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

Research output: Contribution to journalArticle

49 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 <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71-9.48; P = 0.001) and 4.08 (95% CI 1.79-9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.

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

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures'. Together they form a unique fingerprint.

  • 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