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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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.
References
Scrivo, R., Vasile, M., Bartosiewicz, I. & Valesini, G. Inflammation as ‘common soil’ of the multifactorial diseases. Autoimmun. Rev. 10, 369–374 (2011).
Lomas, O. C., Tahri, S. & Ghobrial, I. M. The microenvironment in myeloma. Curr. Opin. Oncol. 32, 170–175 (2020).
Monteran, L. & Erez, N. The dark side of fibroblasts: cancer-associated fibroblasts as mediators of immunosuppression in the tumor microenvironment. Front. Immunol. 10, 1835 (2019).
Baryawno, N. et al. A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia. Cell 177, 1915–1932 (2019).
Helbling, P. M. et al. Global transcriptomic profiling of the bone marrow stromal microenvironment during postnatal development, aging, and inflammation. Cell Rep. 29, 3313–3330 (2019).
Cassese, G. et al. Plasma cell survival is mediated by synergistic effects of cytokines and adhesion-dependent signals. J. Immunol. 171, 1684–1690 (2003).
Corre, J. et al. Bone marrow mesenchymal stem cells are abnormal in multiple myeloma. Leukemia 21, 1079–1088 (2007).
Ghobrial, I. M., Detappe, A., Anderson, K. C. & Steensma, D. P. The bone-marrow niche in MDS and MGUS: implications for AML and MM. Nat. Rev. Clin. Oncol. 15, 219–233 (2018).
Das, R. et al. Microenvironment-dependent growth of preneoplastic and malignant plasma cells in humanized mice. Nat. Med. 22, 1351–1357 (2016).
Zavidij, O. et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat. Cancer 1, 493–506 (2020).
Bailur, J. K. et al. Early alterations in stem-like/resident T cells, innate and myeloid cells in the bone marrow in preneoplastic gammopathy. JCI Insight 5, e127807 (2019).
Görgün, G. T. et al. Tumor-promoting immune-suppressive myeloid-derived suppressor cells in the multiple myeloma microenvironment in humans. Blood 121, 2975–2987 (2013).
Favaloro, J. et al. Myeloid derived suppressor cells are numerically, functionally and phenotypically different in patients with multiple myeloma. Leuk. Lymphoma 55, 2893–2900 (2014).
Nooka, A. K. et al. Daratumumab in multiple myeloma. Cancer 125, 2364–2382 (2019).
Moreau, P. et al. Bortezomib, thalidomide, and dexamethasone with or without daratumumab before and after autologous stem-cell transplantation for newly diagnosed multiple myeloma (CASSIOPEIA): a randomised, open-label, phase 3 study. Lancet 394, 29–38 (2019).
Kumar, S. K. et al. Multiple myeloma. Nat. Rev. Dis. Prim. 3, 17046 (2017).
Barrionuevo, N., Gatica, S., Olivares, P., Cabello-Verrugio, C. & Simon, F. Endothelial cells exhibit two waves of P-selectin surface aggregation under endotoxic and oxidative conditions. Protein J. 38, 667–674 (2019).
Barut, B. A. et al. Role of interleukin 6 in the growth of myeloma-derived cell lines. Leuk. Res. 16, 951–959 (1992).
Nishimoto, N. et al. Oncostatin M, leukemia inhibitory factor, and interleukin 6 induce the proliferation of human plasmacytoma cells via the common signal transducer, gp130. J. Exp. Med. 179, 1343–1347 (1994).
Alfaro, C. et al. Tumor-produced interleukin-8 attracts human myeloid-derived suppressor cells and elicits extrusion of neutrophil extracellular traps (NETs). Clin. Cancer Res. 22, 3924–3936 (2016).
Shi, H. et al. Chemokine (C-X-C motif) ligand 1 and CXCL2 produced by tumor promote the generation of monocytic myeloid-derived suppressor cells. Cancer Sci. 109, 3826–3839 (2018).
Schaeuble, K. et al. Attenuation of chronic antiviral T-cell responses through constitutive COX2-dependent prostanoid synthesis by lymph node fibroblasts. PLoS Biol. 17, e3000072 (2019).
Molejon, M. I. et al. Deciphering the cellular source of tumor relapse identifies CD44 as a major therapeutic target in pancreatic adenocarcinoma. Oncotarget 6, 7408–7423 (2015).
Yang, C. et al. Inducible formation of leader cells driven by CD44 switching gives rise to collective invasion and metastases in luminal breast carcinomas. Oncogene 38, 7113–7132 (2019).
Filippi, I. et al. Different adaptive responses to hypoxia in normal and multiple myeloma endothelial cells. Cell. Physiol. Biochem. 46, 203–212 (2018).
Kang, H. S. et al. CD44 plays a critical role in regulating diet-induced adipose inflammation, hepatic steatosis, and insulin resistance. PLoS ONE 8, e58417 (2013).
Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).
Hideshima, T., Chauhan, D., Schlossman, R., Richardson, P. & Anderson, K. C. The role of tumor necrosis factor α in the pathophysiology of human multiple myeloma: therapeutic applications. Oncogene 20, 4519–4527 (2001).
Morris, E. V. et al. Myeloma cells down-regulate adiponectin in bone marrow adipocytes via TNF-α. J. Bone Miner. Res. 35, 942–955 (2020).
Gars, M. L. et al. CD38 contributes to human natural killer cell responses through a role in immune synapse formation. Preprint at bioRxiv, https://doi.org/10.1101/349084 (2019).
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).
Loberg, R. D. et al. CCL2 is a potent regulator of prostate cancer cell migration and proliferation. Neoplasia 8, 578–586 (2006).
Chen, X. et al. CCL2/CCR2 regulates the tumor microenvironment in HER-2/neu-driven mammary carcinomas in mice. PLoS ONE 11, e0165595 (2016).
Moll, G. et al. Mesenchymal stromal cells engage complement and complement receptor bearing innate effector cells to modulate immune responses. PLoS ONE 6, e21703 (2011).
Perretti, M. & D’Acquisto, F. Annexin A1 and glucocorticoids as effectors of the resolution of inflammation. Nat. Rev. Immunol. 9, 62–70 (2009).
Diakos, C. I., Charles, K. A., McMillan, D. C. & Clarke, S. J. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 15, e493–e503 (2014).
Rubartelli, A., Bajetto, A., Allavena, G., Cozzolino, F. & Sitia, R. Post-translational regulation of interleukin 1β secretion. Cytokine 5, 117–124 (1993).
Erez, N., Truitt, M., Olson, P. & Hanahan, D. Cancer-associated fibroblasts are activated in incipient neoplasia to orchestrate tumor-promoting inflammation in an NF-κB-dependent manner. Cancer Cell 17, 135–147 (2010).
Tong, D. et al. The roles of the COX2/PGE2/EP axis in therapeutic resistance. Cancer Metastasis Rev. 37, 355–368 (2018).
Li, A., Chen, P., Leng, Y. & Kang, J. Histone deacetylase 6 regulates the immunosuppressive properties of cancer-associated fibroblasts in breast cancer through the STAT3–COX2-dependent pathway. Oncogene 37, 5952–5966 (2018).
Santi, A., Kugeratski, F. G. & Zanivan, S. Cancer associated fibroblasts: the architects of stroma remodeling. Proteomics 18, e1700167 (2018).
Miyai, Y., Esaki, N., Takahashi, M. & Enomoto, A. Cancer-associated fibroblasts that restrain cancer progression: hypotheses and perspectives. Cancer Sci. 111, 1047–1057 (2020).
Liu, T. et al. Cancer-associated fibroblasts: an emerging target of anti-cancer immunotherapy. J. Hematol. Oncol. 12, 86 (2019).
Zhang, H. et al. CXCL2/MIF–CXCR2 signaling promotes the recruitment of myeloid-derived suppressor cells and is correlated with prognosis in bladder cancer. Oncogene 36, 2095–2104 (2017).
Tobin, R. P. et al. IL-6 and IL-8 are linked with myeloid-derived suppressor cell accumulation and correlate with poor clinical outcomes in melanoma patients. Front. Oncol. 9, 1223 (2019).
Kumar, H., Kawai, T. & Akira, S. Pathogen recognition by the innate immune system. Int. Rev. Immunol. 30, 16–34 (2011).
Amarante-Mendes, G. P. et al. Pattern recognition receptors and the host cell death molecular machinery. Front. Immunol. 9, 2379 (2018).
Hope, C. et al. Immunoregulatory roles of versican proteolysis in the myeloma microenvironment. Blood 128, 680–685 (2016).
Vulpis, E. et al. Genotoxic stress modulates the release of exosomes from multiple myeloma cells capable of activating NK cell cytokine production: role of HSP70/TLR2/NF-kB axis. Oncoimmunology 6, e1279372 (2017).
Ping, Z. et al. Activation of NF-κB driven inflammatory programs in mesenchymal elements attenuates hematopoiesis in low-risk myelodysplastic syndromes. Leukemia 33, 536–541 (2019).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).
McInnes, L., Healy, J., Saul, N. & Grossberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2016).
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.
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Authors and Affiliations
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.
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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.
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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.
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.
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.
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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DOI: https://doi.org/10.1038/s41590-021-00931-3
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