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Cancer Res. 2017 Jun 15;77(12):3364-3375. doi: 10.1158/0008-5472.CAN-17-0078. Epub 2017 Apr 5.

Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type-Specific Dynamic Logic Models.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom.
2
Departamento de Física de la Materia Condensada, Condensed Matter Physics Center (IFIMAC) and Instituto Nicolás Cabrera, Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain.
3
Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
4
Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
5
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom.
6
Berlin Institute of Health (BIH), Berlin, Germany.
7
Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany. saezrodriguez@gmail.com nils.bluethgen@charite.de.
8
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom. saezrodriguez@gmail.com nils.bluethgen@charite.de.
9
Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, Aachen, Germany.

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. Cancer Res; 77(12); 3364-75. ©2017 AACR.

PMID:
28381545
DOI:
10.1158/0008-5472.CAN-17-0078
[Indexed for MEDLINE]
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