG, Foth H (2008) Cadmium, cobalt and lead cause stress response, cell cycle deregulation and increased steroid as well as xenobiotic metabolism in primary normal human bronchial epithelial cells which is coordinated by at least nine transcription factors. Arch Toxicol 82:513–24CrossRefGoogle Scholar
  6. Hermes M (2007) Konditionale Expression von Her2/NeuT: Einfluss auf die Zell- und Tumorenentwicklung. PhD thesis, University of LeipzigGoogle Scholar
  7. Kim J, Kim JH (2007) Difference-based clustering of short time-course microarray data with replicates. Bioinformatics 8:253Google Scholar
  8. Krahn U (2008) Identifikation von Clustern in Gene-Expressions-Zeitreihen zur Analyse der Zellentwicklung. Diploma thesis, TU DortmundGoogle Scholar
  9. Petry IB, Fieber E, Schmidt M, Gehrmann M, Gebhard S, Hermes M, Schormann W, Selinski S, Freis E, Schwender H, Brulport M, Ickstadt K, Rahnenfuhrer J, Maccoux L, West J, Kolbl H, Schuler M, Hengstler JG (2010) ERBB2 induces an antiapoptotic expression pattern of Bcl-2 family members in node-negative breast cancer. Clin Cancer Res 16(2):451–460CrossRefGoogle Scholar
  10. R Development Core Team (2010) R: A language and environment for statistical computing. Vienna, Austria, URL
  11. Rody A, Holtrich U, Pusztai L, Liedtke C, Gaetje R, Ruckhaeberle E, Solbach C, Hanker L, Ahr A, Metzler D, Engels K, Karn T, Kaufmann M (2009) T-cell metagene predicts a favourable prognosis in estrogen receptor negative and HER2 positive breast cancers. Breast Cancer Res 11:R15CrossRefGoogle Scholar
  12. Schmidt M, Bohm D, von Torne C, Steiner E, Puhl A, Pilch H, Lehr HA, Hengstler JG, Kolbl H, Gehrmann M (2008) The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res 68:5405–5413CrossRefGoogle Scholar
  13. Shamir R, Maron-Katz A, Tanay A, Linhart C, Steinfeld I, Sharan R, Shiloh Y, Elkon R (2005) EXPANDER - an integrative program suite for microarray data analysis. BMC Bioinformatics 6:232CrossRefGoogle Scholar
  14. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL (1987) Human breast cancer: Correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235:177–182CrossRefGoogle Scholar
  15. Tanay A (2005) Computational analysis of transcriptional programs: Function and evolution. PhD thesis, Tel Aviv UniversityGoogle Scholar
  16. Trost TM, Lausch EU, Fees SA, Schmitt S, Enklaar T, Reutzel D, Brixel LR, Schmidtke P, Maringer M, Schiffer IB, Heimerdinger CK, Hengstler JG, Fritz G, Bockamp EO, Prawitt D, Zabel BU, Spangenberg C (2005) Premature senescence is a primary fail-safe mechanism of ERBB2-driven tumorigenesis in breast carcinoma cells. Cancer Res 65:840–849Google Scholar
  17. Wang X, Wu M, Li Z, Chan C (2008) Short time-series microarray analysis: Methods and challenges. BMC Syst Biol 2:58zbMATHCrossRefGoogle Scholar
  18. Winter SC, Buffa FM, Silva P, Miller C, Valentine HR, Turley H, Shah KA, Cox GJ, Corbridge RJ, Homer JJ, Musgrove B, Slevin N, Sloan P, Price P, West CM, Harris AL (2007) Relation of a hypoxia metagene derived from head and neck cancer to prognosis of multiple cancers. Cancer Res 67(7):3441–3449CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Evgenia Freis
    • 1
    • 2
  • Silvia Selinski
    • 2
  • Jan G. Hengstler
    • 2
  • Katja Ickstadt
    • 1
  1. 1.Department of StatisticsDortmund University of TechnologyDortmundGermany
  2. 2.Leibniz Research Centre for Working Environment and Human Factors (IfADo)Dortmund University of TechnologyDortmundGermany

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