Nagi G. Ayad

This study identifies RAC1 as a druggable target in Shh-dependent medulloblastoma by showing its role in regulating GLI1 and GLI2 transcription and demonstrating the efficacy of the inhibitor GYS32661 in reducing tumor growth.

The paper integrates single-cell RNA sequencing data with drug response signatures to identify drug-sensitive and resistant glioblastoma cell states and predict synergistic drug combinations.

This paper develops DrugSeq, a computational platform that predicts drug sensitivities in medulloblastoma subgroups, identifying subgroup-specific therapies and potential drug repositioning candidates.

OrthologAL converts non-human transcriptomics data to human orthologs for drug discovery, preserving data structure and enabling use with human-annotated databases.

This study identifies MAT2A and AHCY as critical for glioblastoma survival by regulating antioxidant metabolism, showing their inhibition reduces cell viability and oxidative stress.

The paper identifies three single-cell vulnerability states in glioblastoma using transcriptomic data and CRISPR screens, showing differential drug sensitivity and spatial distribution.

This study identifies rapid resistance to BET inhibitors in glioblastoma mediated by FGFR1 and shows that co-targeting BET and FGFR1 synergizes to reduce tumor growth.