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Gubin, M.

Publications and source records attributed to Gubin, M..

2 recordsLinked to original sources

Macrophage-Dendritic Cell-T-Cell Tetrads Orchestrate Antitumor Immunity and Response to Checkpoint Blockade

Immune checkpoint inhibitors (ICIs) elicit durable responses in only a subset of patients with solid tumors, underscoring the need to define the cellular architectures that govern effective antitumor immunity. Here we identify a spatially organized multicellular immune unit comprising macrophages, cDC1s, CD4 T-cells, and CD8 T-cells that emerges in response to anti-CTLA-4 or dual checkpoint blockade. We term these structures tetrads. Using multiplexed imaging and spatial transcriptomics in mouse and human tumors, we show that tetrads assemble early during immune priming, depend on the ICOS-ICOSL pathway, and are enriched for ICOS Th1-like CD4 T cells and ICOSLhigh cDC1s. CD8 T-cells within tetrads exhibit an activated, non-terminally differentiated state, while tetrad-associated macrophages display an interferon-{gamma}-responsive program that sustains CD8 T-cell function and prevents dysfunction. Functionally, ICOSL cDC1s are required for tumor eradication in vivo. In patients with bladder cancer treated with neoadjuvant dual checkpoint blockade, tetrad, but not triad or dyad formation correlates with clinical response. These findings establish tetrads as a fundamental cellular unit coordinating antitumor immunity and responsiveness to ICIs.

immunology↗

Literature-scaled immunological gene set annotation using AI-powered immune cell knowledge graph (ICKG)

Large scale application of single-cell and spatial omics in models and patient samples has led to the discovery of many novel gene sets, particularly those from an immunotherapeutic context. However, the biological meaning of those gene sets has been interpreted anecdotally through over-representation analysis against canonical annotation databases of limited complexity, granularity, and accuracy. Rich functional descriptions of individual genes in an immunological context exist in the literature but are not semantically summarized to perform gene set analysis. To overcome this limitation, we constructed immune cell knowledge graphs (ICKGs) by integrating over 24,000 published abstracts from recent literature using large language models (LLMs). ICKGs effectively integrate knowledge across individual, peer-reviewed studies, enabling accurate, verifiable graph-based reasoning. We validated the quality of ICKGs using functional omics data obtained independently from cytokine stimulation, CRISPR gene knock-out, and protein-protein interaction experiments. Using ICKGs, we achieved rich, holistic, and accurate annotation of immunological gene sets, including those that were unannotated by existing approaches and those that are in use for clinical applications. We created an interactive website (https://kchen-lab.github.io/immune-knowledgegraph.github.io/) to perform ICKG-based gene set annotations and visualize the supporting rationale.

bioinformatics↗