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A novel spontaneous hepatocellular carcinoma mouse model for studying T-cell exhaustion in the tumor microenvironment.

ABSTRACT: Immunotherapy has ushered in a new era of cancer therapy, and this is also applicable to therapy of hepatocellular carcinoma (HCC). In this context, effective development of therapeutic strategies requires an HCC mouse model with known tumor-associated antigens (TAAs) and an HCC growth reporter. We created such a model using hydrodynamic injection and a transposon system to introduce AKT and NRAS and open reading frames (ORFs) encoding surrogate tumor antigens and luciferase into chromosomes of hepatocytes to induce nodular and diffuse tumors in the liver. TAA-specific CD8⁺ T cells were detected during HCC progression; however, these showed exhausted-like phenotypes and were unable to control tumor growth. Myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAM) from the tumor microenvironment were found to contribute to the suppression of the CD8⁺ T-cell response. The transposon-based Akt/N-Ras-induced HCC mouse model we developed enables researchers to monitor tumor growth non-invasively and to quantify and characterize endogenous or adoptively transferred TAA-specific CD8⁺ T-cell responses. These features make it a suitable preclinical model for exploration and evaluation of immune checkpoint inhibitors and cell-based immunotherapies for HCC treatment.

SUBMITTER: Liu YT

PROVIDER: S-EPMC6286542 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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A novel spontaneous hepatocellular carcinoma mouse model for studying T-cell exhaustion in the tumor microenvironment.

Liu Yu-Tzu YT Tseng Tai-Chung TC Soong Ruey-Shyang RS Peng Chun-Yi CY Cheng Yu-Hsing YH Huang Shiu-Feng SF Chuang Tsung-Hsien TH Kao Jia-Horng JH Huang Li-Rung LR

Journal for immunotherapy of cancer 20181207 1

Immunotherapy has ushered in a new era of cancer therapy, and this is also applicable to therapy of hepatocellular carcinoma (HCC). In this context, effective development of therapeutic strategies requires an HCC mouse model with known tumor-associated antigens (TAAs) and an HCC growth reporter. We created such a model using hydrodynamic injection and a transposon system to introduce AKT and NRAS and open reading frames (ORFs) encoding surrogate tumor antigens and luciferase into chromosomes of ...[more]

PMID: 30526672

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Project description:Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is the third leading cause of tumor-related mortality worldwide. In recent years, the emergency of immune checkpoint inhibitor (ICI) has revolutionized the management of HCC. Especially, the combination of atezolizumab (anti-PD1) and bevacizumab (anti-VEGF) has been approved by the FDA as the first-line treatment for advanced HCC. Despite great breakthrough in systemic therapy, HCC continues to portend a poor prognosis owing to drug resistance and frequent recurrence. The tumor microenvironment (TME) of HCC is a complex and structured mixture characterized by abnormal angiogenesis, chronic inflammation, and dysregulated extracellular matrix (ECM) remodeling, collectively contributing to the immunosuppressive milieu that in turn prompts HCC proliferation, invasion, and metastasis. The tumor microenvironment coexists and interacts with various immune cells to maintain the development of HCC. It is widely accepted that a dysfunctional tumor-immune ecosystem can lead to the failure of immune surveillance. The immunosuppressive TME is an external cause for immune evasion in HCC consisting of 1) immunosuppressive cells; 2) co-inhibitory signals; 3) soluble cytokines and signaling cascades; 4) metabolically hostile tumor microenvironment; 5) the gut microbiota that affects the immune microenvironment. Importantly, the effectiveness of immunotherapy largely depends on the tumor immune microenvironment (TIME). Also, the gut microbiota and metabolism profoundly affect the immune microenvironment. Understanding how TME affects HCC development and progression will contribute to better preventing HCC-specific immune evasion and overcoming resistance to already developed therapies. In this review, we mainly introduce immune evasion of HCC underlying the role of immune microenvironment, describe the dynamic interaction of immune microenvironment with dysfunctional metabolism and the gut microbiome, and propose therapeutic strategies to manipulate the TME in favor of more effective immunotherapy.

Project description:IntroductionHepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system. RNA methylation plays an important role in tumorigenesis and metastasis, which could alter gene expression and even function at multiple levels, such as RNA splicing, stability, translocation, and translation. In this study, we aimed to conduct a comprehensive analysis of RNA methylation-related genes (RMGs) in HCC and their relationship with survival and clinical features.MethodsA retrospective analysis was performed using publicly available HCC-related datasets. The differentially expressed genes (DEGs) between HCC and controls were identified from TCGA-LlHC and intersected with RMGs to obtain differentially expressed RNA methylation-related genes (DERMGs). Regression analysis was used to screen for prognostic genes and construct risk models. Simultaneously, clinical, immune infiltration and therapeutic efficacy analyses were performed. Finally, multivariate cox regression was used to identify independent risk factors, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression levels of the core genes of the model.ResultsA 21-gene risk model for HCC was established with excellent performance based on ROC curves and survival analysis. Risk scores correlated with tumor grade, pathologic T, and TNM stage. Immune infiltration analysis showed correlations with immune scores, 11 immune cells, and 30 immune checkpoints. Low-risk patients showed a higher susceptibility to immunotherapy. The risk score and TNM stage were independent prognostic factors. qRT-PCR confirmed higher expression of PRDM9, ALPP, and GAD1 in HCC.ConclusionsThis study identified RNA methylation-related signature genes in HCC and constructed a risk model that predicts patient outcomes and reflects the immune microenvironment. Prognostic genes are involved in complex regulatory mechanisms, which may be useful for cancer diagnosis, prognosis, and therapy.

Project description:Background: In recent years, immunotherapy has changed the therapeutic landscape of hepatocellular carcinoma (HCC). Since the efficacy of immunotherapy is closely related to the tumor microenvironment (TME), in this study, we constructed a prognostic model based on TME to predict the prognosis and immunotherapy effect of HCC patients. Methods: Transcriptome and follow-up data of 374 HCC patients were acquired from the TCGA Cancer Genome Atlas (TCGA) database. The immune/stromal/estimate scores (TME scores) and tumor purity were calculated using the ESTIMATE algorithm and the module most associated with TME scores were screened by the weighted gene co-expression network analysis (WGCNA). A TME score-related prognostic model was constructed and patients were divided into a high-risk group and a low-risk group. Kaplan-Meier survival curves and receiver operator characteristic curve (ROC) were used to evaluate the performance of the TME risk prognostic model and validated with the external database International Cancer Genome Consortium (ICGC) cohort. Combined with clinicopathologic factors, a prognostic nomogram was established. The nomogram's ability to predict prognosis was assessed by ROC, calibration curve, and the decision curve analysis (DCA). Gene Set Enrichment Analyses (GSEA) were conducted to explore the underlying biological functions and pathways of this risk signature. Moreover, the possible correlation of risk signature with TME immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, single-nucleotide polymorphisms (SNPs), and drug sensitivity were assessed. Finally, real-time PCR was used to verify the gene expression levels in normal liver cells and cancer cells. Results: KM survival analysis results indicated that high immune/stromal/estimate score groups were closely associated with a better prognosis, while the tumor purity showed a reverse trend (p < 0.01). WGCNA demonstrated that the yellow module was significantly correlated with the TME score. The 5-genes TME risk signature was built to predict the prognosis of patients with HCC including DAB2, IL18RAP, RAMP3, FCER1G, and LHFPL2. Patients with a low-risk score have higher levels of tumor-infiltrating immune cells and higher expression of immune checkpoints, which may be more sensitive to immunotherapy. Conclusion: It provided a theoretical basis for predicting the prognosis and personalized treatment of patients with HCC.

Dataset Information

A novel spontaneous hepatocellular carcinoma mouse model for studying T-cell exhaustion in the tumor microenvironment.

Publications

A novel spontaneous hepatocellular carcinoma mouse model for studying T-cell exhaustion in the tumor microenvironment.

Similar Datasets

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