A Mouse Model of Cholangiocarcinoma Uncovers a Role for Tensin-4 in Tumor Progression.

BACKGROUND AND AIMS: Earlier diagnosis and treatment of intrahepatic cholangiocarcinoma (iCCA) are necessary to improve therapy, yet limited information is available about initiation and evolution of iCCA precursor lesions. Therefore, there is a need to identify mechanisms driving formation of precancerous lesions and their progression toward invasive tumors using experimental models that faithfully recapitulate human tumorigenesis. APPROACH AND RESULTS: To this end, we generated a mouse model which combines cholangiocyte-specific expression of KrasG12D with 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) diet-induced inflammation to mimic iCCA development in patients with cholangitis. Histological and transcriptomic analyses of the mouse precursor lesions and iCCA were performed and compared with human analyses. The function of genes overexpressed during tumorigenesis was investigated in human cell lines. We found that mice expressing KrasG12D in cholangiocytes and fed a DDC diet developed cholangitis, ductular proliferations, intraductal papillary neoplasms of bile ducts (IPNBs), and, eventually, iCCAs. The histology of mouse and human IPNBs was similar, and mouse iCCAs displayed histological characteristics of human mucin-producing, large-duct-type iCCA. Signaling pathways activated in human iCCA were also activated in mice. The identification of transition zones between IPNB and iCCA on tissue sections, combined with RNA-sequencing analyses of the lesions supported that iCCAs derive from IPNBs. We further provide evidence that tensin-4 (TNS4), which is stimulated by KRASG12D and SRY-related HMG box transcription factor 17, promotes tumor progression. CONCLUSIONS: We developed a mouse model that faithfully recapitulates human iCCA tumorigenesis and identified a gene cascade which involves TNS4 and promotes tumor progression.

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

BACKGROUND & AIMS: Alterations of individual genes variably affect the development of hepatocellular carcinoma (HCC). Thus, we aimed to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRNs). METHODS: Using data from The Cancer Genome Atlas, from the LIRI-JP (Liver Cancer - RIKEN, JP project), and from our transcriptomic, transfection and mouse transgenic experiments, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of ß-catenin (CTNNB1). We further generated and validated a quantitative mathematical model of the GRN using human cell lines and in vivo expression data. RESULTS: We found that LIN28B and CTNNB1 form a GRN with SMARCA4, Let-7b (MIRLET7B), SOX9, TP53 and MYC. GRN functionality is detected in HCC and gastrointestinal cancers, but not in other cancer types. GRN status negatively correlates with HCC prognosis, and positively correlates with hyperproliferation, dedifferentiation and HGF/MET pathway activation, suggesting that it contributes to a transcriptomic profile typical of the proliferative class of HCC. The mathematical 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 conditions, and irreversibly in HCC. The mathematical model is available via a web-based tool which can evaluate the GRN status of HCC samples and predict the impact of therapeutic agents on the GRN. CONCLUSIONS: We conclude that identification and modelling of the GRN provide insights into the prognosis of HCC and the mechanisms by which tumor-promoting genes impact on HCC development. LAY SUMMARY: Hepatocellular carcinoma (HCC) is a heterogeneous disease driven by the concomitant deregulation of several genes functionally organized as networks. Here, we identified a gene regulatory network involved in a subset of HCCs. This subset is characterized by increased proliferation and poor prognosis. We developed a mathematical model which uncovers the dynamics of the network and allows us to predict the impact of a therapeutic agent, not only on its specific target but on all the genes belonging to the network.