4.8 Article

A recurrent inactivating mutation in RHOA GTPase in angioimmunoblastic T cell lymphoma

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NATURE GENETICS
卷 46, 期 4, 页码 371-+

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NATURE PUBLISHING GROUP
DOI: 10.1038/ng.2916

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资金

  1. Samsung Biomedical Research Institute [SP2-B2-04, GE1-B2-071]
  2. Samsung Cancer Research Institute [SCRI-12-02]
  3. National Research Foundation of Korea [NRF-2012M3A9D1054744, NRF-2012M3A9B9036673, NRF-2011-0019745]
  4. GIST
  5. ERCSB
  6. Ewha Global Top5 Grant of Ewha Womans University
  7. Ministry of Science, ICT & Future Planning, Republic of Korea [GIST-08] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  8. National Research Council of Science & Technology (NST), Republic of Korea [KGM2111312] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  9. National Research Foundation of Korea [2012M3A9D1054744, 2011-0019747, 2011-0019745] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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The molecular mechanisms underlying angioimmunoblastic T cell lymphoma (AITL), a common type of mature T cell lymphoma of poor prognosis, are largely unknown. Here we report a frequent somatic mutation in RHOA (encoding p. Gly17Val) using exome and transcriptome sequencing of samples from individuals with AITL. Further examination of the RHOA mutation encoding p. Gly17Val in 239 lymphoma samples showed that the mutation was specific to T cell lymphoma and was absent from B cell lymphoma. We demonstrate that the RHOA mutation encoding p.Gly17Val, which was found in 53.3% (24 of 45) of the AITL cases examined, is oncogenic in nature using multiple molecular assays. Molecular modeling and docking simulations provided a structural basis for the loss of GTPase activity in the RHOA Gly17Val mutant. Our experimental data and modeling results suggest that the RHOA mutation encoding p. Gly17Val is a driver mutation in AITL. On the basis of these data and through integrated pathway analysis, we build a comprehensive signaling network for AITL oncogenesis.

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