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Breast cancer remotely imposes a myeloid bias on haematopoietic stem cells by reprogramming the bone marrow niche

Nature Cell Biology volume 25, pages 1736–1745 (2023)Cite this article

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Abstract

Myeloid cell infiltration of solid tumours generally associates with poor patient prognosis and disease severity1,2,3,4,5,6,7,8,9,10,11,12,13. Therefore, understanding the regulation of myeloid cell differentiation during cancer is crucial to counteract their pro-tumourigenic role. Bone marrow (BM) haematopoiesis is a tightly regulated process for the production of all immune cells in accordance to tissue needs14. Myeloid cells differentiate during haematopoiesis from multipotent haematopoietic stem and progenitor cells (HSPCs)15,16,17. HSPCs can sense inflammatory signals from the periphery during infections18,19,20,21 or inflammatory disorders22,23,24,25,26,27. In these settings, HSPC expansion is associated with increased myeloid differentiation28,29. During carcinogenesis, the elevation of haematopoietic growth factors supports the expansion and differentiation of committed myeloid progenitors5,30. However, it is unclear whether cancer-related inflammation also triggers demand-adapted haematopoiesis at the level of multipotent HSPCs. In the BM, HSPCs reside within the haematopoietic niche which delivers HSC maintenance and differentiation cues31,32,33,34,35. Mesenchymal stem cells (MSCs) are a major cellular component of the BM niche and contribute to HSC homeostasis36,37,38,39,40,41. Modifications of MSCs in systemic disorders have been associated with HSC differentiation towards myeloid cells22,42. It is unknown if MSCs are regulated in the context of solid tumours and if their myeloid supportive activity is impacted by cancer-induced systemic changes. Here, using unbiased transcriptomic analysis and in situ imaging of HSCs and the BM niche during breast cancer, we show that both HSCs and MSCs are transcriptionally and spatially modified. We demonstrate that breast tumour can distantly remodel the cellular cross-talks in the BM niche leading to increased myelopoiesis.

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Fig. 1: Breast tumours remotely rewire HSPC differentiation towards myelopoiesis.
Fig. 2: HSCsLT of tumour-bearing mice are myeloid-primed.
Fig. 3: Breast tumours remodel the BM stroma.
Fig. 4: The myeloid supportive activity of MSCs is increased in tumour-bearing mice.

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Data availability

Source data for quantifications shown in figures are provided with the manuscript. Bulk and scRNA-seq data can be found on Gene Expression Omnibus under accession code GSE243964. Mus musculus genome GRCm38.p4 is available at https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001635.24/. Any other data are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

All codes and packages used in this study are either publicly available or can be found on GitHub (https://github.com/TeamPerie/Gerber_et_al_2023).

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Acknowledgements

We thank A.-M. Lennon-Duménil and all INSERM U932. We thank E. Gomez-Perdiguero, A. Voissière, D. Gros and L. Jean-Pierre. J.H. team is supported by Fondation ARC (PJA2021060003760), Ligue contre le cancer (RS22/75-24) and the European Research Council (ERC). Y.G.-F. is supported by FRM (FDT202106013018). E.P. team is supported by FRM, ANR-10-IDEX-0001-02 PSL, LabEx DCBIOL, CIC IGR-Curie 1428 and INCa-DGOS-Inserm_12554. This work has received support under the programme France 2030 launched by the French Government. High-throughput sequencing was performed by the ICGex NGS platform of the Institut Curie supported by the grants ANR-10-EQPX-03 and ANR-10-INBS-09-08, by the ITMO-Cancer Aviesan (Plan Cancer III) and by the SiRIC-Curie program (SiRIC Grant INCa-DGOS-465 and INCa-DGOS- Inserm_12554). L.P. team received support from ANR-11-LABX-0038 and ANR-10-IDEX-0001-02 and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme 758170-Microbar. D.P. laboratory is supported by grants from FRM (AJE20181239593), ATIP-AVENIR, Fondation ARC (R19084KS—RSE20008KSA) and ‘Ville de Paris Emergence’ (R20192KK—RPH20192KKA).

Author information

Author notes

  1. These authors jointly supervised this work: Eliane Piaggio, Julie Helft.

Authors and Affiliations

  1. Institut Curie, Immunity and Cancer, PSL University, INSERM U932, Paris, France

    Yohan Gerber-Ferder, Yoann Missolo-Koussou, Marine Dubois, Christine Sedlik & Eliane Piaggio

  2. Université Paris Cité, INSERM U932, Paris, France

    Yohan Gerber-Ferder

  3. PSL University, Institut Curie Research Center, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France

    Jason Cosgrove & Leïla Perié

  4. Institut Cochin, Leukemia and Niche Dynamics Laboratory, Université Paris Cité, INSERM, CNRS, Paris, France

    Aleria Duperray-Susini & Diana Passaro

  5. Institut Cochin, Phagocytes and Cancer Immunology Laboratory, Université Paris Cité, INSERM U1016, CNRS UMR8104, Paris, France

    Kateryna Stepaniuk & Julie Helft

  6. Cancer Research Center of Lyon, Centre Léon Bérard, Université Claude Bernard Lyon 1, UMR INSERM 1052 CNRS 5286, Lyon, France

    Manuela Pereira-Abrantes & Nathalie Bendriss-Vermare

  7. Institut Curie, ICGex Next-Generation Sequencing Platform, PSL University, Paris, France

    Sonia Lameiras & Sylvain Baulande

  8. Institut Curie, Single Cell Initiative, PSL University, Paris, France

    Sonia Lameiras & Sylvain Baulande

  9. Institut Pasteur, Dendritic Cells and Adaptive Immunity Unit, Université Paris Cité, Paris, France

    Pierre Guermonprez

  10. Institut Pasteur, Université Paris Cité, CNRS UMR3738, Paris, France

    Pierre Guermonprez

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  1. Yohan Gerber-Ferder
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Contributions

Conceptualization: Y.G.-F. and J.H.; methodology: Y.G.-F., J.C., L.P., D.P., E.P. and J.H.; formal analysis: J.C., Y.M.-K., A.D.-S., M.D., K.S. and S.L.; investigation: Y.G.-F., J.C., Y.M.-K. and J.H.; resources: C.S., S.B., M.P.-A., N.B.-V., P.G., L.P., D.P., E.P. and J.H.; writing original draft: Y.G.-F. and J.H. Writing, review and editing: Y.G.-F., J.C., L.P., D.P., E.P. and J.H.; visualization: Y.G.-F., J.C., Y.M.-K., A.D.-S. and J.H.; supervision: D.P., L.P., E.P. and J.H.; funding acquisition: E.P. and J.H.

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Correspondence to Julie Helft.

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Extended data

Extended Data Fig. 1 Breast tumors remotely rewire HSPC differentiation towards myelopoiesis in various breast cancer models.

a, Number of EdU+ cycling LSK cells in the BM of WT (n = 6) or PyMT mice (n = 6, pool of 2 experiments). b, Lin−Sca1+Kit+ cell frequency and cell number in tumor-free (n = 9) or Py230 tumor-bearing mice (n = 9, pool of 2 experiments) and Neu mice (n = 6, one experiment). c, Representative pictures of femurs from tumor-free (n = 3) and Py230 (n = 3) or Neu mice (n = 3). d, e, Representative tissue histology of humerus sections from WT (d) and PyMT mice (e) stained with hematoxylin and eosin (performed twice on 2 WT and 2 PyMT mice). Epiphysis of humerus are a 4.6x zoom with 200μM scale bar. 40x zoom with a 20μM scale bar images from the endosteum and the BM of the same bone are also depicted. f, Total count of live cells in the BM of WT (n = 10) and PyMT mice (n = 11, pool of 4 experiments). g, Correlation between tumor weight and LSK cell number in the BM of PyMT (n = 9, pool of 3 experiments) and Py230 tumor-bearing mice (n = 7, pool of 2 experiments). h, i, Number of LSK cells at different tumor development stages in PyMT (h) or Py230 tumor-bearing mice (i). j, Cell number of CD45+Lin(CD19, NKp46, CD3, B220)- myeloid and Lin− lymphoid cells in the blood of tumor-free (n = 7), Py230 tumor-bearing (n = 7, pool of 2 experiments), WT (n = 5) and Neu (n = 4, one experiment) mice. k, l, Blood cell number of Lin-CD11b+Ly6G+ Neutrophils (k) and CD11b+Ly6G−Ly6CHi Monocytes (l) in WT (n = 7) and PyMT (n = 8, pool of 3 experiments), tumor-free (n = 7) and Py230 tumor-bearing mice (n = 7, pool of 2 experiments) or WT (n = 5) and Neu mice (n = 4, one experiment). m, n, Cell number of CD45+Lin(CD19, NKp46, CD3, B220)- myeloid and Lin− lymphoid cells in the blood of WT and PyMT mice (n = 6, pool of 2 experiments) (m) or tumor-free (n = 9, pool of 3 experiments) and Py230 tumor-bearing (n = 5 for 100mm3 and n = 3 for 500mm3 and 1500mm3) (n) at different tumor development stages. Data are the mean ± s.d. P value < 0.05 are depicted and were obtained using two-tailed t-test (a, b, f, k and l) corrected for multiple comparison using Holm-Šídák method (h, j and m), one-way Anova (i), two-way Anova (n) or simple linear regression (g).

Source data

Extended Data Fig. 2 Transcriptional rewiring of HSPCs in the BM during breast cancer.

a, Heatmap showing normalized expression of the top 10 most variable genes for each cluster (see Fig. 1H). b, Violin plots showing expression distributions of Meis1, Myl10, Gata1, Elane, Mpo, Dntt, Mki67, Top2a. c, UMAP showing the expression of HSC, MPP2, MPP3 and MPP4 gene signatures from Wilson et al., 2008 and Pietras et al., 2015. Gradient shows the background corrected mean expression of all genes in the signature, calculated using AddModuleScore function. d, Violin plots showing expression distribution of the PyMT response signature (upregulated DEG between PyMT and WT LSK) across clusters (see Fig. 1h). e, Frequency of WT and PyMT-derived cells in each cluster. f, Gating strategy to define LSK cell subsets by FACS. g, LSK cell subsets frequency in tumor-free (n = 9) or Py230 tumor-bearing mice (n = 9, pool of 2 experiments) and Neu mice (n = 6, one experiment). h, Frequencies and cell number of quiescent (TMRELow) and active (TMREHigh) HSCsLT in tumor-free (n = 3) or Py230 tumor-bearing mice (n = 3, one experiment), measured by FACS using TMRE staining. i, UMAP showing isolation of WT and PyMT G2M/S cycling cells from LSK scRNAseq dataset. Cell cycle was assigned based on gene expression profiles using the cyclone method in the scran package. j, UMAP showing the expression of MPP2, MPP3 and MPP4 gene signatures from Pietras et al., 2015 among cycling cells. k, Cell number-normalized density plot showing the cell distribution across the cycling cell dataset. l, Bone marrow cells were enriched for hematopoietic progenitor and stained with EdU to assess cell cycling activity. Frequency of EdU+LSK cell subsets by FACS (n = 6 WT and. 6 PyMT mice, pool of 2 experiments). Data are the mean ± s.d. P value < 0.05 are depicted and were obtained using two-tailed t-test (g, h, and l).

Source data

Extended Data Fig. 3 HSCsLT of tumor-bearing mice are myeloid-primed.

a, Frequency and cell number of total CD11b+ cells obtained after in vitro HSCLT myeloid differentiation from WT and PyMT mice (see Fig. 2a). Dots represent individual replicate wells (n = 9 mice, pool of 4 experiments). b, Total cell number obtained after in vitro HSCLT myeloid differentiation from tumor-free or Py230 tumor-bearing mice (n = 3, technical triplicates for each individual mouse, pool of 2 experiments). c, Frequency and cell numbers of Ly6G+CD11bint cells obtained after in vitro HSCLT myeloid differentiation from tumor-free or Py230 tumor-bearing mice (n = 3, triplicates for each individual mouse, pool of 2 experiments). d, e, Frequency of CD45.2+ donor-derived cells among TCRß+ T cells after the #1 transplant (d)(n = 17 WT and 12 PyMT mice at 2 and 4 weeks, n = 9 WT and 7 PyMT mice at 8 and 12 weeks, n = 16 WT and 12 PyMT mice at 16 weeks, pool of 2 experiments) and after the #2 transplant (e)(n = 14 WT and 11 PyMT mice at 2 weeks, n = 14 WT and 12 PyMT mice at 4, 8, 12 and 16 weeks, pool of 2 experiments) of HSCsLT from WT or PyMT mice in irradiated CD45.1+ recipients. Data are the mean ± s.d. P value < 0.05 are depicted and were obtained using two-tailed t-test (a, b, and c) corrected for multiple comparison using Holm-Šídák method (d and e).

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Extended Data Fig. 4 Transcriptional rewiring of MSCs during breast cancer.

a, Concentration of OPG in the serum of WT or PyMT mice at different tumor development stages (n = 3 mice of 8, 12, 18 and 22w old and n = 4 mice of 24w old, pool of 2 experiments). b, Concentration of osteogenic factors in the serum of tumor-free (n = 5) or Py230 tumor-bearing mice (n = 4 for ANGPT2, n = 5 for OPG and WISP1, pool of 2 experiments). c, Concentration of OPG (n = 3 independent experiments) and WISP1 (n = 2 independent experiments) in PyMT tumor-conditioned media or Py230 cell line cultured media (n = 2 independent experiments). d, Concentration of osteogenic factors in the BM fluid of tumor-free (n = 5) and Py230 tumor-bearing mice (n = 5, pool of 2 experiments). e, Quantification of CD31−CD51+ stromal cells in tumor-free (n = 10) or Py230 tumor-bearing mice (n = 10, pool of 2 experiments). f, Heatmap of selected DEGs between clusters to identify cell subsets. g, Number of DEGs between PyMT and WT cells with adjusted p value < 0.05 for each cluster. h, Top5 pathways enriched in WT (grey) and PyMT (blue) MSCs according to REACTOME database. Data are the mean ± s.d. P value < 0.05 are depicted and were obtained using two-tailed t-test (a, b, d, and e).

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Extended Data Fig. 5 Breast tumors remodel the BM stroma.

a, UMAP showing the expression of Vcam1, Cd200 and Pdpn by niche subsets analyzed by scRNAseq. b, FACS gating strategy of niche subsets to identify: CD31+VCAM1+CD200Low SECs, CD31+VCAM1−CD200High AECs, CD51+VCAM1+ MSCs, CD51+VCAM1−PDPN+ fibroblasts, CD51+VCAM1−PDPN−CD200+ osteoblasts. c, d, e, Frequency of BM CD31+ and CD31−CD51+ niche subsets analyzed by FACS in (c) WT and PyMT mice at different tumor development stages: 8-week-old (n = 5, pool of 2 experiments), 16-week-old (n = 4, pool of 2 experiments) and older than 20 weeks (n = 10 WT and 13 PyMT mice, pool of 4 experiments) (d) tumor-free (n = 3) and Py230 tumor-bearing mice (n = 3 at 500 mm3 and 1500 mm3 and n = 4 at 100 mm3, one experiment) and (e) WT (n = 5) or Neu mice older than 20 weeks (n = 4, one experiment). f, Principal component (PC) analysis using the top 1500 most variable genes of 10 000 sorted MSCs from tumor-free (grey) or Py230 tumor-bearing mice (orange) analyzed through bulk RNA sequencing (n = 4). Volcano plot showing the DEGs from MSCs of tumor-free and Py230 tumor-bearing mice. g, VCAM1 geometric mean of fluorescence intensity on MSC of control (n = 8) and PyMT (n = 11, pool of 4 experiments) or Py230 tumor-bearing mice (n = 10, pool of 2 experiments) or WT (n = 5) and Neu mice (n = 4, one experiment) assessed by FACS. h-i, The distance of each HSCLT from niche cells was calculated as described. The spot function was used to identify HSCLT based on the expression of CD150 and Lin. Volumetric iso-surfaces to define the bone and MSCs were generated using the intensity derived from the Col1 and LepR. The distance between each HSCLT and the closest surface pixel was then measured using ‘Distance Transformation’ tool using Imaris XT extension. h, Left plot: quantification of HSCLT distances to Col1+ bone in tumor-free (n = 1; HSCLT = 446) and Py230 tumor-bearing mice (n = 1; HSCLT = 236). Right plot: quantification of HSCLT distances to Col1+ bone in tumor-free (n = 2; HSCLT = 443) and PyMT tumor-bearing mice (n = 1; HSCLT = 613). i, Quantification of HSCLT distances to MSCs in tumor-free (n = 2; HSCLT = 443) and PyMT tumor-bearing mice (n = 1; HSCLT = 613). Data are the mean ± s.d. P value < 0.05 are depicted and were obtained using two-tailed t-test (d, e, and g) or two-tailed Mann-Whitney U test (c).

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Gerber-Ferder, Y., Cosgrove, J., Duperray-Susini, A. et al. Breast cancer remotely imposes a myeloid bias on haematopoietic stem cells by reprogramming the bone marrow niche. Nat Cell Biol 25, 1736–1745 (2023). https://doi.org/10.1038/s41556-023-01291-w

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