Biology subjects

Paradis, V.

Publications and source records attributed to Paradis, V..

4 recordsLinked to original sources

bioRxiv2025

ZEB1 regulates BCL2 in cancer-associated fibroblasts to promote cholangiocarcinoma chemoresistance to gemcitabine and cisplatin

Intrahepatic cholangiocarcinoma (iCCA) is an agressive tumour from the biliary tree that is characterized by a prominent desmoplastic stroma mainly composed of cancer-associated fibroblasts (CAF) and a poor prognosis due to its late clinical presentation and the lack of effective non-surgical treatments. Current therapies still include chemotherapeutic combinations of gemcitabine and cisplatin for the majority of the patients showing poor results due the apparition of resistance. This situation led us to interrogate the potential role in the development of chemoresistance of ZEB1, an EMT-inducing transcription factor (EMT-TF) that we previously identified as a pro tumorigenic factor in tumour cells and CAF of iCCA. By analysing human CCA samples and sc-RNAseq public databases, we show here that ZEB1 is present in the tumour microenvironment of all iCCA patients tested and is prominently expressed by CAF. Using cellular models of CAF, we show that cells depleted for ZEB1 are more sensitive to gemcitabine and cisplatin, via a mechanism involving the regulation of the anti-apoptotic gene BCL2. Moreover, ZEB1 expressing CAF protect iCCA tumour cells against the toxicity of the chemotherapeutic drugs, an event that could be reversed by a BCL2 inhibitor venetoclax. Therefore, our results point to the use of BCL2 inhibitors to improve the efficacy of current chemotherapeutic regimens of gemcitabine and cisplatin in iCCA patients.

cancer biology↗

bioRxiv2024

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

ObjectiveThe transcriptomic classification of intrahepatic cholangiocarcinomas (iCCA) has been recently refined from two to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely used in the clinic because of technical limitations, including insufficient tissue material or the cost of molecular analyses. Here, we assessed a self-supervised learning (SSL) model for predicting iCCA transcriptomic classes on whole-slide digital histological images (WSIs) DesignTranscriptomic classes defined from RNAseq data were available for all samples. The SSL method, called Giga-SSL, was used to train our model on a discovery set of 766 biopsy slides (n=137 cases) and surgical samples (n=109 cases) from 246 patients in a five-fold cross-validation scheme. The model was validated in The Cancer Genome Atlas (TCGA) (n= 29) and a French external validation set (n=32). ResultsOur model showed good to very good performance in predicting the four most frequent transcriptomic class in the discovery set (area under the curve [AUC]: 0.63-0.84), especially for the hepatic stem-like class (37% of cases, AUC 0.84). The model performed equally well in predicting these four transcriptomic classes in the two validation sets, with AUCs ranging from 0.76 to 0.80 in the TCGA set and 0.62 to 0.92 in the French external set. ConclusionWe developed and validated an SSL-based model for predicting iCCA transcriptomic classes on routine histological slides of biopsy and surgical samples, which may impact iCCA management by predicting prognosis and guiding the treatment strategy.

cancer biology↗

bioRxiv2022

PACpAInt: a deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides

Pancreatic ductal adenocarcinoma (PAC) is a highly heterogeneous and plastic tumor with different transcriptomic molecular subtypes that hold great prognostic and theranostic values. We developed PACpAInt, a multistep approach using deep learning models to determine tumor cell type and their molecular phenotype on routine histological preparation at a resolution enabling to decipher complete intratumor heterogeneity on a massive scale never achieved before. PACpAInt effectively identified molecular subtypes at the slide level in three validation cohorts and had an independent prognostic value. It identified an interslide heterogeneity within a case in 39% of tumors that impacted survival. Diving at the cell level, PACpAInt identified "pure" classical and basal-like main subtypes as well as an intermediary phenotype and hybrid tumors that co-carried both classical and basal-like phenotypes. These novel artificial intelligence-based subtypes, together with the proportion of basal-like cells within a tumor had a strong prognostic impact.

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↗