生物
免疫系统
转录组
多发性骨髓瘤
发病机制
基因表达谱
癌症研究
细胞毒性T细胞
计算生物学
肿瘤微环境
微阵列分析技术
串扰
细胞
核糖核酸
疾病
微阵列
临床意义
基因
细胞培养
抑制器
基因组学
深度测序
免疫学
细胞生长
骨髓
细胞周期
遗传学
生物信息学
医学
作者
Lu Jiang,Cheng-Lin Liu,Yu-Liang Zhang,Niu Qiao,Jianfeng Li,Shuangshuang Yang,X Shen,Meng-Ping Chen,Sheng-Yue Wang,Ting Wu,Bing Chen,Shu-Ting Yu,Fangying Jiang,Shuai Wang,Yuan-Fang Liu,Yan Wang,Y Tao,Z Chen,Jian‐Qing Mi,Jian Hou
标识
DOI:10.1073/pnas.2537965123
摘要
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.
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