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Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma

Nature Cancer volume 1, pages 493–506 (2020)Cite this article

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Abstract

Precursor states of multiple myeloma (MM) and its native tumor microenvironment need in-depth molecular characterization to better stratify and treat patients at risk. Using single-cell RNA sequencing of bone marrow cells from precursor stages, monoclonal gammopathy of unknown significance and smoldering MM, to full-blown MM alongside healthy donors, we demonstrate early immune changes during patient progression. We find that natural killer cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. As early as smoldering MM, we show loss of granzyme K+ memory cytotoxic T cells and show their critical role in MM immunosurveillance in mouse models. Finally, we report major histocompatibility complex class II dysregulation in CD14+ monocytes, which results in T-cell suppression in vitro. These results provide a comprehensive map of immune changes at play over the evolution of premalignant MM, which will help develop strategies for immune-based patient stratification.

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Fig. 1: The immune landscape in healthy-donor and MM samples.
Fig. 2: Heterogeneous composition of T cells in diseased BM microenvironment.
Fig. 3: Skewed differentiation of cytotoxic T cells in patients with MM at an early disease stage.
Fig. 4: IFN type I target genes are upregulated in patients compared to healthy donors.
Fig. 5: Dysregulation of MHC class II in CD14+ monocytes in the MM environment.
Fig. 6: CD14-expressing monocytes from the myeloma environment can enhance proliferation of myeloma cells and suppress T-cell activation.

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Data availability

The scRNA-seq data that support the findings of this study have been deposited with the Gene Expression Omnibus under accession code GSE124310. Raw sequencing data have been deposited with the database of Genotypes and Phenotypes (phs001323.v1.p1). Previously published microarray data that were reanalyzed in this study are available under accession code GSE6477. Source data for Figs. 1–6 and Extended Data Figs. 2–4 and 6–9 have been provided in corresponding Source Data tables. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The single-cell RNA data was processed using cellranger v.2.0.1 (https://www.10xgenomics.com/) and analyzed with the R package Seurat v.2.3.1 (https://satijalab.org/seurat/). Gene expression signatures were extracted using our SignatureAnalyzer algorithm available on github (https://github.com/broadinstitute/getzlab-SignatureAnalyzer).

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Acknowledgements

We thank the Dana-Farber/Harvard Cancer Center in Boston for the use of the Specialized Histopathology Core for histology and immunohistochemistry service. The Dana-Farber/Harvard Cancer Center is supported in part by a National Cancer Institute Cancer Center Support Grant no. NIH 5 P30 CA06516. G.G. was partially funded by the Paul C. Zamecnik Chair in Oncology, Massachusetts General Hospital Cancer Center. N.J.H. was partially funded by G.G. startup funds at Massachusetts General Hospital, R01 (principal investigator I.M.G.) and the Harvard Graduate Program in Biophysics, Harvard University. Molecular Biophysics Training Grant NIH/NIGMS T32 GM008313 (N.J.H.; principal investigtor J. M. Hogle). This study was partially funded by two National Institutes of Health grants (no. NIH R01 CA 205954 and R01CA181683), the Leukemia and Lymphoma Society, the Multiple Myeloma Research Foundation and Stand Up to Cancer.

Author information

Author notes

  1. These authors contributed equally: Oksana Zavidij, Nicholas J. Haradhvala, Tarek H. Mouhieddine.

  2. These authors jointly supervised this work: Jamil Azzi, Gad Getz, Irene M. Ghobrial.

Authors and Affiliations

  1. Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Oksana Zavidij, Tarek H. Mouhieddine, Romanos Sklavenitis-Pistofidis, Mairead Reidy, Mahshid Rahmat, Nang K. Su, Michael P. Agius, Jihye Park, Salomon Manier, Mark Bustoros, Daisy Huynh, Marzia Capelletti, Brianna Berrios, Chia-Jen Liu, Jennifer L. Guerriero, Eliezer M. Van Allen & Irene M. Ghobrial

  2. Harvard Medical School, Boston, MA, USA

    Oksana Zavidij, Tarek H. Mouhieddine, Romanos Sklavenitis-Pistofidis, Songjie Cai, Mairead Reidy, Mahshid Rahmat, Abdallah Flaifel, Michael P. Agius, Jihye Park, Salomon Manier, Mark Bustoros, Marzia Capelletti, Chia-Jen Liu, Jennifer L. Guerriero, Melissa Goldman, Eliezer M. Van Allen, Steven A. McCarroll, Jamil Azzi, Gad Getz & Irene M. Ghobrial

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Oksana Zavidij, Nicholas J. Haradhvala, Tarek H. Mouhieddine, Romanos Sklavenitis-Pistofidis, Mairead Reidy, Mahshid Rahmat, Michael P. Agius, Jihye Park, Mark Bustoros, Marzia Capelletti, Meng Xiao He, Yosef E. Maruvka, Melissa Goldman, Eliezer M. Van Allen, Steven A. McCarroll, Gad Getz & Irene M. Ghobrial

  4. Massachusetts General Hospital, Boston, MA, USA

    Nicholas J. Haradhvala & Gad Getz

  5. Harvard Biophysics Graduate Program, Harvard University, Boston, MA, USA

    Nicholas J. Haradhvala & Meng Xiao He

  6. Transplantation Research Center, Renal Division, Brigham and Women’s Hospital, Boston, MA, USA

    Songjie Cai & Jamil Azzi

  7. Specialized Histopathology Core, Brigham and Women’s Hospital, Boston, MA, USA

    Abdallah Flaifel & Benjamin Ferland

  8. Department of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA

    Esteban Braggio & Rafael Fonseca

Authors
  1. Oksana Zavidij
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  2. Nicholas J. Haradhvala
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  3. Tarek H. Mouhieddine
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Contributions

O.Z., T.H.M., I.M.G., N.J.H. and G.G. conceived and designed the study. T.H.M. collected patient data and performed cytogenetic analyses. O.Z. generated the single-cell sequencing and CyTOF data, and performed in vivo experiments. O.Z. and T.H.M. performed in vitro experiments. N.J.H. processed and analyzed the single-cell sequencing data. O.Z. and N.J.H. integrated and interpreted the data. M.G., M.C., J.L.G. and S.A.M. assisted in setup of the study. J.P., M.X.H., C.-J.L., Y.E.M. and E.M.V.A. performed preliminary analyses. N.K.S., D.H. and B.B. managed clinical samples. R.F., E.B., B.F., A.F., S.C., M.P.A., M. Reidy, M. Rahmat, S.M. and M.B. performed validation experiments. I.M.G., G.G. and J.A. supervised the study. O.Z., N.J.H., T.H.M., R.S.P., G.G. and I.M.G. wrote the manuscript.

Corresponding authors

Correspondence to Gad Getz or Irene M. Ghobrial.

Ethics declarations

Competing interests

G.G. receives research funds from IBM and Pharmacyclics and is an inventor on patent applications related to MuTect, ABSOLUTE, MutSig, MSMuTect and POLYSOLVER. I.M.G. has a consulting/advisory role with GSK, AbbVie and Bristol Myers Squibb. I.M.G has a consulting role with Sanofi, Janssen Pharmaceuticals, Takeda, Celgene, Karyopharm, GNS, Cellectar Biosciences, Medscape, Genetech, Adaptive Biotechnologies, Aptitude, Curio Science, Magenta Therapeutics and Oncopeptides. I.M.G. received research funding/ honoraria from Celgene, Takeda, Bristol Myers Squibb, Janssen Pharmaceuticals and Amgen.

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Extended data

Extended Data Fig. 1 Marker genes demonstrating cell type identity of immune cell clusters.

tSNE representation of CD45 cell populations. In each subplot, cells are colored by log-normalized expression values for a given cell type specific marker gene.

Extended Data Fig. 2 Single-cell RNA sequencing results indicate changes in NK cell compartment as compared to healthy donors.

a, A significant increase in the fraction of NK cells in patients with malignant cells expressing IgG heavy chain. Fractions of NK cells are plotted for patients grouped by immunoglobulin heavy chain. Violin plots show minimum, median, and maximum values. A two-tailed t-test was performed on n=19 patient samples with df=12. b, Spearman correlation between NK cell fraction and CXCR4+ subset frequency calculated on 10,0000 samples with replacement of data points. 95% confidence interval is shown. c, Grouping of samples above and below median values for NK cell frequencies and CXCR4+ subset fractions. Points on the median were assigned in the conservative direction (that is to obtain a less significant p-value). A two-sided fisher-exact test was performed on n=23 patient samples.

Source data

Extended Data Fig. 3 Compositional alterations in immune populations of diseased patients as compared to healthy donors.

CyTOF profiling of 4 healthy donors and 13 MGUS-MM patients show a, significantly increased numbers of CD3+CD4+ T cells in BM aspirates of the patients as compared to healthy BM with mean values of 2.3 and 11.12 for CD3+CD4+, 1.6 and 3.7 for NK, and 1.5 and 0.5 for dendritic cells (DC) in healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD. b, Plasmacytoid DC (pDC) are significantly enriched in healthy BM as compared to MM patients with mean values of 1.2 and 0.26 respectively. Mean values for monocytic DC (mDC) are 3.7 and 3.2 for healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent average deviation. c, Classical monocytes (c Monoc) are enriched in BM aspirates of healthy donors compared to MM patients with respective mean values of 67.7 and 26.5. Mean values for non-classical monocytes (nc Monoc) were 13.1 and 44.4 for healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent average deviation. d, Proportion of CD66b+ cells in BM aspirates of SMM patients and healthy donors. Mean values are 23.8 and 20.6 for healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD.

Source data

Extended Data Fig. 4 Transcriptional alterations in cytotoxic T cell populations of diseased patients as compared to healthy donors.

a, Distribution of the Granzyme expression in T-cell cluster. GZMA+GZMB− corresponds to the Granzyme K expressing cells. b, Normalized expression values for four exhaustion-related genes across different T cell subsets, pooling cells across patients. c, Normalized expression values for four exhaustion-related genes across different donors and patients, pooling cells across T subsets. d, Heatmap of immune checkpoint molecules expression levels on different subsets of bone marrow T cells in SMM patients (n=8) as compared to healthy donors (n=4). Individual patients show increased levels of PD-1 and TIGIT in GrB expressing effector cells. Colored scale represents transformed ratio of protein expression. Barplots show variable expression of TIGIT (with mean values of 0.01 vs 0.12), PD-1 (0.52 vs. 0.55) and TIM-3 (0.39 vs. 0.51) in GrB-expressing T effectors from healthy BM and SMM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD. e, Healthy memory cells show significantly higher expression of PD-1 compared to those of SMM patients with mean values of 19.5 and 4.7 respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD.

Source data

Extended Data Fig. 5 IFN type-1 signaling increases in plasma cells during disease progression.

Expression of ISG-15 a, and MX1 b, increases during disease progression. Significant difference is observed between MGUS and MM stage, indicating an increase of IFN signals at later stages in the disease progression (GSE6477). Box plot displays the first quartile, median and third quartile for the gene expression levels, bars indicate the minimum and maximum value. Significant difference between groups was tested using one-way ANOVA and Tukey multiple comparison tests for healthy donors (ND, n=15), MGUS (n=21), SMM (n=23) and newly diagnosed MM (n=75).

Extended Data Fig. 6 Marker genes demonstrating cell type identity of monocytic clusters.

a, Mean expression of MHC II encoding genes. Violin plots show minimum, median, and maximum values. A BH-corrected two-tailed t-test was performed on n=32 patient samples. b, Heatmap of expression values for the top 10 genes with enriched expression in all monocytes discovered by k-nearest neighbors’ subclustering. Expression values are centered and normalized for each gene.

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Extended Data Fig. 7 Dysregulation of HLA-DR surface representation in monocytes from diseased environment.

a, qPCR data demonstrate significant increase of HLA-DR expression in CD14+ monocytes after co-culture with MM cell lines. CD14+ cells alone and those co-cultured with B cells were used as a control. Median ratios were 1.54 for B cells, 2.07 for MM1.S cells, 3.28 for KMS-18, 8.34 for RPMI and 3.12 for OPM2 cells as compared to CD14+ control (1.0). Bars represent SD. Experiment was performed twice with independent donors in triplicates. Representative data from one experiment is shown. b, Immunofluorescence staining of tissue microarrays from MGUS, SMM and MM patients (n=45, TMA performed in triplicates, total of 135 BM sections analysed) demonstrates prevalent intracellular accumulation of HLA-DR (green) in CD14-expressing monocytes (red) in disease settings. Membrane-bound localization of HLA-DR was observed in healthy bone marrow monocytes. (yellow arrows point on cells with HLA-DR localized to the cell membrane, white arrows point on cells with HLA-DR accumulated in the cytoplasm.

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Extended Data Fig. 8 MARCH-1 dependent internalization of HLA-DR in CD14+ monocytes in myeloma environment.

a, Gating strategy for HLA-DR on CD14+ cells. b, Knockdown of MARCH-1 by siRNA rescues presentation of HLA-DR molecules on the surface of CD14+ monocytes co-cultured with MM cells. Representative FACS profiles show higher numbers of HLA-DR+ cells after MARCH-1 knockdown with siRNA (experiment performed 3 times with 3 different donors using 2 different cell lines/2 different siRNA for MARCH-1, non-targeting si-RNA is used as a control). c, Mean fluorescence intensity demonstrates the two to 4.5-fold increase in the levels of HLA-DR protein expression on the cell surface of CD14+ cells. d, qPCR data for relative expression of MARCH-1 in CD14+ cells after siRNA knockdown as compared to the si-RNA control. The assay was performed twice with independent donors/2 cell lines/2 siRNAs, performed in triplicates. Representative data from one experiment is shown. In MM1.S cells, siRNA knockdown leads to reduction of MARCH-1 expression to 0.46 and 0.32 (median value, amplified with primer pair 2) and to 0.40 and 0.26 (median value, amplified with primer pair 3) as compared to control siRNA (median value, 1.0). In OPM2 cells, siRNA knockdown leads to reduction of MARCH-1 expression to 0.65 and 0.7 (median value, amplified with primer pair 2) and to 0.29 and 0.36 (median value, amplified with primer pair 3) as compared to control siRNA (median value, 1.0). Bars represent STDEVP. e, CD14+ cells with lower levels of MARCH-1 have increased HLA-DR protein on their cell surface. The experiment was performed twice with two independent donors/2 cell lines/2 siRNAs. f, Stronger correlation of DAPI and HLA-DR localization in MM1.S and OPM2 cells in control cells as compared to those after MARCH-1-siRNA transfection. The experiment was performed twice with two independent donors/2 cell lines/2 siRNAs.

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Extended Data Fig. 9 CD14+ monocytes do not show the M-MDSC phenotype after co-culture with MM cells.

Expression of different markers for a, macrophages/monocytes and b, MDSCs on CD14+ cells in patients and healthy donors. c, CD14+ cells from healthy donors were co-cultured with MM cells. FACS analysis was performed on day 3 after co-culture. Representative results from one out of two independent experiments performed with two healthy donors/2 different cell lines. Due to restricted cell numbers, no replicates could be used. All donors have similar distribution of cells as compared to controls.

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Extended Data Fig. 10 Alterations in tumor microenvironment start from the precursor stages of the MM and exhibit heterogeneous changes in the immune cell repertoire.

Illustration of immune alterations observed during progression. Bars begin at the stage in which they are first observed in our dataset.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. Patient data and clinical information. List of clinical measurements for samples used for single-cell RNA sequencing. Supplementary Table 2. Sample information. Bead selection and quality metrics for single-cell RNA samples. Supplementary Table 3. Marker genes demonstrating cell type identity of immune cell clusters. List of top 10 differentially expressed genes with average log fold change (mean expression in within-cluster cells compared to mean expression of all out-of-cluster cells) and FDR-corrected rank-sum P values in immune cell clusters discovered by k-nearest neighbors clustering on cells from n = 32 patient samples.

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Data source. Primary data generated in experiments

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Zavidij, O., Haradhvala, N.J., Mouhieddine, T.H. et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat Cancer 1, 493–506 (2020). https://doi.org/10.1038/s43018-020-0053-3

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