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Blood. 2018 Aug 9;132(6):587-597. doi: 10.1182/blood-2018-03-840132. Epub 2018 Jun 8.

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

Author information

1
Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, AR.
2
Celgene Corporation, San Francisco, CA.
3
Cancer Research and Biostatistics, Seattle, WA.
4
Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA.
5
Celgene Corporation, Summit, NJ.
6
Celgene Institute of Translational Research Europe, Sevilla, Spain.
7
Rancho BioSciences, San Diego, CA.
8
Multiple Myeloma Research Foundation, Norwalk, CT.
9
Translational Genomics Research Institute, Phoenix, AZ.
10
Department of Oncology, University of Milan, Milan, Italy.
11
Department of Hematology, University of Nantes, Nantes, France.
12
Cedars-Sinai Samuel Oschin Cancer Center, Los Angeles, CA.
13
Department of Hematology, Mayo Clinic, Scottsdale, AZ.
14
Department of Medicine V, Hematology and Oncology, University Hospital of Heidelberg, Heidelberg, Germany.
15
German Cancer Research Center, Heidelberg, Germany.
16
Department of Internal Medicine II, Wurzburg University, Wurzburg, Germany.
17
Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
18
Centro Investigacion Medica Aplicada, Clinica Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra, Centro de Investigación Biomédica en Red de Oncología, Pamplona, Spain.
19
Winship Cancer Institute, Emory University, Atlanta, GA.
20
Department of Haematology, Newcastle University, Newcastle, United Kingdom.
21
Centre de Recherche en Cancérologie de Toulouse Institut National de la Santé et de la Recherche Médicale U1037, Toulouse, France; and.
22
L'Institut Universitaire du Cancer de Toulouse Oncopole, Centre Hospitalier Universitaire, Toulouse, France.

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.

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