Network-based approaches for multi-target therapies: Application to Triple Negative Breast Cancer
Francesca Vitali, PhD
Postdoctoral Research Fellow, The University of Pavia
Thursday, May 5th
2:00 pm | Kiewit Auditorium
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The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, the construction of biological networks has enabled substantial advances in understanding the phenomena underlying complex diseases such as cancer. We developed a novel bioinformatics pipeline to identify promising multi-target drugs by integrating available knowledge through network-based models. Particularly, given a certain disease, a Protein-Protein Interaction (PPI) network specific for the pathology is built by combining different data and knowledge sources. Next, the application of a score function allows the selection of best-ranked target combinations. A list of drug candidates is subsequently extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is then exploited to identify the most promising candidates for the execution of in vitro studies.
We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. The findings have been confirmed by a number of in vitro experiments, whose results demonstrated the method ability to filter candidates for drug repurposing.