Biomedicine

Model Therapies

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Science  30 Aug 2013:
Vol. 341, Issue 6149, pp. 938
DOI: 10.1126/science.341.6149.938-b

If cellular signaling pathways were discrete and linear, controlling signals gone awry—like those from growth-promoting receptor tyrosine kinases often linked to cancer—would be straightforward. But these pathways form entangled and dynamic networks, and inhibiting signal transmission at one node, although successful in the short term, is often thwarted by regulatory mechanisms that keep cells healthy by rendering them robust to perturbations. Two groups have used a combination of mathematical modeling and experiments to identify strategies that may more effectively fight excess signaling by the ErbB family of receptors, which is associated with breast cancer. Kirouac et al. used their model to search for combinations of two or three inhibitors that would overcome adaptive feedback and validated these effects in cell and animal models of cancer. Meyer et al. used a model, data from public databases, and their own experiments to identify a second receptor, AXL, which allowed cancer cells to resist the effects of ErbB receptor inhibitors. In this scenario, ligand-independent activating interactions between receptors of the ErbB family and AXL appeared to be crucial, suggesting that reducing receptor number or activity is more likely to be effective than treatments that target ligand-induced activation of the receptors.

Sci. Signal. 6, ra68; ra66 (2013).

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