State of the art in silico tools for the study of signaling pathways in cancer

Abstract

In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided.

Keywords: bioinformatics; cancer; networks; pathways; systems biology.

Figure 1

Biological networks represent various types of molecular interactions, including (1) enzyme catalysis, (2) the post-transcriptional control of gene expression by proteins, (3) the effect of metabolites on gene transcription mediated by a protein, (4) protein interactions, (5) the effect of a downstream metabolite on transcription, (6) feedback inhibition/activation of an enzyme by a downstream metabolite, and (7) the exchange of a metabolite outside of the system. The solid lines represent direct interactions, and the discontinuous lines represent possible interactions in tumor cells.

Figure 2

A map of the VEGF signaling pathway (as an example of an important target in cancer therapy) obtained from KEGG is shown [41]. The gene expression studies are used as an independent predictive method for the prognosis. In cancer genomics studies, tremendous effort has been devoted to pathway-based analysis. Pathway analysis is a promising tool to identify the mechanisms that underlie disease, the adaptive physiological compensatory responses and new avenues for investigation. Different pathways have different biological functions. Thus, studying each pathway separately is reasonable. Among the many pathways, only a few have been shown to have predictive power for the development of cancer. In this sense, KEGG could be a useful tool to identify genetic signatures [52].

Figure 3

An illustration of the multi-scale visualization of biological interactions using VisANT’s metagraph capability. VisANT [54] is an integrative software platform for the visualization, mining, analysis and modeling of biological networks. This software extends the applications of GO for network visualization, analysis and inference. The image shows the biological networks between the molecules that belong to the VEGF signaling pathway. (1) VEGF, (2) VEGFR2, (3) COX2 and (4) p33.

Figure 4

Cytoscape is an open-source bioinformatics software platform for visualizing molecular interaction networks and integrating these networks with other state data. Cytoscape was used to model the interaction networks for VEGF-A (also called vascular permeability factor (VPF). The software found 56 interaction networks at a pathway level, and one of these interaction networks, the HIF-2-α network, is shown with 40 nodes and 114 edges. The importance of this figure is to provide the reader understanding of the level of complexity to which we refer when we speak of interactions at the gene or protein level illustrating this by VEGF-A pathway. Each node represents a molecule that interacts with HIF-2-α; in this case 40 molecules interact with this important marker in different malignancies, through 114 edges.

Figure 5

The cancer pathways obtained from KEGG are shown [41]. The complexity of the carcinogenic mechanisms leads to heterogeneity in the molecular phenotypes, pathology, and prognosis of cancers. Systems biology approaches leverage the signature genes as a representation of the changes in the signaling pathways, instead of interpreting the relevance between each gene and the resulting phenotype. At the bottom right side of the figure is represented the complex system of communication that exists in the signaling pathways in cancer (KEGG map05200). Within this complex of roads we can find among others, the signaling pathways that characterize kidney cancer (KEGG map05211).

Figure 6

The challenge of understanding intracellular activity is being addressed by computational approaches, such as the Virtual Cell System. While this graphical representation of the VEGF network provides additional information not available by considering the individual pathways separately, it is still a vast simplification. The graphic is merely a static representation of several dynamic processes occurring concurrently with several intertwined feedback loops. The only way to effectively study the effect of either mutations or therapeutic interventions is to create a quantitative model of the network that integrates the dynamics of the individual pathways and their interconnections, which can be simulated on a computer. By representing aspects of an in silico cell, the model can then be used to explore a variety of questions.

Figure 7

The ability to extract meaningful data from ever-expanding databases is an important area of development in computational oncology. Specifically, the relationship between genes and cancer is being documented by data-mining from large databases. In this figure, the interaction network for the markers involved in neoplasms is shown. DisGeNET [90] is a plugin for Cytoscape to query and analyze a network representation of human gene-disease databases. This figure illustrates the importance of addressing problems, such as which are the genes annotated to prostate, sarcoma or retinoblastoma neoplasms, for example, in expert-curated databases.