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)

Abstract

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.

Notes

Acknowledgements

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.

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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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