Single-cell RNA-seq reveals clonal diversity and prognostic genes of relapsed multiple myeloma.
Single-cell RNA-seq reveals clonal diversity and prognostic genes of relapsed multiple myeloma.
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单细胞 RNA seq 揭示复发性多发性骨髓瘤的克隆多样性和预后基因
DOI：
10.1002/ctm2.757
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发表时间：
2022-03
影响因子：
10.6
通讯作者：
Du J
中科院分区：
文献类型：
--
作者：
He H;Li Z;Lu J;Qiang W;Jiang S;Xu Y;Fu W;Zhai X;Zhou L;Qian M;Du J
多发性骨髓瘤（MM）是一种临床和生物学异质性浆细胞恶性肿瘤。尽管进行了广泛的研究，但疾病异质性和复发仍然是MM治疗的一大挑战。我们试图剖析这种疾病，并通过应用单细胞技术识别用于患者分层和治疗结果预测的新生物标志物。我们对来自18例新诊断MM（NDMM; n = 12）或难治性/复发性MM（RRMM; n = 6）患者队列的骨髓样本同时进行了单细胞RNA测序（scRNA-seq）和可变多样性连接区域靶向测序（scVDJ-seq）。我们使用scVDJ-seq数据分析恶性克隆型，并通过基于基因表达谱的CCA算法进行数据整合和细胞类型注释。此外，我们通过比较NDMM和RRMM数据集确定了疾病状态特异性基因和模块，并在MMRF CoMMpass研究的更大MM队列中探索了结果。我们发现，无论是诊断或复发的样品中的所有骨髓瘤细胞占主导地位的一个主要的克隆，与一些亚克隆在几个样品（n = 5）。接下来，我们研究了骨髓瘤细胞的通用转录特征，并确定了8个与这种疾病相关的Meta程序，特别是Meta程序1和8（M1和M8），它们分别与细胞周期和应激反应相关。此外，我们将恶性浆细胞分为8个簇，发现簇2/6/7中的细胞数量仅在复发样品中更高。此外，我们确定了几个有吸引力的候选生物标志物（例如SMAD 1和STMN 1），这些候选生物标志物与我们数据集中的疾病进展和复发相关，并与CoMMPass数据集中的总生存期相关。我们的数据提供了对MM异质性的深入了解，并强调了肿瘤内异质性的相关性，并发现了可能成为有效治疗的新型生物标志物。单细胞RNA测序和可变多样性连接区靶向测序显示了克隆多样性。研究了骨髓瘤细胞的通用转录特征，并确定了与这种疾病相关的8个Meta程序。生物标志物的几个有吸引力的候选者（例如，SMAD 1和STMN 1），这些指标通过功能研究进行了验证，并在CoMMPass数据集中证实了总生存期相关性。
Multiple myeloma (MM) is a clinically and biologically heterogeneous plasma‐cell malignancy. Despite extensive research, disease heterogeneity and relapse remain a big challenge in MM therapeutics. We tried to dissect this disease and identify novel biomarkers for patient stratification and treatment outcome prediction by applying single‐cell technology. We performed single‐cell RNA sequencing (scRNA‐seq) and variable‐diversity‐joining regions‐targeted sequencing (scVDJ‐seq) concurrently on bone marrow samples from a cohort of 18 patients with newly diagnosed MM (NDMM; n = 12) or refractory/relapsed MM (RRMM; n = 6). We analysed the malignant clonotypes using scVDJ‐seq data and conducted data integration and cell‐type annotation through the CCA algorithm based on gene expression profiling. Furthermore, we identified disease status‐specific genes and modules by comparison of NDMM and RRMM datasets and explored the findings in a larger MM cohort from the MMRF CoMMpass study. We found that all the myeloma cells in either diagnosed or relapsed samples were dominated by a major clone, with a few subclones in several samples (n = 5). Next, we investigated the universal transcriptional features of myeloma cells and identified eight meta‐programs correlated with this disease, especially meta‐programs 1 and 8 (M1 and M8), which were the most significant and related to cell cycle and stress response, respectively. Furthermore, we classified the malignant plasma cells into eight clusters and found that the cell numbers in clusters 2/6/7 were exclusively higher in relapsed samples. Besides, we identified several attractive candidates for biomarkers (e.g. SMAD1 and STMN1) associated with disease progression and relapse in our dataset and related to overall survival in the CoMMpass dataset. Our data provide insights into the heterogeneity of MM as well as highlight the relevance of intra‐tumour heterogeneity and discover novel biomarkers that might be a potent therapy. Single‐cell RNA sequencing and variable‐diversity‐joining regions‐targeted sequencing revealed clonal diversity. The universal transcriptional features of myeloma cells were investigated, and eight meta‐programs correlated with this disease were identified. Several attractive candidates for biomarkers (e.g., SMAD1 and STMN1) associated with disease progression were identified, which were validated by functional investigation and confirmed overall survival related in the CoMMpass dataset.
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影响因子：
45.3
作者：
Decaux, Olivier;Lode, Laurence;Minvielle, Stephane
通讯作者：
Minvielle, Stephane
DOI：
10.1158/1078-0432.ccr-09-2831
发表时间：
2010-03-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Dickens NJ;Walker BA;Leone PE;Johnson DC;Brito JL;Zeisig A;Jenner MW;Boyd KD;Gonzalez D;Gregory WM;Ross FM;Davies FE;Morgan GJ
通讯作者：
Morgan GJ
影响因子：
3.6
作者：
Averbeck, Marco;Kuhn, Stephanie;Polte, Tobias
通讯作者：
Polte, Tobias
影响因子：
1.2
作者：
Jiang, Yanwen;Nie, Kui;Elemento, Olivier
通讯作者：
Elemento, Olivier
影响因子：
11.4
作者：
Kuiper, R.;Broyl, A.;Sonneveld, P.
通讯作者：
Sonneveld, P.