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JISTIC: Identification of Significant Targets in Cancer

Felix Sanchez-Garcia12*, Uri David Akavia23, Eyal Mozes2 and Dana Pe'er23

Author Affiliations

1 Department of Computer Science, Columbia University, New York, NY, USA

2 Department of Biological Sciences, Columbia University, New York, NY, USA

3 Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA

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BMC Bioinformatics 2010, 11:189  doi:10.1186/1471-2105-11-189

Published: 14 April 2010

Abstract

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. Importantly, the additional regions detected by JISTIC are enriched for oncogenes and genes involved in cell-cycle and proliferation.

Conclusions

JISTIC is an easy-to-install platform independent implementation of GISTIC that outperforms the original algorithm detecting more relevant candidate genes and regions. The software and documentation are freely available and can be found at: http://www.c2b2.columbia.edu/danapeerlab/html/software.html webcite