A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer

Abstract

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, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning 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. Our “in-silico” findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing.

In-Silico Methodologies for Cancer Multidrug Optimization

“…Francesca Vitali et al [39], applied network-based modelling to identify multi-target drugs for triple negative breast cancer. They constructed a network of disease proteins (DPs) and their interactors.…”

Exploring Wound-Healing Genomic Machinery with a Network-Based Approach

“…BC measures the local properties of the network, while BCoeff measures global ones. The BR of a node n was calculated as proposed by Vitali et al [34,35] following the equation: BR(n) = RWBC(n) × BCoeff(n) where RWBC is the random walk betweenness centrality [65] and the BCoeff is computed as: $\mathrm{BCoeff}\left(\mathrm{n}\right)=\frac{D{\left(n\right)}^{-1}}{{60\%true\sum }_{v\in N\left(n\right)}D{\left(v\right)}^{-1}}$ where D ( n ) is the degree of node n (i.e., the number of neighbors of n), and N ( n ) is the set of neighbors of node n .…”

Autologous micrograft accelerates endogenous wound healing response through ERK-induced cell migration

“…The PPI network was constructed using the repository STRING v.10.5 (20). We downloaded from STRING all the PPIs related to the mouse organism and, according with our previous works (42,43), we retained the PPIs based on database or experimental evidences and with STRING confidence score higher than 700. These interactions are considered the most reliable, for more details please refer to (42,43).…”

“…We downloaded from STRING all the PPIs related to the mouse organism and, according with our previous works (42,43), we retained the PPIs based on database or experimental evidences and with STRING confidence score higher than 700. These interactions are considered the most reliable, for more details please refer to (42,43). We constructed the network starting from the protein codified by the DEGs resulting from the previous RNA-Seq data related to AMG treatment at 5 hours.…”