Elizabeth M. Jaffee

The paper calibrates a spatial QSP model using CODEX data to predict TME states and identify biomarkers for HCC immunotherapy response.

This study shows antigen-presenting cancer-associated fibroblasts (apCAFs) in murine pancreatic tumors influence regulatory T cell phenotype and function through CXCL9 and CCL22, affecting immune checkpoint blockade response.

The paper introduces dominoSignal, a software tool that statistically tests differential cell signaling from single-cell data to infer changes in cell-cell communication networks.

This study integrates single-cell and spatial transcriptomics to identify prognostic fibroblast subpopulations in thyroid cancer progression.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

The paper introduces FunCN, a method to analyze cellular neighborhoods in PDAC, revealing distinct immune interactions in primary and metastatic sites.

The paper investigates how cancer-associated fibroblasts (CAF) influence tumor cell behavior in pancreatic ductal adenocarcinoma by driving EMT and altering cell subtypes, with IL-8 playing a key modulatory role.

The paper uses virtual patient models to evaluate biomarkers for predicting response to PD-1 blockade in TNBC, finding that early on-treatment biomarkers and blood-based markers offer better performance.

This study uses spatial multi-omics to identify TLS-associated immune niches in PDAC that correlate with improved survival in immunotherapy responders.

The paper characterizes a novel syngeneic murine breast cancer model (NT2.5-LM) that exhibits rapid and widespread metastases, enabling study of metastatic progression and therapeutic responses.

The paper introduces computational tools for NMF analysis of single-cell RNA-seq data, demonstrating their use in quantifying cell state transitions in pancreatic cancer.

This paper introduces SpaceMarkers, a method to infer molecular changes from cell-cell interactions in spatial transcriptomics data, applied to tumor-immune interactions in metastasis and immunotherapy.

This paper introduces EVAsc, a method to analyze expression variation in scRNA-seq data to quantify tumor heterogeneity across cell types, subtypes, and patients.