DRUG AND SINGLE-CELL GENE EXPRESSION INTEGRATION IDENTIFIES SENSITIVE AND RESISTANT GLIOBLASTOMA CELL POPULATIONS

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.