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Pourquier, D.

Publications and source records attributed to Pourquier, D..

2 recordsLinked to original sources

Cancer-associated Fibroblast Spatial Heterogeneity and EMILIN1 Expression in Cancer Stroma Modulate TGF-beta Activity and CD8+ T-Cell Infiltration in Breast Cancer

The tumor microenvironment (TME) and its multifaceted interactions with cancer cells are major targets for cancer treatment. Single-cell technologies have brought major insights into the TME, but the resulting complexity frequently precludes conclusions on function. Therefore, we combined single-cell RNA sequencing and spatial transcriptomic data to explore the relationship between different cancer-associated fibroblast (CAF) populations and immune cell exclusion in breast tumors. Our data show for the first time the degree of spatial organization of different CAF populations in breast cancer. We found that IL-iCAFs, Detox-iCAFs, and IFN{gamma}-iCAFs tended to cluster together, while Wound-myCAFs, TGF{beta}-myCAFs, and ECM-myCAFs formed another group that overlapped with elevated TGF-{beta} signaling. Differential gene expression analysis of areas with CD8+ T-cell infiltration/exclusion within the TGF-{beta} signaling-rich zones identified elastin microfibrillar interface protein 1 (EMILIN1) as a top modulated gene. EMILIN1, a TGF-{beta} inhibitor, was upregulated in IFN{gamma}-iCAFs directly modulating TGF{beta} immunosuppressive function. Histological analysis of 74 breast cancer samples confirmed that high EMILIN-1 expression in the tumor margins was related to high CD8+ T-cell infiltration, consistent with our spatial gene expression analysis. High EMILIN-1 expression was also associated with better prognosis of patients with breast cancer, underscoring its functional significance for the recruitment of cytotoxic T cells into the tumor area. In conclusion, our data show that correlating TGF-{beta} signaling to a CAF subpopulation is not enough because proteins with TGF-{beta}-modulating activity originating from other CAF subpopulations can alter its activity. Therefore, therapeutic targeting should remain focused on biological processes rather than on specific CAF subtypes.

cancer biology↗

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different bulk datasets, including new Visium liver metastasis ST data, with experimental validation of selected interactions. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

bioinformatics↗