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A Pan-Cancer Analysis of Transcriptome Changes Associated with Somatic Mutations in U2AF1 Reveals Commonly Altered Splicing Events

Overview of attention for article published in PLOS ONE, January 2014
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Title
A Pan-Cancer Analysis of Transcriptome Changes Associated with Somatic Mutations in U2AF1 Reveals Commonly Altered Splicing Events
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0087361
Pubmed ID
Authors

Angela N. Brooks, Peter S. Choi, Luc de Waal, Tanaz Sharifnia, Marcin Imielinski, Gordon Saksena, Chandra Sekhar Pedamallu, Andrey Sivachenko, Mara Rosenberg, Juliann Chmielecki, Michael S. Lawrence, David S. DeLuca, Gad Getz, Matthew Meyerson

Abstract

Although recurrent somatic mutations in the splicing factor U2AF1 (also known as U2AF35) have been identified in multiple cancer types, the effects of these mutations on the cancer transcriptome have yet to be fully elucidated. Here, we identified splicing alterations associated with U2AF1 mutations across distinct cancers using DNA and RNA sequencing data from The Cancer Genome Atlas (TCGA). Using RNA-Seq data from 182 lung adenocarcinomas and 167 acute myeloid leukemias (AML), in which U2AF1 is somatically mutated in 3-4% of cases, we identified 131 and 369 splicing alterations, respectively, that were significantly associated with U2AF1 mutation. Of these, 30 splicing alterations were statistically significant in both lung adenocarcinoma and AML, including three genes in the Cancer Gene Census, CTNNB1, CHCHD7, and PICALM. Cell line experiments expressing U2AF1 S34F in HeLa cells and in 293T cells provide further support that these altered splicing events are caused by U2AF1 mutation. Consistent with the function of U2AF1 in 3' splice site recognition, we found that S34F/Y mutations cause preferences for CAG over UAG 3' splice site sequences. This report demonstrates consistent effects of U2AF1 mutation on splicing in distinct cancer cell types.

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Geographical breakdown

Country Count As %
United States 1 <1%
Netherlands 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 159 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 28%
Researcher 37 23%
Student > Master 15 9%
Student > Doctoral Student 12 7%
Professor > Associate Professor 8 5%
Other 22 13%
Unknown 23 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 33%
Biochemistry, Genetics and Molecular Biology 53 33%
Medicine and Dentistry 15 9%
Computer Science 4 2%
Mathematics 3 2%
Other 9 6%
Unknown 25 15%