Biology subjects

Perret, C.

Publications and source records attributed to Perret, C..

3 recordsLinked to original sources

bioRxiv2023

CD4 T cells and neutrophils contribute to epithelial-mesenchymal transition in breast cancer

Epithelial-mesenchymal transition (EMT) is a central oncogenic mechanism, contributing both to transformation and metastatic dissemination. Inflammation and innate immune cells are known to favor EMT induction, but the role of adaptive immunity still remains unclear. Using an original murine mammary tumor model in immune cell subpopulation depletion experiments, we demonstrated that tumor cells maintain their epithelial phenotype in mice deficient for adaptive immune response, but undergo EMT in the presence of T-cells. This phenotypic conversion involves the major contribution of CD4 T cells, but not CD8 T cells nor B cells, undoubtedly demonstrating the pro-EMT role of CD4 T cells specifically among adaptive immune cells. Moreover, combined intra-tumor immune infiltrate and transcriptomic analyses of murine mammary tumors with various EMT phenotype revealed an inverse correlation between mesenchymal tumor cell and intratumoral neutrophil proportions, due to the reduced ability of mesenchymal cells to recruit neutrophils. Last, selective in vivo depletion of neutrophils and transcriptomic analysis of human breast tumor cohorts demonstrated the pro-EMT role of neutrophils and suggest a cooperation with CD4 T cells in EMT promotion. Collectively, our data highlight a novel mechanism of EMT regulation by both innate and adaptive immune compartments.

cancer biology↗

bioRxiv2021

Deleting in vivo β-catenin degradation domain in mouse hepatocytes drives hepatocellular carcinoma or hepatoblastoma-like tumors

Background and aimsOne-third of hepatocellular carcinomas (HCCs) have mutations that activate the {beta}-catenin pathway with mostly CTNNB1 mutations. Mouse models using Adenomatous polyposis coli (Apc) loss-of-functions (LOF) are widely used to mimic {beta}-catenin-dependent tumorigenesis. Considering the low prevalence of APC mutations in human HCCs we aimed to generate hepatic tumors through CTNNB1 exon 3 deletion ({beta}cat{Delta}ex3) and to compare them to hepatic tumors with Apc LOF engineered through a frameshift in exon 15 (Apcfs-ex15). MethodsWe used hepatic-specific and inducible Cre-lox mouse models as well as a hepatic-specific in vivo CRISPR/Cas9 approach using AAV vectors, to generate Apcfs-ex15 and {beta}cat{Delta}ex3 hepatic tumors harboring activation of the {beta}-catenin pathway. Tumors generated by the Cre-lox models were analyzed phenotypically using immunohistochemistry and were selected for transcriptomic analysis using RNA-sequencing. Mouse RNAseq data were compared to human RNAseq data (normal tissues (8), HCCs (48) and hepatoblastomas (9)) in an integrative analysis. Tumors generated via CRISPR were analyzed using DNA sequencing and immunohistochemistry. ResultsMice with {beta}cat{Delta}ex3 alteration in hepatocytes developed liver tumors. Generated tumors were indistinguishable from those arising in Apcfs-ex15 mice. Both Apcfs-ex15 and {beta}cat{Delta}ex3 mouse models induced two phenotypically distinct tumors (differentiated or undifferentiated). Integrative analysis of human and mouse tumors showed that mouse differentiated tumors are close to human well differentiated CTNNB1-mutated tumors, while undifferentiated ones are closer to human mesenchymal hepatoblastomas, and are activated for YAP signaling. ConclusionApcfs-ex15 and {beta}cat{Delta}ex3 mouse models similarly induce tumors transcriptionally close to either well differentiated {beta}-Catenin activated human HCCs or mesenchymal hepatoblastomas.

cancer biology↗

bioRxiv2018

OPTIMIR, a novel algorithm for integrating available genome-wide genotype data into miRNA sequence alignment analysis.

Next-generation sequencing is an increasingly popular and efficient approach to characterize the full set of microRNAs (miRNAs) present in human biosamples. MiRNAs detection and quantification still remain a challenge as they can undergo different post transcriptional modifications and might harbor genetic variations (polymiRs) that may impact on the alignment step. We present a novel algorithm, OPTIMIR, that incorporates biological knowledge on miRNA editing and genome-wide genotype data available in the processed samples to improve alignment accuracy. OPTIMIR was applied to 391 human plasma samples that had been typed with genome-wide genotyping arrays. OPTIMIR was able to detect genotyping errors, suggested the existence of novel miRNAs and highlighted the allelic imbalance expression of polymiRs in heterozygous carriers. OPTIMIR is written in python, and freely available on the GENMED website (http://www.genmed.fr/index.php/fr/) and on Github (github.com/FlorianThibord/OptimiR).

bioinformatics↗