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. 2020 Jun 19;10(6):70.
doi: 10.1038/s41408-020-0336-z.

Genomic analysis of primary plasma cell leukemia reveals complex structural alterations and high-risk mutational patterns

Affiliations

Genomic analysis of primary plasma cell leukemia reveals complex structural alterations and high-risk mutational patterns

Carolina Schinke et al. Blood Cancer J. .

Abstract

Primary plasma cell leukemia (pPCL) is a rare and aggressive form of multiple myeloma (MM) that is characterized by the presence of ≥20% circulating plasma cells. Overall survival remains poor despite advances of anti-MM therapy. The disease biology as well as molecular mechanisms that distinguish pPCL from non-pPCL MM remain poorly understood and, given the rarity of the disease, are challenging to study. In an attempt to identify key biological mechanisms that result in the aggressive pPCL phenotype, we performed whole-exome sequencing and gene expression analysis in 23 and 41 patients with newly diagnosed pPCL, respectively. The results reveal an enrichment of complex structural changes and high-risk mutational patterns in pPCL that explain, at least in part, the aggressive nature of the disease. In particular, pPCL patients with traditional low-risk features such as translocation t(11;14) or hyperdiploidy accumulated adverse risk genetic events that could account for the poor outcome in this group. Furthermore, gene expression profiling showed upregulation of adverse risk modifiers in pPCL compared to non-pPCL MM, while adhesion molecules and extracellular matrix proteins became increasingly downregulated. In conclusion, this is one of the largest studies to dissect pPCL on a genomic and molecular level.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1. The prevalence of copy number alterations and driver mutations in pPCL compared to MM.
a pPCL is enriched for adverse risk copy number alterations compared to MM, *p < 0.05. Significant genes are labeled, with the exception of markers on chromosomes associated with hyperdiploidy. b The frequency of driver mutations in pPCL and MM, *p < 0.05 (chi-square test); +p < 0.005 (after multiple test correction).
Fig. 2
Fig. 2. Mutational burden and the prevalence of the APOBEC signature in pPCL.
a The median burden of nonsynonymous mutations increases with disease stage. pPCL (median 99, range 74–468), MGUS (median 20, range 1–66), and MM (median 64, range 11–2263), ***p < 0.001. b The prevalence of the APOBEC signature in the pPCL cohort. Only three patients (13% of total group and 33% of patients with MAF translocations) had a high percentage of the APOBEC signature, which is lower than reported in non-pPCL MM. c The amount of nonsynonymous mutations per cytogenetic subgroup is shown in (a). The median number of nonsynonymous mutations is relatively high in each subgroup: t(11;14) (median = 84, mean = 84), hyperdiploidy (HRD) (median = 104, mean = 100) and MAF including t(14;16) and t(14;20) (median = 112, mean = 171). There was no statistical significant difference between these groups. d The mutational load within the maf-subgroup between patients with a high APOBEC contribution compared to those with a not substantially increased APOBEC contribution.
Fig. 3
Fig. 3. Gene expression differences in pPCL.
a Unsupervised clustering of matched CD138+ cells from BM aspirates and PB from the same patients (n = 15). b Gene expression profiling comparing differentially expressed genes between non-pPCL MM and pPCL with at least a twofold change (p < 0.05, FDR < 0.01). c Expression of the top four upregulated genes by MM stage and in healthy donors (HD). d The top four downregulated genes by MM stage and in HD.

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