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Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy

Oncogene volume 39, pages 3803–3820 (2020)Cite this article

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Abstract

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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Fig. 1: Simulation framework to analyze the adaptive resistance to drugs.
Fig. 2: Reconstruction of a large-scale signaling network of CRC.
Fig. 3: Mapping genomic information to cell line-specific CRC networks.
Fig. 4: Identification of combinatorial targets for overcoming adaptive resistance.
Fig. 5: Combination effect of SRCi and MEKi in KRAS-mutant CRC cells.
Fig. 6: Combination effect of SRCi and BRAFi in BRAF-mutant CRC cells.
Fig. 7: Proposed generic model for the role of Src in adaptive resistance.

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All codes are available from the authors upon request.

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Acknowledgements

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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  1. These authors contributed equally: Sang-Min Park, Chae Young Hwang

Authors and Affiliations

  1. Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea

    Sang-Min Park, Chae Young Hwang, Jihye Choi, Chang Young Joung & Kwang-Hyun Cho

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  1. Sang-Min Park
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Contributions

K-HC, S-MP, and CYH conceived the study and designed computational simulations and experiments. S-MP conducted modeling and analysis. CYH performed experiments. JC provided experimental support, and CYJ provided analytical support. S-MP, CYH and K-HC wrote the paper. K-HC designed the project and supervised the study.

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Correspondence to Kwang-Hyun Cho.

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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

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