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Molecular signatures of multiple myeloma progression through single cell RNA-Seq.

ABSTRACT: 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 having the lowest expression of genes involved in the oxidative phosphorylation, Myc targets, and mTORC1 signaling pathways (p?

SUBMITTER: Jang JS

PROVIDER: S-EPMC6318319 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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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]

PMID: 30607001

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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.

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.

Dataset Information

Molecular signatures of multiple myeloma progression through single cell RNA-Seq.

Publications

Molecular signatures of multiple myeloma progression through single cell RNA-Seq.

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