ArticleNature communications2017
Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed.
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- Cancer-associated fibroblast-derived fibulin-5 promotes epithelial-mesenchymal transition in diffuse-type gastric cancer via cAMP response element-binding protein pathway, showing poor prognosis.Experimental & molecular medicine · 2025Article
- Article
- Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors.Alzheimer's research & therapy · 2024Article
- Canalizing kernel for cell fate determination.Briefings in bioinformatics · 2024Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on "Structure-Property" Relationship Classification Models.Life (Basel, Switzerland) · 2023Article
- Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors.Scientific reports · 2023Article
- Critical transition and reversion of tumorigenesis.Experimental & molecular medicine · 2023Review
- A Cell-Fate Reprogramming Strategy Reverses Epithelial-to-Mesenchymal Transition of Lung Cancer Cells While Avoiding Hybrid States.Cancer research · 2023Article
- Global stabilizing control of large-scale biomolecular regulatory networks.Bioinformatics (Oxford, England) · 2023Article
- Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics.Communications biology · 2022Article
- Therapeutics Targeting p53-MDM2 Interaction to Induce Cancer Cell Death.International journal of molecular sciences · 2022Review
- Single-cell analysis of p53 transitional dynamics unravels stimulus- and cell type-dependent signaling output motifs.BMC biology · 2022Article
- Effective connectivity determines the critical dynamics of biochemical networks.Journal of the Royal Society, Interface · 2022Article
- Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging.Scientific reports · 2021Article
- The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Predicting chemosensitivity using drug perturbed gene dynamics.BMC bioinformatics · 2021Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.