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The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape

Abstract

Progression and persistence of malignancies are influenced by the local tumor microenvironment, and future eradication of currently incurable tumors will, in part, hinge on our understanding of malignant cell biology in the context of their nourishing surroundings. Here, we generated paired single-cell transcriptomic datasets of tumor cells and the bone marrow immune and stromal microenvironment in multiple myeloma. These analyses identified myeloma-specific inflammatory mesenchymal stromal cells, which spatially colocalized with tumor cells and immune cells and transcribed genes involved in tumor survival and immune modulation. Inflammatory stromal cell signatures were driven by stimulation with proinflammatory cytokines, and analyses of immune cell subsets suggested interferon-responsive effector T cell and CD8+ stem cell memory T cell populations as potential sources of stromal cell–activating cytokines. Tracking stromal inflammation in individuals over time revealed that successful antitumor induction therapy is unable to revert bone marrow inflammation, predicting a role for mesenchymal stromal cells in disease persistence.

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Fig. 1: Identification of myeloma-specific inflammatory MSCs.
Fig. 2: Distinct localization of CD44+ iMSCs in bone marrow.
Fig. 3: Potential activators of iMSCs.
Fig. 4: Activation recapitulates stromal inflammation.
Fig. 5: The myeloma immune microenvironment at single-cell resolution.
Fig. 6: Predicted interactions of iMSCs with myeloma cells.
Fig. 7: Predicted interactions in the myeloma microenvironment.
Fig. 8: Bone marrow inflammation persists after successful antitumor therapy.

Data availability

The single-cell RNA sequencing datasets of the immune and non-hematopoietic bone marrow microenvironment and plasma cells of newly diagnosed individuals with MM and individuals without myeloma are available on ArrayExpress, no. E-MTAB-9139. The bulk RNA-sequencing datasets of the non-hematopoietic bone marrow microenvironment of individuals with MM at diagnosis and after induction treatment are available on ArrayExpress, no. E-MTAB-9285. Files summarizing receptor–ligand interactions as computed by CellphoneDB between the non-hematopoietic, CD38, CD38+SDC1 and CD38+SDC1+ datasets are available through GitHub (https://github.com/MyelomaRotterdam/Microenvironment). Source data are provided with this paper.

Code availability

The code generated during this study to analyze the single-cell datasets is available through GitHub at https://github.com/MyelomaRotterdam/Microenvironment.

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Acknowledgements

We thank members of Myeloma Research Rotterdam and the Department of Hematology for critical discussions and reading of the manuscript, and the participants, families and nurses for their contributions to this study. We thank N. Leimkühler (Department of Hematology, Erasmus MC, the Netherlands) for help with the collection of healthy bone marrow biopsies and E. Bindels (Department of Hematology, Erasmus MC, the Netherlands) for help with sequencing. This work was supported by grants from the European Myeloma Network (EMN, grant HO95-EMN02-FB01102018 to P.S.) and ZonMw (grant 95103008 to P.S.). C.K. is supported by the Jose Carreras Leukaemia Foundation (grant DJCLS 17R/2018), the Deutsche Krebshilfe (grant 70112392), Deutsche Forschungsgemeinschaft (grant KH331/2–3) and the intramural funding of the faculty of Medicine at University Hospital of Muenster (grant Kha2/002/20). This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 707404 (to Z.K.). The opinions expressed in this document reflect only the authors’ views. The European Commission is not responsible for any use that may be made of the information it contains.

Author information

Affiliations

Authors

Contributions

T.C. and P.S. conceptualized the study. T.C. and M.D.J. were responsible for the study methodology. M.D.J., Z.K., N.P. and S.T. were responsible for study investigation. M.D.J., Z.K., N.P., S.T., R.H., M.S. and T.C. analyzed the data. D.H.O.B., P.V.D.W, P.K.B., C.K., J.V., P.M., M.V.D. and A.B. provided resources for the study. M.D.J., R.H. and M.S. curated the data. M.D.J. contributed to data visualization. M.D.J. and T.C. wrote the original draft, and all authors reviewed and edited the final manuscript. P.S. and Z.K. acquired funding. T.C., A.B. and P.S. supervised the study, and T.C. and P.S. were responsible for project administration.

Corresponding authors

Correspondence to Pieter Sonneveld or Tom Cupedo.

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Competing interests

J.V. is an employee of Janssen; A.B. consults for Celgene, Janssen, Amgen and Takeda; P.S. consults for and receives research support from Celgene, Janssen, Karyopharm and SkylineDx; P.M. is on the advisory boards of and receives honoraria from Janssen, BMS/Celgene, Amgen and Abbvie. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks Nicola Giuliani, Shannon Turley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Related to Figs. 1, 4 and 6.

(a) FACS gating strategy for total non-hematopoietic cells or bone marrow mesenchymal stromal cells from CD45-depleted bone marrow mononuclear cells (MNCs) from aspirates for bulk RNA sequencing and single-cell RNA sequencing experiments (b) FACS gating strategy of major immune cell populations in bone marrow aspirates for qPCR. (c) FACS gating strategy of CD38- and CD38+ hematopoietic cells from non-depleted bone marrow aspirate MNCs for single-cell RNA sequencing experiments.

Extended Data Fig. 2 Related to Fig. 1.

(a) Bar graph depicting relative abundance of non-hematopoietic cells of total bone marrow aspirates. Data points represent patients (myeloma, n = 40; treated, n = 16; control, n = 8) (b) Transcription of FN1 and DCN in non-hematopoietic cells (c) Violin plots depicting transcription of collagen genes in the 8 clusters of non-hematopoietic cells (d) Transcription of PDGFRA, PDGFRB and ACTA2 in non-hematopoietic cells (e) Transcription of CD36 and THBD in non-hematopoietic cells (f) Transcription of SP7 in non-hematopoietic cells (g) Circle diagrams depicting cluster fractions of total in control- and myeloma datasets (h) Bar plot depicting fraction of cells per cluster per patient (left Y-axis); line graph depicting total number of sequenced non-hematopoietic cells per patient (right Y-axis) (i) Bar graph depicting patient-specific iMSC-fraction of total mesenchymal stromal cells, segregated by R-ISS stage. Datapoints represent patients (R-ISS stage I, n = 4; II, n = 7; III, n = 2) (j) Spearman (two-tailed) correlation of iMSC-fraction of total mesenchymal stromal cells with percentage of CD38+CD138+ plasma cells in aspirates taken at diagnosis. Datapoints represent patients (k) Heatmap of top 50 differentially expressed genes up in MSC1/2 compared to MSC3/4/5 (left) or down in MSC1/2 compared to MSC3/4/5 (right) as described in Supplementary Table 5. The data in (a) and (i) are presented as mean ± standard error of the mean (SEM). Significance was calculated using Mann-Whitney U (two-tailed). NS; p > 0.05.

Source data

Extended Data Fig. 3 Related to Figs. 1 and 3.

(a) UMAP of the integrated dataset of 389 cells from freshly aspired bone marrow of 1 newly diagnosed myeloma patient showing 7 clusters identified by integrated analysis, accompanied by UMAPs showing transcription of CXCL12 (MSCs) and CD36 (endothelial cells). Colors represent clusters (b) Transcription of inflammatory genes in fresh non-hematopoietic cells shown in (a) (c) Spearman (two-tailed) correlation of iMSC-fraction of total mesenchymal stromal cells with inflammatory protein levels in bone marrow plasma. Datapoints represent patients (d) Monocle 3 pseudotime analysis of mesenchymal stromal cells in controls. ‘S’ represents arbitrary selected starting point of trajectory calculations.

Extended Data Fig. 4 Related to Figs. 2 and 4.

(a) Flow cytometric gating strategy to determine CD44 protein expression on CD271+ MSCs (b) FACS gating strategy of CD56dim, CD56bright and CD56brightCD27+ cells for qPCR (c) Flow cytometric gating strategy of CD38+CD3+CD8+CD45RA+CD27+FAS+ stem cell memory T cells, CD38+CD3-CD56bright NK cells and CD38+CD3-CD56brightCD27+ NK cells in bone marrow MNCs. Same sample as used for FACS in (b).

Extended Data Fig. 5 Related to Fig. 4.

(a) Transcription of NCR1, FCGR3A, IL4 and RORC in the CD38- dataset (b) Transcription of interferon-response genes IFIT1, IFIT2 and IFIT3 in the CD38- dataset (c) Transcription of FCGR3A in the CD38+ dataset (d) Transcription of CD27 in the CD38+ dataset (e) Transcription of GZMK and GZMB in NK cell subsets of the myeloma bone marrow (n = 5) as determined by qPCR. Data are presented as mean ± standard error of the mean (SEM). Significance was calculated using Mann-Whitney U (two-tailed). **; p ≤0.01 (f) Original UMAPs of CD38- dataset split by patient. CBM: non-myeloma control; PH: patient with hyperdiploid tumor; PT: patient with tumor harboring translocations (g) Original UMAPs of CD38+ dataset split by patient. CBM: non-myeloma control; PH: patient with hyperdiploid tumor; PT: patient with tumor harboring translocations.

Source data

Extended Data Fig. 6 Related to Fig. 4.

(a) UMAP of 32,412 CD38+SDC1+ myeloma cells of 13 myeloma patients showing 7 clusters identified by integrated analysis. Colors represent clusters (b) Transcription of marker genes in the CD38+SDC1+ myeloma cell dataset (c) Original UMAPs of CD38+SDC1+ myeloma cell dataset split by patient. PH: patient with hyperdiploid tumor; PT: patient with tumor harboring translocations (d) Transcription of TNF and IL1B in the CD38+SDC1+ myeloma cell dataset.

Extended Data Fig. 7 Related to Fig. 6.

(a) Presence of canonical immune subset transcripts in bulk RNA sequencing datasets of purified MSC from newly-diagnosed myeloma (blue, n=14) and non-myeloma control patients (green, n=4) (b) Transcription of stromal cell subset markers comparing myeloma (blue) to non-myeloma (green) (c) Transcription of genes encoding extracellular matrix (ECM) proteins comparing myeloma (blue) to non-myeloma (green) (d) Transcription of canonical immune subset transcripts in bulk RNA sequencing datasets of purified MSC comparing post-induction treatment (blue, n = 9) to diagnosis (green, n = 9) (e) Transcription of stromal cell subset markers comparing post-induction treatment (blue) to diagnosis (green) (f) Transcription of genes encoding ECM proteins comparing post-induction treatment (blue) to diagnosis (green) (g) Heatmap of top differentially expressed genes up or down in treated patients compared to newly diagnosed samples as described in Supplementary Table 13. The data in (a), (b), (c), (d), (e) and (f) are presented as mean ± standard error of the mean (SEM). Significance was calculated using the Wald test (two-tailed), followed by Benjamini Hochberg correction. NS; p >0.05, *; p ≤0.05, **; p ≤0.01. TPM; transcripts per million.

Source data

Extended Data Fig. 8 Hypothetical model.

Myeloma cells present in the bone marrow either activate stromal cells directly or trigger local inflammation, leading to the production of inflammatory mediators. Activation of local MSCs leads to formation of two subsets of iMSC (MSC2 and MSC1). iMSCs start to transcribe genes that might support MM cell survival and attract and modulate immune subsets, including myeloid cells.

Supplementary information

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de Jong, M.M.E., Kellermayer, Z., Papazian, N. et al. The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape. Nat Immunol 22, 769–780 (2021). https://doi.org/10.1038/s41590-021-00931-3

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