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<p>We hypothesized that gene networks converged between plasma cell development and multiple myeloma (MM) pathogenesis may better explain the clinical heterogeneity in individual MM patients. Here, we transformed the reciprocal expression pattern between a gene module centered on the expression of <i>MCL1</i> and another gene module represented by the expression of <i>TLR10</i> from individual MM transcriptomes into a myeloma classification score (MCS). MCS enabled prognostication in 1738 patients with newly diagnosed MM (NDMM) and 319 patients with relapsed/refractory MM (RRMM) from our own observational cohort, from MMRF, and from five prospective trials. MCS also enabled the discrimination between progressive and stable disease states in the longitudinal samples from 85 patients in MMRF and the PETHEMA/GEM2012MENOS65 trial. MCS further enabled prediction of the responses to bortezomib-based treatment in 425 patients enrolled in the HOVON-65/GMMG-HD4 and APEX (039) trials, and the response to carfilzomib-based treatment in 86 transplantation-eligible patients from MMRF. MCS-defined risk groups harbored distinct targetable pathways, including unfolded protein response, replication stress, genomic instability and interferon responses. However, MCS-based individualized prediction of prognosis and treatment response was independent of the hitherto available prognostic models. Thus, MCS represents an alternative biomarker for risk stratification and prediction of treatment response in individual MM patients.</p>

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Plasma cell development-based prediction of prognosis and treatment response in individual multiple myeloma

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Sitao Jiang,
  • Yuan Yang,
  • Man Yao,
  • Ayaz Ali Samo,
  • Pengli Xiao,
  • Qinglan Cao,
  • Jiuyi Li,
  • Huijuan Wang,
  • Wenming Chen,
  • Xuzhang Lu,
  • Yin Wu,
  • Xiaolong Fan

摘要

We hypothesized that gene networks converged between plasma cell development and multiple myeloma (MM) pathogenesis may better explain the clinical heterogeneity in individual MM patients. Here, we transformed the reciprocal expression pattern between a gene module centered on the expression of MCL1 and another gene module represented by the expression of TLR10 from individual MM transcriptomes into a myeloma classification score (MCS). MCS enabled prognostication in 1738 patients with newly diagnosed MM (NDMM) and 319 patients with relapsed/refractory MM (RRMM) from our own observational cohort, from MMRF, and from five prospective trials. MCS also enabled the discrimination between progressive and stable disease states in the longitudinal samples from 85 patients in MMRF and the PETHEMA/GEM2012MENOS65 trial. MCS further enabled prediction of the responses to bortezomib-based treatment in 425 patients enrolled in the HOVON-65/GMMG-HD4 and APEX (039) trials, and the response to carfilzomib-based treatment in 86 transplantation-eligible patients from MMRF. MCS-defined risk groups harbored distinct targetable pathways, including unfolded protein response, replication stress, genomic instability and interferon responses. However, MCS-based individualized prediction of prognosis and treatment response was independent of the hitherto available prognostic models. Thus, MCS represents an alternative biomarker for risk stratification and prediction of treatment response in individual MM patients.