Researchers developed a model to identify subgroups of patients with newly diagnosed multiple myeloma (MM) to use as a tool for prognostication and management, with classification incorporating both genomic and transcriptomic characteristics. They described the model in a recent report in the journal Scientific Advances.
The researchers noted that many models for MM have been developed over the years; however, they explained that models for MM have not incorporated both genomic and transcriptomic characteristics. They employed the concept of patient similarity networks (PSNs), which function similarly to a social network in connecting patients based on similarities in genomic and transcriptomic profiles and clinical data, to build a tool to classify and predict outcomes for patients with MM.
The model, called MM-PSN, is a classifier constructed using data from patients with newly diagnosed MM from the Multiple Myeloma Research Foundation Commpass study. Results from whole genome sequencing, whole exome sequencing, and ribonucleic acid-sequencing data were derived from analyses of 655 tumor samples. The MM-PSN classifier was used to find essential genes in MM, identify potential drugs for patient subgroups, and estimate survival outcomes.
Translocations and copy number alterations were found to contribute the most to the MM-PSN classifier. Three main clusters of patients were evident with the model. Group 1 was characterized by hyperdiploidy, group 2 was characterized by translocations t(4;14) related to MMSET/FGFR3 and t(14;16) related to MAF, and group 3 was characterized by translocation t(11;14) related to CCND1. Group 1 included 357 patients, or 54.5% of the total. Group 2 had 166 patients (25.3%), and group 3 had 132 patients (20.15%). Across these groups, a total of 12 subgroups were additionally found.
Analysis of gene essentiality showed 213 genes to be essential, of which some may serve as targets. An example was overexpression of CCND2, which was a common vulnerability found in group 2 and in 2 subgroups of group 1.
Progression-free survival was shortest for patients in group 2, compared with groups 1 and 3 (P <.01). There was a trend toward shorter overall survival for patients in group 2 than for patients in group 1 (P =.05).
Increased relapse risk and shorter survival were found with the combination of the t(4;14) translocation and 1q gain, in comparison with t(4;14) alone. Induction therapy and autologous stem cell transplantation were also found to be unable to compensate for the negative influence of 1q gain on prognosis.
“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-naïve patients with MM,” the researchers concluded in their report.
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