Description:
- Bioinformatics
- Personal genomics
Abstract
USC researchers have designed the first tool that bridges genomic data mining and prior cancer search knowledge. This statistical model leverages personal genomic information with a large biological database to facilitate cancer diagnostics and personalize cancer treatment.
Benefit
- User-friendly tool
- Facilitate cancer diagnostics
- Increase accuracy of cancer detection and mutation prioritization
Market Application
Cancer is caused by somatic mutations. These mutations vary on a case-to-case basis. Integration of personal genomic material allows for better understanding of the cancer and treatment selection. While there are computational tools that have been developed to identify cancer driver genes, it remains a challenge to identify these on an individual basis and to design drug therapies. Current methods fail to exceed 81% accuracy of detecting real cancer driver mutations.
Publications
iCAGES: integrated Cancer Genome Score for comprehensively prioritizing driver genes in personal cancer genomes, Genome Medicine 2016
Other
- Experimentally validated
- Available for exclusive and non-exclusive license
Status: Software available