Molecular signatures of multiple myeloma progression through single cell RNA-Seq.
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ABSTRACT:
SUBMITTER: Jang JS
PROVIDER: S-EPMC6318319 | biostudies-literature | 2019 Jan
REPOSITORIES: biostudies-literature
Publications
Molecular signatures of multiple myeloma progression through single cell RNA-Seq.
Jang Jin Sung JS Li Ying Y Mitra Amit Kumar AK Bi Lintao L Abyzov Alexej A van Wijnen Andre J AJ Baughn Linda B LB Van Ness Brian B Rajkumar Vincent V Kumar Shaji S Jen Jin J
Blood cancer journal 20190103 1
We used single cell RNA-Seq to examine molecular heterogeneity in multiple myeloma (MM) in 597 CD138 positive cells from bone marrow aspirates of 15 patients at different stages of disease progression. 790 genes were selected by coefficient of variation (CV) method and organized cells into four groups (L1-L4) using unsupervised clustering. Plasma cells from each patient clustered into at least two groups based on gene expression signature. The L1 group contained cells from all MGUS patients havi ...[more]
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Project description:Multiple myeloma (MM) is a malignant plasma cell disorder with well-defined clonal genetic/cytogenetic abnormalities. However, cellular heterogeneity is a key factor in MM’s progression, therapeutic decision, and response to treatment. Single cell whole transcriptome profiling (scRNA-Seq) offers an opportunity to dissect this molecular heterogeneity during MM progression to better understand the disease and guide rational therapy. Here, we examined 597 CD138 positive cells from 15 patients at different stages of MM progression using scRNA-Seq. We selected 790 genes based on a Coefficient of Variation (CV) approach which organized cells into four clusters (L1-L4) based on unsupervised clustering. Plasma cells from each patient contained a mixed population of plasma cells at different state of aggressiveness based on gene expression signature reflecting the inter-cellular heterogeneous nature of MM. Cells in the L1 group is characterized by low level expression of genes involved in the oxidative phosphorylation, Myc targets, and mTORC1 signaling pathway having most cells from MGUS patients (p < 1.2x10-14). In contrast, low level of these genes in L1 group increased progressively and were the highest in the L4 group containing only cells from high-risk MM patients with t(4;14) translocations. Furthermore, 44 genes consistently overexpressed by pair-wised comparisons of the four groups strongly associated with a reduced overall survival in MM patients (APEX trial, p < 0.0001; Hazard Ratio (HR), 1.83; 95% CI, 1.33 to 2.52), particularly those in the bortezomib treated group (p < 0.0001; HR, 2.00; 95% CI, 1.39 to 2.89). No survival significance was observed for the dexamethasone treated group. Our study at the resolution of single cells showed that there is a mixed population of cells in each patient at different stages of MM progression and these cells can be organized into four different subgroups (L1 to L4). Consistent overexpression of the 44 genes from L1 to L4 groups is associated with patient outcome and treatment response. Our results show that oxidative phosphorylation, Myc target, and mTORC1 signaling genes are significant pathways for MM progression and affect MM prognosis and treatment stratification.
2018-11-17 | GSE118900 | GEO
Project description:Molecular Signatures of Multiple Myeloma Progression through Single Cell RNA-Seq
| PRJNA487220 | ENA
Project description:Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data were performed to investigate the clonal evolution characteristics and to find novel prognostic targets in MM. The scRNA-seq data were analyzed by the Seurat pipeline and Monocle 2 to identify MM cell branches with different differentiation states. Marker genes in each branch were uploaded to the STRING database to construct the Protein-Protein Interaction (PPI) network, followed by the detection of hub genes by Cytoscape software. Using bulk RNA-seq data, Kaplan-Meier (K-M) survival analysis was then carried out to determine prognostic biomarkers in MM. A total of 342 marker genes in two branches with different differentiation states were identified, and the top 20 marker genes with the highest scores in the network calculated by the MCC algorithm were selected as hub genes in MM. Furthermore, K-M survival analysis revealed that higher NDUFB8, COX6C, NDUFA6, USMG5, and COX5B expression correlated closely with a worse prognosis in MM patients. Moreover, ssGSEA and Pearson analyses showed that their expression had a significant negative correlation with the proportion of Tcm (central memory cell) immune cells. Our findings identified NDUFB8, COX6C, NDUFA6, USMG5, and COX5B as novel prognostic biomarkers in MM, and also revealed the significance of genetic heterogeneity during cell differentiation in MM prognosis.
| S-EPMC9776050 | biostudies-literature
Project description:BackgroundThe development of multiple myeloma (MM) is considered to involve a multistep transformation process, but the role of cytogenetic abnormalities and molecular alterations in determining the cell fate of multiple myeloma (MM) remains unclear. Here, we have analyzed single cell RNA-seq data and bulk gene profiles to reveal a novel signature associated with MM development.MethodsThe scRNA-seq data from GSE118900 was used to profile the transcriptomes of cells from MM patients at different stages. Pseudotemporal ordering of the single cells was performed using Monocle package to feature distinct transcriptomic states of the developing MM cells. The bulk microarray profiles from GSE24080 and GSE9782 were applied to identify a signature associated with MM development.ResultsThe 597 cells were divided into 7 clusters according to different risk levels. They were initiated mainly from monoclonal gammopathy of undetermined significance (MGUS), newly diagnosed MM (NDMM), or relapsed and/or refractory myeloma (RRMM) with cytogenetically favorable t(11;14), moved towards the cells from smoldering MM (SMM) or NDMM without t(11;14) or t(4;14), and then finally to cells from SMM or RRMM with t(4;14). Based on the markers identified in the late stage, the bulk data was used to develop a 20-gene signature stratifying patients into high and low-risk groups (GSE24080: HR = 3.759, 95% CI 2.746-5.145; GSE9782: HR = 2.612, 95% CI 1.894-3.603), which was better than the previously published gene signatures (EMC92, UAMS70, and UAMS17) and International Staging System. This signature also succeeded in predicting the clinical outcome of patients treated with bortezomib (HR = 2.884, 95% CI 1.994-4.172, P = 1.89e-8). The 20 genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients with MM.ConclusionOur comprehensive analyses offered new insights in MM development, and established a 20-gene signature as an independent biomarker for MM.
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Project description:Single-cell sequencing methods have emerged as powerful tools for identification of heterogeneous cell types within defined brain regions. Application of single-cell techniques to study the transcriptome of activated neurons can offer insight into molecular dynamics associated with differential neuronal responses to a given experience. Through evaluation of common whole-cell and single-nuclei RNA-sequencing (snRNA-seq) methods, here we show that snRNA-seq faithfully recapitulates transcriptional patterns associated with experience-driven induction of activity, including immediate early genes (IEGs) such as Fos, Arc and Egr1. SnRNA-seq of mouse dentate granule cells reveals large-scale changes in the activated neuronal transcriptome after brief novel environment exposure, including induction of MAPK pathway genes. In addition, we observe a continuum of activation states, revealing a pseudotemporal pattern of activation from gene expression alone. In summary, snRNA-seq of activated neurons enables the examination of gene expression beyond IEGs, allowing for novel insights into neuronal activation patterns in vivo.
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| S-EPMC6887585 | biostudies-literature
Project description:Genomic characterization of cancer has enabled identification of numerous molecular targets, which has led to significant advances in personalized medicine. However, with few exceptions, precision medicine approaches in the plasma cell malignancy multiple myeloma (MM) have had limited success, likely owing to the subclonal nature of molecular targets in this disease. Targeted therapies against FGFR3 have been under development for the past decade in the hopes of targeting aberrant FGFR3 activity in MM. FGFR3 activation results from the recurrent transforming event of t(4;14) found in ∼15% of MM patients, as well as secondary FGFR3 mutations in this subgroup. To evaluate the effectiveness of targeting FGFR3 in MM, we undertook a phase 2 clinical trial evaluating the small-molecule FGFR1-4 inhibitor, erdafitinib, in relapsed/refractory myeloma patients with or without FGFR3 mutations (NCT02952573). Herein, we report on a single t(4;14) patient enrolled on this study who was identified to have a subclonal FGFR3 stop-loss deletion. Although this individual eventually progressed on study and succumbed to their disease, the intended molecular response was revealed through an extensive molecular characterization of the patient's tumor at baseline and on treatment using single-cell genomics. We identified elimination of the FGFR3-mutant subclone after treatment and expansion of a preexisting clone with loss of Chromosome 17p. Altogether, our study highlights the utility of single-cell genomics in targeted trials as they can reveal molecular mechanisms that underlie sensitivity and resistance. This in turn can guide more personalized and targeted therapeutic approaches, including those that involve FGFR3-targeting therapies.
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Project description:The objective of this study is to identify genes involved in the pathogenesis of multiple myeloma (MM). In this study, we performed gene expression profiling of a total of 29 samples consisting of 19 clinical specimens, 7 myeloma cell lines and 3 normal samples. The microarray data were analysed by GeneSpring Software v14.9. A total of 5124 DEGs were identified in MM group vs control group (fold change≥2.0; P<0.05). Out of 5124 DEGs, 2696 and 2428 genes were up-regulated and down-regulated, respectively in MM group compared to the normal control group.
2022-07-20 | GSE72213 | GEO