Background: Primary plasma cell leukemia (pPCL) is a rare and aggressive form of multiple myeloma (MM) with an extremely poor prognosis and distinct biological and clinical features. Because of its low incidence and its heterogeneity, biological knowledge about pPCL is lacking especially molecular process responsible for its aggressiveness. Here, we took advantage of a large series of pPCL to describe the genomic and transcriptomic landscape of pPCL, to identify potential driver mutations and pathways, and to determine their clinical impacts.

Methods: To address these issues, we performed a targeted DNA sequencing and a RNA sequencing of sorted bone marrow plasma cells collected at the time of diagnosis from 96 patients with pPCL between 2014 and 2020. We compared their genomic profiles with those of 907 MM at diagnosis previously obtained in our laboratory and their transcriptomic profiles with those of 300 MM at diagnosis obtained from the IFM2009/DFCI trial (NCT01191060). Copy number aberrations (CNA), single nucleotide variants (SNV), translocations, mutations, gene expression (GE) and gene set enrichment were analyzed and correlated with clinical information (overall survival and progression-free survival).

Results: Genome analysis highlighted a specific genomic profile of pPCL. Indeed, hyperdiploid karyotypes were less frequent in pPCL compared with MM (20% vs 57%, p<0,001). We found a high prevalence of translocations involving the heavy chain locus (IGH) in pPCL with higher incidences of t(11;14) (51% vs 23%, p<0,001) and t(14;16) (14% vs 3%, p<0,001), but an identical incidence of t(4;14) (11% vs 10%, p=0,7). pPCL presented more adverse cytogenetic abnormalities such as del(17p) (30% vs 9,5%, p<0,001), 1q gain (53% vs 32%, p<0,001) and del(1p32) (24% vs 9%, p<0,001). Among the 246 recurrently mutated genes in MM, mutations of TP53 (21% vs 5%, p<0,001) and IRF4 (11% vs 4%, p<0,005) were significantly more frequent in pPCL. Furthermore, pPCL presented high-risk genomic features with an increased proportion of Double Hit profiles (27% vs 5%, p<0,001) with more bi-allelic inactivation of TP53 (17% vs 3%, p<0,001) and more amp1q on the background of International Staging System III (11% vs 5%, p<0,005). Interestingly, by comparing genomic profiles from pPCL with and without t(11;14) we found two distinctive patterns. Indeed pPCL with t(11;14) showed more TP53 mutations and more bi-allelic inactivation of TP53. While pPCL without t(11;14) showed more adverse cytogenetic abnormalities such as trisomy 21, 1q gains and del(1p32). These results suggest two distinctive oncogenic mechanisms.

RNA-seq analysis showed also a specific transcriptional landscape of pPCL. Indeed, unsupervised hierarchical clustering of gene expression profiles demonstrated two distinct clusters between pPCL and MM. Gene set enrichment analysis identified a significantly higher expression of genes involved in MYC Targets and G2M checkpoint, and a significantly lower expression of genes involved in P53 pathway, hypoxia and TNF alpha signaling via NF-κB. Furthermore, pPCL with and without t(11;14) presented two distinct transcriptomic patterns, in particular for genes implicated in the apoptotic machinery. Three members of the BCL2 family were differentially expressed with BCL2 and PMAIP1 [NOXA] significantly overexpressed and BCL2L1 significantly underexpressed in pPCL with t(11;14).

Median PFS and OS of patients with pPCL were respectively at 11 and 15 months. Presence of TP53 mutations was associated with a significantly lower PFS (4 months, p<0,05) and OS (5 months, p<0,05). Neither the IgH translocations nor the ploidy status predicted for survival.

Conclusion: To our knowledge, we present the study on the largest series of patients with pPCL. Our results provide new information on both genomic and transcriptomic landscape of pPCL. Despite their heterogeneity, pPCL present a specific mutational landscape with high prevalence of t(11;14) and high-risk genomic features. These results help to better understand oncogenicity and the aggressive behavior of pPCL and support the use of new treatment strategies such as BCL2 inhibitor (Venetoclax) for pPCL with t(11;14).

Disclosures

Perrot:Amgen, BMS/Celgene, Janssen, Sanofi, Takeda: Consultancy, Honoraria, Research Funding. Hulin:Celgene/Bristol-Myers Squibb, Janssen, GlaxoSmithKline, and Takeda: Honoraria.

Author notes

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Asterisk with author names denotes non-ASH members.