Mutational landscape reflects the biological continuum of plasma cell dyscrasias

• Verfasst von: Rossi, Alessandra [VerfasserIn]
• Verfasst von: Hegenbart, Ute [VerfasserIn]
• Titel: Mutational landscape reflects the biological continuum of plasma cell dyscrasias
• Verf.angabe: A Rossi, M Voigtlaender, S Janjetovic, B Thiele, M Alawi, M März, A Brandt, T Hansen, J Radloff, G Schön, U Hegenbart, S Schönland, C Langer, C Bokemeyer and M Binder
• Fussnoten: Gesehen am 24.04.2018
• Titel Quelle: Enthalten in: Blood cancer journal
• Jahr Quelle: 2017
• Band/Heft Quelle: 7(2017,2) Artikel-Nummer e537, 8 Seiten
• ISSN Quelle: 2044-5385
• DOI: doi:10.1038/bcj.2017.19
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1572341505

Abstract

We subjected 90 patients covering a biological spectrum of plasma cell dyscrasias (monoclonal gammopathy of undetermined significance (MGUS), amyloid light-chain (AL) amyloidosis and multiple myeloma) to next-generation sequencing (NGS) gene panel analysis on unsorted bone marrow. A total of 64 different mutations in 8 genes were identified in this cohort. NRAS (28.1%), KRAS (21.3%), TP53 (19.5%), BRAF (19.1%) and CCND1 (8.9%) were the most commonly mutated genes in all patients. Patients with non-myeloma plasma cell dyscrasias showed a significantly lower mutational load than myeloma patients (0.91±0.30 vs 2.07±0.29 mutations per case, P=0.008). KRAS and NRAS exon 3 mutations were significantly associated with the myeloma cohort compared with non-myeloma plasma cell dyscrasias (odds ratio (OR) 9.87, 95% confidence interval (CI) 1.07-90.72, P=0.043 and OR 7.03, 95% CI 1.49-33.26, P=0.014). NRAS exon 3 and TP53 exon 6 mutations were significantly associated with del17p cytogenetics (OR 0.12, 95% CI 0.02-0.87, P=0.036 and OR 0.05, 95% CI 0.01-0.54, P=0.013). Our data show that the mutational landscape reflects the biological continuum of plasma cell dyscrasias from a low-complexity mutational pattern in MGUS and AL amyloidosis to a high-complexity pattern in multiple myeloma. Our targeted NGS approach allows resource-efficient, sensitive and scalable mutation analysis for prognostic, predictive or therapeutic purposes.