ASH 2020: Multiple Myeloma Patient Similarity Network Identifies Prognostic Subgroups With Distinct Genetic and Clinical Features

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An analysis by Bhalla et al of multiomics data from newly diagnosed patients with multiple myeloma has identified 3 main patient groups and 12 prognostic subgroups (as well as potential vulnerabilities in each subgroup) based on five data types generated from genomic and transcriptomic patient profiling. The Multiple Myeloma Patient Similarity Network (MM-PSN) classification uncovered novel associations between distinct hallmarks of the cancer—with significant prognostic implications—and allowed further refinement of risk stratification. These findings were presented at the 2020 American Society of Hematology (ASH) Annual Meeting & Exposition (Abstract 3154).

Multiple myeloma is the second most common blood cancer (after non-Hodgkin lymphoma) diagnosed in the United States. This year, it is estimated that about 32,270 people will be diagnosed with multiple myeloma, and despite advances in more effective treatments, nearly 13,000 will die from the disease.

Significant progress has been made over the past 20 years in understanding the molecular heterogeneity of multiple myeloma. However, current classification systems based on fluorescence in situ hybridization or karyotyping do not fully capture the diversity observed in the patient population, and response to treatment still varies significantly within the same cytogenetic groups of patients.

Study Methodology

The researchers used whole-genome, whole-exome, and RNA sequencing to generate data from bone marrow aspirates (CD138+ cells) and peripheral blood (control) of 655 newly diagnosed patients with multiple myeloma enrolled in the MMRF CoMMpass Study. Copy number alterations and translocations were identified from whole-genome sequencing data; somatic single-nucleotide variations were identified from whole-exome sequencing data; and gene-level counts and gene fusions were inferred from RNA sequencing data.

In the MM-PSN, the researchers integrated five data types—gene expression, gene fusions, copy number alterations, somatic mutations, and chromosomal translocations—with clinical data from each patient. Spectral clustering of MM-PSN data revealed 3 main patient groups and 12 subgroups of highly similar patients, each characterized by specific patterns of genetic and molecular features.

Group 1 included 357 patients (54.5%) mainly enriched for hyperdiploidy, MYC translocations, and NRAS mutations, and was comprised of four subgroups. Group 2 included 166 patients (25.3%) and was overall enriched for the MMSET and MAF translocations. Group 3 included 132 patients (20.15%) and was comprised of three subgroups.



  • MM-PSN classification uncovered novel associations between distinct hallmarks of the cancer with significant prognostic implications and allowed further refinement of risk stratification.
  • This approach integrates multiomics data from multiple myeloma patients and reveals greater molecular and clinical heterogeneity than current cytogenetic classifications. The classification system is a valuable and accessible resource that can be employed in most clinical settings.

Survival analysis by the researchers revealed several novel prognostic findings that may improve current cytogenetic risk classification systems. For example, while patients with hyperdiploidy are considered to have better prognosis, their analysis found that concurrent gain (1q) identifies a subgroup of patients with hyperdiploidy at higher risk (1c) for relapse.

Similarly, the MMSET translocation t(4;14) is currently considered to confer poor prognosis and identifies high-risk disease in the Revised Multiple Myeloma International Staging System. The researchers’ findings demonstrate significant heterogeneity within this group, in which patients in subgroup 2e MMSET translocation+gain (1q) had the poorest prognosis in terms of both progression-free and overall survival in the entire cohort, while those in subgroup 2a (MMSET translocation alone) had a significantly better prognosis, comparable to that of the hyperdiploidy subgroups.

This finding has important implications, according to the researchers, because it shows that MMSET translocation does not always confer poor prognosis and that only a fraction of patients with concurrent gain (1q) are high-risk. Additionally, their model revealed a protective effect conferred by gain (15q), whose presence determined a significantly longer progression-free and overall survival, even after adjusting for gain (1q).

Further analysis of pathways enriched by gene expression in the subgroups found specific enrichment for interleukin signaling in group 1; growth, proliferation, and differentiation pathways in group 2; and inflammation and other immune-related pathways in group 3.

The researchers further identified therapeutic vulnerabilities and treatment options that may be important in a subgroup-specific manner; for example, CDK6 in the MAF translocation subgroup 2b, IGF1R in the MMSET translocation+gain (1q) subgroup 2e, and CCND2 across several subgroups of the hyperdiploidy and MMSET/MAF translocation groups. Drug repurposing analysis identified novel potential therapeutic options in a subgroup-specific manner, such as eportin 1 (XPO1) inhibitors in hyperdiploidy subgroup 1b and MAPK inhibitors in MMSET translocation+gain (1q) subgroup 2e.

Clinical Significance

The study authors concluded, “MM-PSN integrates multiomics data from [patients with] multiple myeloma and reveals greater molecular and clinical heterogeneity than current cytogenetic classifications. Ongoing research is focused on a deeper investigation of the molecular mechanisms driving each subgroup and further validation of treatment options identified for specific subgroups.”

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The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.