Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response
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Published on: November 22, 2021
Minsoo Choi1, Jue Shi2, Yanting Zhu2
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Abstract:
Cancer is a complex disease involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The heterogeneous nature of cancer, which results in highly variable drug response, is a major obstacle to developing effective cancer therapy. Previous studies of cancer therapeutic response mostly focus on static analysis of genome-wide alterations, thus they are unable to unravel the dynamic, network-specific origin of variation. Here we present a network dynamics-based approach to integrate cancer genomics with dynamics of biological network for drug response prediction and design of drug combination. We select the p53 network as an example and analyze its cancer-specific state transition dynamics under distinct anticancer drug treatments by attractor landscape analysis. Our results not only enable stratification of cancer into distinct drug response groups, but also reveal network-specific drug targets that maximize p53 network-mediated cell death, providing a basis to design combinatorial therapeutic strategies for distinct cancer genomic subtypes.
Insights
This study introduces a network dynamics approach to predict cancer drug response by analyzing genomic alterations and biological network behavior. It enables personalized cancer therapy design by identifying network-specific targets for improved treatment strategies.
Area of Science:
- Oncology
- Systems Biology
- Computational Biology
Background:
- Cancer is a complex disease driven by genomic alterations affecting cellular signaling networks.
- Cancer's heterogeneity leads to variable drug responses, hindering effective therapy development.
- Current therapeutic response studies often use static genomic analysis, missing dynamic network origins of variation.
Purpose of the Study:
- To develop a network dynamics-based approach for predicting cancer drug response.
- To integrate cancer genomics with biological network dynamics for personalized drug design.
- To identify network-specific targets for combinatorial cancer therapy.
Main Methods:
- Utilized a network dynamics approach integrating cancer genomics with biological network dynamics.
- Employed attractor landscape analysis to study cancer-specific p53 network state transitions.
- Analyzed dynamics under distinct anticancer drug treatments.
Main Results:
- Successfully stratified cancers into distinct drug response groups.
- Identified network-specific drug targets that enhance p53 network-mediated cell death.
- Demonstrated the approach's utility in predicting drug response and designing combination therapies.
Conclusions:
- A network dynamics approach offers a novel method for cancer drug response prediction.
- This method facilitates the design of personalized combinatorial therapeutic strategies for distinct cancer subtypes.
- Understanding network dynamics is crucial for overcoming cancer heterogeneity in treatment.
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