JISTIC: Identification of Significant Targets in Cancer

Background: Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect. Results: We present JISTIC, a tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer. JISTIC is an improvement over the widely used GISTIC algorithm. We compared the performance of JISTIC versus GISTIC on a dataset of glioblastoma copy number variation, JISTIC finds 173 significant regions, whereas GISTIC only finds 103 significant regions. Imp...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Felix Sanchez-Garcia, Uri David Akavia, Eyal Mozes, Dana Pe'er
• Keywords: BMCBI 2010, Cancer, Copy Number, Copy Number Aberrations