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DOK2 Inhibits EGFR-Mutated Lung Adenocarcinoma

Overview of attention for article published in PLOS ONE, November 2013
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Title
DOK2 Inhibits EGFR-Mutated Lung Adenocarcinoma
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0079526
Pubmed ID
Authors

Alice H. Berger, Ming Chen, Alessandro Morotti, Justyna A. Janas, Masaru Niki, Roderick T. Bronson, Barry S. Taylor, Marc Ladanyi, Linda Van Aelst, Katerina Politi, Harold E. Varmus, Pier Paolo Pandolfi

Abstract

Somatic mutations in the EGFR proto-oncogene occur in ~15% of human lung adenocarcinomas and the importance of EGFR mutations for the initiation and maintenance of lung cancer is well established from mouse models and cancer therapy trials in human lung cancer patients. Recently, we identified DOK2 as a lung adenocarcinoma tumor suppressor gene. Here we show that genomic loss of DOK2 is associated with EGFR mutations in human lung adenocarcinoma, and we hypothesized that loss of DOK2 might therefore cooperate with EGFR mutations to promote lung tumorigenesis. We tested this hypothesis using genetically engineered mouse models and find that loss of Dok2 in the mouse accelerates lung tumorigenesis initiated by oncogenic EGFR, but not that initiated by mutated Kras. Moreover, we find that DOK2 participates in a negative feedback loop that opposes mutated EGFR; EGFR mutation leads to recruitment of DOK2 to EGFR and DOK2-mediated inhibition of downstream activation of RAS. These data identify DOK2 as a tumor suppressor in EGFR-mutant lung adenocarcinoma.

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The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 13%
Student > Bachelor 2 9%
Researcher 2 9%
Student > Postgraduate 2 9%
Student > Ph. D. Student 2 9%
Other 4 17%
Unknown 8 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 26%
Medicine and Dentistry 4 17%
Agricultural and Biological Sciences 3 13%
Computer Science 1 4%
Mathematics 1 4%
Other 0 0%
Unknown 8 35%