A study conducted by Mount Sinai investigators determined that the co-occurrence of t(4;14) and 1q gain was effective at identifying newly diagnosed patients with multiple myeloma who were at high risk of relapse.
A new model that utilizes DNA and RNA sequencing data to identify genetic alterations, the Multiple Myeloma Patient Similarity Network (MM-PSN), successfully identified 12 distinct subgroups of multiple myeloma not previously described, according to data presented in Science Advance.1
These disease subtypes were defined by alteration patterns and enriched for gene vulnerability. As a result, the model could have potential for informing targeted therapy selection in certain patients.
“Our findings have immediate implications for the development of novel precision medicine tools and clinical trials, as different subgroups of patients may respond to different targeted and immuno-oncology therapies based on their genomic and transcriptomic profiles,” lead author Alessandro Lagana, PhD, assistant professor of Oncological Sciences at The Tisch Cancer Institute at Mount Sinai, said in a press release.2 “These studies are fundamental to advancing our understanding of myeloma pathology and pave the way for future research into drug repurposing approaches aimed at novel therapies tailored to specific patient subgroups.”
When compared with t(4;14) alone, the co-occurrence of t(4;14) and 1q gain together are a better indicator of patients with newly diagnosed multiple myeloma who were at significantly higher risk of relapse and shorter survival. Additionally, findings from the study indicated that 1q is the most important single lesion capable of identifying a high risk of relapse and may complement categorization by the International Staging Systems.
Five different types of data were obtained through DNA and RNA sequencing of samples from newly diagnosed patients to create the MM-PSN model. For every sample, investigators used gene expression and gene fusion data from RNA sequencing, somatic single-nucleotide variations from whole-exome sequencing, copy number alterations, and translocation calls from whole-genome sequencing.
Three main groups and 12 subgroups with distinct genetic and molecular features were identified in the analysis of MM-PSN, which revealed a diversity otherwise not known within previously defined disease subtypes, including hyperdiploid.
“Our recent network model of newly diagnosed [multiple myeloma] based on gene co-expression, MMNet, revealed a clear molecular separation between patients with immunoglobulin translocations and hyperdiploidy and identified 3 novel subtypes characterized by cytokine signaling, immune signatures, and MYC translocations,” the study authors wrote.
A total of 655 patients with newly diagnosed multiple myeloma were included in the analysis, with a median age of 63 years. Most patients were male (50.1%), White (64.1%), and had stage II disease (51.8%). Group 1 featured 357 patientsenriched for mutations in NRAS and an LSAMP:RPL18 gene fusion; group 2 had 166 patients and was enriched for mutations in FGFR3, DIS3, and MAX; and group 3 included 132 patients enriched for mutations in CCND1 and NRAS. Group 1 included 4 subgroups, group 2 included 5 subgroups, and group 3 included 3 subgroups.
“These studies are fundamental to advance our understanding of multiple myeloma pathology and paves the way for future research into drug repurposing approaches aimed at novel therapies tailored to specific patient subgroups,” the investigators concluded.