bioRxivImmunologyPreprint
Immune repertoire profiling reveals extensive clonal expansion of T and B cells in CXCL9-rich niches in sinonasal cancer
J.S. Alvarez, Eva Gracia VillacampaiD, Daniel Labuz, Mariia V. GurylevaiD, Egon Urgard, Åsa KågedaliD, Oscar Solmell, Anders Nasman, Javier Escudero MorlanesiD, Junjie MaiD, Niels ØdumiD, Adnane AchouriD, Susanna Kumlien GeoréniD, Benedict J. ChambersiD, Ben MurrelliD, Lars‐Olaf CardelliD, Camilla EngblomiD, Kim ThraneiD, Jonathan M. CoquetiD
Abstract
Sinonasal cancer (SNC) is a rare and aggressive head and neck cancer with limited therapeutic options and an incompletely defined immune microenvironment. Although histopathology remains central to clinical tumor evaluation, immune profiling typically requires tissue dissociation and therefore loses the spatial relationships that shape antitumor immunity. Here, we combined single-cell RNA-sequencing approaches, spatial transcriptomics and Spatial V(D)J, a technique we recently pioneered, to analyse the cellular, spatial, and clonal architecture of SNC. We found that SNC tumors were marked by a robust T cell infiltration, including substantial proportions of classical tissue resident memory (TRM) and exhausted (TExh) CD8+ T cells. This inflamed phenotype was accompanied by an immense infiltration of suppressive regulatory T (Treg) cells, which differentiated towards a Tbet+CXCR3+ phenotype. Spatially, expanded Treg, CD8 T and B cell clones concentrated within specialized peritumoral immune niches enriched for cancer-associated fibroblasts, CXCL9 tumor-associated macrophages and LAMP3IDO1 dendritic cells. T cell clones occupying these niches were clonally related to those infiltrating epithelial tumor regions, linking lymphoid hubs to the broader tumor immune response. Together, these data identify SNC as an inflamed but highly immunoregulatory tumor type and reveal a spatially organized clonal architecture in which suppressive and cytotoxic lymphocyte states coexist within CXCL9 myeloid niches.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 0
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