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

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.

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Déclaration de conflit d'intérêts

Declaration of interests The authors declare the following competing interests: Complexdata S.R.L. has filed a patent application related to the present work. Inventors: F.F.-C., S.Z., and C.A.M.L.P.; patent status: pending. Date of application: 13/12/2019. Application number: 102019000023946. The patent concerns a method to screen breast cancer patients using transcriptomic data and Boolean networks. F.F.-C., S.Z., and C.A.M.L.P. each hold a 9.5% share of Complexdata S.R.L.