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Entropy Measures Quantify Global Splicing Disorders in Cancer

Overview of attention for article published in PLoS Computational Biology, March 2008
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Title
Entropy Measures Quantify Global Splicing Disorders in Cancer
Published in
PLoS Computational Biology, March 2008
DOI 10.1371/journal.pcbi.1000011
Pubmed ID
Authors

William Ritchie, Samuel Granjeaud, Denis Puthier, Daniel Gautheret

Abstract

Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes.

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Geographical breakdown

Country Count As %
United States 8 10%
United Kingdom 1 1%
Germany 1 1%
France 1 1%
Unknown 71 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 30%
Student > Ph. D. Student 17 21%
Professor > Associate Professor 8 10%
Student > Master 7 9%
Student > Doctoral Student 6 7%
Other 15 18%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 50%
Biochemistry, Genetics and Molecular Biology 22 27%
Computer Science 5 6%
Medicine and Dentistry 4 5%
Mathematics 2 2%
Other 3 4%
Unknown 5 6%