What question did this study set out to answer?

The aim is to identify effective treatment combinations for glioblastoma by analyzing the cellular responses to drugs at a single-cell level.

March 25, 2026

Abstract B032: Drug and single-cell gene expression integration identifies heterogeneity-aware synergistic combinations for glioblastoma

Key Points

The aim is to identify effective treatment combinations for glioblastoma by analyzing the cellular responses to drugs at a single-cell level.
Developed the scFOCAL framework for analyzing drug sensitivity in glioblastoma cells.
Conducted single-cell RNA sequencing on newly diagnosed and recurrent GBM tumors.
Leveraged LINCS L1000 database to identify compounds with diverse transcriptional signatures in GBM.
Validated findings through in vitro, ex vivo, and in vivo experiments.
Identified specific drug combinations that show synergy in targeting various GBM cell states.
Demonstrated that inhibition of Olig2 combined with Depatux-M led to enhanced anti-tumor effects in vivo.
Highlighted the potential of scFOCAL in predicting sensitivity and resistance to therapies based on cellular states.

Abstract

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 use the Olig2 inhibitor CT-179 as a reference drug to identify additional small molecules that would maximize the cell-drug discordance across the GBM transcriptional landscape. Our analysis leads to the combination of Olig2 inhibition with treatment with Depatux-M, of with which we demonstrate synergy in vivo. 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. Citation Format: Robert K. Suter, Anna M. Jermakowicz, Rithvik Veeramachaneni, Matthew D'Antuono, Longwei Zhang, Rishika Chowdary, Simon Kaeppeli, Madison Sharp, Pravallika Palwai, Vasileios Stathias, Grace Baker, Luz Ruiz, Winston Walters, Maria Cepero, Danielle Burgenske, Edward B. Reilly, Anatol Oleksijew, Mark G. Anderson, Sion Ll. Williams, Michael E. Ivan, Ricardo J. Komotar, Macarena I. De La Fuente, Gregory Stein, Alexandre Wojcinski, Santosh Kesari, Jann N. Sarkaria, Stephan C. Schürer, Nagi G. Ayad. Drug and single-cell gene expression integration identifies heterogeneity-aware synergistic combinations for glioblastoma abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86 (6Suppl): Abstract nr B032.

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Cite This Study

Suter et al. (Mon,) studied this question.

synapsesocial.com/papers/69c37b54b34aaaeb1a67d960 https://doi.org/https://doi.org/10.1158/1538-7445.brain26-b032

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