Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Thomas Karn, Lajos Pusztai, Uwe Holtrich, Takayuki Iwamoto, Christine Y. Shiang, Marcus Schmidt, Volkmar Müller, Christine Solbach, Regine Gaetje, Lars Hanker, Andre Ahr, Cornelia Liedtke, Eugen Ruckhäberle, Manfred Kaufmann, Achim Rody

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43 Citations (Scopus)

Abstract

Background: Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes. Methodology/Principal Findings: We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases). Conclusions/Significance: Using two distinct false discovery rate thresholds, 25% and

Original languageEnglish
Article numbere28403
JournalPLoS One
Volume6
Issue number12
DOIs
Publication statusPublished - Dec 29 2011
Externally publishedYes

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ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Karn, T., Pusztai, L., Holtrich, U., Iwamoto, T., Shiang, C. Y., Schmidt, M., Müller, V., Solbach, C., Gaetje, R., Hanker, L., Ahr, A., Liedtke, C., Ruckhäberle, E., Kaufmann, M., & Rody, A. (2011). Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures. PLoS One, 6(12), [e28403]. https://doi.org/10.1371/journal.pone.0028403