Freddie Charles Hamdy

This paper introduces ARMS DNAseq, a scalable spatial genomic assay that maps copy number subclones in archival tissue, revealing tumor evolution and subclone associations with morphology and transcriptomic features.

This paper introduces a 3D microdissection method combining CNC milling and light-sheet microscopy to study tumor evolution in complex 3D structures.

This study evaluates the impact of a 3D pathology workflow on DNA, RNA, and protein integrity, showing minimal effect on amplifiability and transcriptomic profiles.

This paper develops a germline polygenic score (PRSagg) to predict prostate cancer aggressiveness and validates it across multiple datasets.

This study shows IGF-1 suppresses immune-related pathways in prostate cancer cells, reducing antigen presentation and increasing PD-L1, which may explain poor immunotherapy response.

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

The paper evaluates the accuracy of transcript-based inferred tumor phylogenies compared to DNA-based phylogenies in prostate cancer using single-cell, spatial transcriptomic, and bulk data.

The paper uses spatial transcriptomics to analyze copy number variations in benign and malignant tissues, revealing clonal patterns and suggesting early cancer evolution mechanisms.

This study improves a polygenic hazard score for prostate cancer by adding 120 SNPs, enhancing hazard ratios but not positive predictive value of PSA testing.

This paper evaluates how sample size affects polygenic hazard score models for prostate cancer, finding that 20-30k samples are needed for discovery models, but 10k suffice if SNPs are pre-established.

The paper evaluates a polygenic hazard score for prostate cancer screening, showing it improves positive predictive value by adjusting screening age based on genetic risk.

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.

This paper develops a genetic risk score to personalize prostate cancer screening age based on polygenic hazard scores and validates its predictive power in a separate dataset.