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@ARTICLE{Alberge:301516,
      author       = {J.-B. Alberge and A. K. Dutta and A. Poletti and T. H. H.
                      Coorens and E. D. Lightbody and R. Toenges and X. Loinaz and
                      S. Wallin and A. Dunford and O. Priebe and J. Dagan and C.
                      J. Boehner and E. Horowitz and N. K. Su and H. Barr and L.
                      Hevenor and K. Towle and R. Beesam and J. B. Beckwith and J.
                      Perry and D. M. Cordas Dos Santos and L. Bertamini and P. T.
                      Greipp and K. Kübler$^*$ and P. F. Arndt and C. Terragna
                      and E. Zamagni and E. M. Boyle and K. Yong and G. Morgan and
                      B. A. Walker and M. A. Dimopoulos and E. Kastritis and J.
                      Hess and R. Sklavenitis-Pistofidis and C. Stewart and G.
                      Getz and I. M. Ghobrial},
      title        = {{G}enomic landscape of multiple myeloma and its precursor
                      conditions.},
      journal      = {Nature genetics},
      volume       = {57},
      number       = {6},
      issn         = {1061-4036},
      address      = {London},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {DKFZ-2025-01053},
      pages        = {1493-1503},
      year         = {2025},
      note         = {2025 Jun;57(6):1493-1503},
      abstract     = {Reliable strategies to capture patients at risk of
                      progression from precursor stages of multiple myeloma (MM)
                      to overt disease are still missing. We assembled a
                      comprehensive collection of MM genomic data comprising 1,030
                      patients (218 with precursor conditions) that we used to
                      identify recurrent coding and non-coding candidate drivers
                      as well as significant hotspots of structural variation. We
                      used those drivers to define and validate a simple 'MM-like'
                      score, which we could use to place patients' tumors on a
                      gradual axis of progression toward active disease. Our MM
                      precursor genomic map provides insights into the time of
                      initiation and cell-of-origin of the disease, order of
                      acquisition of genomic alterations and mutational processes
                      found across the stages of transformation. Taken together,
                      we highlight here the potential of genome sequencing to
                      better inform risk assessment and monitoring of MM precursor
                      conditions.},
      cin          = {BE01},
      ddc          = {570},
      cid          = {I:(DE-He78)BE01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40399554},
      doi          = {10.1038/s41588-025-02196-0},
      url          = {https://inrepo02.dkfz.de/record/301516},
}