Dataset Information

Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma.

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.

SUBMITTER: Walker BA

PROVIDER: S-EPMC6097138 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma.

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

Blood 20180608 6

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 mor ...[more]

PMID: 29884741

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Project description:Multiple Myeloma (MM) arises from malignant transformation and deregulated proliferation of clonal plasma cells (PCs) harbouring heterogeneous molecular anomalies. The effect of evolving mutations on clone fitness and their cellular prevalence shapes the progressing myeloma genome and impacts clinical outcomes. Although clonal heterogeneity in MM is well established, which subclonal mutations emerge/persist/perish with progression in MM and which of these can be targeted therapeutically remains an open question. In line with this, we have sequenced pairwise whole exomes of 62 MM patients collected at two time points, i.e., at diagnosis and on progression. Somatic variants were called using a novel ensemble approach where a consensus was deduced from four variant callers (Illumina's Dragen, Strelka2, SomaticSniper and SpeedSeq) and actionable/druggable gene targets were identified. A marked intraclonal heterogeneity was observed. Branching evolution was observed among 72.58% patients, of whom 64.51% had low TMBs (<10) and 61.29% had 2 or more founder clones. The hypermutator patients (with high TMB levels ≥10 to ≤100) showed a significant decrease in their TMBs from diagnosis (median TMB 77.11) to progression (median TMB 31.22). A distinct temporal fall in subclonal driver mutations was identified recurrently across diagnosis to progression e.g., in PABPC1, BRAF, KRAS, CR1, DIS3 and ATM genes in 3 or more patients suggesting such patients could be treated early with target specific drugs like Vemurafenib/Cobimetinib. An analogous rise in driver mutations was observed in KMT2C, FOXD4L1, SP140, NRAS and other genes. A few drivers such as FAT4, IGLL5 and CDKN1A retained consistent distribution patterns at two time points. These findings are clinically relevant and point at consideration of evaluating multi time point subclonal mutational landscapes for designing better risk stratification strategies and tailoring time to time risk adapted combination therapies in future.

Dataset Information

Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma.

Publications

Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma.

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