Animal Modeling of Pediatric Liver Cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Whitlock RS
PROVIDER: S-EPMC7072332 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
Publications
Animal Modeling of Pediatric Liver Cancer.
Whitlock Richard S RS Yang Tianyou T Vasudevan Sanjeev A SA Woodfield Sarah E SE
Cancers 20200122 2
Hepatoblastoma (HB) is the most common pediatric liver malignancy. Management of HB requires multidisciplinary efforts. The 5-year overall survival of this disease is about 80% in developed countries. Despite advances in the care of these patients, survival in recurrent or treatment-refractory disease is lower than 50%. This is due to more complex tumor biology, including hepatocellular carcinoma (HCC)-like mutations and expression of aggressive gene signatures leading to chemoresistance, vascul ...[more]
Similar Datasets
Project description:Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.
| S-EPMC5263056 | biostudies-literature
Project description:Mouse models of medulloblastoma have proven to be instrumental in understanding disease mechanisms, particularly the role of epigenetic and molecular drivers, and establishing appropriate preclinical pipelines. To date, our research community has developed murine models for all four groups of medulloblastoma, each of which will be critical for the identification and development of new therapeutic approaches. Approaches to modeling medulloblastoma range from genetic engineering with CRISPR/Cas9 or in utero electroporation, to orthotopic and patient-derived orthotopic xenograft systems. Each approach or model presents unique advantages that have ultimately contributed to an appreciation of medulloblastoma heterogeneity and the clinical obstacles that exist for this patient population.
| S-EPMC7317774 | biostudies-literature
Project description:When mimicking epileptic processes in a laboratory setting, it is important to understand the differences between experimental models of seizures and epilepsy. Because human epilepsy is defined by the appearance of multiple spontaneous recurrent seizures, the induction of a single acute seizure without recurrence does not constitute an adequate epilepsy model. Animal models of epilepsy might be useful for various tasks. They allow for the investigation of pathophysiological mechanisms of the disease, the evaluation, or the development of new antiepileptic treatments, and the study of the consequences of recurrent seizures and neurological and psychiatric comorbidities. Although clinical relevance is always an issue, the development of models of pediatric epilepsies is particularly challenging due to the existence of several key differences in the dynamics of human and rodent brain maturation. Another important consideration in modeling pediatric epilepsy is that "children are not little adults," and therefore a mere application of models of adult epilepsies to the immature specimens is irrelevant. Herein, we review the models of pediatric epilepsy. First, we illustrate the differences between models of pediatric epilepsy and models of the adulthood consequences of a precipitating insult in early life. Next, we focus on new animal models of specific forms of epilepsies that occur in the developing brain. We conclude by emphasizing the deficiencies in the existing animal models and the need for several new models.
| S-EPMC3337021 | biostudies-literature
Project description:Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that is a combination of two steps: first predicting movement behavioral states and second predicting the animal's velocity. We specify this framework at the individual level as well as for collective movement. We use Random Forests, Neural and Recurrent Neural Networks to compare performance predicting one step ahead as well as long range simulations. We compare results against a custom constructed Stochastic Differential Equation (SDE) model. We apply this approach to high resolution ant movement data. We found that the individual level Machine Learning and Deep Learning methods outperformed the SDE model for one step ahead prediction. The SDE model did comparatively better at simulating long range movement behaviour. Of the Machine Learning and Deep Learning models the Long Short Term Memory (LSTM) individual level model did best at long range simulations. We also applied the Random Forest and LSTM individual level models to model gull migratory movement to demonstrate the generalizability of this framework. Machine Learning and deep learning models are easier to specify compared to traditional parametric movement models which can have restrictive assumptions. However, machine learning and deep learning models are less interpretable than parametric movement models. The type of model used should be determined by the goal of the study, if the goal is prediction, our study provides evidence that machine learning and deep learning models could be useful tools.
| S-EPMC7384613 | biostudies-literature
Project description:The development of hepatoblastoma (HBL) is associated with failure of hepatic stem cells (HSC) to differentiate into hepatocytes. Despite intensive investigations, mechanisms of the failure of HSC to differentiate are not known. We found that oncogene Gankyrin (Gank) is involved in the inhibition of differentiation of HSC via triggering degradation of tumor suppressor proteins (TSPs) Rb, p53, C/EBPα and HNF4α. Our data show that the activation of a repressor of Gank, farnesoid X receptor, FXR, after initiation of liver cancer by Diethylnitrosamine (DEN) prevents the development of liver cancer by inhibiting Gank and rescuing tumor suppressor proteins. We next analyzed FXR-Gank-Tumor suppressor pathways in a large cohort of HBL patients which include 6 controls and 53 HBL samples. Systemic analysis of these samples and RNA-Seq approach revealed that the FXR-Gank axis is activated; markers of hepatic stem cells are dramatically elevated and hepatocyte markers are reduced in HBL samples. In the course of these studies, we found that RNA binding protein CUGBP1 is a new tumor suppressor protein which is reduced in all HBL samples. Therefore, we generated CUGBP1 KO mice and examined HBL signatures in the liver of these mice. Micro-array studies revealed that the HBL-specific molecular signature is developed in livers of CUGBP1 KO mice at very early ages. Thus, we conclude that FXR-Gank-TSPs-Stem cells pathway is a key determinant of liver cancer in animal models and in pediatric liver cancer. Our data provide a strong basis for development of FXR-Gank-based therapy for treatment of patients with hepatoblastoma.
| S-EPMC5862323 | biostudies-literature
Project description:Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.
| S-EPMC5766105 | biostudies-literature
Project description:Yttrium-90 (90Y) radioembolization is a minimally invasive procedure increasingly used for advanced liver cancer treatment. In this method, radioactive microspheres are injected into the hepatic arterial bloodstream to target, irradiate, and kill cancer cells. Accurate and precise treatment planning can lead to more efficient and safer treatment by delivering a higher radiation dose to the tumor while minimizing the exposure of the surrounding liver parenchyma. Treatment planning primarily relies on the estimated radiation dose delivered to tissue. However, current methods used to estimate the dose are based on simplified assumptions that make the dosimetry results unreliable. In this work, we present a computational model to predict the radiation dose from the 90Y activity in different liver segments to provide a more realistic and personalized dosimetry. Computational fluid dynamics (CFD) simulations were performed in a 3D hepatic arterial tree model segmented from cone-beam CT angiographic data obtained from a patient with hepatocellular carcinoma (HCC). The microsphere trajectories were predicted from the velocity field. 90Y dose distribution was then calculated from the volumetric distribution of the microspheres. Two injection locations were considered for the microsphere administration, a lobar and a selective injection. Results showed that 22% and 82% of the microspheres were delivered to the tumor, after each injection, respectively, and the combination of both injections ultimately delivered 49% of the total administered 90Y microspheres to the tumor. Results also illustrated the nonhomogeneous distribution of microspheres between liver segments, indicating the importance of developing patient-specific dosimetry methods for effective radioembolization treatment.
| S-EPMC7580665 | biostudies-literature
Project description:Classification of human liver cancer into biologically distinct subgroups suggests origin from different hepatic lineage cells. To clarify contribution of lineage stage in liver oncogenesis, we transduced H-Ras and SV40LT into hepatic progenitor cells (HPC), hepatoblasts (HB) and terminally differentiated adult hepatocytes (AH). Regardless of origin, all transformed cell types acquired common cancer stem cell characteristics in vitro and in vivo. However, expression analysis distinguished tumors of different cellular origin underscoring the contribution of lineage/stage-dependent genetic changes in malignant transformation. Notably, AH-derived tumors showed specific enrichment of c-Myc target genes. These data establish that any hepatic lineage cell can be a target for transformation and acquire common cancer stem cell traits via activation of diverse cell type specific pathways. Profiling of individual tumors derived by Ras/SV40 transformation of hepatic progenitor cell (oval cells), hepatoblasts (fetal) and adult hepatocytes
2013-10-01 | E-GEOD-41312 | biostudies-arrayexpress
Project description:Liver transplantation (LT) remains the gold standard treatment for end stage liver disease in the pediatric population. For liver based metabolic disorders (LBMDs), the decision for LT is predicated on a different set of paradigms. With improved outcomes post-transplantation, LT is no longer merely life saving, but has the potential to also significantly improve quality of life. This review summarizes the clinical presentation, medical treatment and indications for LT for some of the common LBMDs. We also provide a practical update on the dilemmas and controversies surrounding the indications for transplantation, surgical considerations and prognosis and long terms outcomes for pediatric LT in LBMDs. Important progress has been made in understanding these diseases in recent years and with that we outline some of the new therapies that have emerged.
| S-EPMC8568579 | biostudies-literature
Project description:The lack of a preclinical model of progressive non-alcoholic steatohepatitis (NASH) that recapitulates human disease is a barrier to therapeutic development.A stable isogenic cross between C57BL/6J (B6) and 129S1/SvImJ (S129) mice were fed a high fat diet with ad libitum consumption of glucose and fructose in physiologically relevant concentrations and compared to mice fed a chow diet and also to both parent strains.Following initiation of the obesogenic diet, B6/129 mice developed obesity, insulin resistance, hypertriglyceridemia and increased LDL-cholesterol. They sequentially also developed steatosis (4-8weeks), steatohepatitis (16-24weeks), progressive fibrosis (16weeks onwards) and spontaneous hepatocellular cancer (HCC). There was a strong concordance between the pattern of pathway activation at a transcriptomic level between humans and mice with similar histological phenotypes (FDR 0.02 for early and 0.08 for late time points). Lipogenic, inflammatory and apoptotic signaling pathways activated in human NASH were also activated in these mice. The HCC gene signature resembled the S1 and S2 human subclasses of HCC (FDR 0.01 for both). Only the B6/129 mouse but not the parent strains recapitulated all of these aspects of human NAFLD.We here describe a diet-induced animal model of non-alcoholic fatty liver disease (DIAMOND) that recapitulates the key physiological, metabolic, histologic, transcriptomic and cell-signaling changes seen in humans with progressive NASH.We have developed a diet-induced mouse model of non-alcoholic steatohepatitis (NASH) and hepatic cancers in a cross between two mouse strains (129S1/SvImJ and C57Bl/6J). This model mimics all the physiological, metabolic, histological, transcriptomic gene signature and clinical endpoints of human NASH and can facilitate preclinical development of therapeutic targets for NASH.
| S-EPMC5012902 | biostudies-literature