A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer - Fig 7

<p>(a) PathEFF_MC index for each simulated treatment in every pathway; (b) DugEFF(D) and the related noDrugEFF(D) for each drug combination; (c) <i>EFFECT</i> index for each simulated drug administration</p

Collection of data sources used for matrix tri-factorization, their size and number of edges.

<p>Collection of data sources used for matrix tri-factorization, their size and number of edges.</p

Network constraints to select TP nodes.

<p>In Fig 4(a) hubs are highlighted in pink. Note that these nodes are discarded as potential TPs. In Fig 4(b) orange nodes correspond to the bridging nodes, while in Fig 4(c) druggable nodes are depicted in dark green. The node size is proportional to its degree (i.e. number of neighbors).</p

Conversion table of KEGG associations into Boolean rules.

<p>Conversion table of KEGG associations into Boolean rules.</p

List of network Target Proteins TP.

<p>The column Freq. reports the protein frequency in the significant triplets.</p

Evaluation of proliferation rate of TNBC cells (MDA-MB-231) and luminal-like breast cancer cells (MCF7).

<p>Evaluation of proliferation rate of TNBC cells (MDA-MB-231) and luminal-like breast cancer cells (MCF7).</p

Overview of the proposed approach.

<p>(1) A PPI network is constructed starting from a list of disease proteins (DPs); then a list of target candidates (TPs) for drug synergy is obtained based on topological network properties; (2) A score function, called Topolgical Score of Drug Synergy (TSDS), assigns a score to each combination of TPs allowing the selection of significant multi-target combinations; (3) TP combinations are further augmented through the application of a data fusion approach. Here, the integration of several data sources [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162407#pone.0162407.ref026" target="_blank">26</a>] allows to obtain a list of known and predicted drug-target interactions; (4) The biological pathways related to disease progression are extracted; the pathways are represented with Boolean Networks (BNs); (5) BNs are simulated taking into account drug activities to understand biological pathways alterations through different pharmaceutical interventions. Finally, <i>in vitro</i> studies to validate the ability of the method to propose potential therapies can be carried on taking into account the results obtained from the previous phases.</p

Known and predicted drugs associated with significant TP nodes.

<p>Known and predicted drugs associated with significant TP nodes.</p

TNBC PPI Network.

<p>In the network the 43 DP seed nodes are highlighted in red while the 33 TP nodes are depicted by blue diamonds. The node size depends on the Bridging Centrality values as shown in the graph below the figure.</p

Boolean Network of the Jak-STAT signalling pathway.

<p>Boolean Network of the Jak-STAT signalling pathway.</p