Related Experiment Videos
Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type-Specific Dynamic Logic Models
Federica Eduati1, Victoria Doldàn-Martelli1,2, Bertram Klinger3,4
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom.
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.
Insights
Understanding cancer cell signaling dynamics improves drug sensitivity predictions beyond genomic biomarkers. This approach identifies novel drug combinations to overcome resistance and advance personalized cancer medicine.
Area of Science:
- Cancer Research
- Systems Biology
- Pharmacology
Background:
- Genomic biomarkers for kinase inhibitor sensitivity in cancer treatment have limited impact on patient stratification.
- Kinase inhibitors affect cellular signaling dynamics, which in turn influence drug response.
Purpose of the Study:
- To investigate the association between cell-specific dynamic signaling pathways and drug sensitivity in colorectal cancer.
- To identify novel biomarkers and drug combinations for overcoming cancer drug resistance.
Main Methods:
- Measured 14 phosphoproteins across 43 conditions (5 stimuli, 7 inhibitors) in 14 colorectal cancer cell lines.
- Developed cell-specific dynamic logic models of signaling networks.
- Used model parameters as features to predict sensitivity to 27 drugs.
Main Results:
- Signaling dynamics parameters strongly correlated with drug sensitivity for 14 drugs, including 9 without known genomic biomarkers.
- Validated a novel drug combination (MEK inhibitor + GSK3 inhibitor) to overcome resistance, a finding not predicted by genomic data.
- Demonstrated that signaling dynamics provide insights beyond static genotypes.
Conclusions:
- Cellular signaling dynamics are crucial for predicting drug sensitivity and overcoming cancer resistance.
- Integrating dynamic signaling information with genomic data is essential for advancing personalized cancer medicine.
- This approach can identify novel therapeutic strategies and biomarkers missed by traditional genomic analyses.