Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning

Kalifa Manjang, Shailesh Tripathi, Olli Yli-Harja, Matthias Dehmer, Galina Glazko, Frank Emmert-Streib

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.

Original languageEnglish
Article number156
Pages (from-to)156
JournalScientific Reports
Issue number1
Publication statusPublished - Dec 2021


  • Biomarkers, Tumor/genetics
  • Breast Neoplasms/diagnosis
  • Female
  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Prognosis
  • Transcriptome


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