Multiple myeloma is a mostly incurable disease with a high relapse rate and a high level of genomic heterogeneity. Recent research published in Science Advances developed a patient similarity network (PSN) for newly diagnosed multiple myeloma (MM-PSN).
Being able to classify patients based on their gene expression can help apply targeted therapies. A PSN groups patients based on the similarity of their genomic and transcriptomic profiles. The current MM-PSN used data from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study.
To develop the PSN, researchers used data from 655 tumor samples from patients with newly diagnosed multiple myeloma enrolled in the study. The researchers found 3 main groups and 12 subgroups of patients based on the genetic data.
The 3 main groups were based on translocations or hyperdiploidy (HD). The first group was enriched for HD and t(8;14) translocation of MYC, the second group was enriched for translocations t(4;14)of MMSET/FGFR3 and t(14;16) of MAF, and the third group was enriched for translocation t(11;14) of CCND1.
Patients with a gain of the long arm of chromosome 1 was observed in about 40% of MM cases and was a high-risk feature. A total of 6 subgroups within the PSN were enriched for gain(1q).
In this dataset, the authors found that patients with gain(1q) and t(4;14) translocation involving MMSET had the worst outcomes. But patients with tMMSET alone was not a poor prognostic indicator. The authors note that tMMSET has traditionally been considered high risk.
Patients with gain(15q) had longer progression-free survival (PFS) and overall survival (OS). The researchers found correlations between MM-PSN subgroups and response to therapy. This can guide choices for precision medicine. The role of gain(1q) in this study has particular significance to multiple myeloma management.
“While the prognostic impact of gain(1q) has been previously investigated and established in numerous studies, our network model and analysis have revealed a much higher significance and centrality of this genetic lesion in risk assessment of treatment-naive patients with MM,” the authors wrote.
Bhalla S, Melnekoff DT, Aleman A, et al. Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications. Sci Adv. 2021;7(47):eabg9551. doi:10.1126/sciadv.abg9551