Sanghee Cho

The paper investigates cell engulfment in colorectal cancer, showing it creates competitive metabolic niches linked to clinical outcomes.

Prostate-TriMod integrates visual morphology, spatial tissue maps, and clinical outcomes for prostate cancer to enable multimodal AI research.

This paper identifies a five-protein stem cell signature using a deep neural network that predicts relapse in stage III colorectal cancer patients.

The paper develops TOPAZ, a tool that predicts cancerous cells/glands and analyzes immune cell spatial relationships in prostate cancer, finding that immune cell distance from cancer glands correlates with recurrence risk.

This study uses high-resolution spatial proteomics to characterize colorectal cancer consensus molecular subtypes at single-cell resolution, revealing subtype-specific immune and metabolic features.

The paper investigates the spatial relationship between T cells and cancer cells in stage III CRC patients, finding that closer proximity of T cells to cancer cells is associated with better disease-free survival.

This study compares tumor cell phenotype and heterogeneity differences between IDH1 mutant and wild-type gliomas using integrated multi-omics data.