Molecular insight into the interplay among heterogeneous plasmacytes and microenvironment cells and their clinical relevance in myeloma - Lu Jiang; Cheng-Lin Liu; Yu-Liang Zhang; Niu Qiao; Jian-Feng Li; Shuang-Shuang Yang; Xiao-Zhong Shen; Meng-Ping Chen; Sheng-Yue Wang; Ting Wu; Bing Chen; Shu-Ting Yu; et al. (Proceedings of the National Academy of Sciences, 2026)
Multiple myeloma (MM) develops with the acquisition of genetic abnormalities in plasmacytes and changes in microenvironment cells (MECs). Despite the progress in understanding MM disease mechanism through omics studies, the genomic/transcriptomic profiling remains limited in Chinese MM patients. Here, we collected 277 newly diagnosed MM (NDMM) patients in the Shanghai MM Omics (SMMO) project. Analysis of 267 cases with whole-genome/whole-exome sequencing and RNA sequencing (RNA-seq) identified three genetic groups (MY, HRD, and MS/CD). Using single-cell RNA sequencing (scRNA-seq), we investigated 59 NDMM subjects from SMMO, 20 relapsed cases from public database and 30 normal controls. Eight subpopulations of plasmacytes from NDMM (mSP1–mSP8) were defined, each showing unique signatures while forming a differentiation trajectory. The mSP2 is worth noting due to its high proliferative property. Regarding MECs, we found T cell subsets including T-helpers, Tregs, and cytotoxic T cells all in dysfunctional status and increased myeloid-derived suppressor cells such as macrophages mainly in M2 polarization, both constituting a milieu in favor of MM cell growth and immune escape. Furthermore, we scrutinized the crosstalk between MM cells and MECs and that among distinct MECs. A dynamic, comprehensive MM pathogenesis network was revealed, with a number of ligand–receptor pairs. Importantly, the mSP2 signature can be projected to the MM cell RNA-seq data of 235 patients to generate a Score100 with prognostic value in SMMO. Via multivariate analysis of the International Staging System, Consensus Genomic Staging, and Score100, we propose a practical MM stratification model for evaluating aggressive myeloma.