Predicting the risk of metastasis in triple negative breast cancer patients
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. Researchers from the Center for Complexity and Biosystems at the University of Milan lead by Caterina La Porta introduced and validated ARIADNE, a general algorithmic strategy to assess the risk of metastasis in patients with triple negative breast cancer. The results are published in Cell Systems.
Triple negative breast cancer is a subtype of breast cancer with poorer prognosis with respect to the other subtypes and no specific therapeutic strategy. It is therefore of great importance to identify as soon as possible the patients with the highest risk to develop metastasis and those with lower risk ARIADNE is able to identify patients whose tumor cells are more aggressive since they are in a hybrid state, in between the highly motile “mesenchymal” state and the more tissue-like “epithelial” state. “These hybrid cells are expected to be more aggressive and drive metastasis,” explains Caterina La Porta who coordinated the study “but it is not easy to identify them from the biopsy of a patient.” ARIADNE uses a sophisticated algorithm that maps gene expression data obtained from the biopsy into the states of a computational model epithelial- mesenchymal network. “Using this mapping, it was possible to stratify patients according to their prognosis, as we showed by validating the strategy with three independent cohorts of triple negative breast cancer patients” concludes Caterina La Porta.
ARIADNE will be commercialized by Complexdata S.R.L a spinoff of the University of Milan. Complexdata cleared all the steps to obtain the CE Mark for ARIADNE as a type 2 medical device. ARIADNE 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.