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Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models

Item Type:Article
Title:Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models
Creators Name:Eduati, F. and Doldan-Martelli, V. and Klinger, B. and Cokelaer, T. and Sieber, A. and Kogera, F. and Dorel, M. and Garnett, M.J. and Bluethgen, N. and Saez-Rodriguez, J.
Abstract:Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line–specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes.
Keywords:Antineoplastic Agents, Colorectal Neoplasms, Neoplasm Drug Resistance, Protein Kinase Inhibitors, Signal Transduction, Statistical Models, Tumor Biomarkers, Tumor Cell Line
Source:Cancer Research
ISSN:0008-5472
Publisher:American Association for Cancer Research (U.S.A.)
Volume:77
Number:12
Page Range:3364-3375
Date:15 June 2017
Official Publication:https://doi.org/10.1158/0008-5472.CAN-17-0078
PubMed:View item in PubMed

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