Analysis of heterogeneity within genetic murine models of HCC
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ABSTRACT:
PROVIDER: PRJNA1154667 | ENA |
REPOSITORIES: ENA
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Project description:Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer related mortality worldwide1,2. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulated progressive driver mutations3, with hepatocytes the most likely cell of origin2. However, the landscape of driver mutations in HCC is independent of the underlying aetiologies4. Despite an increasing range of systemic treatment options for advanced HCC outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations5,6. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC7. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human-mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with histopathology. In a proof-of-principle analysis, we verified response to standard of care treatment and used a linked in vitro-in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy.
2024-12-03 | GSE276089 | GEO
Project description:Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer related mortality worldwide. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulated progressive driver mutations, with hepatocytes the most likely cell of origin. However, the landscape of driver mutations in HCC is independent of the underlying aetiologies. Despite an increasing range of systemic treatment options for advanced HCC outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human-mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with histopathology. In a proof-of-principle analysis, we verified response to standard of care treatment and used a linked in vitro-in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy.
2024-12-02 | GSE273806 | GEO
Project description:Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer related mortality worldwide. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulated progressive driver mutations, with hepatocytes the most likely cell of origin. However, the landscape of driver mutations in HCC is independent of the underlying aetiologies. Despite an increasing range of systemic treatment options for advanced HCC outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human-mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with histopathology. In a proof-of-principle analysis, we verified response to standard of care treatment and used a linked in vitro-in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy.
2024-12-02 | GSE275443 | GEO
Project description:Human-correlated genetic HCC and HCC-organoid models identify combination therapy for precision medicine
| PRJNA1150922 | ENA
Project description:Transcriptional Heterogeneity within the Glucocorticoid Response
| PRJNA594939 | ENA
Project description:In this study, we identified and validated a molecular classification of hepatocellular carcinoma (HCC) based on 42 fatty acid degradation (FAD) genes in clinical samples. We further searched PubMed for the RNA sequencing datasets of mouse models to identify the FAD subtypes in mouse HCC models. A total of 90 samples were collected from five publicly available datasets including 11 mouse HCC models. In addition, the transcriptome sequencing data of 8 samples from our two mouse models (NRAS.MYC.ND and AKT1.MYC.KD) were also included. This dataset aims to explore the transcriptomic characteristics of these two models.
2023-05-01 | GSE202070 | GEO
Project description:Human-correlated genetic HCC models identify combination therapy for precision medicine
| PRJNA1143392 | ENA
Project description:Up to 41% of hepatocellular carcinomas (HCCs) result from activating mutations in the CTNNB1 gene encoding β-catenin. β-catenin has dual cellular functions as a component of the Wnt signaling pathway and adherens junctions. HCC-associated CTNNB1 mutations stabilize the β-catenin protein, leading to nuclear and/or cytoplasmic localization of β-catenin and downstream activation of Wnt target genes. In patient HCC samples, β-catenin nuclear and cytoplasmic localization are typically patchy, even among HCC with highly active CTNNB1 mutations. The functional and clinical relevance of this heterogeneity in β-catenin activation are not well understood. To define mechanisms of β-catenin-driven HCC initiation, we generated a Cre-lox system that enabled switching on activated β-catenin in 1) a small number of hepatocytes in early development; or 2) the majority of hepatocytes in later development or adulthood. We discovered that switching on activated β-catenin in a subset of larval hepatocytes was sufficient to drive HCC initiation. To determine the role of Wnt/β-catenin signaling heterogeneity later in hepatocarcinogenesis, we performed RNA-seq analysis of zebrafish β-catenin-driven HCC. Ingenuity Pathway Analysis of differentially expressed genes in the Cre-lox HCC model revealed that “Cancer” and “Liver Tumor” categories were significantly altered, indicating transcriptional similarities with human HCC and other vertebrate HCC models. At the single-cell level, 2.9% to 15.2% of hepatocytes from zebrafish β-catenin-driven HCC expressed two or more of the Wnt target genes axin2, mtor, glula, myca, and wif1, indicating focal activation of Wnt signaling in established tumors. Thus, heterogeneous β-catenin activation drives HCC initiation and persists throughout hepatocarcinogenesis.
2019-09-27 | GSE137787 | GEO
Project description:Identification of FAD subtypes in mouse HCC models
| PRJNA834137 | ENA