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Updated: Jan 18, 2026
A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells
Published on: March 28, 2021
DRUG AND SINGLE-CELL GENE EXPRESSION INTEGRATION IDENTIFIES SENSITIVE AND RESISTANT GLIOBLASTOMA CELL POPULATIONS
Robert K Suter1,2, Anna M Jermakowicz1, Rithvik Veeramachaneni1
1Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA.
Abstract:
Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we developed a novel framework, ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings in vitro, ex vivo, and in vivo, and identified a novel combination of an OLIG2 inhibitor and Depatux-M for GBM. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM and that it can be applied to identify new synergistic combinations.
Insights
Glioblastoma drug discovery is advanced by ISOSCELES, a new framework predicting cell sensitivity. This tool identifies novel drug combinations for glioblastoma (GBM) by analyzing transcriptional states.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Glioblastoma (GBM) is a lethal brain cancer with limited treatments.
- Intratumor heterogeneity, with diverse neoplastic cell states, complicates therapeutic development.
- Predicting differential drug sensitivity across GBM cell states is crucial.
Purpose of the Study:
- To develop a novel computational framework, ISOSCELES, for quantifying glioblastoma cellular drug sensitivity and resistance.
- To identify compounds targeting specific GBM cell states using transcriptional signatures.
- To discover new synergistic therapeutic combinations for glioblastoma.
Main Methods:
- Developed ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures).
- Utilized single-cell RNA sequencing data from GBM tumors.
- Integrated data with the LINCS L1000 database for transcriptional response signatures.
Main Results:
- ISOSCELES quantified the cellular drug sensitivity and resistance landscape in GBM.
- Identified compounds with transcriptional response signatures discordant with distinct GBM cell states.
- Validated findings *in vitro*, *ex vivo*, and *in vivo*, discovering a novel OLIG2 inhibitor and Depatux-M combination.
Conclusions:
- ISOSCELES effectively identifies GBM cell states sensitive and resistant to targeted therapies.
- The framework can be applied to discover novel synergistic drug combinations for glioblastoma.
- This approach holds promise for advancing GBM therapeutic development by addressing heterogeneity.
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