Maria Olga Hernandez

This study establishes patient-derived KS xenografts that maintain KSHV infection and recapitulate KS tumor gene signatures, enabling analysis of tumor microenvironment cell communications.

This study defines the spatial architecture of intrahepatic cholangiocarcinoma using single-cell spatial proteomics and a graph-based deep learning framework to identify multicellular spatial patterns and their association with patient outcomes.

This study identifies a STING-STAT3-SOX18 axis driving EndMT and epigenetic reprogramming in SAVI lung fibrosis, suggesting STING as a therapeutic target.

The paper uses spatial single-cell data to identify tumor cell clusters (villages) and their microenvironmental dependencies, showing their impact on patient outcomes.

The paper tracks premalignant lesions in head and neck cancer using intravital microscopy and identifies myeloid cell organization as a predictor of lesion regression or progression.

This study uses deep learning and spatial data to show how tumor architecture and cell states co-evolve, influencing colorectal cancer outcomes.