Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identifying mouse models for skin cancer using the Mouse Tumor Biology Database.

ABSTRACT: In recent years, the scientific community has generated an ever-increasing amount of data from a growing number of animal models of human cancers. Much of these data come from genetically engineered mouse models. Identifying appropriate models for skin cancer and related relevant genetic data sets from an expanding pool of widely disseminated data can be a daunting task. The Mouse Tumor Biology Database (MTB) provides an electronic archive, search and analysis system that can be used to identify dermatological mouse models of cancer, retrieve model-specific data and analyse these data. In this report, we detail MTB's contents and capabilities, together with instructions on how to use MTB to search for skin-related tumor models and associated data.

SUBMITTER: Begley DA

PROVIDER: S-EPMC4183210 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Identifying mouse models for skin cancer using the Mouse Tumor Biology Database.

Begley Dale A DA Krupke Debra M DM Neuhauser Steven B SB Richardson Joel E JE Schofield Paul N PN Bult Carol J CJ Eppig Janan T JT Sundberg John P JP

Experimental dermatology 20141001 10

In recent years, the scientific community has generated an ever-increasing amount of data from a growing number of animal models of human cancers. Much of these data come from genetically engineered mouse models. Identifying appropriate models for skin cancer and related relevant genetic data sets from an expanding pool of widely disseminated data can be a daunting task. The Mouse Tumor Biology Database (MTB) provides an electronic archive, search and analysis system that can be used to identify ...[more]

PMID: 25040013

Similar Datasets

Web-based access to mouse models of human cancers: the Mouse Tumor Biology (MTB) Database.

Project description:The Mouse Tumor Biology (MTB) Database serves as a curated, integrated resource for information about tumor genetics and pathology in genetically defined strains of mice (i.e., inbred, transgenic and targeted mutation strains). Sources of information for the database include the published scientific literature and direct data submissions by the scientific community. Researchers access MTB using Web-based query forms and can use the database to answer such questions as 'What tumors have been reported in transgenic mice created on a C57BL/6J background?', 'What tumors in mice are associated with mutations in the Trp53 gene?' and 'What pathology images are available for tumors of the mammary gland regardless of genetic background?'. MTB has been available on the Web since 1998 from the Mouse Genome Informatics web site (http://www.informatics.jax.org). We have recently implemented a number of enhancements to MTB including new query options, redesigned query forms and results pages for pathology and genetic data, and the addition of an electronic data submission and annotation tool for pathology data.

| S-EPMC29782 | biostudies-literature

Mouse tumor biology database (MTB): enhancements and current status.

Project description:The Mouse Tumor Biology Database (MTB) is a Web-based resource that provides access to information on tumor frequency and latency, genetics and pathology in genetically defined mice (transgenics, targeted mutations and inbred strains). MTB is designed to serve as an information resource for cancer genetics researchers who use the laboratory mouse as a model system for understanding human disease processes. Data in MTB are obtained from the primary scientific literature and direct submissions by the research community. MTB is accessible from the Mouse Genome Informatics Web site (http://www. informatics.jax.org). User support is available for MTB via Email at mgi-help@informatics.jax.org

| S-EPMC102417 | biostudies-literature

The Mouse Tumor Biology Database (MTB): a central electronic resource for locating and integrating mouse tumor pathology data.

Project description:The Mouse Tumor Biology Database (MTB) is designed to provide an electronic data storage, search, and analysis system for information on mouse models of human cancer. The MTB includes data on tumor frequency and latency, strain, germ line, and somatic genetics, pathologic notations, and photomicrographs. The MTB collects data from the primary literature, other public databases, and direct submissions from the scientific community. The MTB is a community resource that provides integrated access to mouse tumor data from different scientific research areas and facilitates integration of molecular, genetic, and pathologic data. Current status of MTB, search capabilities, data types, and future enhancements are described in this article.

| S-EPMC3130112 | biostudies-literature

Identifying cancer origin using circulating tumor cells.

Project description:Circulating tumor cells (CTCs) have become an established clinical evaluation biomarker. CTC count provides a good correlation with the prognosis of cancer patients, but has only been used with known cancer patients, and has been unable to predict the origin of the CTCs. This study demonstrates the analysis of CTCs for the identification of their primary cancer source. Twelve mL blood samples were equally dispensed on 6 CMx chips, microfluidic chips coated with an anti-EpCAM-conjugated supported lipid bilayer, for CTC capture and isolation. Captured CTCs were eluted to an immunofluorescence (IF) staining panel consisting of 6 groups of antibodies: anti-panCK, anti-CK18, anti-CK7, anti-TTF-1, anti-CK20/anti-CDX2, and anti-PSA/anti-PSMA. Cancer cell lines of lung (H1975), colorectal (DLD-1, HCT-116), and prostate (PC3, DU145, LNCaP) were selected to establish the sensitivity and specificity for distinguishing CTCs from lung, colorectal, and prostate cancer. Spiking experiments performed in 2mL of culture medium or whole blood proved the CMx platform can enumerate cancer cells of lung, colorectal, and prostate. The IF panel was tested on blood samples from lung cancer patients (n = 3), colorectal cancer patients (n = 5), prostate cancer patients (n = 5), and healthy individuals (n = 12). Peripheral blood samples found panCK(+) and CK18(+) CTCs in lung, colorectal, and prostate cancers. CTCs expressing CK7(+) or TTF-1(+), (CK20/ CDX2)(+), or (PSA/ PSMA)(+) corresponded to lung, colorectal, or prostate cancer, respectively. In conclusion, we have designed an immunofluorescence staining panel to identify CTCs in peripheral blood to correctly identify cancer cell origin.

| S-EPMC4910938 | biostudies-literature

The Mouse Genome Database (MGD): mouse biology and model systems.

Project description:The Mouse Genome Database, (MGD, http://www.informatics.jax.org/), integrates genetic, genomic and phenotypic information about the laboratory mouse, a primary animal model for studying human biology and disease. MGD data content includes comprehensive characterization of genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data within MGD are obtained from diverse sources including manual curation of the biomedical literature, direct contributions from individual investigator's laboratories and major informatics resource centers such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development of data and semantic standards such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. MGD provides a data-mining platform that enables the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the association of gene trap data with mouse genes and a new batch query capability for customized data access and retrieval.

| S-EPMC2238849 | biostudies-literature

Mouse Tumor Models for Advanced Cancer Immunotherapy.

Project description:Recent advances in the development of new methods of cancer immunotherapy require the production of complex cancer animal models that reliably reflect the complexity of the tumor and its microenvironment. Mice are good animals to create tumor models because they are low cost, have a short reproductive cycle, exhibit high tumor growth rates, and can be easily genetically modified. However, the obvious problem of these models is the high failure rate observed in human clinical trials after promising results obtained in mouse models. In order to increase the reliability of the results obtained in mice, the tumor model should reflect the heterogeneity of the tumor, contain components of the tumor microenvironment, in particular immune cells, to which the action of immunotherapeutic drugs are directed. This review discusses the current immunocompetent and immunocompromised mouse models of human tumors that are used to evaluate the effectiveness of immunotherapeutic agents, in particular chimeric antigen receptor (CAR) T-cells and immune checkpoint inhibitors.

| S-EPMC7312663 | biostudies-literature

IDENTIFYING CANCER SPECIFIC METABOLIC SIGNATURES USING CONSTRAINT-BASED MODELS.

Project description:Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.

| S-EPMC5173378 | biostudies-literature

Identifying novel therapeutic agents using xenograft models of pediatric cancer.

Project description:In the USA, the overall cure rate for all childhood cancers is seventy percent, and in many patients that ultimately fail curative therapy, initial responses to current multimodality treatments (surgery, radiation therapy and chemotherapy) is good, with overall 5-year event-free survival approaching 80 %. However, current approaches to curative therapy result in significant morbidity and long-term sequelae, including cardiac dysfunction and cognitive impairment. Furthermore, dose-intensive chemotherapy with conventional agents has not significantly improved outcomes for patients that present with advanced or metastatic disease. Classical cytotoxic agents remain the backbone for curative therapy of both hematologic and solid tumors of childhood. While 'molecularly' targeted agents have shown some clinical activity, responses are often modest and of short duration; hence, there is a need to identify new classes of cytotoxic agent that are effective in patients at relapse and that have reduced or different toxicity profiles to normal tissues. Here we review the pediatric preclinical testing program experience of testing novel agents, and the value and limitations of preclinical xenograft models and genetically engineered mouse models for developing novel agents for treatment of childhood cancer.

| S-EPMC4966996 | biostudies-literature

The Mouse Genome Database (MGD): integrating biology with the genome.

Project description:The Mouse Genome Database (MGD) is one component of the Mouse Genome Informatics (MGI) system (http://www.informatics.jax.org), a community database resource for the laboratory mouse. MGD strives to provide a comprehensive knowledgebase about the mouse with experiments and data annotated from both literature and online sources. MGD curates and presents consensus and experimental data representations of genetic, genotype (sequence) and phenotype information including highly detailed reports about genes and gene products. Primary foci of integration are through representations of relationships between genes, sequences and phenotypes. MGD collaborates with other bioinformatics groups to curate a definitive set of information about the laboratory mouse and to build and implement the data and semantic standards that are essential for comparative genome analysis. Recent developments in MGD discussed here include an extensive integration of the mouse sequence data and substantial revisions in the presentation, query and visualization of sequence data.

| S-EPMC308859 | biostudies-other

Genetically engineered mouse models for studying radiation biology.

Project description:Genetically engineered mouse models (GEMMs) are valuable research tools that have transformed our understanding of cancer. The first GEMMs generated in the 1980s and 1990s were knock-in and knock-out models of single oncogenes or tumor suppressors. The advances that made these models possible catalyzed both technological and conceptual shifts in the way cancer research was conducted. As a result, dozens of mouse models of cancer exist today, covering nearly every tissue type. The advantages inherent to GEMMs compared to in vitro and in vivo transplant models are compounded in preclinical radiobiology research for several reasons. First, they accurately and robustly recapitulate primary cancers anatomically, histopathologically, and genetically. Reliable models are a prerequisite for predictive preclinical studies. Second, they preserve the tumor microenvironment, including the immune, vascular, and stromal compartments, which enables the study of radiobiology at a systems biology level. Third, they provide exquisite control over the genetics and kinetics of tumor initiation, which enables the study of specific gene mutations on radiation response and functional genomics in vivo. Taken together, these facets allow researchers to utilize GEMMs for rigorous and reproducible preclinical research. In the three decades since the generation of the first GEMMs of cancer, advancements in modeling approaches have rapidly progressed and expanded the mouse modeling toolbox with techniques such as in vivo short hairpin RNA (shRNA) knockdown, inducible gene expression, site-specific recombinases, and dual recombinase systems. Our lab and many others have utilized these tools to study cancer and radiobiology. Recent advances in genome engineering with CRISPR/Cas9 technology have made GEMMs even more accessible to researchers. Here, we review current and future approaches to mouse modeling with a focus on applications in preclinical radiobiology research.

| S-EPMC6363345 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data