Richard J. R. Elliott

A comprehensive pharmacological survey across heterogeneous patient-derived GBM stem cell models

This study screens a large compound library across patient-derived GBM stem cells to identify effective drugs targeting tumor heterogeneity and presents validated hits for further exploration.

SS18-SSX co-opts P300 to sustain oncogenic transcription independent of SWI/SNF activity

This study shows SS18-SSX sustains oncogenic transcription without needing SWI/SNF activity, identifies P300 as a critical co-factor, and demonstrates targeting P300 or SWI/SNF as therapeutic strategies.

Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models

This study identifies three compounds targeting glioblastoma stem cells using machine learning and high-throughput screening of patient-derived models.

Phenotypic Screening Identifies Flunarizine as an Inhibitor of Radiotherapy-Induced Astrocyte Reactivity with Therapeutic Potential in Glioblastoma

This paper identifies Flunarizine as a compound that inhibits radiation-induced astrocyte reactivity and improves survival in a glioblastoma model.

Drug combinations targeting FAK and MEK overcomes tumour heterogeneity in glioblastoma

This study identifies a synergistic combination of FAK and MEK inhibitors that effectively targets glioblastoma heterogeneity and improves therapeutic outcomes.

Dyr726, a brain-penetrant inhibitor of PI3Kα, Type III receptor tyrosine kinases, and WNT signalling

This paper designs and evaluates Dyr726, a brain-penetrant multi-kinase inhibitor that targets PI3Kα, receptor tyrosine kinases, and WNT signaling, showing efficacy in preclinical cancer models.

Machine learning recognises senescence in glioblastoma and discovers senescence-inducing compounds

The paper uses machine learning to identify senescent glioblastoma cells and discovers compounds that induce senescence in these cells.

Morphological profiling by cell painting in human neural progenitor cells classifies hit compounds in a pilot drug screen for Alzheimer's disease

The paper uses cell painting to identify drugs that reverse SORL1 deletion phenotypes in neural progenitor cells, revealing potential therapeutic targets for Alzheimer's disease.