Drew M. Pardoll

The paper designs an AND-gated CAR T cell receptor called MANTIS that uses extracellular size differences to spatially regulate signaling for precise antigen recognition.

This study shows that γδ17 T cells influence fibrosis by altering ECM composition and vascularity through interactions with stromal cells.

This study shows that tissue injury accelerates tumor growth and reduces response to immunotherapy in mice, but biomaterials that enhance wound healing can improve outcomes and ICB response.

This study shows immune checkpoint inhibitors enhance type 2 immunity and regeneration in ECM-treated wounds by promoting Treg/TH2 plasticity and pro-regenerative macrophages.

The paper identifies Treg clonal expansion and immunosuppressive functions in fibrosis induced by synthetic implants, showing their role in modulating stromal cell behavior.

This paper engineers effector cells resistant to bacterial toxins to deliver immunotoxins specifically to cancer cells and introduces a platform for therapeutic cell type development.

The paper identifies riboflavin-auxotrophic Enterococcus spp. that synergize with metabolites to boost MR1 expression and activate MAIT cells in the tumor microenvironment.

This study examines how dual checkpoint blockade with Anti-PD-1 and Anti-LAG-3 affects T cell clones in glioblastoma, showing increased clonal expansion and differentiation into effector T cells.

Clonotrace integrates gene expression and clonotype data to infer cell state transitions and fate biases, improving understanding of lineage dynamics in various biological systems.

The paper develops a spatial biomarker, DONUTS, to predict treatment response in lung cancer by analyzing the niche surrounding CD8+FoxP3+ T-cells.

This study shows antibiotic-induced microbiota depletion in mice impairs the pro-regenerative immune response to a biological scaffold, reducing myocyte regeneration and increasing fibrosis.

The paper corrects gene models for PVRIG in scRNA-seq and bulk data, improving its detection and enabling accurate functional analysis.

DeepTCR uses deep learning to analyze T-cell receptor sequences, improving classification and prediction in immunology and clinical settings.

AI-MHC improves Class I and Class II HLA-binding predictions using a deep learning framework that integrates allele information and achieves state-of-the-art performance.