Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups

Niccolo Bolli, Giulia Biancon, Matahi Moarii, Silvia Gimondi, Yilong Li, Chiara de Philippis, Francesco Maura, Vijitha Sathiaseelan, Yu-Tzu Tai, Laura Mudie, Sarah O'Meara, Keiran Raine, Jon W Teague, Adam P Butler, Cristiana Carniti, Moritz Gerstung, Tina Bagratuni, Efstathios Kastritis, Meletios Dimopoulos, Paolo CorradiniKenneth C Anderson, Philippe Moreau, Stephane Minvielle, Peter J Campbell, Elli Papaemmanuil, Herve Avet-Loiseau, Nikhil C Munshi

Research output: Contribution to journalArticlepeer-review

Abstract

In multiple myeloma, next-generation sequencing (NGS) has expanded our knowledge of genomic lesions, and highlighted a dynamic and heterogeneous composition of the tumor. Here we used NGS to characterize the genomic landscape of 418 multiple myeloma cases at diagnosis and correlate this with prognosis and classification. Translocations and copy number abnormalities (CNAs) had a preponderant contribution over gene mutations in defining the genotype and prognosis of each case. Known and novel independent prognostic markers were identified in our cohort of proteasome inhibitor and immunomodulatory drug-treated patients with long follow-up, including events with context-specific prognostic value, such as deletions of the PRDM1 gene. Taking advantage of the comprehensive genomic annotation of each case, we used innovative statistical approaches to identify potential novel myeloma subgroups. We observed clusters of patients stratified based on the overall number of mutations and number/type of CNAs, with distinct effects on survival, suggesting that extended genotype of multiple myeloma at diagnosis may lead to improved disease classification and prognostication.

Original languageEnglish
Pages (from-to)2604-2616
Number of pages13
JournalLeukemia
Volume32
Issue number12
DOIs
Publication statusPublished - Dec 2018

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