| Literature DB >> 29884741 |
Brian A Walker1, Konstantinos Mavrommatis2, Christopher P Wardell1, T Cody Ashby1, Michael Bauer1, Faith E Davies1, Adam Rosenthal3, Hongwei Wang3, Pingping Qu3, Antje Hoering3, Mehmet Samur4, Fadi Towfic5, Maria Ortiz6, Erin Flynt5, Zhinuan Yu5, Zhihong Yang5, Dan Rozelle7, John Obenauer7, Matthew Trotter6, Daniel Auclair8, Jonathan Keats9, Niccolo Bolli10, Mariateresa Fulciniti4, Raphael Szalat4, Philippe Moreau11, Brian Durie12, A Keith Stewart13, Hartmut Goldschmidt14, Marc S Raab14,15, Hermann Einsele16, Pieter Sonneveld17, Jesus San Miguel18, Sagar Lonial19, Graham H Jackson20, Kenneth C Anderson4, Herve Avet-Loiseau21,22, Nikhil Munshi4, Anjan Thakurta5, Gareth J Morgan1.
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.Entities:
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Year: 2018 PMID: 29884741 PMCID: PMC6097138 DOI: 10.1182/blood-2018-03-840132
Source DB: PubMed Journal: Blood ISSN: 0006-4971 Impact factor: 22.113