Multiple myeloma has remarkable genetic heterogeneity, which can make it challenging to identify proper prognostication and clinical management for patients.
The creation of a multiomics patient similarity network of multiple myeloma allowed researchers to identify 12 distinct subgroups defined by 5 data types generated from 655 patients, according to research published in Science Advances.
Multiple myeloma has remarkable genetic heterogeneity, which can make it challenging to identify proper prognostication and clinical management for patients, according to the study authors. The disease is a mostly incurable cancer that impacts more than 30,000 patients each year in the United States, with a median survival of approximately 6 years.
Although many patients initially respond to treatment, most relapse and become refractory as they undergo multiple lines of therapy. In particular, approximately 15% of patients are identified as high-risk and typically relapse within 2 years of diagnosis.
Several classifications for multiple myeloma based on gene expression have been proposed in recent decades. The translocation/cyclin D (TC) classification includes 8 groups characterized by various chromosomal translocations and the up regulation of the cyclin D genes, whereas the University of Arkansas for Medical Sciences classification proposes 7 clusters with some overlap of the TC classes and enriched for clinically relevant features and differential response to therapy.
To create a new classification system and identify distinct subgroups of multiple myeloma, researchers generated MM-PSN, a multiomics patient similarity network based on whole-exome sequencing, whole-genome sequencing, and RNA sequencing. Data were obtained from 655 tumor samples from newly diagnosed patients with multiple myeloma enrolled in the MMRF CoMMpass study, using the similarity network fusion method.
Translocations and copy number alterations provided the strongest contribution to MM-PSN, followed by gene expression, gene fusions, and SNVs. Researchers then applied spectral clustering to determine groups of highly similar patients sharing features across the 5 data types.
An evaluation of the network suggested that 3 was the optimal number of clusters, and a differential feature analysis of those clusters found that they had several things in common. First, they were enriched for hyperdiploidy and the t(8;14) translocation of MYC, as well as translocations t(4;14) of MMSET/FGFR3 and t(14;16) of MAF. In addition, they found translocation of t(11;14) of CCND1, as well.
Each group was labeled based on these features. Group 1 included 357 patients and was further enriched for mutations in NRAS and an LSAMP:RPL18 gene fusion. Group 2 included 166 patients and was enriched for mutations in FGFR3, DIS3, and MAX.
Group 3 included 132 patients and was enriched for mutations in CCND1 and NRAS. To further dissect intragroup heterogeneity, researchers reapplied spectral clustering within each group and found 12 subgroups.
This analysis has significant implications for identifying the optimal treatment and clinical features for each patient, according to the researchers. For instance, a survival analysis of the 3 main groups showed that patients in group 2 had shorter progression-free survival times compared to patients in groups 1 and 3, as well as shorter overall survival compared to patients in group 1.
Researchers also found different enrichments for various pathways in each group. Group 1 was overall enriched for inflammation and immune evasion pathways, group 2 was enriched for proliferative pathways, and group 3 was overall enriched for pathways associated with replicative immortality and evasion of growth suppression.
Finally, the analysis found that co-occurrence of t(4;14) and 1q gain identified patients with a significantly higher risk of relapse and a shorter survival time compared to t(4;14) as a single lesion. They noted that 1q gain was the most important single lesion conferring a high risk of relapse.
Bhalla S, Melnekoff D, Aleman A, Leshchanko V, et al. Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications. Science Advances; November 17, 2021. Accessed December 13, 2021. doi:10.1126/sciadv.abg9551.