Predicting Cancer’s Next Move: Integrative Models of Signalling and Resistance
Dr. Lan Nguyen
South Australian immunoGENomics Cancer Institute (SAiGENCI), the University of Adelaide
[Abstract]
The clinical efficacy of targeted cancer therapies is profoundly limited by adaptive resistance, a rapid, dynamic rewiring of cellular signalling networks that bypasses therapeutic intervention. Predicting and overcoming this complex network-level behaviour remains a central challenge in oncology. Our intuition about linear pathways often fails to anticipate these non-obvious responses.
To address this challenge, we have developed an integrative research program combining mechanistic modelling and experimental validation in an iterative cycle. We construct predictive, dynamic models of oncogenic signalling networks, calibrated with quantitative proteomics and functional data, to simulate drug perturbations and uncover hidden vulnerabilities. Applying this approach across a range of cancers, including PI3K-driven and FGFR-driven breast cancer, we have identified diverse mechanisms of adaptive resistance to targeted therapy and predicted synergistic drug combinations to overcome them. Notably, in FGFR4-driven cancers, our model explained the paradoxical rebound of AKT signalling and correctly predicted that co-targeting AKT itself – not its upstream activator PI3K – was the superior synergistic strategy in triple-negative breast cancer. We further demonstrated the context-dependency of this rewiring, predicting and validating an ERK-mediated escape route in liver cancer.
Beyond application to therapy, our modelling framework has uncovered new biological insights. We identified a core set of resistance-prone network motifs — such as coupled negative feedback and incoherent feed-forward loops — that predispose signalling systems to rebound behaviour. Our web-based tool, NetScan, now enables prospective identification of these motifs in human signalling networks.
Together, these findings highlight the value of network-based models in revealing hidden logic in cellular signalling and provide a path toward designing personalised and effective therapeutic strategies.
Date： July 14, 2025 (Mon.) 16:00 – 17:00 Lab-only seminar