Purpose: Primary plasma cell leukemia (pPCL) is an aggressive subtype of multiple myeloma, which is distinguished from newly diagnosed multiple myeloma (NDMM) on the basis of the presence of ≥ 20% circulating tumor cells (CTCs). A molecular marker for pPCL is currently lacking, which could help identify NDMM patients with high-risk PCL-like disease, despite not having been recognized as such clinically.
Methods: A transcriptomic classifier for PCL-like disease was bioinformatically constructed and validated by leveraging information on baseline CTC levels, tumor burden, and tumor transcriptomics from 154 patients with NDMM included in the Cassiopeia or HO143 trials and 29 patients with pPCL from the EMN12/HO129 trial. Its prognostic value was assessed in an independent cohort of 2,139 patients with NDMM from the HOVON-65/GMMG-HD4, HOVON-87/NMSG-18, EMN02/HO95, MRC-IX, Total Therapy 2, Total Therapy 3, and MMRF CoMMpass studies.
Results: High CTC levels were associated with the expression of 1,700 genes, independent of tumor burden (false discovery rate < 0.05). Of these, 54 genes were selected by leave-one-out cross-validation to construct a transcriptomic classifier representing PCL-like disease. This not only demonstrated a sensitivity of 93% to identify pPCL in the validation cohort but also classified 10% of NDMM tumors as PCL-like. PCL-like MM transcriptionally and cytogenetically resembled pPCL, but presented with significantly lower CTC levels and tumor burden. Multivariate analyses in NDMM confirmed the significant prognostic value of PCL-like status in the context of Revised International Staging System stage, age, and treatment, regarding both progression-free (hazard ratio, 1.64; 95% CI, 1.30 to 2.07) and overall survival (hazard ratio, 1.89; 95% CI, 1.42 to 2.50).
Conclusion: pPCL was identified on the basis of a specific tumor transcriptome, which was also present in patients with high-risk NDMM, despite not being clinically leukemic. Incorporating PCL-like status into current risk models in NDMM may improve prognostic accuracy.