Matthew J. Lazzara

This study identifies plectin as a biomarker of aggressive pancreatic cancer, showing it suppresses T cell activity and that targeting it with an antibody enhances anti-tumor immunity.

This study isolates and characterizes HMEC-1 extracellular vesicles, showing they enhance wound closure and have dual inflammatory effects on endothelial cells.

This paper identifies an optimal thermal dose for focused ultrasound ablation that balances tumor destruction with immunotherapy access using multimodal PET imaging.

The paper models PTPRJ's role in regulating cell phenotypes through RTK signaling pathways in cancer cells, identifying context-dependent mechanisms.

The paper develops computational models of the EGFR-ERK pathway to show that low RAF abundance creates signaling bottlenecks and stochastic dynamics, affecting ERK activation and RAS-RAF interactions in cancer cells.

This paper develops a method to create stable microfluidic channels within granular hydrogels using selective crosslinking, enabling controlled perfusion and dynamic cellular environments.

This study evaluates a PTPRJ-targeting peptide agonist's effects on lung cancer cells, revealing broad signaling changes, DNA damage, and growth inhibition.

The paper identifies ERK as a dominant driver of EMT heterogeneity in pancreas cancer cells using mutual information modeling and imaging, showing its role is independent of MEK inhibition and growth factors.

The paper shows hypoxic fibroblasts in pancreas cancer use histone methylation and ROS-mediated NF-κB activation to produce growth factors that drive epithelial-mesenchymal transition in malignant cells.

The paper develops a computational model to predict cell responses to EGFR ligands by integrating mechanistic and data-driven approaches, revealing how ligand affinity and signaling pathways influence phenotypes.

The paper develops a reaction-diffusion model to predict the spatial persistence of GAB1-SHP2 complexes downstream of EGFR activation and validates it with experiments.

The paper develops and validates agent-based models to predict multicell patterns driven by signaling-induced differential adhesion and uses machine learning to map parameters to patterns.