Format

Send to

Choose Destination
Nucleic Acids Res. 2015 Jan;43(Database issue):D844-8. doi: 10.1093/nar/gku770. Epub 2014 Sep 4.

The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice.

Author information

1
Department of Obstetrics, Gynecology & Women's Health, University of Minnesota, Minneapolis, MN 55455, USA.
2
Masonic Cancer Center Biostatistics and Bioinformatics Core, University of Minnesota, Minneapolis, MN 55455, USA Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
3
Department of Obstetrics, Gynecology & Women's Health, University of Minnesota, Minneapolis, MN 55455, USA Masonic Cancer Center Biostatistics and Bioinformatics Core, University of Minnesota, Minneapolis, MN 55455, USA.
4
Department of Obstetrics, Gynecology & Women's Health, University of Minnesota, Minneapolis, MN 55455, USA Department of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, MN 55455, USA Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA star0044@umn.edu.

Abstract

Identification of cancer driver gene mutations is crucial for advancing cancer therapeutics. Due to the overwhelming number of passenger mutations in the human tumor genome, it is difficult to pinpoint causative driver genes. Using transposon mutagenesis in mice many laboratories have conducted forward genetic screens and identified thousands of candidate driver genes that are highly relevant to human cancer. Unfortunately, this information is difficult to access and utilize because it is scattered across multiple publications using different mouse genome builds and strength metrics. To improve access to these findings and facilitate meta-analyses, we developed the Candidate Cancer Gene Database (CCGD, http://ccgd-starrlab.oit.umn.edu/). The CCGD is a manually curated database containing a unified description of all identified candidate driver genes and the genomic location of transposon common insertion sites (CISs) from all currently published transposon-based screens. To demonstrate relevance to human cancer, we performed a modified gene set enrichment analysis using KEGG pathways and show that human cancer pathways are highly enriched in the database. We also used hierarchical clustering to identify pathways enriched in blood cancers compared to solid cancers. The CCGD is a novel resource available to scientists interested in the identification of genetic drivers of cancer.

PMID:
25190456
PMCID:
PMC4384000
DOI:
10.1093/nar/gku770
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center