Biology subjects

Zucman-Rossi, J.

Publications and source records attributed to Zucman-Rossi, J..

5 recordsLinked to original sources

bioRxiv2025

Hepatocyte expression of fetal insulin receptor isoform contributes to the promotion of liver cancer through non-cell autonomous mechanisms

The insulin receptor (INSR) exists in two isoforms, INSR-A and INSR-B, resulting from alternative splicing of the INSR pre-mRNA. INSR-B mediates the metabolic and mitogenic effects of insulin in the adult liver, while INSR-A is expressed during development. Recently, INSR-A has been detected in pathological murine and human livers. Here, we develop an in vivo CRISPR/Cas9 strategy to assess the impact of INSR-A on mouse liver homeostasis and susceptibility to carcinogenesis. We find that INSR-A expression in hepatocytes leads to the spontaneous development of liver tumours and also increases tumour initiation in a context of {beta}-catenin-driven liver carcinogenesis. Mechanistically, this is attributed to the higher intrinsic capacity of INSR-A expressing hepatocytes to enter apoptosis, rendering the microenvironment more inflammatory, thus making way for the proliferation of preneoplastic cells. Collectively, our data highlight a novel function for INSR-A in promoting liver cancer via non-cell autonomous mechanisms.

cancer biology↗

bioRxiv2022

Discovering cryptic splice mutations in cancers via a deep neural network framework

Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house liver cancer series, SpliceAI uncovers many cryptic splice mutations, located outside essential splice sites, that validate at a high rate in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 18,115 tumors, revealing >100,000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing >100 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis reveals the mutational processes that give rise to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

bioinformatics↗

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↗

bioRxiv2019

MS.liverK: an R package for transcriptome-based computation of molecular subtypes and functional signatures in liver cancer

SummaryLiver cancer is a highly heterogeneous disease in terms of etiology, tissue and cellular morphology, tumor molecular characteristics, microenvironment composition and prognosis. Several studies, based on tumor gene-expression profiling (GEP) data, have dissected the molecular heterogeneity of liver cancer. They resulted in various tools, either delineating homogeneous tumor subtypes or calculating molecular scores of prognostic or biological functions. Here, we present MS.liverK, an easy-to-use R package providing a comprehensive implementation of these tools, for research use. Availability and implementationThe MS.liverK R package is available from GitHub (https://github.com/cit-bioinfo/MS.liverK).

bioinformatics↗

bioRxiv2018

Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma

Alterations of individual genes variably affect development of hepatocellular carcinoma (HCC), prompting the need to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRN). Here, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of CTNNB1 ({beta}-CATENIN). LIN28B and CTNNB1 form a functional GRN with SMARCA4 (BRG1), Let-7b, SOX9, TP53 and MYC. GRN activity is detected in HCC and gastrointestinal cancers; it negatively correlates with HCC prognosis and contributes to a transcriptomic profile typical of the proliferative class of HCC. Using data from The Cancer Genome Atlas and from transcriptomic, transfection and mouse transgenic experiments, we generated and validated a quantitative mathematical model of the GRN. The model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal condition, and irreversibly in HCC. We conclude that identification and modelling of the GRN provides insight into prognosis, mechanisms of tumor-promoting genes and response to pharmacological agents in HCC.

cancer biology↗