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Published on: July 15, 2015
Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines
Wei Zhao1, Jun Li2, Mei-Ju M Chen2
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Abstract:
Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of "protein-drug" connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.
Insights
This study maps cancer cell protein responses to drug perturbations, revealing insights into drug resistance and sensitivity. The findings aid in identifying effective cancer drug combinations and treatments.
Area of Science:
- Perturbation biology
- Cancer cell line modeling
- Proteomics
Background:
- Quantitative cellular behaviors and disease mechanisms are poorly understood due to a lack of large-scale protein response data in cancer cell lines.
- Existing resources do not capture the complex protein expression changes induced by drug perturbations.
Purpose of the Study:
- To create a comprehensive resource of perturbed protein expression profiles in cancer cell lines in response to drug compounds.
- To leverage this data to understand drug resistance, predict drug sensitivity, and identify optimal drug combinations.
- To establish a "protein-drug" connectivity map and a user-friendly data portal for the research community.
Main Methods:
- Utilized reverse-phase protein arrays (RPPA) to generate perturbed expression profiles.
- Analyzed data from over 12,000 cancer cell line samples.
- Investigated responses to approximately 170 drug compounds, focusing on ~210 clinically relevant proteins.
Main Results:
- Integrated perturbed protein response signals provided mechanistic insights into drug resistance.
- Demonstrated increased predictive power for drug sensitivity through response signal integration.
- Identified effective drug combinations by analyzing protein-drug interactions.
- Developed a systematic map of "protein-drug" connectivity.
Conclusions:
- The generated resource offers a valuable tool for investigating cancer cell behaviors and treatment response dependencies.
- This work enables a broad range of biomedical applications, including personalized medicine and drug discovery.
- The data portal facilitates community access and utilization of these crucial perturbation biology insights.
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