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Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma

Brian A. Walker, Konstantinos Mavrommatis, Christopher P. Wardell, T. Cody Ashby, Michael Bauer, Faith E. Davies, Adam Rosenthal, Hongwei Wang, Pingping Qu, Antje Hoering, Mehmet Samur, Fadi Towfic, Maria Ortiz, Erin Flynt, Zhinuan Yu, Zhihong Yang, Dan Rozelle, John Obenauer, Matthew Trotter, Daniel Auclair, Jonathan Keats, Niccolo Bolli, Mariateresa Fulciniti, Raphael Szalat, Philippe Moreau, Brian Durie, A. Keith Stewart, Hartmut Goldschmidt, Marc S. Raab, Hermann Einsele, Pieter Sonneveld, Jesus San Miguel, Sagar Lonial, Graham H. Jackson, Kenneth C. Anderson, Herve Avet-Loiseau, Nikhil Munshi, Anjan Thakurta and Gareth J. Morgan

Key points

  • Using the largest set of newly diagnosed myeloma patients we have identified 63 mutated driver genes.

  • We identify oncogenic dependencies, particularly relating to primary translocations, indicating a non-random accumulation of genetic hits.

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 1,273 newly diagnosed patients with multiple myeloma we identify 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 mutations in driver genes are associated with a worse outcome, as are those with 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 pre-determined by the primary events upon which further dependencies are built, giving rise to a non-random accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

  • Submitted March 19, 2018.
  • Accepted June 4, 2018.