Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

Abstract

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model's components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results.

Keywords: cancer; computational modeling; in silico perturbation analysis; signal transduction; therapeutic targets.

Figure 1

Overview of the method used to assess influential components in the model.

Figure 2

Comparison of the most influential components across simulated environmental conditions. (A) Inactivating perturbations, (B) activating perturbations.

Figure 3

Enriched biological processes in the most influential components under environmental conditions, and inactivating perturbations. (A) Death (B) growth (C) motility and (D) quiescence.

Figure 4

Enriched biological processes in the most influential components under environmental conditions, and activating perturbations. (A) Death (B) growth (C) motility and (D) quiescence.

Figure 5

Distribution of essential genes in the most influential components. X-axis = environmental conditions, Y-axis = ratio of essential genes in total selected most or least influential components in (A) most influential vs. least influential components under activating perturbations, (B) most influential vs. least influential components under inactivating perturbations, (C) essential genes in most influential under inactivating vs. activating perturbations, (D) essential genes in least influential components under inactivating vs. activating perturbations.

Figure 6

Distribution of druggable proteins within the most influential vs. least influential components. (A) Inactivating perturbations, (B) activating perturbations. X-axis = environmental conditions, Y-axis = ratio of druggable proteins in total most or least influential components.

Figure 7

Visualization of the most affected components (KST value = 1) as a result of perturbing the most influential druggable components. (A) Inactivating perturbations, (B) activating perturbations. Orange colored eclipeses = most influential druggable components; squares = affected components; orange colored squares = affected druggable components; components with blue borders = experimentally found to be associated with cancer.

Figure 8

Visualization of the upstream components affecting the most influential components. (A) Inactivating perturbations, (B) activating perturbations. Gray colored nodes = the most influential components, and white colored nodes = not most influential components. The directions of arrows are from the source (upstream component) to the target (most influential components).

Figure 9

The regulatory circuit connecting IP3R1 and PI3K and downstream components. Edges with arrow = activation. Edges with oval end = inhibition.