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@ARTICLE{Walker:142199,
      author       = {B. A. Walker and K. Mavrommatis and C. P. Wardell and T. C.
                      Ashby and M. Bauer and F. E. Davies and A. Rosenthal and H.
                      Wang and P. Qu and A. Hoering and M. Samur and F. Towfic and
                      M. Ortiz and E. Flynt and Z. Yu and Z. Yang and D. Rozelle
                      and J. Obenauer and M. Trotter and D. Auclair and J. Keats
                      and N. Bolli and M. Fulciniti and R. Szalat and P. Moreau
                      and B. Durie and A. K. Stewart and H. Goldschmidt and M.-S.
                      Raab$^*$ and H. Einsele and P. Sonneveld and J. San Miguel
                      and S. Lonial and G. H. Jackson and K. C. Anderson and H.
                      Avet-Loiseau and N. Munshi and A. Thakurta and G. J. Morgan},
      title        = {{I}dentification of novel mutational drivers reveals
                      oncogene dependencies in multiple myeloma.},
      journal      = {Blood},
      volume       = {132},
      number       = {6},
      issn         = {0006-4971},
      address      = {Stanford, Calif.},
      publisher    = {HighWire Press},
      reportid     = {DKFZ-2019-00013},
      pages        = {587-597},
      year         = {2018},
      abstract     = {Understanding the profile of oncogene and tumor suppressor
                      gene mutations with their interactions and impact on the
                      prognosis of multiple myeloma (MM) can improve the
                      definition of disease subsets and identify pathways
                      important in disease pathobiology. Using integrated genomics
                      of 1273 newly diagnosed patients with MM, we identified 63
                      driver genes, some of which are novel, including IDH1, IDH2,
                      HUWE1, KLHL6, and PTPN11 Oncogene mutations are
                      significantly more clonal than tumor suppressor mutations,
                      indicating they may exert a bigger selective pressure.
                      Patients with more driver gene abnormalities are associated
                      with worse outcomes, as are identified mechanisms of genomic
                      instability. Oncogenic dependencies were identified between
                      mutations in driver genes, common regions of copy number
                      change, and primary translocation and hyperdiploidy events.
                      These dependencies included associations with t(4;14) and
                      mutations in FGFR3, DIS3, and PRKD2; t(11;14) with mutations
                      in CCND1 and IRF4; t(14;16) with mutations in MAF, BRAF,
                      DIS3, and ATM; and hyperdiploidy with gain 11q, mutations in
                      FAM46C, and MYC rearrangements. These associations indicate
                      that the genomic landscape of myeloma is predetermined by
                      the primary events upon which further dependencies are
                      built, giving rise to a nonrandom accumulation of genetic
                      hits. Understanding these dependencies may elucidate
                      potential evolutionary patterns and lead to better treatment
                      regimens.},
      cin          = {G170},
      ddc          = {610},
      cid          = {I:(DE-He78)G170-20160331},
      pnm          = {317 - Translational cancer research (POF3-317)},
      pid          = {G:(DE-HGF)POF3-317},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29884741},
      pmc          = {pmc:PMC6097138},
      doi          = {10.1182/blood-2018-03-840132},
      url          = {https://inrepo02.dkfz.de/record/142199},
}