Title |
An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer
|
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Published in |
Nature Biotechnology, November 2009
|
DOI | 10.1038/nbt.1584 |
Pubmed ID | |
Authors |
Xiao-Song Wang, John R Prensner, Guoan Chen, Qi Cao, Bo Han, Saravana M Dhanasekaran, Rakesh Ponnala, Xuhong Cao, Sooryanarayana Varambally, Dafydd G Thomas, Thomas J Giordano, David G Beer, Nallasivam Palanisamy, Maureen A Sartor, Gilbert S Omenn, Arul M Chinnaiyan |
Abstract |
Cancer genomes contain many aberrant gene fusions-a few that drive disease and many more that are nonspecific passengers. We developed an algorithm (the concept signature or 'ConSig' score) that nominates biologically important fusions from high-throughput data by assessing their association with 'molecular concepts' characteristic of cancer genes, including molecular interactions, pathways and functional annotations. Copy number data supported candidate fusions and suggested a breakpoint principle for intragenic copy number aberrations in fusion partners. By analyzing lung cancer transcriptome sequencing and genomic data, we identified a novel R3HDM2-NFE2 fusion in the H1792 cell line. Lung tissue microarrays revealed 2 of 76 lung cancer patients with genomic rearrangement at the NFE2 locus, suggesting recurrence. Knockdown of NFE2 decreased proliferation and invasion of H1792 cells. Together, these results present a systematic analysis of gene fusions in cancer and describe key characteristics that assist in new fusion discovery. |
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Geographical breakdown
Country | Count | As % |
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United States | 5 | 3% |
Canada | 4 | 3% |
Netherlands | 2 | 1% |
Norway | 2 | 1% |
Germany | 2 | 1% |
Sweden | 1 | <1% |
Singapore | 1 | <1% |
Hong Kong | 1 | <1% |
Unknown | 133 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 45 | 30% |
Student > Ph. D. Student | 39 | 26% |
Professor > Associate Professor | 20 | 13% |
Student > Master | 11 | 7% |
Student > Doctoral Student | 7 | 5% |
Other | 24 | 16% |
Unknown | 5 | 3% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 88 | 58% |
Biochemistry, Genetics and Molecular Biology | 25 | 17% |
Medicine and Dentistry | 16 | 11% |
Computer Science | 8 | 5% |
Engineering | 3 | 2% |
Other | 5 | 3% |
Unknown | 6 | 4% |