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Updated: Mar 11, 2026
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Integrating network reconstruction with mechanistic modeling to predict cancer therapies
Melinda Halasz1,2, Boris N Kholodenko3,2,4, Walter Kolch1,2,4
1Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland. walter.kolch@ucd.ie tapesh.santra@ucd.ie melinda.halasz@ucd.ie.
Abstract:
Signal transduction networks are often rewired in cancer cells. Identifying these alterations will enable more effective cancer treatment. We developed a computational framework that can identify, reconstruct, and mechanistically model these rewired networks from noisy and incomplete perturbation response data and then predict potential targets for intervention. As a proof of principle, we analyzed a perturbation data set targeting epidermal growth factor receptor (EGFR) and insulin-like growth factor 1 receptor (IGF1R) pathways in a panel of colorectal cancer cells. Our computational approach predicted cell line-specific network rewiring. In particular, feedback inhibition of insulin receptor substrate 1 (IRS1) by the kinase p70S6K was predicted to confer resistance to EGFR inhibition, suggesting that disrupting this feedback may restore sensitivity to EGFR inhibitors in colorectal cancer cells. We experimentally validated this prediction with colorectal cancer cell lines in culture and in a zebrafish (Danio rerio) xenograft model.
Insights
Computational analysis identified cancer-specific signal network rewiring. Targeting feedback inhibition of insulin receptor substrate 1 (IRS1) may restore sensitivity to epidermal growth factor receptor (EGFR) inhibitors in colorectal cancer.
Area of Science:
- Oncology
- Computational Biology
- Systems Biology
Background:
- Cancer cells exhibit altered signal transduction networks.
- Identifying these network alterations is crucial for developing effective cancer therapies.
Purpose of the Study:
- To develop a computational framework for identifying, reconstructing, and modeling rewired signaling networks.
- To predict potential therapeutic targets for cancer intervention using this framework.
Main Methods:
- Developed a computational framework to analyze noisy perturbation response data.
- Applied the framework to analyze epidermal growth factor receptor (EGFR) and insulin-like growth factor 1 receptor (IGF1R) pathways in colorectal cancer cells.
- Experimentally validated computational predictions in cell lines and a zebrafish xenograft model.
Main Results:
- The computational approach identified cell line-specific network rewiring in colorectal cancer.
- Predicted that feedback inhibition of insulin receptor substrate 1 (IRS1) by p70S6K confers resistance to EGFR inhibition.
- Experimental validation confirmed the predicted feedback loop's role in drug resistance.
Conclusions:
- The developed computational framework can effectively model rewired signaling networks in cancer.
- Disrupting the IRS1-p70S6K feedback loop is a potential strategy to overcome resistance to EGFR inhibitors in colorectal cancer.
- This approach holds promise for personalized cancer treatment strategies.
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