Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups
Bolli N., Biancon G., Moarii M., Gimondi S., Li Y., Philippis CD., Maura F., Sathiaseelan V., Tai YT., Mudie L., O’Meara S., Raine K., Teague JW., Butler AP., Carniti C., Gerstung M., Bagratuni T., Kastritis E., Dimopoulos M., Corradini P., Anderson K., Moreau P., Minvielle S., Campbell PJ., Papaemmanuil E., Avet-Loiseau H., Munshi NC.
© 2017 Macmillan Publishers Limited. All rights reserved. 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 changes (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 IMiD-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.