Biology subjects

Boissiere, F.

Publications and source records attributed to Boissiere, F..

3 recordsLinked to original sources

bioRxiv2023

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↗

bioRxiv2023

RIP140 regulates HES1 oscillatory expression and mitogenic activity in colon cancer cells

BackgroundThe transcription factor RIP140 (Receptor Interacting Protein of 140 kDa) regulates intestinal homeostasis and tumorigenesis through the Wnt signaling. In this study, we have investigated its effect on the Notch/HES1 signaling pathway. MethodsThe impact on HES1 expression and activity was evaluated in colorectal cancer (CRC) cell lines and in transgenic mice, invalidated or not for the Rip140 gene in the intestinal epithelium. A tumor microarray and transcriptomic data sets were used to investigate RIP140 and HES1 expression in relation with patient survival. Statistical comparisons were performed with Mann-Whitney or Kruskal-Wallis or Chi2 tests. ResultsIn CRC cells, RIP140 positively regulated HES1 gene expression at the transcriptional level via an RBPJ/NICD-mediated mechanism. In support of these in vitro data, RIP140 and HES1 expression significantly correlated in mouse intestine and in a cohort of CRC samples, analyzed by immunohistochemistry, thus supporting the positive regulation of HES1 gene expression by RIP140. Interestingly, when the Notch pathway is fully activated, RIP140 exerted a strong inhibition of HES1 gene transcription controlled by the level of HES1 itself. Moreover, RIP140 directly interacts with HES1 and reversed its mitogenic activity in human CRC cells. In line with this observation, HES1 levels were associated with a better patient survival only when tumors expressed high levels of RIP140. ConclusionsOur data identify RIP140 as a key regulator of the Notch/HES1 signaling pathway with a dual effect on HES1 gene expression at the transcriptional level and a strong impact on colon cancer cell proliferation.

cancer biology↗

bioRxiv2022

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↗