An in silico screen links gene expression signatures to drug response in glioblastoma stem cells
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Updated: Apr 20, 2026
A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells
Published on: March 28, 2021
G Riddick1, H Song1, S L Holbeck2
1Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Abstract:
Cancer stem cells (CSCs) are thought to promote resistance to chemotherapeutic drugs in glioblastoma, the most common and aggressive primary brain tumor. However, the use of high-throughput drug screens to discover novel small-molecule inhibitors for CSC has been hampered by their instability in long-term cell culture. We asked whether predictive models of drug response could be developed from gene expression signatures of established cell lines and applied to predict drug response in glioblastoma stem cells. Predictions for active compounds were confirmed both for 185 compounds in seven established glioma cell lines and 21 compounds in three glioblastoma stem cells. The use of established cell lines as a surrogate for drug response in CSC lines may enable the large-scale virtual screening of drug candidates that would otherwise be difficult or impossible to test directly in CSCs.
Insights
Predictive models using gene expression data can identify effective glioblastoma drugs. This approach overcomes challenges with culturing cancer stem cells (CSCs), enabling virtual screening for new glioblastoma treatments.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Glioblastoma (GBM) is an aggressive brain tumor.
- Cancer stem cells (CSCs) contribute to GBM drug resistance.
- CSCs are challenging to culture for drug screening.
Purpose of the Study:
- To develop predictive models for drug response in glioblastoma stem cells (CSCs).
- To assess the utility of established cell lines as surrogates for CSC drug response.
- To enable large-scale virtual screening of potential CSC-targeting drugs.
Main Methods:
- Utilized gene expression signatures from established glioma cell lines.
- Developed predictive models to forecast drug response.
- Validated predictions using both established cell lines and CSCs.
Main Results:
- Successfully predicted drug responses in established glioma cell lines (185 compounds).
- Confirmed predictions in glioblastoma stem cell lines (21 compounds).
- Established cell lines can serve as effective surrogates for CSC drug screening.
Conclusions:
- Gene expression-based predictive models are feasible for identifying glioblastoma drugs.
- This method circumvents difficulties in culturing CSCs.
- Enables efficient virtual screening for novel glioblastoma therapeutics targeting CSCs.
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