Targeted drugs aim to treat cancer by directly inhibiting oncogene activity or oncogenic pathways, but drug resistance frequently emerges. Due to the intricate dynamics of cancer signaling networks, which contain complex feedback regulations, cancer cells can rewire these networks to adapt to and counter the cytotoxic effects of a drug, thereby limiting the efficacy of targeted therapies. To identify a combinatorial drug target that can overcome such a limitation, we developed a Boolean network simulation and analysis framework and applied this approach to a large-scale signaling network of colorectal cancer with integrated genomic information. We discovered Src as a critical combination drug target that can overcome the adaptive resistance to the targeted inhibition of mitogen-activated protein kinase pathway by blocking the essential feedback regulation responsible for resistance. The proposed framework is generic and can be widely used to identify drug targets that can overcome adaptive resistance to targeted therapies.
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We thank Dr Ki-Sun Kwon (Korea Research Institute of Bioscience and Biotechnology) for the rabbit polyclonal anti-GAPDH antibody, Nancy R. Gough (BioSerendipity, LLC) for editorial assistance, and Sea Choi for her editorial assistance during revision. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea Government, the Ministry of Science and ICT (2017R1A2A1A17069642 and 2015M3A9A7067220).
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Park, SM., Hwang, C.Y., Choi, J. et al. Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy. Oncogene 39, 3803–3820 (2020). https://doi.org/10.1038/s41388-020-1255-y