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Enhancing prognostic power in multiple myeloma using a plasma cell signature derived from single-cell RNA sequencing.

Jian-Rong LiShahram Arsang-JangYan ChengFumou SunAnita D SouzaBinod DhakalParameswaran N HariQuillan HuangPaul AuerYong LiRaul UrrutiaFenghuang ZhanJohn D ShaughnessySiegfried JanzJing DongChao Cheng
Published in: Blood cancer journal (2024)
Multiple myeloma (MM) is a heterogenous plasma cell malignancy, for which the established prognostic models exhibit limitations in capturing the full spectrum of outcome variability. Leveraging single-cell RNA-sequencing data, we developed a novel plasma cell gene signature. We evaluated and validated the associations of the resulting plasma cell malignancy (PBM) score with disease state, progression and clinical outcomes using data from five independent myeloma studies consisting of 2115 samples (1978 MM, 65 monoclonal gammopathy of undetermined significance, 35 smoldering MM, and 37 healthy controls). Overall, a higher PBM score was significantly associated with a more advanced stage within the spectrum of plasma cell dyscrasias (all p < 0.05) and a shorter overall survival in MM (hazard ratio, HR = 1.72; p < 0.001). Notably, the prognostic effect of the PBM score was independent of the International Staging System (ISS) and Revised ISS (R-ISS). The downstream analysis further linked higher PBM scores with the presence of cytogenetic abnormalities, TP53 mutations, and compositional changes in the myeloma tumor immune microenvironment. Our integrated analyses suggest the PBM score may provide an opportunity for refining risk stratification and guide decisions on therapeutic approaches to MM.
Keyphrases
  • single cell
  • rna seq
  • multiple myeloma
  • high throughput
  • stem cells
  • gene expression
  • lymph node
  • deep learning
  • genome wide
  • artificial intelligence
  • free survival