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Application of microarray analyses to identify genes involved in radiation-induced fibrosis

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Background

Among breast cancer patients receiving ionizing radiation (IR) treatment, a subgroup shows adverse long-term effects in the normal tissue. Radiation-induced fibrosis (RIF) is one of the most serious complications, and risk of RIF is a dose-limiting factor in the treatment of breast cancer patients with IR. The mechanisms whereby IR induces RIF are not fully understood. However, several observations indicate that the variation in normal tissue sensitivity and the consequent risk of developing late morbidity may be genetically determined. The aim of this study was to obtain a comprehensive overview of the changes in gene expression after IR and to identify genes that can be used to predict risk of RIF, using microarray analyses.

Materials and methods

Normal fibroblasts were achieved from 41 patients treated with postmastectomy radiotherapy in Aarhus, Denmark, from 1978 to 1982 and subsequently evaluated in detail with regard to development of RIF. The fibroblasts were grown to early confluency before they received radiation. Total RNA was isolated both before and after radiation, labelled and hybridized to cDNA microarrays consisting of 15,000 cDNAs and ESTs [1]. Expression profiles were identified using hierarchical cluster analyses [2]. Statistically significant changes in gene expression were identified using significance analysis of microarrays (SAM), and predictive genes were identified using prediction analysis for microarrays (PAM) [3].

Results and conclusion

Microarray data were first analyzed in order to identify radiation-responsive genes. While several genes were involved in known IR response pathways such as cell cycling, proliferation and stress, a substantial fraction of the genes were involved in processes not previously associated with IR response. Of particular interest are genes involved in extracellular matrix composition. SAM analyses were also applied to identify genes in which the expression level correlated with the level of fibrosis. PAM analyses identified a limited set of predictive genes that may provide a basis for a diagnostic tool in the identification of patients with adverse responses to radiation, and to improve and optimize radiotherapy at the individual level.

References

  1. 1.

    Norwegian Microarray Consortium – A National FUGE Platform for DNA Microarray Technology. [http://www.mikromatrise.no]

  2. 2.

    Michael Eisen Laboratory, Lawrence Berkeley National Laboratory and University of California at Berkeley. [http://rana.lbl.gov/EisenSoftware.htm]

  3. 3.

    Health Research and Policy, and Statistics, Stanford University. [http://www-stat.stanford.edu/~tibs]

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Acknowledgements

Supported by the EMBIO, University of Oslo, Norway and the Danish Cancer Society.

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Rødningen, O., Alsner, J., Hastie, T. et al. Application of microarray analyses to identify genes involved in radiation-induced fibrosis. Breast Cancer Res 7, P4.21 (2005) doi:10.1186/bcr1151

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Keywords

  • Breast Cancer Patient
  • Microarray Analysis
  • Ionize Radiation
  • Hierarchical Cluster Analysis
  • cDNA Microarrays