Lung mesenchymal cells elicit lipid storage in neutrophils that fuel breast cancer lung metastasis

Abstract

Acquisition of a lipid-laden phenotype by immune cells has been defined in infectious diseases and atherosclerosis but remains largely uncharacterized in cancer. Here, in breast cancer models, we found that neutrophils are induced to accumulate neutral lipids upon interaction with resident mesenchymal cells in the premetastatic lung. Lung mesenchymal cells elicit this process through repressing the adipose triglyceride lipase (ATGL) activity in neutrophils in prostaglandin E2-dependent and -independent manners. In vivo, neutrophil-specific deletion of genes encoding ATGL or ATGL inhibitory factors altered neutrophil lipid profiles and breast tumor lung metastasis in mice. Mechanistically, lipids stored in lung neutrophils are transported to metastatic tumor cells through a macropinocytosis–lysosome pathway, endowing tumor cells with augmented survival and proliferative capacities. Pharmacological inhibition of macropinocytosis significantly reduced metastatic colonization by breast tumor cells in vivo. Collectively, our work reveals that neutrophils serve as an energy reservoir to fuel breast cancer lung metastasis.

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Fig. 1: Neutrophils acquire a lipid-laden phenotype in the premetastatic lung.
Fig. 2: Lung CD140a+ MCs drive lipid accumulation in neutrophils.
Fig. 3: A critical role of the PGE2–HIF1α–HILPDA axis in lung MC-triggered lipid storage in neutrophils.
Fig. 4: Genetic ablation of Atgl in host neutrophils leads to more aggressive lung metastasis of breast cancer.
Fig. 5: Lung-infiltrating neutrophils transfer their stored lipid to metastatic tumor cells.
Fig. 6: Neutrophil-derived lipids enhance the proliferative capacity of metastatic tumor cells.
Fig. 7: Blockage of macropinocytosis inhibits metastatic colonization in vivo.

Data availability

RNA-seq data are deposited in ArrayExpress and are available under accession code E-MTAB-9128. For lung metastasis-free survival analysis of patients with breast cancer, the published microarray dataset was used (GSE2603). For correlation analysis of signature genes in breast cancer lung metastasis samples, the published microarray dataset (GSE14018) was used. All other data supporting the findings of this study are available within the article and its supplementary information files and on reasonable request from the corresponding author. A Nature Research Reporting Summary for this article is available as a supplementary information file. Source data are provided with this paper.

References

  1. 1.

    Liu, Y. & Cao, X. Characteristics and significance of the pre-metastatic niche. Cancer Cell 30, 668–681 (2016).

    CAS  PubMed  Google Scholar 

  2. 2.

    Peinado, H. et al. Pre-metastatic niches: organ-specific homes for metastases. Nat. Rev. Cancer 17, 302–317 (2017).

    CAS  PubMed  Google Scholar 

  3. 3.

    Wculek, S. K. & Malanchi, I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature 528, 413–417 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Coffelt, S. B. et al. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature 522, 345–348 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Albrengues, J. et al. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science 361, eaao4227 (2018).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Spiegel, A. et al. Neutrophils suppress intraluminal NK cell–mediated tumor cell clearance and enhance extravasation of disseminated carcinoma cells. Cancer Discov. 6, 630–649 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Massagué, J. & Obenauf, A. C. Metastatic colonization by circulating tumour cells. Nature 529, 298–306 (2016).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Dupuy, F. et al. PDK1-dependent metabolic reprogramming dictates metastatic potential in breast cancer. Cell Metab. 22, 577–589 (2015).

    CAS  PubMed  Google Scholar 

  9. 9.

    Christen, S. et al. Breast cancer-derived lung metastases show increased pyruvate carboxylase-dependent anaplerosis. Cell Rep. 17, 837–848 (2016).

    CAS  PubMed  Google Scholar 

  10. 10.

    Loo, J. M. et al. Extracellular metabolic energetics can promote cancer progression. Cell 160, 393–406 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Nieman, K. M. et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat. Med. 17, 1498–1503 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Messmer, M. N., Netherby, C. S., Banik, D. & Abrams, S. I. Tumor-induced myeloid dysfunction and its implications for cancer immunotherapy. Cancer Immunol. 64, 1–13 (2015).

    CAS  Google Scholar 

  13. 13.

    Heckmann, B. L., Zhang, X., Xie, X. & Liu, J. The G0/G1 switch gene 2 (G0S2): regulating metabolism and beyond. Biochim. Biophys. Acta 1831, 276–281 (2013).

    CAS  PubMed  Google Scholar 

  14. 14.

    Padmanabha Das, K. M. et al. Hypoxia-inducible lipid droplet-associated protein inhibits adipose triglyceride lipase. J. Lipid Res. 59, 531–541 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Munir, R., Lisec, J., Swinnen, J. V. & Zaidi, N. Lipid metabolism in cancer cells under metabolic stress. Br. J. Cancer 120, 1090–1098 (2019).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Vallochi, A. L., Teixeira, L., Oliveira, K. D. S., Maya-Monteiro, C. M. & Bozza, P. T. Lipid droplet, a key player in host–parasite interactions. Front. Immunol. 9, 1022 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Chistiakov, D. A., Melnichenko, A. A., Myasoedova, V. A., Grechko, A. V. & Orekhov, A. N. Mechanisms of foam cell formation in atherosclerosis. J. Mol. Med. 95, 1153–1165 (2017).

    CAS  PubMed  Google Scholar 

  18. 18.

    Missaglia, S., Coleman, R. A., Mordente, A. & Tavian, D. Neutral lipid storage diseases as cellular model to study lipid droplet function. Cells 8, 187 (2019).

    CAS  PubMed Central  Google Scholar 

  19. 19.

    Nielsen, T. S., Jessen, N., Jørgensen, J. O., Møller, N. & Lund, S. Dissecting adipose tissue lipolysis: molecular regulation and implications for metabolic disease. J. Mol. Endocrinol. 52, R199–R222 (2014).

    CAS  PubMed  Google Scholar 

  20. 20.

    Zhang, X. H. et al. Latent bone metastasis in breast cancer tied to Src-dependent survival signals. Cancer Cell 16, 67–78 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Charoentong, P. et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18, 248–262 (2017).

    CAS  PubMed  Google Scholar 

  22. 22.

    Nakanishi, M. & Rosenberg, D. W. Multifaceted roles of PGE2 in inflammation and cancer. Semin. Immunopathol. 35, 123–137 (2013).

    CAS  PubMed  Google Scholar 

  23. 23.

    Sugimoto, Y. & Narumiya, S. Prostaglandin E receptors. J. Biol. Chem. 282, 11613–11617 (2007).

    CAS  PubMed  Google Scholar 

  24. 24.

    Dennis, E. A. & Norris, P. C. Eicosanoid storm in infection and inflammation. Nat. Rev. Immunol. 15, 511–523 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Wang, D. & Dubois, R. N. Eicosanoids and cancer. Nat. Rev. Cancer 10, 181–193 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Zhang, X. et al. Inhibition of intracellular lipolysis promotes human cancer cell adaptation to hypoxia. Elife 6, e31132 (2017).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Knight, M., Braverman, J., Asfaha, K., Gronert, K. & Stanley, S. Lipid droplet formation in Mycobacterium tuberculosis infected macrophages requires IFN-γ/HIF-1α signaling and supports host defense. PLoS Pathog. 14, e1006874 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Palazon, A., Goldrath, A. W., Nizet, V. & Johnson, R. S. HIF transcription factors, inflammation, and immunity. Immunity 41, 518–528 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Passegue, E., Wagner, E. F. & Weissman, I. L. JunB deficiency leads to a myeloproliferative disorder arising from hematopoietic stem cells. Cell 119, 431–443 (2004).

    CAS  PubMed  Google Scholar 

  30. 30.

    den Brok, M. H., Raaijmakers, T. K., Collado-Camps, E. & Adema, G. J. Lipid droplets as immune modulators in myeloid cells. Trends Immunol. 39, 380–392 (2018).

    Google Scholar 

  31. 31.

    Veglia, F. et al. Fatty acid transport protein 2 reprograms neutrophils in cancer. Nature 569, 73–78 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Stewart, T. J. & Abrams, S. I. Altered immune function during long-term host-tumor interactions can be modulated to retard autochthonous neoplastic growth. J. Immunol. 179, 2851–2859 (2007).

    CAS  PubMed  Google Scholar 

  33. 33.

    DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 7, 11–20 (2008).

    CAS  Google Scholar 

  34. 34.

    Pascual, G. et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 541, 41–45 (2017).

    CAS  Google Scholar 

  35. 35.

    Zhang, M. et al. Adipocyte-derived lipids mediate melanoma progression via FATP proteins. Cancer Discov. 8, 1006–1025 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Wang, Y. Y. et al. Mammary adipocytes stimulate breast cancer invasion through metabolic remodeling of tumor cells. JCI insight 2, e87489 (2017).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Adrover, J. M., Nicolas-Avila, J. A. & Hidalgo, A. Aging: a temporal dimension for neutrophils. Trends Immunol. 37, 334–345 (2016).

    CAS  PubMed  Google Scholar 

  38. 38.

    Schild, T., Low, V., Blenis, J. & Gomes, A. P. Unique metabolic adaptations dictate distal organ-specific metastatic colonization. Cancer Cell 33, 347–354 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Flaherty, S. E. III, Grijalva, A., Xu, X., Ables, E., Nomani, A. & Ferrante, A. W. Jr. A lipase-independent pathway of lipid release and immune modulation by adipocytes. Science 363, 989–993 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Lim, J. P. & Gleeson, P. A. Macropinocytosis: an endocytic pathway for internalising large gulps. Immunol. Cell Biol. 89, 836–843 (2011).

    CAS  PubMed  Google Scholar 

  41. 41.

    Commisso, C. et al. Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells. Nature 497, 633–637 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Kim, S. M. et al. PTEN deficiency and AMPK activation promote nutrient scavenging and anabolism in prostate cancer cells. Cancer Discov. 8, 866–883 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Rejman, J., Bragonzi, A. & Conese, M. Role of clathrin- and caveolae-mediated endocytosis in gene transfer mediated by lipo- and polyplexes. Mol. Ther. 12, 468–474 (2005).

    CAS  PubMed  Google Scholar 

  44. 44.

    Shaul, M. E. & Fridlender, Z. G. Tumour-associated neutrophils in patients with cancer. Nat. Rev. Clin. Oncol. 16, 601–620 (2019).

    PubMed  Google Scholar 

  45. 45.

    Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Spiegelman, B. M. & Flier, J. S. Obesity and the regulation of energy balance. Cell 104, 531–543 (2001).

    CAS  PubMed  Google Scholar 

  47. 47.

    Lehuede, C., Dupuy, F., Rabinovitch, R., Jones, R. G. & Siegel, P. M. Metabolic plasticity as a determinant of tumor growth and metastasis. Cancer Res. 76, 5201–5208 (2016).

    PubMed  Google Scholar 

  48. 48.

    Zhang, Y. & Commisso, C. Macropinocytosis in cancer: a complex signaling network. Trends Cancer 5, 332–334 (2019).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ohs, I. et al. Restoration of natural killer cell antimetastatic activity by IL12 and checkpoint blockade. Cancer Res. 77, 7059–7071 (2017).

    CAS  PubMed  Google Scholar 

  50. 50.

    Basnet, H. Flura-seq identifies organ-specific metabolic adaptations during early metastatic colonization. Elife 8, e43627 (2019).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Breitkopf, S. B. et al. A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source. Metabolomics 13, 30 (2017).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank S.I. Abrams (Roswell Park Comprehensive Cancer Center) for providing the AT3 breast cancer cell line, Y. Kang (Princeton University) for providing the MDA-4175 breast cancer cell line and R.A. Weinberg (Whitehead Institute for Biomedical Research) for providing the lentiviralvector expressing mouse Csf3. This work was supported by grants from the National Institutes of Health (R00-CA188093 to G.R.; R37-CA237307 to G.R.; P30-CA034196 to G.R., L.D.S. and S.L.; R35−GM133562 to S.L.; and R24-OD026440 and R01-AI132963 to L.D.S.), the U.S. Department of Defense (W81XWH-18-1-0013 to G.R.), Leukemia Research Foundation New Investigator Grant (to S.L.) and The Jackson Laboratory Director’s Innovation Fund (19000-17-31 to S.L.). Q.L. is supported by the Pyewacket Fund at The Jackson Laboratory. We acknowledge critical comments from E. T. Liu, N. A. Rosenthal, K. Seburn and C. Robinett. We also thank G. Stafford for RNA-seq analysis and W. Schott for cell sorting, as well as The Jackson Laboratory Scientific Service for assistance.

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Authors

Contributions

G.R., P.L. and M.L. conceived the project, designed the study and performed the data analysis. P.L., M.L., J.S., Z.G., L.H. and Q.L. performed the experiments. L.D.S. provided critical assistance on experimental design. X.H.-F.Z. provided essential experimental materials. B.L. and X.H.-F.Z. provided assistance on clinical dataset analysis and experimental design; X.C. and S.L. performed the clinical dataset analysis and provided assistance on statistical analysis. G.R., P.L., M.L. and L.D.S. interpreted the data and wrote the manuscript.

Corresponding author

Correspondence to Guangwen Ren.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Lung neutrophils acquire a lipid-laden phenotype in the orthotopic 4T1 tumor model.

a–d, Transcriptional profiles of BM, PB and lung neutrophils sorted from the orthotopic 4T1 tumor-bearing BALB/cJ mice (n = 3). Volcano plots showing fold-change and p-value for the comparison of lung versus BM neutrophils (a), and PB versus BM neutrophils (b) based on the RNA-seq data. P values were determined by unpaired two-tailed t-test and smaller than 0.05 were considered significant. A schematic diagram showing the major pathway and key factors in lipid metabolism (c). A heat map depicts expression levels of the major lipid metabolic genes (d). e, Upper: a diagram showing the pre-metastatic and metastatic stages in the orthotopic 4T1 tumor model; lower: Representative immunostaining of neutrophils (Ly6G) in lung sections of naïve and 4T1 tumor-bearing BALB/cJ mice (n = 4 from 2 independent experiments). Scale bars, 20 μm. f, Intracellular lipids in neutrophils detected by BODIPY 493/503 staining under microscope (n = 4 mice from 2 independent experiments). Scale bars, 5 μm. g, The flow cytometry gating strategy is shown: neutrophils were identified as the Ly6G+Ly6Clow cell population which was gated on CD45+CD11b+ cells. h, Measurement of total lipid contents in neutrophils isolated from naïve and 4T1 tumor-bearing mice by BODIPY 493/503 staining and flow cytometry (n = 6). i, Cellular TG contents in neutrophils (n = 5). In f and i, neutrophils were isolated from the indicated tissues and organs of orthotopic 4T1 tumor-bearing mice at the pre-metastatic stage. n represents biologically independent animals. Data are mean ± s.e.m. and P values were determined by one-way ANOVA with Tukey’s multiple comparisons test (h and i). ns, not significant. Source data

Extended Data Fig. 2 Up-regulation of genes encoding ATGL inhibitory factors led to reduced TG hydrolysis in pre-metastatic lung-infiltrating neutrophils.

a–h, Levels of total triglyceride (TG) (a), cholesteryl ester (CE) (b), phosphatidylethanolamine (PE) (c), phosphatidylcholine (PC) (d), phosphatidylserine (PS) (e), phosphatidylinositol (PI) (f), ceramides (Cer) (g) and phosphatidylglycerol (PG) (h) in PB and lung neutrophils isolated from the pre-metastatic stage of orthotopic 4T1 tumor-bearing mice (n = 5), were determined by liquid chromatography-mass spectrometry. Relative levels of these lipid were also shown in Fig. 1f. The exact values of specific chemical species for each lipid type were provided in Source Data file. i-l, Neutrophils were isolated from the BM, PB and lung tissues of orthotopic 4T1 tumor-bearing mice (n = 5) at the pre-metastatic stage and compared for relative mRNA expression (to Rps18) of Hilpda, Cidec, G0s2, Atgl and Abhd5 by qRT-PCR (i); HILPDA, CIDEC and G0S2 protein expression by Western blotting with GAPDH as a loading control (j); relative TG hydrolase activity (k); and cellular lipase activity (l). n represents biologically independent animals. Data are mean ± s.e.m. and P values were determined by one-way ANOVA with Tukey’s multiple comparisons test (i and l) or unpaired two-tailed t-test (k). ns, not significant. Source data

Extended Data Fig. 3 Neutrophil-specific genetic ablation of Hilpda or Cidec did not lead to significant changes on lung metastases of breast cancer in vivo.

af, The neutrophil-targeting cKO mice (a-c, Hilpda-cKO; df, Cidec-cKO), and their wild type littermates were orthotopically implanted with AT3-g-csf cells. On day 15, the relative lipid levels in lung neutrophils were determined by BODIPY 493/503 staining and flow cytometry (a and d) (n = 5 per group), and the resected primary tumors were weighed (b and e). At the end point (day 30), spontaneous lung metastases of WT and cKO mice were quantified (c and f). n = 13 (WT group) and 14 (Hilpda-cKO group) for b, c; n = 15 (WT group) and 14 (Cidec-cKO group) for e, n = 10 (WT group) and 12 (Cidec-cKO group) for f. g, A schematic diagram showing the modified experimental lung metastasis model employed in this study. Mice were first orthotopically implanted with non-labeled AT3-g-csf cells to induce a neutrophil-high host condition. Luciferase-labeled AT3-Luc cells were then implanted intravenously on day 10, a time point within the pre-metastatic stage. On day 15, the primary tumors were resected. At the end point, the lung metastatic progression of AT3-Luc cells was detected by ex vivo BLI. h, Following the above modified experimental lung metastasis model, the lung metastatic progression of AT3-Luc cells in WT and Cidec-cKO recipient mice was determined by ex vivo BLI. Representative BLI images of the harvested lungs are shown and red lines indicate blank wells without lung tissues (left) (n = 11). n represents biologically independent animals. Data are mean ± s.e.m. and P values were determined by unpaired two-tailed t-test. ns, not significant. Source data

Extended Data Fig. 4 Tumor cells absorb lipids from lipid-laden lung neutrophils.

a, Representative images from 2 biologically independent experiments showing lipid transfer from BODIPY FL C16-loaded neutrophils to 4T1-mCherry cells. Arrowheads indicate neutrophils. Scale bars, 20 μm. b, 4T1-mCherry or AT3-mCherry cells were mono-cultured or co-cultured with their respective tumor-bearing mice-derived neutrophils that were pre-loaded with BODIPY FL C16. Then the intensity of BODIPY in tumor cells was examined by flow cytometry. Representative of 3 biologically independent experiments. c, The cellular TG contents of AT3-mCherry cells after monoculture or co-culture with neutrophils. Data are mean ± s.d. from 3 biologically independent cell cultures. d, Measurement of the lipids in early lung-colonizing AT3-mCherry cells in G-CSF-pretreated mice without and with anti-Ly6G-based neutrophil depletion (see Methods) (n = 5). Data are mean ± s.e.m. e, The lipid transfer from BODIPY FL C16-loaded lung neutrophils to the indicated tumor cells in vivo as determined by flow cytometry (see Methods) (n = 4). Representative flow cytometry histograms are shown. f, As depicted in the left panel, AT3-mCherry or 4T1-mCherry cells were mono-cultured or co-cultured with PB or lung neutrophils. In one experimental group, lung neutrophils were pre-activated by phorbol 12-myristate 13-acetate (PMA) and hydrogen peroxide (H2O2). The total lipid levels in tumor cells were determined by BODIPY 493/503 staining and flow cytometry. Data are mean ± s.d. from 5 (4T1-mCherry) or 3 (AT3-mCherry) biologically independent cell cultures. Neutrophils used throughout this figure were isolated from the pre-metastatic stage of 4T1 tumor-bearing mice or AT3-g-csf tumor-bearing mice. n represents biologically independent animals. P values were determined by one-way ANOVA with Tukey’s multiple comparisons test. ns, not significant. Source data

Extended Data Fig. 5 Neutrophil-derived lipids enhance the proliferative capacity of metastatic tumor cells.

a, Oxygen consumption rates (OCR) of AT3 cells, upon mono-culture or co-culture with PB or lung neutrophils (left). The amount of OCR derived from fatty acid oxidation was quantified as the magnitude of the response to etomoxir (right). b–d, Measurement of the proliferative capacities of the indicated tumor cells without and with co-culture with PB or lung neutrophils (see Methods). e–g, Comparison of the metastatic colonization potentials of the indicated tumor cells (e, AT3-Luc; f, MCF7-Luc; and g, MDA-4175-Luc) without and with co-culture with PB or lung neutrophils (see Methods). Representative BLI images of the recipient mice and quantification of BLI signals within the lung areas are shown (n = 5 for e and n = 6 for f, g). h, i, PB and lung neutrophils were isolated from AT3-g-csf tumor-bearing WT or Atgl-cKO mice (pre-metastatic stage). The intracellular lipids in neutrophils were detected by BODIPY 493/503 staining under microscope (representative of n = 5 mice). Scale bars, 5 μm (h). The cellular TG contents of neutrophils were measured (n = 5) (i). j, 4T1-Luc cells, upon co-culture with PB or lung neutrophils isolated from AT3-g-csf tumor-bearing WT or Atgl-cKO mice (pre-metastatic stage), were intravenously injected into NSG mice (see Methods). Representative BLI images of the recipient mice and quantification of BLI signals within the lung areas are shown (n = 6). km, As depicted in k, the effects of EIPA treatment on lung colonization by 4T1 tumor cells (l) and on primary tumor growth (m) were determined (see Methods) (n = 10). Neutrophils throughout this figure were isolated from the pre-metastatic stage of 4T1 tumor-bearing mice (c, d, f, g) or AT3-g-csf tumor-bearing mice (a, b and e), except otherwise stated. n represents biologically independent animals. Data are mean ± s.d. from 6 (a) or 4 (b–d) biologically independent cell cultures, and mean ± s.e.m. for e-g, i, j, l, m. P values were determined by two-way ANOVA with Sidak’s multiple comparisons test (b–d, mono-culture versus lung Neu co-culture), one-way ANOVA with Tukey’s multiple comparisons test (a, e–g, i and j) or unpaired two-tailed t-test (l, m). ns, not significant. Source data

Extended Data Fig. 6 Both lipid- and neutrophil-associated gene expression signatures are related with lung metastasis in human breast cancer patients.

Kaplan–Meier plots of lung metastasis-free survival of breast cancer patients, stratified by expression of indicated gene signature sets in their primary tumors (GEO accession number: GSE2603, n = 82 patients). A risk score was calculated for each sample which was defined as a linear combination of expression values of genes in one signature set weighted by their estimated Cox model regression coefficients. If the risk score for one sample was in the top 20th percentile of the risk scores, then it was classified into high-risk group, otherwise into low-risk group. P values were calculated by a 2-sided log-rank test. The used gene signature sets are derived from Gene Ontology (GO): Biological Process of MsigDB v.7.1, including GO_POSITIVE_REGULATION_OF_LIPID_CATABOLIC_PROCESS (M14107, 25 genes); GO_POSITIVE_REGULATION_OF_LIPID_TRANSPORT (M11731, 61 genes); GO_LIPID_OXIDATION (M15880, 101 genes); GO_NEUTROPHIL_MIGRATION (M25402, 119 genes); GO_REGULATION_OF_NEUTROPHIL_CHEMOTAXIS (M29283, 30 genes); and GO_NEUTROPHIL_EXTRAVASATION (M24616, 13 genes).

Extended Data Fig. 7 A lung MC → neutrophil → tumor cell metabolic axis in the lung metastatic niche.

A schematic diagram depicts how lung neutrophils are stimulated by lung resident MCs to accumulate lipids, and in turn transport their stored lipids to metastatic tumor cells for survival and proliferation leading to accelerated lung metastasis in a breast cancer model.

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Li, P., Lu, M., Shi, J. et al. Lung mesenchymal cells elicit lipid storage in neutrophils that fuel breast cancer lung metastasis. Nat Immunol 21, 1444–1455 (2020). https://doi.org/10.1038/s41590-020-0783-5

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