Candidate Cancer Gene Database


NAR Molecular Biology Database Collection entry number 1803
Starr, Timothy; Abbott, Kenneth; Nyre, Erik; Abrahante, Juan; Ho, Yen-Yi; Isaksson Vogel, Rachel
1Department of Obstetrics, Gynecology & Women's Health, University of Minnesota, Minneapolis, MN, 55455, USA 2Masonic Cancer Center Biostatistics and Bioinformatics Core, University of Minnesota, Minneapolis, MN, 55455, USA 3Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA 4Department of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, MN, 55455, USA 5Masonic Cancer Center, University of Minnesota, Minneapolis, MN, 55455, USA

Database Description

Identification of cancer driver genes is crucial to advancement of cancer therapeutics. The search for causative cancer driver genes in the human genome is complicated by the presence of passenger mutations which appear in overwhelming numbers, hiding low-penetrance cancer drivers. We have developed a method of identifying low-penetrance drivers by performing forward genetic screens in mice using DNA transposons as mutagens. Molecular analysis of the tumors arising in these mice allows for identification of transposon common insertion sites (CISs), where the frequency of transposon insertion is greater than expected by chance. Genes disrupted by the transposon insertions in these CISs are identified as candidate cancer driver genes. Our lab, along with many other labs, have used this method to identify thousands of candidate cancer driver genes. Unfortunately, the findings are scattered across dozens of publications using different mouse genome builds and strength metrics. We developed the Candidate Cancer Gene Database (CCGD, http://ccgd-starrlab.oit.umn.edu/) to improve access to these findings and facilitate meta-analyses. The CCGD is a manually curated database of candidate driver genes based on transposon CISs from all published, transposon-based forward genetic screens in mice. Users can quickly locate information on a gene of interest or generate a list of driver genes associated with a particular tumor type. The CCGD accepts queries by study and cancer type as well as by human, mouse, rat, fly, zebrafish, or yeast gene symbol or gene ID. The database supports export into a comma-separated file or a BED formatted file. Gene detail pages provide many links to external resources such as databases and genome browsers. To reduce curation burden, we automated many tasks involving processing of downloads from NCBI Gene, NCBI HomoloGene, Sanger CGC, and Sanger COSMIC. As the CCGD incorporates additional studies, it will facilitate identification of pathway and gene mutations specific to subsets of cancer. To demonstrate this potential, we performed a modified gene set enrichment analysis (GSEA) using KEGG pathwa

Acknowledgements

The authors would like to thank David Largaespada, Vincent Keng and Robert Cormier for their assistance in developing the CCGD. The authors would also like to thank the Minnesota Supercomputing Institute for use of their supercomputers.

References

1. Starr, T.K. and Largaespada, D.A. (2005) Cancer Gene Discovery using the Sleeping Beauty Transposon. Cell Cycle, 4. 1744-1748.
2. Copeland, N.G. and Jenkins, N.A. (2010) Harnessing transposons for cancer gene discovery. Nat Rev Cancer, 10, 696-706.

Subcategory: Cancer gene databases

Go to the abstract in the NAR 2015 Database Issue.
Oxford University Press is not responsible for the content of external internet sites