Related Experiment Video
Updated: Jun 30, 2026
Transposon Mediated Integration of Plasmid DNA into the Subventricular Zone of Neonatal Mice to Generate Novel Models of Glioblastoma
Published on: February 22, 2015
Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations
Robert K Suter1, Anna M Jermakowicz2, Rithvik Veeramachaneni2
1Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. RKS82@Georgetown.edu.
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 and plasticity. GBM tumors contain neoplastic cells within a fluid spectrum of diverse transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we develop scFOCAL (Single-Cell Framework for -Omics Connectivity and Analysis via L1000), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identify compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states, and leverage this capability to predict combination synergy. We validate the significance of these findings in vitro, ex vivo, and in vivo, and identify a combination of an OLIG2 inhibitor and Depatux-M for the treatment of GBM. Our studies suggest that scFOCAL identifies cell states that are sensitive and resistant to targeted therapies in GBM using a measure of cell and drug connectivity, which can be applied to identify new synergistic combinations.
Insights
Glioblastoma (GBM) drug discovery is advanced by scFOCAL, a new platform predicting cell state drug responses. This identifies a novel combination therapy for treating this lethal brain cancer.
Area of Science:
- Neuro-oncology
- Computational Biology
- Pharmacology
Background:
- Glioblastoma (GBM) is a lethal brain cancer with limited treatment options.
- Intratumor heterogeneity and plasticity impede therapeutic development.
- Predicting drug sensitivity across diverse GBM cell states is crucial.
Purpose of the Study:
- To develop a computational framework, scFOCAL, for analyzing single-cell drug sensitivity in GBM.
- To identify compounds and predict synergistic combinations targeting distinct GBM cell states.
- To validate therapeutic strategies in preclinical models.
Main Methods:
- Single-cell RNA sequencing of GBM tumors.
- Utilizing the LINCS L1000 database for compound screening.
- Developing scFOCAL for cell-drug connectivity analysis.
- In vitro, ex vivo, and in vivo validation of predicted drug combinations.
Main Results:
- scFOCAL quantifies the cellular drug sensitivity and resistance landscape in GBM.
- Identified compounds with transcriptional responses discordant to specific GBM cell states.
- Predicted and validated synergistic drug combinations, including an OLIG2 inhibitor and Depatux-M.
- Demonstrated scFOCAL's ability to identify cell states sensitive and resistant to targeted therapies.
Conclusions:
- scFOCAL provides a novel approach to understanding GBM cell state-specific drug responses.
- The platform facilitates the identification of effective targeted therapies and combination strategies for GBM.
- This work offers a promising direction for developing new treatments for glioblastoma.
More Related Videos
10:28Flow Cytometry-based Drug Screening System for the Identification of Small Molecules That Promote Cellular Differentiation of Glioblastoma Stem Cells
Published on: January 10, 2018
05:29A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells
Published on: March 28, 2021
