scholarly journals Mouse models of human cancer and the need for more translational research

2008 ◽  
Author(s):  
Martin Fenner
Keyword(s):  
Mouse Model ◽  
Translational Research ◽  
Synovial Sarcoma ◽  
Mouse Models ◽  
Human Disease ◽  
Human Cancer ◽  
Neuropsychiatric Disorders ◽  
International Congress

One of the opening lectures this Saturday of the International Congress of Genetics was held by Mario Capecchi. His talked was entitled Modeling human disease in the mouse: from cancer to neuropsychiatric disorders. In the first half he described his mouse model of synovial sarcoma. ...

scholarly journals A missense mutation in Grm6 reduces but does not eliminate mGluR6 expression or rod depolarizing bipolar cell function

2017 ◽  
Vol 118 (2) ◽  
pp. 845-854 ◽  
Author(s):  
Neal S. Peachey ◽  
Nazarul Hasan ◽  
Bernard FitzMaurice ◽  
Samantha Burrill ◽  
Gobinda Pangeni ◽  
...  
Keyword(s):  
Mouse Model ◽  
Missense Mutation ◽  
Mouse Models ◽  
Human Disease ◽  
Bipolar Cell ◽  
Night Blindness ◽  
Cell Function ◽  
Congenital Stationary Night Blindness ◽  
Depolarizing Bipolar Cell

This article describes a mouse model of the human disease complete congenital stationary night blindness in which the mutation reduces but does not eliminate GRM6 expression and bipolar cell function, a phenotype distinct from that seen in other Grm6 mouse models.

scholarly journals Mice, Models, Microbiota: How Can We More Accurately Reflect Human Disease?

2021 ◽  
Vol 15 (1) ◽  
pp. 8
Author(s):  
Rahman Ladak ◽  
Dana Philpott
Keyword(s):  
Gut Microbiota ◽  
Translational Research ◽  
Mouse Models ◽  
Human Disease ◽  
Specific Pathogen Free ◽  
Germ Free ◽  
Stage Of Development ◽  
Specific Pathogen

With growing evidence that human disease is affected by the microbiota, many researchers have sought to modulate the microbiomes of mice to improve translational research. Altering their microbiomes, which are usually germ-free or specific pathogen-free, might allow mice to more accurately model human disease and hence produce more applicable findings. However, this has been difficult to apply to individual projects due to the disparity of explained methods and results. In this review, we first describe the immunological functions of the gut microbiota and the methods of altering mice microbiota, from transplantation route to age of transplantation to microbiota source. We then present an approach for how the gut microbiota might be considered when modelling human disease in mice. By organizing findings by type of disease - neurological, immunological, chronic inflammatory, and cancer - we propose that mouse models can be improved by considering the source of the microbiota, the presence or absence of certain microbial phyla, and by timing the transplantation during a physiologically relevant stage of development, such as the first five weeks of life.

scholarly journals Translating translational research: mouse models of human disease

2013 ◽  
Vol 10 (5) ◽  
pp. 373-374 ◽  
Author(s):  
Amanda M Burkhardt ◽  
Albert Zlotnik
Keyword(s):  
Translational Research ◽  
Mouse Models ◽  
Human Disease

scholarly journals A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

2018 ◽  
Author(s):  
Douglas K. Brubaker ◽  
Elizabeth A. Proctor ◽  
Kevin M. Haigis ◽  
Douglas A. Lauffenburger
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mouse Model ◽  
Mouse Models ◽  
Differentially Expressed Genes ◽  
Inflammatory Disease ◽  
Human Disease ◽  
Model Systems ◽  
Learning Approach

ABSTRACTThe high failure rate of therapeutics showing promise in mouse disease models to translate to patients is a pressing challenge in biomedical science. However, mouse models are a useful tool for evaluating mechanisms of disease and prioritizing novel therapeutic agents for clinical trials. Though retrospective studies have examined the fidelity of mouse models of inflammatory disease to their respective human in vivo conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for prospective inference of disease-associated human in vivo differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human in vivo biology from mouse model datasets. We found that a semi-supervised artificial neural network identified significantly more true human in vivo associations than interpreting mouse experiments directly (95% CI on F-score for mouse experiments [0.090, 0.175], neural network [0.278, 0.375], p = 0.00013). Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches combining mouse and human data for biological inference provides the most accurate assessment of human in vivo disease and therapeutic mechanisms. The task of translating insights from model systems to human disease contexts may therefore be better accomplished by the use of systems modeling driven approaches.Author SummaryComparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts. Here, we address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse experiment datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and pathways as if the model system experiment had been run in the human cohort. A semi-supervised version of a feed forward artificial neural network was the most efficacious model for translating experimentally derived mouse molecule-phenotype associations to the human in vivo disease context. We find that computational generalization of signaling insights from mouse to human contexts substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients.

scholarly journals Takes one to B1a: Dismantling the origin of mantle cell lymphoma

2021 ◽  
Vol 218 (10) ◽  
Author(s):  
Anna E. Beaudin
Keyword(s):  
Mouse Model ◽  
Mantle Cell Lymphoma ◽  
Mouse Models ◽  
Human Disease ◽  
Cell Lymphoma ◽  
Cell Of Origin ◽  
Cyclin D2 ◽  
B1a Cells ◽  
Mantle Cell

Therapeutic discovery for mantle cell lymphoma (MCL) has been hindered by a lack of preclinical mouse models that recapitulate human disease. In this issue, Pieters and colleagues (2021. J. Exp. Med.https://doi.org/10.1084/jem.20202280) establish a novel mouse model of MCL driven by overexpression of cyclin D2 and identify fetal-derived B1a cells as putative cell of origin for MCL.

scholarly journals Evaluation Fucoidan Extracts From Undaria pinnatifida and Fucus vesiculosus in Combination With Anticancer Drugs in Human Cancer Orthotopic Mouse Models

2017 ◽  
Vol 17 (3) ◽  
pp. 755-761 ◽  
Author(s):  
Maryam Burney ◽  
Lata Mathew ◽  
Anjali Gaikwad ◽  
Elizabeth K. Nugent ◽  
Anneliese O. Gonzalez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Mouse Model ◽  
Mouse Models ◽  
Undaria Pinnatifida ◽  
Human Cancer ◽  
Fucus Vesiculosus ◽  
Cancer Models ◽  
Mcf 7

Objective: To determine the activity of fucoidan from Undaria pinnatifida (UPF) and Fucus vesiculosus (FVF) when given in combination of chemotherapy drugs using selected human breast or ovarian cancer orthotopic mouse models. Methods: Mice were inoculated with 1 × 106 cells of TOV-112d, MCF-7, or ZR-75 subcutaneously or SKOV3-GFP-Luc intraperitoneally on day 0. MCF-7 and ZR-75 mice were administered with estradiol valerate 2 mg/kg in 0.2 mL castor oil subcutaneously two days prior to cell inoculation. Mice were randomized to one of six arms (N = 10/arm) paclitaxel, UPF/paclitaxel, FVF/paclitaxel, tamoxifen, UPF/tamoxifen, or FVF/tamoxifen. Tumors were measured three times per week for 28 days. Results: Improved activity was observed with UPF or FVF in combination with tamoxifen in both the MCF-7 and ZR-75D breast cancer mouse models. Decreased activity of paclitaxel was observed when given in combination with UPF or FVF in both breast cancer mouse models. The combination of FVF/tamoxifen in the TOV-112d ovarian cancer mouse model had improved activity but no there was difference observed with the UPF/tamoxifen in either ovarian cancer mouse model. No difference was observed with combination of UPF or FVF with paclitaxel in human ovarian cancer SKOV3 or TOV-112d orthotopic mouse models. Conclusion: This study did confirm that UPF/FVF in combination with tamoxifen did not decrease tamoxifen activity in both breast and ovarian cancer, with some potential to improve activity compared to tamoxifen alone in breast cancers. Previous in vitro studies had suggested UPF and FVF had overall synergistic activity with paclitaxel; however, in the current in vivo human cancer mouse model studies there was no change in paclitaxel activity when given in combination with UPF or FVF in either of the two human ovarian cancer models. Furthermore, this study demonstrated that UPF or FVF given in combination with paclitaxel had a potential antagonistic effect in breast cancer models. Additional studies are warranted to delineate mechanisms contributing to variation in the in vivo activity when given in combination with paclitaxel. As a first step, a clinical pharmacokinetic study evaluating impact of FVF/UPF given in combination with chemotherapy in patients with solid tumors is underway.

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