Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

An in vivo screen identifies ependymoma oncogenes and tumor-suppressor genes.

ABSTRACT: Cancers are characterized by non-random chromosome copy number alterations that presumably contain oncogenes and tumor-suppressor genes (TSGs). The affected loci are often large, making it difficult to pinpoint which genes are driving the cancer. Here we report a cross-species in vivo screen of 84 candidate oncogenes and 39 candidate TSGs, located within 28 recurrent chromosomal alterations in ependymoma. Through a series of mouse models, we validate eight new ependymoma oncogenes and ten new ependymoma TSGs that converge on a small number of cell functions, including vesicle trafficking, DNA modification and cholesterol biosynthesis, identifying these as potential new therapeutic targets.

SUBMITTER: Mohankumar KM

PROVIDER: S-EPMC4520751 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

An in vivo screen identifies ependymoma oncogenes and tumor-suppressor genes.

Mohankumar Kumarasamypet M KM Currle David S DS White Elsie E Boulos Nidal N Dapper Jason J Eden Christopher C Nimmervoll Birgit B Thiruvenkatam Radhika R Connelly Michele M Kranenburg Tanya A TA Neale Geoffrey G Olsen Scott S Wang Yong-Dong YD Finkelstein David D Wright Karen K Gupta Kirti K Ellison David W DW Thomas Arzu Onar AO Gilbertson Richard J RJ

Nature genetics 20150615 8

Cancers are characterized by non-random chromosome copy number alterations that presumably contain oncogenes and tumor-suppressor genes (TSGs). The affected loci are often large, making it difficult to pinpoint which genes are driving the cancer. Here we report a cross-species in vivo screen of 84 candidate oncogenes and 39 candidate TSGs, located within 28 recurrent chromosomal alterations in ependymoma. Through a series of mouse models, we validate eight new ependymoma oncogenes and ten new ep ...[more]

PMID: 26075792

Similar Datasets

In vivo oncogenomics screen identifies novel ependymoma oncogenes and tumor suppressor genes within large chromosomal alterations

Project description:Cancers are characterized by non-random, chromosome copy number variations that presumably contain oncogenes and tumor suppressor genes (TSGs). The affected loci are often large, making it difficult to pinpoint which genes are driving the cancer. Here, we report a cross-species, in vivo screen of 84 candidate oncogenes and 39 candidate TSGs, located within 28 recurrent chromosomal alterations in ependymoma. Through a series of mouse models we validate eight new ependymoma oncogenes and 11 TSGs that dysregulate a small number of cell functions including vesicle trafficking and cholesterol biosynthesis; pinpointing these as potential points for therapeutic intervention. Mouse cortical tumors were excised. These tumors derived from cells expressing one of 3 genes: RAB3A, BC74C, or ZNF668

2015-04-28 | E-GEOD-67497 | biostudies-arrayexpress

In vivo oncogenomics screen identifies novel ependymoma oncogenes and tumor suppressor genes within large chromosomal alterations

2015-04-28 | GSE67497 | GEO

Somatic selection distinguishes oncogenes and tumor suppressor genes.

Project description:MotivationFunctions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited.ResultsWe show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms.Availability and implementationAn R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7703750 | biostudies-literature

Classical oncogenes and tumor suppressor genes: a comparative genomics perspective.

Project description:We have curated a reference set of cancer- related genes and reanalyzed their sequences in the light of molecular information and resources that have become available since they were first cloned. Homology studies were carried out for human oncogenes and tumor suppressors, compared with the complete proteome of the nematode, Caenorhabditis elegans, and partial proteomes of mouse and rat and the fruit fly, Drosophila melanogaster. Our results demonstrate that simple, semi-automated bioinformatics approaches to identifying putative functionally equivalent gene products in different organisms may often be misleading. An electronic supplement to this article provides an integrated view of our comparative genomics analysis as well as mapping data, physical cDNA resources and links to published literature and reviews, thus creating a "window" into the genomes of humans and other organisms for cancer biology.

| S-EPMC1507562 | biostudies-literature

Bioinformatic Analysis of Circadian Expression of Oncogenes and Tumor Suppressor Genes.

Project description:BackgroundCircadian rhythms are physiological and behavioral cycles with a period of approximately 24 hours that control various functions including gene expression. Circadian disruption is associated with a variety of diseases, especially cancer. Although some of the oncogenes and tumor suppressor genes (TSGs) are known as clock-controlled genes (CCGs), the analysis and annotation of circadian expression of most human oncogenes and TSGs are still lacking. This study aims to investigate the circadian expression of a list of human oncogenes and TSGs.MethodsA bioinformatic analysis was conducted on a gene library comprising 120 genes to investigate the circadian expression of human oncogenes and TSGs. To achieve this purpose, the genotranscriptomic data were retrieved from COSMIC and analyzed by R statistical software. Furthermore, the acquired data were analyzed at the transcriptomic and proteomic levels using several publicly available databases. Also, the significance of all analyses was confirmed statistically.ResultsAltogether, our results indicated that 7 human oncogenes/TSGs may be expressed and function in a circadian manner. These oncogenes/TSGs showed a circadian expression pattern at CircaDB database and associated with at least one of the circadian genes/CCGs based on both genotranscriptomic and correlation analyses.ConclusionsAlthough 4 of 7 finally outputted genes have been previously reported to be clock controlled, heretofore there is no report about the circadian expression of 3 other genes. Considering the importance of oncogenes/TSGs in the initiation and progression of cancer, further studies are suggested for the identification of exact circadian expression patterns of these 3 human oncogenes/TSGs.

| S-EPMC5734456 | biostudies-literature

A single-copy Sleeping Beauty transposon mutagenesis screen identifies new PTEN-cooperating tumor suppressor genes.

Project description:The overwhelming number of genetic alterations identified through cancer genome sequencing requires complementary approaches to interpret their significance and interactions. Here we developed a novel whole-body insertional mutagenesis screen in mice, which was designed for the discovery of Pten-cooperating tumor suppressors. Toward this aim, we coupled mobilization of a single-copy inactivating Sleeping Beauty transposon to Pten disruption within the same genome. The analysis of 278 transposition-induced prostate, breast and skin tumors detected tissue-specific and shared data sets of known and candidate genes involved in cancer. We validated ZBTB20, CELF2, PARD3, AKAP13 and WAC, which were identified by our screens in multiple cancer types, as new tumor suppressor genes in prostate cancer. We demonstrated their synergy with PTEN in preventing invasion in vitro and confirmed their clinical relevance. Further characterization of Wac in vivo showed obligate haploinsufficiency for this gene (which encodes an autophagy-regulating factor) in a Pten-deficient context. Our study identified complex PTEN-cooperating tumor suppressor networks in different cancer types, with potential clinical implications.

| S-EPMC5409503 | biostudies-literature

An integrated in vitro and in vivo high-throughput screen identifies treatment leads for ependymoma.

Project description:Using a mouse model of ependymoma-a chemoresistant brain tumor-we combined multicell high-throughput screening (HTS), kinome-wide binding assays, and in vivo efficacy studies, to identify potential treatments with predicted toxicity against neural stem cells (NSC). We identified kinases within the insulin signaling pathway and centrosome cycle as regulators of ependymoma cell proliferation, and their corresponding inhibitors as potential therapies. FDA approved drugs not currently used to treat ependymoma were also identified that posses selective toxicity against ependymoma cells relative to normal NSCs both in vitro and in vivo, e.g., 5-fluorouracil. Our comprehensive approach advances understanding of the biology and treatment of ependymoma including the discovery of several treatment leads for immediate clinical translation.

| S-EPMC3172881 | biostudies-literature

RNA-DNA differences are rarer in proto-oncogenes than in tumor suppressor genes.

Project description:It has long been assumed that DNA sequences and corresponding RNA transcripts are almost identical; a recent discovery, however, revealed widespread RNA-DNA differences (RDDs), which represent a largely unexplored aspect of human genome variation. It has been speculated that RDDs can affect disease susceptibility and manifestations; however, almost nothing is known about how RDDs are related to disease. Here, we show that RDDs are rarer in proto-oncogenes than in tumor suppressor genes; the number of RDDs in coding exons, but not in 3'UTR and 5'UTR, is significantly lower in the former than the latter, and this trend is especially pronounced in non-synonymous RDDs, i.e., those cause amino acid changes. A potential mechanism is that, unlike proto-oncogenes, the requirement of tumor suppressor genes to have both alleles affected to cause tumor 'buffers' these genes to tolerate more RDDs.

| S-EPMC3270091 | biostudies-literature

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.

Project description:Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.

| S-EPMC7673741 | biostudies-literature

Integrated transcriptome interactome study of oncogenes and tumor suppressor genes in breast cancer.

Project description:Breast cancer is the leading cause for mortality among women worldwide. Dysregulation of oncogenes and tumor suppressor genes is the major reason for the cause of cancer. Understanding these genes will provide clues and insights about their regulatory mechanism and their interplay in cancer. In the present study, an attempt is made to compare the functional characteristics and interactions of oncogenes and tumor suppressor genes to understand their biological role. 431 breast cancer samples from seven publicly available microarray datasets were collected and analysed using GEO2R tool. The identified 416 differentially expressed genes were classified into five gene sets as oncogenes (OG), tumor suppressor genes (TSG), druggable genes, essential genes and other genes. The gene sets were subjected to various analysis such as enrichment analysis (viz., GO, Pathways, Diseases and Drugs), network analysis, calculation of mutation frequencies and Guanine-Cytosine (GC) content. From the results, it was observed that the OG were having high GC content as well as high interactions than TSG. Moreover, the OG are found to have frequent mutations than TSG. The enrichment analysis results suggest that the oncogenes are involved in positive regulation of cellular protein metabolic process, macromolecule biosynthetic process and majorly in cell cycle and focal adhesion pathway in cancer. It was also found that these oncogenes are involved in other diseases such as skin diseases and viral infections. Collagenase, paclitaxel and docetaxel are some of the drugs found to be enriched for oncogenes.

| S-EPMC6411624 | 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