Researchers create a novel model for multiple myeloma, stratifying the cancer’s severity via DNA and RNA sequencing

Mount Sinai researchers have developed a new model that uses DNA and RNA sequencing data from hundreds of patients to identify specific genes and genetic alterations responsible for never-before-defined subtypes of a blood cancer called multiple myeloma. They also identified potential targeted treatments based on the findings, as reported in Science Advances in November.

This is the first study using multi-omics, the integration and analysis of multiple data types, to create a computational model of multiple myeloma, which the scientists named the Multiple Myeloma Patient Similarity Network (MM-PSN). The genes identified in the analysis included some associated with a high risk of relapse.

“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,” said lead author Alessandro Lagana, PhD, Assistant Professor of Oncological Sciences at The Tisch Cancer Institute at Mount Sinai. “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.”

Researchers believe that MM-PSN captures the complexity of multiple myeloma by associating patients with highly similar DNA and RNA profiles to form more granular and homogeneous classes than achieved by previous classifications. Within the MM-PSN model, researchers represented patients as nodes, much as in a social network, that are connected with one another based on how similar their DNA and RNA profiles are.

To create MM-PSN, researchers analyzed five different types of data obtained from DNA and RNA sequencing of 655 newly diagnosed multiple myeloma patients. The analysis of MM-PSN identified three main groups and 12 subgroups enriched for distinct genetic and molecular features, revealing remarkable diversity within previously defined disease subtypes —such as hyperdiploid and MMSET-translocated, which are chromosome abnormalities—and novel insights into the occurrence of primary and secondary genomic alterations within each patient’s cancer.

Network generation and identification of groups and subgroups

(A) Somatic genetic variants and transcriptomic features from WES, WGS, and RNA-seq data from 655 patients in the MMRF CoMMpass study was used to generate an MM-PSN using the SNF approach. Edges connecting patients in the network represent similarity based on one or more feature type (e.g., orange edges in the sample network represent similarity based on SNVs and magenta edges represent similarity based on all the types of features). Spectral clustering was used to identify patient groups and then reapplied to identify subgroups enriched for specific features. (B) Representation of MM-PSN where nodes (patients) are colored according to the three main groups identified by spectral clustering. (C) The plot shows the contribution of the different data types to the fused matrix, in terms of normalized mutual information (NMI). (D) Eigen gap (maximum) and rotation cost (minimum) were used to determine 3 as the optimal number of clusters. (E) Overview of MM-PSN patient groups and subgroups. The heatmap shows characterization of the three main groups and 12 subgroups of MM-PSN based on their enrichment for the different genomic and transcriptomic features. GSS, genomic scar score.

One of the biggest findings of the MM-PSN is the an abnormality within an area of chromosome 1 being the most important single genetic variant associated with a high risk of relapse; the study suggests that it should now be incorporated into international myeloma staging systems. Researchers also identified new classes of high-risk patients beyond current classifications in multiple myeloma, including one of patients at highest risk of relapse and shortest overall survival, and another that is often associated with more favorable outcomes.

SourceThe Mount Sinai Hospital / Mount Sinai School of Medicine

Bhalla S, Melnekoff DT, Aleman A, Leshchenko V, Restrepo P, Keats J, Onel K, Sawyer JR, Madduri D, Richter J, Richard S, Chari A, Cho HJ, Dudley JT, Jagannath S, Laganà A, Parekh S. (2021) Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications. Sci Adv 7(47):eabg9551. [article]

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