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A stromal inflammasome Ras safeguard against Myc-driven lymphomagenesis

Nature Immunology volume 26, pages 53–67 (2025)Cite this article

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Abstract

The inflammasome plays multifaceted roles in cancer, but less is known about its function during premalignancy upon initial cell transformation. We report a homeostatic function of the inflammasome in suppressing malignant transformation through Ras inhibition. We identified increased hematopoietic stem cell (HSC) proliferation within the bone marrow of inflammasome-deficient mice. HSCs within an inflammasome-deficient stroma expressed a Ras signature associated with increased Ras pathway- and cancer-related transcripts and heightened levels of cytokine, chemokine and growth factor receptors. Stromal inflammasome deficiency established a poised Ras-dependent mitogenic state within HSCs, which fueled progeny B cell lymphomagenesis upon Myc deregulation in a spontaneous model of B cell lymphoma, and shortened its premalignant stage leading to faster onset of malignancy. Thus, the stromal inflammasome preserves tissue balance by restraining Ras to disrupt the most common oncogenic Myc–Ras cooperation and establish a natural defense against transition to malignancy. These findings should inform preventative therapies against hematological malignancies.

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Fig. 1: Inflammasome impairment expands circulating and BM B cell progenitors in premalignant Eμ-myc mice.
Fig. 2: Stromal inflammasome deficiency accelerates malignant disease onset in Eμ-myc mice.
Fig. 3: Inflammasome impairment accelerates BM stem and B cell progenitor proliferation in Eμ-myc mice.
Fig. 4: A heightened HSC Ras signaling signature upon inflammasome deficiency.
Fig. 5: The stromal inflammasome constrains the Ras pathway in HSCs.
Fig. 6: The stromal inflammasome controls HSC surface TNFR1/II and MIP receptors via Ras.
Fig. 7: Canonical NLRP3 inflammasome components control HSC Ras signaling in trans.
Fig. 8: Stromal-derived soluble factor and direct stroma–HSC contact mediate stromal inflammasome control of HSCs.

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Data availability

Source data are provided with this paper.

Code availability

The RNA-seq data have been uploaded to the GEO under accession GSE183695. The code for data processing and analysis in bioinformatics is fully referenced and described in the Methods.

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Acknowledgements

This work was supported by institutional seed funds to J.M.B., grant no. F30CA174313 to A.K. and training grants no. T32AI007605 to A.K. and no. T32DK116970 to K.J.Y.M. J.M.B. and the Blander Lab are also supported by National Institutes of Health grants to Principal Investigator J.M.B. (grant nos. AI170832, AI170897, AI159772, AI178327), and the Binational US–Israel Science Foundation Grant no. 2021299. G.B. was supported by fellowship and career development awards from the Crohn’s & Colitis Foundation and the Robert Wood Johnson Foundation Grant no. 74260. G.I.E. is supported by a Cancer Research UK Program Award no. A29210 (Principal Investigator G.I.E.).

Author information

Author notes

  1. Zachary Hutchins

    Present address: Immunology and Microbial Pathogenesis, College of Life Sciences, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA

  2. Gregory Putzel

    Present address: Microbial Computational Genomic Core Lab, Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA

  3. Dmitry Zhigarev

    Present address: The Wistar Institute, Philadelphia, PA, USA

  4. Baosen Jia

    Present address: Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

  5. Gaetan Barbet

    Present address: Department of Pediatrics, The Child Health Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA

  6. These authors contributed equally: Andrew Kent, Kristel Joy Yee Mon.

Authors and Affiliations

  1. Division of Hematology, University of Colorado School of Medicine, Aurora, CO, USA

    Andrew Kent

  2. Jill Roberts Institute for Research in Inflammatory Bowel Disease, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Kristel Joy Yee Mon, Zachary Hutchins, Gregory Putzel, Dmitry Zhigarev, Alexander Grier, Baosen Jia, Gaetan Barbet & J. Magarian Blander

  3. Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Kristel Joy Yee Mon, Zachary Hutchins, Gregory Putzel, Dmitry Zhigarev, Alexander Grier, Baosen Jia, Gaetan Barbet & J. Magarian Blander

  4. The Francis Crick Institute, London, UK

    Roderik M. Kortlever & Gerard I. Evan

  5. Kings College London, London, UK

    Roderik M. Kortlever & Gerard I. Evan

  6. Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY, USA

    J. Magarian Blander

  7. Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Cornell University, New York, NY, USA

    J. Magarian Blander

  8. Immunology and Microbial Pathogenesis Program, Weill Cornell and Sloan Kettering Institute Graduate School of Medical Sciences, New York, NY, USA

    J. Magarian Blander

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Contributions

A.K., K.J.Y.M. and J.M.B. designed the experiments. A.K. and K.J.Y.M. performed the experiments. A.K., K.J.Y.M. and J.M.B. wrote the manuscript and prepared figures. A.K. and J.M.B. directed the study. B.J. and G.B. sorted HSCs and prepared samples for RNA-seq. G.P. and Z.H. performed RNA-seq data processing and expression analyses. D.Z. performed the cytokine arrays. A.G. performed bioinformatic analyses of published bone marrow stroma scRNA-seq datasets, and A.G. and J.M.B. designed Seurat cluster analysis and cell type annotation. R.M.K. and G.I.E. contributed to the design of cytokine array and Ras inhibition-related experiments.

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Correspondence to J. Magarian Blander.

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Nature Immunology thanks Gabriel Mbalaviele, James Vince and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in colloboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Analyses of GL-7 and IgM expression facilitate characterization of premalignant and malignant Eμ-myc B cells in circulation.

a. Flow cytometry gating on CD19+ B cells from the peripheral blood of representative WT and Eμ-myc mice, at 4 weeks of age, depicting GL-7 and B220 staining within the IgM+ and IgM− fractions of the circulating CD19+ population of B cells. Data represent hundreds of mice genotyped using this gating strategy. b,c, Flow cytometry on and frequencies of circulating pre-B cells at 4, 6, and 12 weeks of age (n ≥ 4 per group). d, Diversity of malignant CD19+ cellular phenotypes. Each plot depicts the circulating CD19+ cells from an individual mouse at the malignant stage of disease. e, Representative Ly6C and F4/80 staining patterns within whole bone marrow. f, Representative conventional flow cytometry gating strategy to delineate B cells, T cells, and neutrophils. Data represent four independent experiments, at least four mice per genotype. g, Absolute numbers of T cells, neutrophils, monocytes and macrophages as defined in (f) from the bone marrow of mice of each genotype, and calculated ratio of macrophages to monocytes (bottom panel). Each dot represents an individual mouse (n ≥ 4 per genotype). h, cytometric bead array quantification of cytokines from bone marrows of mice of the indicated genotypes. Each dot represents an individual mouse (n ≥ 8 per genotype). *=p ≤ 0.05, **=p ≤ 0.01, ***=p ≤ 0.001, calculated by two-tailed unpaired student’s T-test. All quantified data are presented as mean values +/− SEM.

Source data

Extended Data Fig. 2 CyTOF visualization of hematopoietic cell lineages and cell type specific expression of Casp1 and Casp11.

a, Representative SPADE tree analysis of CyTOF data depicting the major hematopoietic stem cell, progenitor, and committed lineage clusters from the mouse bone marrow. b, SPADE tree diagrams from each of the Eu-myc and Casp1−⁄− Casp11−⁄− mouse genotypes showing increased frequency in the B cell progenitor cluster in Eu-myc Casp1−⁄− Casp11−⁄− mouse bone marrow. c,d, Average Casp1 (c) and Casp11 (d) relative expression values, in arbitrary units (AU) extracted for each indicated cell population from the ImmGen database (SC.LT34f.BM = Long-term reconstituting stem cell; SC.LTSL.BM = long-term repopulating hematopoietic stem cell; SC.STSL.BM = short-term repopulating hematopoietic stem cell; SC.MPP34F.BM = Flk2+ multipotent progenitor; SC.ST34F.BM = Flk2− multipotent progenitor; SC.CMP.BM.MM = common myeloid progenitor; SC.MEP.BM = megakaryocyte erythroid progenitor; SC.GMP.BM = granulocyte-monocyte progenitor; SC.CDP.BM = common dendritic cell precursors; SC.MDP.BM = monocyte-dendritic cell precursors; MLP.BM = multilineage progenitor; proB.CLP.BM = common-lymphoid progenitor; proB.FrA.BM = prepro-B cells; preB.FrBC.BM = pro-B cells; preB.FrC.BM = cycling pre-B cells; preB.FrD.BM = small pre-B cells; B.FrE.BM = newly-formed B cells; B.FrF.BM = Bone marrow fraction F B cells). e, Sorting strategy for representative LSK, B cell, macrophage, and monocyte cell populations. f, Relative Casp1 expression levels from each sorted population from (e) as determined by qRT-PCR. g, Relative Casp11 expression levels from each sorted population from (e) as determined by qRT-PCR. Data in e-g represent two independent experiments, totaling 4 biological replicates. *=p ≤ 0.05, **=p ≤ 0.01, ***=p ≤ 0.001, calculated by two-tailed unpaired student’s T-test. All quantified data are presented as mean values +/− SEM.

Source data

Extended Data Fig. 3 Inflammasome impairment alters the frequency but not phenotype of premalignant B cell progenitors.

a, Wright staining of MACS-isolated CD19+ cells from the bone marrow of indicated genotypes. Data is representative of 2 independent experiments with n = 3 total biological replicates/genotype. b, Representative forward scatter (FSC-A) of CD19+ and GL-7+IgM− cells from the indicated genotypes. c, CYTOF viSNE plots of representative bone marrow samples of each genotype with colors indicating cells within the indicated marker gates. d, viSNE cell density diagrams on the same samples as in (c). Data represent three independent experiments with six individual mice total per genotype. e, Representative B cell marker expression from the CyTOF data, comparing genotypes. Data represent three independent experiments with six individual mice total per genotype. f, Intracellular cleaved caspase-3 (CC3) vs. GL-7 staining within the CD19+ compartment. g,h, Quantification of (f) showing % CC3+ in CD19+ gate (g) and the CD19+GL-7+ gate (h) (n ≥ 4 mice per group). All experiments were performed on 4-week-old mice of the indicated genotypes. *=p ≤ 0.05, **=p ≤ 0.01, ***=p ≤ 0.001, calculated by two-tailed unpaired student’s T-test. All quantified data are presented as mean values +/− SEM.

Source data

Extended Data Fig. 4 FACS-sorted HSCs are highly enriched for HSC-specific genes.

a, Flow cytometry contour plots showing gating on lineage−Sca-1+c−-Kit+ (LSK) stem cells prior to FACS-sorting of cells for RNA-sequencing. LSK cells were isolated from total bone marrow cells extracted from femurs and tibias of indicated premalignant 6-week-old mice and stained for specific LSK cells isolation with an antibody mixture containing the Lineage marker cocktail (clones: 145-2C11, RB6-8C5, M1/70, RA3-6B2, Ter-119), anti-c-Kit (clone 2B8) and anti-Sca-1 (clone D7) antibodies. b,c, Histogram of mean normalized counts of sequenced genes from sorted LSK cells showing high enrichment of genes restricted to HSC as reported by the laboratories of Lemischka and Dick29,30, compared to relatively lower enrichment of HSC genes reported by Rossi laboratory31 (b), and highest enrichment compared to signature HSC genes represented in the Hematopoietic Stem Cell Differentiation gene ontology biological process (b,c). Positioning of the average total counts of genes in our sorted LSK HSC cells is indicated as ‘Total’.

Extended Data Fig. 5 Distinct bone marrow profiles of caspase 1 and 11 deficient mice irrespective of Eμ-myc.

a,b, Heatmaps showing normalized expression levels of differentially expressed genes from selected enriched GO terms. Genes shown are differentially regulated by Casp1−⁄− Casp11−⁄− in both Eμ-myc and WT backgrounds. a, Genes in response to TGF-β, positive regulation of vasculature development, and cell-cell signaling by Wnt are shown. b, Genes in response to cellular respiration, mitochondrial respiratory chain complex assembly, mitochondrial organization, mitochondrial ATP synthesis coupled electron transport, and protein targeting to mitochondria are shown. Color scale indicates row z score of normalized read counts. c, Gene Ontology (GO) biological process terms most significantly enriched among genes differentially expressed due to Eμ-myc in both Casp1−⁄− Casp11−⁄− and wild type backgrounds. Common genes indicated in purple in Venn diagrams in Fig. 3c). Enrichment p values were corrected using the method of Benjamini and Hochberg.

Extended Data Fig. 6 The stromal inflammasome controls HSC surface TNFR1/II and MIP receptors via Ras.

a, Cytokine Array (ab193659) map for the Membranes C3 (top) and C4 (bottom) showing the identities of the 96 cytokine targets probed for per membrane. b, Representative cytokine array blots of WT (left) vs Casp1−⁄− Casp11−⁄− (right) bulk bone marrow after 6 h incubation including array controls of Membrane C4. c, Quantification of the top differentially expressed cytokines from the cytokine array blots of WT vs Casp1−⁄− Casp11−⁄− bulk bone marrow after 6 h incubation. [below 10000 units normalized fluorescence]; n = 3 biological replicates per genotype; ns, p > 0.05, p* ≤ 0.05, p*** ≤ 0.001, p**** ≤ 0.0001; 2 WAY Anova with ordinary 2 WAY Anova and SIdak’s multiple comparisons posttest comparing every genotype column mean by cytokine row mean. d, Representative histograms of stroma (CD45–Sca-1–c-Kit–Lin–) expression of indicated receptors from 6 h incubation of bulk bone marrow of adult male WT, Casp1−⁄− Casp11−⁄− Eµ-myc Casp1−⁄− Casp11−⁄− mice and WT bone marrow treated with pan caspase inhibitor QVD-OPH for 2 h. All data are presented as mean values +/− SD.

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Extended Data Fig. 7 Gene expression profiles of inflammasome components in the bone marrow stroma.

a. UMAP visualizations of CD45- (Ptprc-) osteoblast, perivascular, and vascular bone marrow niche cells (n = 19,418) taken from Tikhonova A.N. et al.38. Top: Labeled and color-coded according to algorithmically determined clusters (Seurat clusters). Bottom: Labeled and color-coded according to bone marrow niche (VECAD = vascular, LERP = perivascular, COL1A1 = osteoblast), as determined by the expression of niche marker genes Cdh5 (VECAD), Lepr, and Col1a1. b. UMAP visualization of bone marrow niche cells colored according to the normalized expression of niche marker genes (Cdh5 = vascular, Lepr = perivascular, Col1a1 = osteoblast). c. Violin plots of normalized expression of niche marker genes across all clusters of CD45- (Ptprc-) bone marrow niche cells. d. Table shows our manual cluster annotations of the original Seurat clusters generated from the scRNA-sequencing dataset by Tikhonova A.N. et al.38. Bone marrow endothelial cells (BMEC), mesenchymal stromal cell (MSC), leptin-receptor (LEPR), and osteoblasts. Sub-markers used were based on Tikhonova A.N. et al.1: BMEC: V1 Ly6ahigh and V2 Stab2high and indicated as LEPR+ when applicable for Seurat clusters 6 and 15. LepR MSC: sub-marked as O1, O2, O3 based on low co-expression of Col1a1 or V1, V2 based on low co-expression of VEcad. Osteoblasts: O1 Col16a1highTnnhigh, O2 Fbn1highIgf1high, O3 BglaphighCar3high, low for osteoblast maturation marker (Bglaplo) or when co-expressing Lepr (Seurat clusters 11 and 14). e. Dot plot of the expression of genes of interest (columns) in annotated CD45– (Ptprc–) bone marrow niche cell clusters (rows). Dot size indicates the percent of cells within a cluster that have non-zero expression of the corresponding gene. Dot color corresponds to scaled average normalized expression of a gene within a cluster.

Extended Data Fig. 8 Relative expression of inflammasome genes in bone marrow stromal cells versus bone marrow macrophages.

Average normalized expression values of genes of interest in select cell populations/clusters, including CD45– (Ptprc–) bone marrow niche cells and bone marrow derived macrophages. Relative gene expression in manually annotated Seurat clusters a, VE-Cad+ bone marrow endothelial cells (BMEC), b, LERP+ LeprMSC (Leptin receptor+ mesenchymal stromal cells), and c, COL1A1+ osteoblasts. Manual cluster annotations were conducted as outlined in Extended Data Fig. 7d.

Extended Data Fig. 9 Ras target gene expression levels in WT HSCs isolated from different inflammasome component deficient stroma bearing mice.

Quantitative real time PCR showing the fold change expression of Ras signaling associated, and cancer related genes relative to WT (normalized to B2M at n = 1) of enriched wild type donor CD45.1+ CD45.2– bone marrow HSC (Lin–Sca-1+c-Kit+CD48–) isolated from chimeric WT CD45.2 recipient vs indicated CD45.2+ recipient WT, Nlrp3−⁄−, Pycard−⁄−, Casp1−⁄−, Gsdmd−⁄−, Casp11−⁄−, and Casp1−⁄− Casp11−⁄− mice. n = 3 biological replicates per genotype; p* ≤ 0.05, p** ≤ 0.01; 2 WAY Anova. Only p values ≤ 0.05 are shown. All data are presented as mean values +/− SD.

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Extended Data Fig. 10 Proposed model – The stromal inflammasome maintains tissue equilibrium by restraining Ras activity to impede premalignant-to-malignant transition.

In the absence of inflammasome effectors caspases 1 and 11 in the bone marrow stroma, HSCs exhibited a distinct Ras-transduction signature associated with increased activity in the Ras pathway and an elevated expression of transcripts associated with cancer. Additionally, there was an increased presence of receptors for cytokines, chemokines, and growth-factors on HSCs. These findings suggest a model whereby inflammasome deficiency within the stroma creates a state of heightened Ras-dependent mitogenic processes, which cooperate with Myc upon its deregulation to accelerate the rate of malignant transformation and B cell lymphoma development. Created using BioRender.com and exported under a paid subscription to BioRender.

Supplementary information

Supplementary Information

Supplementary Table 1.

Reporting Summary

Supplementary Data 1

Genes enriched in sorted LSK HSCs compared with other hematopoietic cell types. Excel file shows 322 genes whose expression is enriched specifically in our sorted LSK HSCs compared with all other hematopoietic cell types in the ImmGen database33.

Supplementary Data 2

Lists of genes in GO pathways associated with HSC caspase-1/11 deficiency or Eμ-myc transgene expression. Excel file shows three tabs: Extended clusters, Caspase effect and Eμ-myc effect on the differential expression of genes found from RNA-seq on isolated LSK HSCs from premalignant Eμ-myc and Eμ-myc Casp1−⁄− Casp11−⁄− and their nontransgenic counterpart WT and Casp1−⁄− Casp11−⁄− mice (nontransgenic background).

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Statistical source data for the graphs in Fig. 1d–f, h–j and l.

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Statistical source data for the graphs in Fig. 2a and b.

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Statistical source data for the graphs in Fig. 3f–k.

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Statistical source data for the graphs in Fig. 4g and h.

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Statistical source data for the graphs in Fig. 5c, d, f, g and i.

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Statistical source data for the graphs in Fig. 6b, d and f–h.

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Statistical source data for the graphs in Fig. 7c, d, f, h and j.

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Statistical source data for the graphs in Fig. 8b, d,f, g and i.

Source Data Extended Data Fig. 1

Statistical source data for the graphs in Extended Data Fig. 1c, g and h.

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Statistical source data for the graphs in Extended Data Fig. 2f and g.

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Statistical source data for the graphs in Extended Data Fig. 3g and h.

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Statistical source data for the graphs in Extended Data Fig. 6c.

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Statistical source data for the graphs in Extended Data Fig. 9.

Source Data Fig. 6b and Extended Data Fig. 6b

Cytokine array blots in Fig. 6b and Extended Data Fig 6b.

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Kent, A., Yee Mon, K.J., Hutchins, Z. et al. A stromal inflammasome Ras safeguard against Myc-driven lymphomagenesis. Nat Immunol 26, 53–67 (2025). https://doi.org/10.1038/s41590-024-02028-z

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