Presentation Information
[SS22-05]Mathematical Modelling Meets Cancer Biology: New Insights into Overcoming Drug Resistance
*Sungyoung Shin1, Lan K Nguyen1,2 (1. The University of Adelaide (Australia), 2. Australian Research Council Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS) (Australia))
Keywords:
Mathematical Modelling,Systems Biology,Targeted Therapy,Cancer Signalling,Personalized Medicine
Mathematical modelling has played a pivotal role in advancing cancer biology over the past two decades, driven by the rise of systems biology. During this time, remarkable progress in mechanistic modelling has paralleled expanded elucidation of the complex cancer signalling network. This computational modelling approach has now created a powerful framework for exploring the molecular and cellular landscapes of cancer, contributing to precision oncology, personalized medicine, and drug development. Increasingly, mathematical models are being integrated into clinical workflows to guide decision-making, particularly in the development of targeted cancer therapy.
In this presentation, we explore how computational models are leveraged to investigate cancer cell resistance to targeted therapies, paving the new way for more effective and personalized treatment strategies. Specifically, we are focused on the fibroblast growth factor receptor (FGFR4) signaling pathway, a promising target for treating cancers, including triple-negative breast cancer (TNBC) and liver cancer, and addressing resistance to FGFR4-targeted monotherapy, a significant clinical challenge that necessitates rational combination therapies.
By utilizing computational modelling and in silico simulation approach integrated with experimental validation, we discovered that inhibiting FGFR4 in TNBC cells leads to reactivation of ErbB and AKT, suggesting that dual targeting of FGFR4 may improve treatment outcomes, confirmed experimentally where co-targeting FGFR4 with ErbB or AKT showed a synergistic effect. To enhance the versatility of the computational model, we customized it for specific cancer cell lines by integrating their gene expression data, thereby generating ‘virtual cancer cells.’ Through this approach, we revealed that while AKT reactivation is common, it is not universally applicable. In specific liver cancer cell lines, reactivation of the ERK pathway was observed, indicating that co-targeting FGFR4 and MEK could be more beneficial in this context.These findings underscore the dynamic and adaptive nature of cancer cell responses to therapeutic interventions, highlighting the critical importance of personalized combination therapies. Integrating computational modelling with experimental approaches may facilitate the development of more effective, tailored strategies that address the unique molecular and phenotypic characteristics of distinct cancer types.
In this presentation, we explore how computational models are leveraged to investigate cancer cell resistance to targeted therapies, paving the new way for more effective and personalized treatment strategies. Specifically, we are focused on the fibroblast growth factor receptor (FGFR4) signaling pathway, a promising target for treating cancers, including triple-negative breast cancer (TNBC) and liver cancer, and addressing resistance to FGFR4-targeted monotherapy, a significant clinical challenge that necessitates rational combination therapies.
By utilizing computational modelling and in silico simulation approach integrated with experimental validation, we discovered that inhibiting FGFR4 in TNBC cells leads to reactivation of ErbB and AKT, suggesting that dual targeting of FGFR4 may improve treatment outcomes, confirmed experimentally where co-targeting FGFR4 with ErbB or AKT showed a synergistic effect. To enhance the versatility of the computational model, we customized it for specific cancer cell lines by integrating their gene expression data, thereby generating ‘virtual cancer cells.’ Through this approach, we revealed that while AKT reactivation is common, it is not universally applicable. In specific liver cancer cell lines, reactivation of the ERK pathway was observed, indicating that co-targeting FGFR4 and MEK could be more beneficial in this context.These findings underscore the dynamic and adaptive nature of cancer cell responses to therapeutic interventions, highlighting the critical importance of personalized combination therapies. Integrating computational modelling with experimental approaches may facilitate the development of more effective, tailored strategies that address the unique molecular and phenotypic characteristics of distinct cancer types.