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Updated: Feb 24, 2026
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A Preclinical Mouse Model of Osteosarcoma to Define the Extracellular Vesicle-mediated Communication Between Tumor and Mesenchymal Stem Cells
Published on: May 6, 2018
10.7K
Multitype Network-Guided Target Controllability in Phenotypically Characterized Osteosarcoma: Role of Tumor
Ankush Sharma1,2, Caterina Cinti1, Enrico Capobianco2,3
1Experimental Oncology Unit, UOS - Institute of Clinical Physiology, CNR, Siena, Italy.
Frontiers in Immunology
|August 22, 2017
Summary
Network-guided controllability analysis aids precision oncology by identifying therapeutic targets in osteosarcoma. This approach analyzes protein networks to reveal shared and specific targets across different cancer phenotypes for better diagnostics and treatments.
Area of Science:
- Oncology
- Systems Biology
- Bioinformatics
Background:
- Precision oncology requires tools to identify effective therapeutic targets.
- Cancer phenotypic heterogeneity poses challenges for target identification.
Purpose of the Study:
- To highlight network-guided controllability analysis as a precision oncology tool.
- To identify therapeutic targets by analyzing protein interaction networks in osteosarcoma (OS).
Main Methods:
- Utilized multitype networks to analyze protein regulation circuits in 22 OS cell lines.
- Identified targets within protein sub-complexes based on connectivity patterns.
- Characterized OS cell lines both in vitro and in vivo.
Main Results:
- Discovered critical proteins in OS regulation circuits that are both shared and phenotype-specific.
- Identified emerging targets related to the OS microenvironment, including proteoglycans, cyclins, collagen, laminin, and keratin.
- Found phenotype-specific targets like IGFBP7 and PDGFRA (invasive) and FGFR3 and THBS1 (colony forming).
Conclusions:
- Network-guided controllability analysis is relevant for identifying diagnostic and therapeutic targets in cancer.
- Analyzing interactive targets within protein sub-complexes offers insights into cancer heterogeneity.
- This approach can guide precision oncology strategies by revealing phenotype-specific and shared therapeutic vulnerabilities.
