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<p>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 (<b>S</b>ingle-<b>C</b>ell <b>F</b>ramework for -<b>O</b>mics <b>C</b>onnectivity and <b>A</b>nalysis via <b>L</b>1000), 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.</p>

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Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

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  • 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

摘要

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.