Exploring Multi-Target Therapeutic Strategies for Glioblastoma via Endogenous Network Modeling

Abstract

Medical treatment of glioblastoma presents a significant challenge. A conventional medication has limited effectiveness, and a single-target therapy is usually effective only in the early stage of the treatment. Recently, there has been increasing focus on multi-target therapies, but the vast range of possible combinations makes clinical experimentation and implementation difficult. From the perspective of systems biology, this study conducted simulations for multi-target glioblastoma therapy based on dynamic analysis of previously established endogenous networks, validated with glioblastoma single-cell RNA sequencing data. Several potentially effective target combinations were identified. The findings also highlight the necessity of multi-target rather than single-target intervention strategies in cancer treatment, as well as the promise in clinical applications and personalized therapies.

Keywords: endogenous network theory; glioma; landscape; multi-target therapy; system biology.

Figure 1

Network model and an example of the dynamics equations. (A) The core endogenous network for glioma comprises 25 nodes with 75 edges. A red arrow indicates an activation, while a blue T-shape sets an inhibition on the target node. (B) The dynamic equations for Akt are illustrated as an example. Here, x represents the concentration/activity of the core node, h is the Hill coefficient, k is the inverse of the apparent dissociation constant, and η denotes the degradation rate of the node.

Figure 2

Visualization of the simulation results. (A) Heatmap of simulation with no intervention combined with scRNA-seq data. (B) scRNA-seq data selected for validation. Source: modified from www.gbmseq.org (C–Q). Distributions of modeling results combined with scRNA-seq data with principal component analysis. The title of each block represents the intervention. (C) is from a non-intervention simulation, while (D,E,G,H) are the results of single-target interventions. (F,I) demonstrate the results of dual-target interventions. (J–Q) illustrate the outcome of triple-target interventions. In block (C), the boxed area represents the tumor states obtained from theoretical calculations. In block (C–Q), the numbers indicate the proportion of tumor states among all stable states. In the (A,C–Q) blocks of this figure, AS represents the astrocytes, OPC stands for oligodendrocyte progenitor cells, OG stands for the oligodendrocytes, and “neoplasm” represents the tumor cells. These results came from published experimental data. The “ent” represents the results from the theoretical simulation.