Padmanee Sharma

Eva is a foundation model for tissue imaging data that learns multi-scale spatial representations and performs various tasks like cross-modal inference and patient stratification.

This study shows KDM6A mutations in bladder cancer improve response to immune checkpoint therapy but worsen chemotherapy outcomes through effects on DNA repair and metabolic reprogramming.

The paper identifies tetrads as a multicellular immune unit that correlates with antitumor immunity and response to checkpoint blockade in mouse and human tumors.

This study uses spatial transcriptomics to analyze AML in both medullary and extramedullary tissues, revealing inflammation and trans-differentiation states.

This study uses spatial multi-omics and deep learning to identify immunotherapy response and resistance signatures in hepatocellular carcinoma.

This study uses high-plex spatial proteomics to identify tertiary lymphoid-like structures in AML bone marrow and links them to patient outcomes.

The paper introduces CORAL, a deep generative model for spatial multiomics data that deconvolves low-resolution data into single-cell profiles and identifies spatial domains and cell-cell interactions.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

The paper identifies proinflammatory monocytes/macrophages associated with resistance to PD-1/PD-L1 blockade in urothelial cancer using bulk and single-cell RNA sequencing.