Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

Area of Science:

• Computational biology
• Molecular oncology
• Systems biology

Background:

• Transforming growth factor beta (TGFβ), WNT, and epidermal growth factor receptor (EGFR) ligand pathways are crucial in colorectal cancer (CRC) development.
• Understanding the complex interplay of these signaling networks is essential for targeted therapeutic strategies.

Purpose of the Study:

• To create a dynamic simulation model of key signaling pathways in colorectal cancer.
• To validate the model's predictive capabilities using experimental data from CRC cell lines.
• To explore the potential of computational modeling for personalized cancer treatment.

Main Methods:

• Development of a Molecular Interaction Map (MIM) representing signaling pathways.
• Implementation of dynamic simulations using Ordinary Differential Equations (ODEs) with 447 reactants.
• In silico simulation of physiologic and pathologic (cancer) conditions, including oncoprotein mutations.
• Experimental validation using HCT116 and HT29 colorectal cancer cell lines, measuring mRNA and protein levels under inhibitor treatments.

Main Results:

• The dynamic model accurately simulated colorectal cancer signaling pathways.
• Model predictions correlated well with experimental measurements of MYC, CCND1 mRNA, and phosphorylated AKT/ERK.
• The model successfully predicted outcomes of independent studies and validated findings in HCT116 and HT29 cell lines.
• Simulations demonstrated the feasibility of predictive modeling even with incomplete pathway information.

Conclusions:

• Dynamic modeling of signaling networks, even with partial information, is a viable approach for understanding cancer pathogenesis.
• The developed model shows promise for predicting responses to targeted therapies in colorectal cancer.
• This work opens avenues for developing personalized treatment strategies by combining signaling protein inhibitors.