Identifying therapeutic targets in a combined EGFR-TGFβR signalling cascade using a multiscale agent-based cancer...

Area of Science:

• Oncology
• Molecular Biology
• Computational Biology

Background:

• Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality.
• The epidermal growth factor receptor (EGFR) signaling pathway is frequently dysregulated in NSCLC, presenting a key therapeutic target.
• Targeting specific molecular components within the EGFR pathway holds potential for improved treatment strategies.

Purpose of the Study:

• To evaluate the therapeutic efficacy of targeting various molecular components within the EGFR signaling pathway in NSCLC.
• To rank downstream EGFR signaling molecules based on their therapeutic value for both inhibition and amplification strategies.
• To explore the potential of an in silico model for tailoring personalized molecular treatment regimens.

Main Methods:

• Utilized a pre-existing computational model for non-small cell lung cancer.
• Simulated therapeutic inhibition and amplification of diverse molecular targets within the EGFR pathway.
• Quantitatively assessed and ranked the therapeutic indices of targeted molecules.

Main Results:

• Identified mitogen-activated protein kinase (MAPK) and extracellular signal-regulated kinase (ERK) as optimal targets for both inhibition and amplification.
• Demonstrated that combined parameter perturbations did not always yield additive therapeutic benefits compared to single-target interventions.
• Highlighted the complex interplay of molecular targets within the EGFR pathway.

Conclusions:

• MAPK and ERK are identified as critical therapeutic targets for NSCLC treatment within the EGFR pathway.
• The study underscores the importance of precise target selection and suggests that combined interventions require careful consideration.
• The developed in silico model offers a valuable platform for future research into personalized, molecular-driven cancer therapies.