Category Genomics>Genetic Data Analysis/Tools

Abstract JISTIC is a tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer.

JISTIC is an easy-to-install platform independent implementation of GISTIC that outperforms the original algorithm detecting more relevant candidate genes and regions.

JISTIC is an improvement over the widely used GISTIC algorithm.

The manufacturer's 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.

JISTIC Background --

A comprehensive study of the genomic alterations that occur in cancer is vital for understanding this disease.

Technological advances have made it possible to detect chromosomal regions of amplifications and deletions genome-wide and at high resolution.

Datasets measuring such aberrations in patient tumors are accumulating at a staggering rate for multiple types of cancer.

However, tumors harbor a great number of copy number alterations and it is difficult to distinguish between driver aberrations (functional changes vital for cancer progression) and passenger aberrations (random and with No selective advantage).

Thus, the distinction between driver and passenger mutations has become one of the key challenges in cancer genomics.

A very successful algorithm to address this is "Genomic Identification of Significant Targets in Cancer" (GISTIC), that identifies aberrant regions more likely to drive cancer pathogenesis.

GISTIC calculates the background rate of random chromosomal aberrations and identifies those regions that are aberrant more often than would be expected by chance, with greater weight given to high amplitude events that are less likely to represent random aberrations.

There are other algorithms that tackle this task such as GLAD, RAE and STAC.

However, GISTIC is unique in its ability to combine magnitude and frequency of the alteration into a statistical score. This algorithm has been successfully applied to various datasets and the approach is becoming widespread.

GISTIC identifies those regions of the genome that are aberrant more often than would be expected by chance.

While successful in most scenarios, GISTIC has trouble identifying the relevant sub-region when a very large region is amplified or deleted.

Such large chromosomal aberrations frequently occur in cancer and this leaves the user with two (2) less than optimal options - consider only a single peak within the region, or consider an entire chromosomal arm.

However, the manufacturer's have observed that in many cases there are other small sub-regions for which the aberration is significantly stronger than in the rest of the large region.

Moreover, these regions often contain known oncogenes.

To address this issue, the manufacturers developed JISTIC, a tool that implements all of GISTIC's capabilities, with an additional new variant of the algorithm capable of detecting multiple significant sub-regions within large aberrant regions.

JISTIC Implementation/Conclusions/Output --

JISTIC is based on the GISTIC algorithm (as stated above...).

JISTIC implements the previously published variants of GISTIC (standard, focal and arm-peel-off) and can also deal with Loss of Heterozygosity (LOH) in the same way that the original algorithm does.

More detailed information on GISTIC can be found in the following paper: Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, Vivanco I, Lee JC, Huang JH, Alexander S, et al.: Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci USA 2007, 104(50):20007-20012.

A previous analysis was performed on limited peel-off against standard and focal GISTIC and it demonstrated the superiority of limited peel-off to achieve both better specificity and dramatically increase recall by obtaining a large number of novel peaks.

JISTIC is a significantly improved algorithm to distinguish between driver and passenger copy number aberrations in cancer genomes.

Importantly, it detects a significant number of additional driver regions while maintaining a similar false positive rate.

The manufacturers conclude that both limited peel-off and focal GISTIC should be used together as they provide complementary and significant results.

JISTIC is implemented in Java, and has been tested on Linux and Windows.

It does Not have dependencies to external libraries and can be downloaded as a single Java JAR file.

The execution time for the glioblastoma dataset (mentioned above...) on a standard desktop computer (Intel Xeon W3505 @ 2.53 GHz, 3GB of RAM) was 8 minutes for all variants.

MATLAB scripts are provided in order to visualize the output and obtain different statistics.

JISTIC output can also be converted to the format used by the open-source visualization tool - Integrative Genomics Viewer (IGV) - which can be used to display cancer genomic data using a user-friendly interface.

System Requirements

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Manufacturer Web Site JISTIC

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G6G Abstract Number 20781

G6G Manufacturer Number 104203