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Visualization of Drug Target Interactions in the Contexts of Pathways and Networks With ReactomeFIViz


Visualization of Drug Target Interactions in the Contexts of Pathways and Networks With ReactomeFIViz

Aurora S Blucher et al. F1000Res.


The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

Keywords: Boolean network; Reactome; biological pathway; constrained fuzzy logic modeling; drug interaction visualization; functional interaction network; systems pharmacology; targeted therapy.

Conflict of interest statement

No competing interests were disclosed.


Figure 1.
Figure 1.. The ReactomeFIviz Cytoscape app enables pathway and network-based approaches in precision targeted therapies.
Using ReactomeFIViz, researchers can visualize drug-target interactions according to strength of supporting binding assay evidence, visualize drug-target interactions in the contexts of pathways and functional interaction networks, and perform Boolean Network modeling to investigate the impact of drugs on pathways.
Figure 2.
Figure 2.. Visualizing drug-target interaction evidence for FDA-approved drug sorafenib via a histogram of drug-target assay values categorized by assay types (KD, EC50, IC50, and Ki).
Sorafenib interacts with many targets, even when restricting to target interactions supported by binding assay evidence < 100 nM.
Figure 3.
Figure 3.. Drugs targeting KIT in the “SCF-KIT Signaling” pathway.
Drug-target interactions were fetched from the Cancer Targetome and supported with multiple assay types having values <= 100 nM.
Figure 4.
Figure 4.. Partial diagram of the “Signaling by SCF-KIT” pathway showing the feedback loop that generates the “p-STAT dimers” complex annotated in Reactome.
The entry point of the loop and “p-STAT dimers”, together with four reactions forming the loop, are highlighted in light blue. The reactions are labeled with names in the light-grey boxes. The target of drug sorafenib, KIT protein, is in the upstream of this loop and not shown here.
Figure 5.
Figure 5.. Constrained fuzzy logic simulation results for complex “pSTAT-dimers” in pathway “Signaling by SCF-KIT” from 4 simulations.
Panel A: Simulations conducted with the default setup provided by ReactomeFIVIz. Logic fuzzy values for two simulations, reference_1 and sorafenib_1, are almost the same except bottom values starting from step 18: 0.0 for reference_1 and 0.0024 for sorafenib_1 (see the insert for an example). Panel B: Same as in Panel A except the initial value of PRKCA was reduced from 1.0 to 0.5. Logic fuzzy values prior to time step 18 are the same for both reference and sorafenib simulations. The results were exported from ReactomeFIViz and plotted in Microsoft Excel.
Figure 6.
Figure 6.. The “Signaling by SCF-KIT” pathway with entities highlighted according to relative impact scores calculated from constrained fuzzy logic simulations.
A: Relative impact scores for sorafenib with PRKCA’s initial value equal to 1.0 as fully activated. B: Same as A except that the initial value of PRKCA was reduced from 1.0 to 0.5.
Figure 7.
Figure 7.. Functional interaction network for frequently mutated genes in TCGA ovarian cancer samples.
Only part of the FI network was shown here for drugs targeting protein products of genes in the network. Network nodes are genes mutated in at least 5 TCGA samples, where size of the node indicates the number of samples with the mutated gene. Black edges are FIs between genes, blue edges drug-target interactions with <= 100 nM supporting assay values, dashed edges predicted FIs, solid edges extracted from annotated FIs, -> activation or catalysis FIs, -| inhibition FI, - FIs extracted from complexes or inputs of reactions. EGFR, TNIK, PAK3 and sunitinib malate are highlighted in yellow.
Figure 8.
Figure 8.. An automatic scheme to convert Reactome pathways into Boolean networks by handling Reaction, Complex and EntitySet instances.
A: Convert a typical Reactome reaction into a set of Boolean relationships. B: Convert a complex into an AND relationship between complex subunits and the complex. C: Convert an EntitySet into an OR relationship between set members and the set.

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