Classification of triple-negative breast cancers through a Boolean network model of the epithelial-mesenchymal transition

Cell Syst. 2021 May 19;12(5):457-462.e4. doi: 10.1016/j.cels.2021.04.007. Epub 2021 May 6.

Abstract

Predicting the metastasis risk in patients with a primary breast cancer tumor is of fundamental importance to decide the best therapeutic strategy in the framework of personalized medicine. Here, we present ARIADNE, a general algorithmic strategy to assess the risk of metastasis from transcriptomic data of patients with triple-negative breast cancer, a subtype of breast cancer with poorer prognosis with respect to the other subtypes. ARIADNE identifies hybrid epithelial/mesenchymal phenotypes by mapping gene expression data into the states of a Boolean network model of the epithelial-mesenchymal pathway. Using this mapping, it is possible to stratify patients according to their prognosis, as we show by validating the strategy with three independent cohorts of triple-negative breast cancer patients. Our strategy provides a prognostic tool that could be applied to other biologically relevant pathways, in order to estimate the metastatic risk for other breast cancer subtypes or other tumor types. A record of this paper's transparent peer review process is included in the supplemental information.

Keywords: Boolean network; Triple-negative breast cancer; epithelial-mesenchymal transition; metastasis; personalized medicine; tumor aggressiveness.

MeSH terms

  • Epithelial-Mesenchymal Transition / genetics
  • Humans
  • Peer Review
  • Triple Negative Breast Neoplasms* / genetics