scholarly journals Genomic Perspective on Mouse Liver Cancer Models

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1648
Author(s):  
Sun Young Yim ◽  
Ju-Seog Lee
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mouse Models ◽  
Mouse Liver ◽  
Cancer Genomics ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Genomic Data ◽  
Preclinical Research ◽  
Cancer Models ◽  
Distinct Aspect

Selecting the most appropriate mouse model that best recapitulates human hepatocellular carcinoma (HCC) allows translation of preclinical mouse studies into clinical studies. In the era of cancer genomics, comprehensive and integrative analysis of the human HCC genome has allowed categorization of HCC according to molecular subtypes. Despite the variety of mouse models that are available for preclinical research, there is a lack of evidence for mouse models that closely resemble human HCC. Therefore, it is necessary to identify the accurate mouse models that represent human HCC based on molecular subtype as well as histologic aggressiveness. In this review, we summarize the mouse models integrated with human HCC genomic data to provide information regarding the models that recapitulates the distinct aspect of HCC biology and prognosis based on molecular subtypes.

scholarly journals Integrative genomic analysis of mouse and human hepatocellular carcinoma

2018 ◽  
Vol 115 (42) ◽  
pp. E9879-E9888 ◽  
Author(s):  
Michelle Dow ◽  
Rachel M. Pyke ◽  
Brian Y. Tsui ◽  
Ludmil B. Alexandrov ◽  
Hayato Nakagawa ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mouse Models ◽  
Cancer Genomics ◽  
Genomic Analysis ◽  
Human Tumors ◽  
Low Grade ◽  
Mhc I ◽  
Molecular Features ◽  
Frequent Mutations

Cancer genomics has enabled the exhaustive molecular characterization of tumors and exposed hepatocellular carcinoma (HCC) as among the most complex cancers. This complexity is paralleled by dozens of mouse models that generate histologically similar tumors but have not been systematically validated at the molecular level. Accurate models of the molecular pathogenesis of HCC are essential for biomedical progress; therefore we compared genomic and transcriptomic profiles of four separate mouse models [MUP transgenic, TAK1-knockout, carcinogen-driven diethylnitrosamine (DEN), and Stelic Animal Model (STAM)] with those of 987 HCC patients with distinct etiologies. These four models differed substantially in their mutational load, mutational signatures, affected genes and pathways, and transcriptomes. STAM tumors were most molecularly similar to human HCC, with frequent mutations in Ctnnb1, similar pathway alterations, and high transcriptomic similarity to high-grade, proliferative human tumors with poor prognosis. In contrast, TAK1 tumors better reflected the mutational signature of human HCC and were transcriptionally similar to low-grade human tumors. DEN tumors were least similar to human disease and almost universally carried the Braf V637E mutation, which is rarely found in human HCC. Immune analysis revealed that strain-specific MHC-I genotype can influence the molecular makeup of murine tumors. Thus, different mouse models of HCC recapitulate distinct aspects of HCC biology, and their use should be adapted to specific questions based on the molecular features provided here.

scholarly journals Immune-Related DNA Methylation Data-Based Molecular Classification Associated with the Prognosis of Patients with Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiong-Wen Wang ◽  
Qian Yan ◽  
Bao-Qian Ye ◽  
Bo-Qing Wang ◽  
Wen-Jiang Zheng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Immune Cells ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Training Set ◽  
Cpg Sites ◽  
Immune Related Genes

Abstract Background: The combination of epigenetic drugs and immunotherapy should be able to develop an optimal treatment plan for hepatocellular carcinoma (HCC), yet its mechanism is still in the preliminary exploration stage. The purpose of this study is to analyze the DNA methylation and gene expression profiles of immune-related CpG sites to identify the molecular subtypes and CpG sites related to the prognosis of HCC. Methods: In this study, the DNA methylation and gene expression datasets were downloaded from The Cancer Genome Atlas database, together with immune-related genes downloaded from the immunology database and analysis portal database to explore the prognostic molecular subtypes of HCC. Univariate and multivariate survival analysis was used for selecting the significant methylation sites, and the consensus clustering was performed to find the best molecular subtype associated with the survival of HCC. Next, we used the least absolute shrinkage and selection operator (LASSO) algorithm to construct a prognostic-related model and performed internal verification. Finally, we explored the levels of 16 immune-related genes expression correlate with the infiltration levels of immune cells in HCC. Results: By performing consistent clustering analysis on 830 immune-related CpG sites in 231 samples of a training set, we identified seven subgroups with significant differences in overall survival. Finally, 16 classifiers of immune-related CpG sites were constructed and used in the testing set to verify the prognosis of DNA methylation subgroups, and the results were consistent with the training set. Using the TIMER database, we analyzed 16 immune-related CpG sites expression with the abundance of six types of immune infiltrating cells and found that most are positively correlated with the level of infiltration of multiple immune cells in HCC. Conclusions: This study screened potential immune-related prognostic methylation sites and established a new prognosis model of HCC based on DNA methylation molecular subtype, which may help in the early diagnosis of HCC and developing more effective personalized treatments.

scholarly journals Multiparametric Analyses of Hepatocellular Carcinoma Somatic Mouse Models and Their Associated Tumor Microenvironment

2021 ◽  
Vol 1 (6) ◽  
Author(s):  
Daniel Taranto ◽  
Christel F.A. Ramirez ◽  
Serena Vegna ◽  
Marnix H.P. Groot ◽  
Niels Wit ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Mouse Models

scholarly journals A Bioinformatics Analysis Identifies the Telomerase Inhibitor MST-312 for Treating High-STMN1-Expressing Hepatocellular Carcinoma

2021 ◽  
Vol 11 (5) ◽  
pp. 332
Author(s):  
Szu-Jen Wang ◽  
Pei-Ming Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Kinase Inhibitors ◽  
Cancer Genomics ◽  
Gene Signature ◽  
Migration And Invasion ◽  
Advanced Hcc ◽  
Telomerase Inhibitor ◽  
Cell Cycle Related Gene ◽  
Hcc Cells

Hepatocellular carcinoma (HCC) is a relatively chemo-resistant tumor. Several multi-kinase inhibitors have been approved for treating advanced HCC. However, most HCC patients are highly refractory to these drugs. Therefore, the development of more effective therapies for advanced HCC patients is urgently needed. Stathmin 1 (STMN1) is an oncoprotein that destabilizes microtubules and promotes cancer cell migration and invasion. In this study, cancer genomics data mining identified STMN1 as a prognosis biomarker and a therapeutic target for HCC. Co-expressed gene analysis indicated that STMN1 expression was positively associated with cell-cycle-related gene expression. Chemical sensitivity profiling of HCC cell lines suggested that High-STMN1-expressing HCC cells were the most sensitive to MST-312 (a telomerase inhibitor). Drug–gene connectivity mapping supported that MST-312 reversed the STMN1-co-expressed gene signature (especially BUB1B, MCM2/5/6, and TTK genes). In vitro experiments validated that MST-312 inhibited HCC cell viability and related protein expression (STMN1, BUB1B, and MCM5). In addition, overexpression of STMN1 enhanced the anticancer activity of MST-312 in HCC cells. Therefore, MST-312 can be used for treating STMN1-high expression HCC.

scholarly journals Basal and Luminal Molecular Subtypes in Naturally-Occurring Canine Urothelial Carcinoma are Associated with Tumor Immune Signatures and Dog Breed

2021 ◽  
pp. 1-17
Author(s):  
Breann C. Sommer ◽  
Deepika Dhawan ◽  
Audrey Ruple ◽  
José A. Ramos-Vara ◽  
Noah M. Hahn ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Cancer Progression ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Clinical Stage ◽  
Tumor Characteristics ◽  
Rna Seq ◽  
Advanced Clinical Stage ◽  
Naturally Occurring ◽  
Immune Signatures

BACKGROUND: Improved therapies are needed for patients with invasive urothelial carcinoma (InvUC). Tailoring treatment to molecular subtypes holds promise, but requires further study, including studies in pre-clinical animal models. Naturally-occurring canine InvUC harbors luminal and basal subtypes, mimicking those observed in humans, and could offer a relevant model for the disease in people. OBJECTIVE: To further validate the canine InvUC model, clinical and tumor characteristics associated with luminal and basal subtypes in dogs were determined, with comparison to findings from humans. METHODS: RNA sequencing (RNA-seq) analyses were performed on 56 canine InvUC tissues and bladder mucosa from four normal dogs. Data were aligned to CanFam 3.1, and differentially expressed genes identified. Data were interrogated with panels of genes defining luminal and basal subtypes, immune signatures, and other tumor features. Subject and tumor characteristics, and outcome data were obtained from medical records. RESULTS: Twenty-nine tumors were classified as luminal and 27 tumors as basal subtype. Basal tumors were strongly associated with immune infiltration (OR 52.22, 95%CI 4.68–582.38, P = 0.001) and cancer progression signatures in RNA-seq analyses, more advanced clinical stage, and earlier onset of distant metastases in exploratory analyses (P = 0.0113). Luminal tumors were strongly associated with breeds at high risk for InvUC (OR 0.06, 95%CI 0.01 –0.37, P = 0.002), non-immune infiltrative signatures, and less advanced clinical stage. CONCLUSIONS: Dogs with InvUC could provide a valuable model for testing new treatment strategies in the context of molecular subtype and immune status, and the search for germline variants impacting InvUC onset and subtype.

scholarly journals A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Hong ◽  
Yu Zhou ◽  
Xiangbang Xie ◽  
Wanrui Wu ◽  
Changsheng Shi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Prognostic Significance ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cumulative Distribution ◽  
Gene Module ◽  
Expression Levels

Abstract Background Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. Methods Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. Results We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. Conclusions Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC.

Mouse models for translational preclinical research in immuno-oncology

2016 ◽  
Vol 69 ◽  
pp. S53-S54
Author(s):  
A. Wulf-Goldenberg ◽  
M. Stecklum ◽  
Z. Reiner ◽  
I. Fichtner ◽  
J. Hoffmann
Keyword(s):  
Mouse Models ◽  
Preclinical Research

scholarly journals Development of a Novel Orthotopic Gastric Cancer Mouse Model

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Wonyoung Kang ◽  
Leigh Maher ◽  
Michael Michaud ◽  
Seong-Woo Bae ◽  
Seongyeong Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Mouse Models ◽  
Cancer Metastasis ◽  
Soft Tissues ◽  
Tumor Formation ◽  
Cancer Model ◽  
Orthotopic Model ◽  
Cancer Models ◽  
Organotropic Metastasis ◽  
Nsg Mouse

Abstract Background Gastric cancer metastasis is a highly fatal disease with a five-year survival rate of less than 5%. One major obstacle in studying gastric cancer metastasis is the lack of faithful models available. The cancer xenograft mouse models are widely used to elucidate the mechanisms of cancer development and progression. Current procedures for creating cancer xenografts include both heterotopic (i.e., subcutaneous) and orthotopic transplantation methods. Compared to the heterotopic model, the orthotopic model has been shown to be the more clinically relevant design as it enables the development of cancer metastasis. Although there are several methods in use to develop the orthotopic gastric cancer model, there is not a model which uses various types of tumor materials, such as soft tissues, semi-liquid tissues, or culture derivatives, due to the technical challenges. Thus, developing the applicable orthotopic model which can utilize various tumor materials is essential. Results To overcome the known limitations of the current orthotopic gastric cancer models, such as exposure of tumor fragments to the neighboring organs or only using firm tissues for the orthotopic implantation, we have developed a new method allowing for the complete insertion of soft tissue fragments or homogeneously minced tissues into the stomach submucosa layer of the immunodeficient NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mouse. With this completely-closed transplantation method, tumors with various types of tissue may be used to establish orthotopic gastric cancer models without the risks of exposure to nearby organs or cell leakage. This surgical procedure was highly reproducible in generating forty-eight mouse models with a surgery success rate of 96% and tumor formation of 93%. Among four orthotopic patient-derived xenograft (PDX) models that we generated in this study, we verified that the occurrence of organotropic metastasis in either the liver or peritoneal cavity was the same as that of the donor patients. Conclusion Here we describe a new protocol, step by step, for the establishment of orthotopic xenograft of gastric cancer. This novel technique will be able to increase the use of orthotopic models in broader applications for not only gastric cancer research but also any research related to the stomach microenvironment.

NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi139-vi139
Author(s):  
Jan Lost ◽  
Tej Verma ◽  
Niklas Tillmanns ◽  
W R Brim ◽  
Harry Subramanian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Prediction Accuracy ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Resection ◽  
Support Vector ◽  
Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

Sign in / Sign up

Export Citation Format

Share Document