Human-correlated genetic HCC models identify combination therapy for precision medicine
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ABSTRACT:
PROVIDER: PRJEB77358 | ENA |
REPOSITORIES: ENA
Similar Datasets
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 models identify combination therapy for precision medicine
| PRJNA1143392 | ENA
Project description:Human-correlated genetic HCC and HCC-organoid models identify combination therapy for precision medicine
| PRJNA1150922 | ENA
Project description:Human-correlated genetic HCC organoid models identify combination therapy for precision medicine
| PRJNA1150928 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2024-12-02 | GSE275444 | GEO
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 accumulate progressive driver mutations3, with hepatocytes the most likely cell of origin2. However, the landscape of driver mutations in HCC is broadly 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. Here we generated a suite of genetically driven immunocompetent in vivo and matched in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance and metastasis. 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 the human 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 previously been linked to HCC treatment. Cladribine acts in a highly effective subtype-specific manner in combination with standard-of-care therapy.
| S-EPMC11922762 | biostudies-literature
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:Identification of FAD subtypes in mouse HCC models
| PRJNA834137 | ENA
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