Application of microarrays for the prediction of therapy response in breast cancer

Balazs Györffy, Pawel Surowiak, Hermann Lage

Research output: Contribution to journalReview article

2 Citations (Scopus)


Single genes, which can be used to predict response to therapy in breast cancer, including estrogen receptor (ER), HER-2, metallothionein and the ABC transporters are discussed. With the exception of the ER status, no single tumor marker has been shown to possess a sufficient predictive value to render it clinically useful. To achieve greater predictive power, multiple markers need to be examined and correlated with response to chemotherapy. With the advent of high-throughput quantification of gene expression, simultaneous assessment of thousands of genes is now possible, which allows identification of expression patterns in different breast cancers that might correlate with and, thereby, predict survival or response to treatment. Recent studies using microarrays to investigate survival prediction, chemotherapy resistance and therapy response are discussed. In vivo and in vitro experiments are discussed. Particular interest is given to anthracycline treatment, where in vitro drug resistance data may be useful for patient prognosis prediction. However, different microarray platforms can provide different results for the same experiment. A recommended statistical pathway is still not yet accepted. These problems have to be solved before future diagnostic applications using cDNA microarrays can be developed. In the near future, it can be expected that various microarray studies will be available to analyze hundreds to thousands of patients, selecting and validating predictive gene expression signatures.

Original languageEnglish
Pages (from-to)255-263
Number of pages9
JournalCancer Genomics and Proteomics
Issue number5
Publication statusPublished - Jan 1 2005


  • Breast cancer
  • Differential gene expression
  • Drug resistance
  • Response prediction
  • Review
  • cDNA microarrays

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Cancer Research

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