Presentation Information
[C12-01]Network architectures underpinning signalling rebound-mediated adaptive drug resistance in cancer treatment
*Lan Khuyen Nguyen1,2, Karina Rios2, Milad Ghomlaghi2, Sung-young Shin1,2 (1. Computational Systems Oncology Program, SAiGENCI, The University of Adelaide (Australia), 2. Biomedicine Discovery Institute, Monash University (Australia))
Keywords:
biological network,network structure,adaptive drug resistance,mathematical modelling and simulation,cancer treatment
Targeted therapies are a significant advancement in cancer treatment, disrupting crucial molecules in cell proliferation. However, drug resistance limits their long-term effectiveness. Adaptive resistance, where cancer cells rapidly evolve to evade therapy through complex signaling networks, poses a major challenge to effective treatment. This study employs a multidisciplinary approach, combining advanced mathematical modeling and introducing NetScan, an innovative web-based tool, to analyze network architectures that contribute to adaptive resistance. We use methods like ordinary differential equations-based modeling, statistical analysis, and hierarchical clustering, validated by biological experiments. Our comprehensive computational simulations and analysis of over 16,000 network structures reveal diverse network patterns that drive adaptive drug resistance, challenging traditional views that focus mainly on feedback loops. We also identify networks that cause hyper-activation of target proteins, potentially worsening cancer progression. NetScan, our new tool, enables the real-time identification and visualization of these critical network structures within cellular signaling networks. Validation with high-throughput experimental data confirms that proteins forming specific networks are likely to develop resistance. In conclusion, our findings enhance understanding of network-mediated drug resistance in cancer, offering new directions for developing more effective treatments.