Mouse Tumor Biology (MTB): a database of mouse models for human cancer

Abstract

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers.

Mitochondrial Haplotype Alters Mammary Cancer Tumorigenicity and Metastasis in an Oncogenic Driver–Dependent Manner

“…Just as humans have unique genetic subpopulations with distinct cancer incidence, mice have unique strains that show differences in susceptibility to cancers (4–7). For example, offspring of C57BL/6J and BALB/cJ female mice mated with male transgenic mice carrying the polyoma virus middle T (PyMT) antigen have altered tumor latency and decreased metastatic burden compared with offspring of similarly mated FVB/NJ mice (8).…”

“…Several studies have described changes in transgene behavior on different mouse strains (4, 15, 30). Nuclear modifiers appear to be combination-dependent, i.e., nuclear modifiers are driver-dependent or vice versa .…”

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE) 

“…The common DE genes are represented in Table 2. In order to highlight common molecular mechanisms evoked by RIBE, we exploited the functional enrichment results from three different biomedical ontologies (GO [11,12], Reactome pathways [13,14] and MGI [15][16][17]), as derived by the BioInfoMiner (BIM) [18] interpretation web platform, emphasizing in overlapping semantic terms above a certain level across transcriptomic datasets. More specifically, we identified biological processes that were found significantly overrepresented in at least three out of six DE lists, concerning Bystander & Irradiated samples vs controls with a-particles IR and two out of four with carbon-ion IR (Tables 3 & 4).…”

“…The methodology is described in Koutsandreas et al [66] In order to derive a gene signature characterizing RIBE, we combined different subsets of linker genes, derived from the application of the methodology with different vocabularies. namely GO [11,12], Reactome [13,14], and MGI [15][16][17]. Firstly, we performed functional enrichment analysis and gene prioritization for every gene list of the aforementioned bystander comparisons, resulting in five linker gene lists for GO, five for Reactome and five for MGI vocabularies.…”

Integrating Open Data on Cancer in Support to Tumor Growth Analysis

“…The Cancer Imaging Archive TCIA REST, download [24] CancerData.org -Sharing data for cancer research CancerData download [45] Mouse Tumor Biology -Database MTB download [44] National Biomedical Imaging Archive NBIA REST, download [24] Many Table 2 lists all examined databases providing cancer-related content as download that is free for non-commercial, scientific purposes, sorted by category.…”