Unique molecular assay (UMA): a next-generation sequencing targeted panel for efficient and comprehensive genomic profiling and risk stratification of multiple myeloma
Multiple myeloma (MM) is characterized by genetic abnormalities in plasma cells, requiring precise genomic characterization for effective risk stratification and treatment. This study presents the Unique Molecular Assay (UMA) panel, a targeted DNA-sequencing approach designed to capture critical ge...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ferrata Storti Foundation
2025-05-01
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| Series: | Haematologica |
| Online Access: | https://haematologica.org/article/view/12063 |
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| Summary: | Multiple myeloma (MM) is characterized by genetic abnormalities in plasma cells, requiring precise genomic characterization for effective risk stratification and treatment. This study presents the Unique Molecular Assay (UMA) panel, a targeted DNA-sequencing approach designed to capture critical genomic aberrations in MM, including canonical immunoglobulin heavy chain translocations (t-IgH), copy number alterations (CNAs), and mutations in 82 genes. The UMA panel is the first MM sequencing panel validated against traditional methods like FISH and SNP-arrays across two laboratories for clinical-grade accuracy and reproducibility. The study included 150 patients whose DNA samples were analyzed using the UMA panel, achieving a median coverage of 233X with a requirement of ≥4 million reads per sample. The UMA panel demonstrated high concordance with FISH in detecting both CNAs and t-IgH, achieving a balanced accuracy >93%. Moreover, inter-laboratory validation confirmed the robustness and reliability of the panel on genomic alterations calls. Importantly, UMA panel enabled precise risk stratification based on the R2-ISS staging system, identifying high-risk features such as TP53 mutations and genome-wide CNAs. This comprehensive and cost-effective genomic profiling tool supports clinical decision-making and supports personalized treatment strategies in MM. The validated performance and scalability of the UMA panel suggest its potential to complement traditional diagnostic methods, offering detailed insights into the genomic landscape of MM.
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| ISSN: | 0390-6078 1592-8721 |