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<p>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.</p>

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Genomic landscape of multiple myeloma and its precursor conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月21日

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  • Jean-Baptiste Alberge,
  • Ankit K. Dutta,
  • Andrea Poletti,
  • Tim H. H. Coorens,
  • Elizabeth D. Lightbody,
  • Rosa Toenges,
  • Xavi Loinaz,
  • Sofia Wallin,
  • Andrew Dunford,
  • Oliver Priebe,
  • Johnathan Dagan,
  • Cody J. Boehner,
  • Erica Horowitz,
  • Nang K. Su,
  • Hadley Barr,
  • Laura Hevenor,
  • Katherine Towle,
  • Rashmika Beesam,
  • Jenna B. Beckwith,
  • Jacqueline Perry,
  • David M. Cordas dos Santos,
  • Luca Bertamini,
  • Patricia T. Greipp,
  • Kirsten Kübler,
  • Peter F. Arndt,
  • Carolina Terragna,
  • Elena Zamagni,
  • Eileen M. Boyle,
  • Kwee Yong,
  • Gareth Morgan,
  • Brian A. Walker,
  • Meletios Athanasios Dimopoulos,
  • Efstathios Kastritis,
  • Julian Hess,
  • Romanos Sklavenitis-Pistofidis,
  • Chip Stewart,
  • Gad Getz,
  • Irene M. Ghobrial

摘要

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.