Martin Edwin Gleave

This study uses longitudinal scRNA-seq to track neuroendocrine transdifferentiation in a prostate cancer model, identifying transcriptional changes and key regulators during treatment-induced NEPC progression.

This study uses longitudinal single-cell RNA sequencing to identify RUNX1T1 as a key regulator in the transition and maintenance of treatment-induced neuroendocrine prostate cancer.

This study identifies rare germline variants associated with metastatic prostate cancer using an extreme phenotype cohort and validates their functional impact.

This study identifies lineage-specific transcription factor profiles in prostate cancer progression, revealing dynamic shifts during NE transdifferentiation and proposing a three-phase model.

This study shows AR-V7 forms condensates independently of androgens and AR-FL in CRPC, driving oncogenic transcription through both condensate-dependent and -independent mechanisms.

The paper introduces Deep Docking (D2), a deep learning method for virtual screening of large chemical datasets, achieving significant data reduction and enrichment of top hits.