Research ArticleCancer

Integrating network reconstruction with mechanistic modeling to predict cancer therapies

Sci. Signal.  22 Nov 2016:
Vol. 9, Issue 455, pp. ra114
DOI: 10.1126/scisignal.aae0535

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Translating genomic mutations into drug treatments

A holy grail of systems biology is having sufficient information to use mathematical models to predict therapeutic strategies for patients. Halasz et al. developed a computational approach that used published information and newly acquired experimental data to construct signaling network models that could be tested to discover new potential therapeutic strategies for cancer. They applied this approach to colorectal cancer cell lines and identified a specific network feedback event present in a subset of the cells that was predicted to cause resistance to drugs that target the growth factor receptor EGFR. Testing this prediction in a zebrafish tumor migration model was consistent with the predictions. Thus, modeling patient cell signaling data may eventually aid in identifying the best personalized treatment.

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