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. 2019 Jan 3;9(1):2.
doi: 10.1038/s41408-018-0160-x.

Molecular Signatures of Multiple Myeloma Progression Through Single Cell RNA-Seq

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Free PMC article

Molecular Signatures of Multiple Myeloma Progression Through Single Cell RNA-Seq

Jin Sung Jang et al. Blood Cancer J. .
Free PMC article

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 < 1.2 × 10-14). In contrast, the expression level of these pathway genes increased progressively and were the highest in L4 group containing only cells from MM patients with t(4;14) translocations. A 44 genes signature of consistently overexpressed genes among the four groups was associated with poorer overall survival in MM patients (APEX trial, p < 0.0001; HR, 1.83; 95% CI, 1.33-2.52), particularly those treated with bortezomib (p < 0.0001; HR, 2.00; 95% CI, 1.39-2.89). Our study, using single cell RNA-Seq, identified the most significantly affected molecular pathways during MM progression and provided a novel signature predictive of patient prognosis and treatment stratification.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1. Workflow of scRNA-Seq in MM.
a Schematic illustration of scRNA-Seq from bone marrow aspirate. b Bioinformatics analysis pipeline which includes three components: QC and data conversion, gene selection and profiling, and clustering and survival analyses
Fig. 2
Fig. 2. Identification of signature genes and hierarchical clustering analysis (HCA).
a Selection of genes (n = 790, inside black triangle) with coefficient of variation (CV) ≥ 0.5 (slope of the triangle) and a mean average gene expression value log2 (TPM + 1) ≥ 3 (vertical line of the triangle). Red dots: housekeeping genes (HG). b Unsupervised two-dimensional HC analysis using the 790 genes. Four main branches were formed (L1–L4) by HCA. c Distribution plot of a total of 597 cells from 15 patients. Each black line indicates each cell
Fig. 3
Fig. 3. Expression of protein homeostasis genes among clustering cell groups.
a Relative expression for 18 proteasome subunits genes in L1–L4 groups. p-values and fold changes by ANOVA are shown in Supplemental Table S3. b Relative expression of ATF6 and EIF2A genes within each single cell group. Vertical axis is the log-transformed mean expression values and width indicates frequency of cells at the indicated expression level. *p < 0.05; **p < 0.01; ***p < 0.001
Fig. 4
Fig. 4. Differential expression genes and associated pathways with MM Progression.
a Most significantly up-regulated (FC ≥ 2, p < 0.05) and shared 311 genes when comparing each cell groups to L1. b Identification of 44 genes with most consistently altered in expression levels (FC ≥ 2, p< 0.05) between the adjacent groups and sample violin plots for 4 of 44 shared genes (red circle)
Fig. 5
Fig. 5. Survival analysis using 44 signature gene sets.
Microarray gene expression data from APEX (ac) was used and Kaplan–Meier (KM) survival curve are shown based on the high and low expression status of the signature genes. p-values were generated using Mantel–Cox log-rank test. Bz. Bortezomib; Dex. Dexamethasone, HR hazard ratio, Y-axis percentage of survival, X-axis days of survival from randomization

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References

    1. Rajkumar SV, Kumar S. Multiple myeloma: diagnosis and treatment. Mayo Clin. Proc. 2016;91:101–119. doi: 10.1016/j.mayocp.2015.11.007. - DOI - PMC - PubMed
    1. Morgan GJ, Walker BA, Davies FE. The genetic architecture of multiple myeloma. Nat. Rev. Cancer. 2012;12:335–348. doi: 10.1038/nrc3257. - DOI - PubMed
    1. Broyl A, et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood. 2010;116:2543–2553. doi: 10.1182/blood-2009-12-261032. - DOI - PubMed
    1. Zingone A, Kuehl WM. Pathogenesis of monoclonal gammopathy of undetermined significance and progression to multiple myeloma. Semin. Hematol. 2011;48:4–12. doi: 10.1053/j.seminhematol.2010.11.003. - DOI - PMC - PubMed
    1. Bolli N, et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 2014;5:2997. doi: 10.1038/ncomms3997. - DOI - PMC - PubMed

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