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Neutrophil oxidative stress mediates obesity-associated vascular dysfunction and metastatic transmigration

Nature Cancer volume 2, pages 545–562 (2021)Cite this article

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Abstract

Metastasis is the leading cause of cancer-related deaths, and obesity is associated with increased breast cancer (BC) metastasis. Preclinical studies have shown that obese adipose tissue induces lung neutrophilia associated with enhanced BC metastasis to this site. Here we show that obesity leads to neutrophil-dependent impairment of vascular integrity through loss of endothelial adhesions, enabling cancer cell extravasation into the lung. Mechanistically, neutrophil-produced reactive oxygen species in obese mice increase neutrophil extracellular DNA traps (NETs) and weaken endothelial junctions, facilitating the influx of tumor cells from the peripheral circulation. In vivo treatment with catalase, NET inhibitors or genetic deletion of Nos2 reversed this effect in preclinical models of obesity. Imaging mass cytometry of lung metastasis samples from patients with cancer revealed an enrichment in neutrophils with low catalase levels correlating with elevated body mass index. Our data provide insights into potentially targetable mechanisms that underlie the progression of BC in the obese population.

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Fig. 1: Obesity enhances BC extravasation by modifying the endothelium.
Fig. 2: Obesity increases vascular permeability by downregulating endothelial adhesions.
Fig. 3: Endothelial barrier integrity is regulated by neutrophils during obesity.
Fig. 4: Obesity reprograms neutrophils to increase ROS production.
Fig. 5: Neutrophil-ROS modulates vascular integrity and enhances BC extravasation during obesity in a reversible manner.
Fig. 6: Obesity alters the secretory profile of neutrophils and enhances NETosis.
Fig. 7: Nos2 knockout reduces cancer cell extravasation in obese hosts.
Fig. 8: The obesity–neutrophil axis is observed in patients with cancer with lung metastatic disease.

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Data availability

Numerical and western blot source data for Figs. 1–8 and Extended Data Figs. 1–8 are provided with this paper. Any additional data that support the findings of this study are available from the corresponding author upon reasonable request. Further information on research design is available in the Nature Research Reporting Summary linked to this article. All RNA-seq data were deposited in the Gene Expression Omnibus under the accession codes GSE165442 (lung neutrophils, Figs. 4a–d and 6c and Extended Data Fig. 6f) and GSE165441 (lung endothelial cells, Fig. 2a–c). Databases used for RNA-seq analysis included MSigDB v.7.2 http://www.gsea-msigdb.org/gsea/msigdb/index.jsp, DoRothEA v.1.3.0 https://saezlab.github.io/dorothea/ and IPA v.01-13 https://digitalinsights.qiagen.com.

Code availability

MATLAB code used for IMC cell segmentation is available upon request.

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Acknowledgements

We thank the Goodman Cancer Research Centre and Life Sciences Complex at McGill University for core facility support, including the Single Cell and Imaging Mass Cytometry Platform (SCIMAP, Y.W.), Histology facility (Cleber Moraes), the Comparative Medicine and Animal Resource Centre and the Flow Cytometry facility. SCIMAP is supported by a McGill MI4 Platform grant and the Fraser Memorial Trust. This study was funded by the Susan G. Komen Foundation (D.F.Q., grant no. CCR18548032), Canada Foundation for Innovation (D.F.Q., JELF-37488; L.A.W., JELF-39178), Canadian Institutes of Health Research (D.F.Q., PJT-159742; L.A.W., PJT-162137) and the Breast Cancer Research Foundation (A.J.D., BCRF-19-034). D.F.Q. is supported by a Tier II Canada Research Chair in Tumor Microenvironment Research. L.A.W. is supported by a Rosalind and Morris Goodman Chair in Lung Cancer Research. S.A.C.M is supported by a Charlotte and Leo Karassik Foundation Fellowship, a Canada Graduate Scholarship from the Canadian Institutes of Health Research and a Canderel Graduate Studentship Award from the Goodman Cancer Research Centre.

Author information

Authors and Affiliations

  1. Rosalind and Morris Goodman Cancer Research Centre, McGill University, Montreal, Quebec, Canada

    Sheri A. C. McDowell, Robin B. E. Luo, Azadeh Arabzadeh, Samuel Doré, Nicolas C. Bennett, Valérie Breton, Elham Karimi, Ryan R. Yang, Bozena Samborska, Lucas J. M. Perus, Dan Moldoveanu, Yuhong Wei, Benoit Fiset, Roni F. Rayes, Ian R. Watson, Lawrence Kazak, Jonathan D. Spicer, Logan A. Walsh & Daniela F. Quail

  2. Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

    Sheri A. C. McDowell, Nicolas C. Bennett, Lucas J. M. Perus & Daniela F. Quail

  3. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Robin B. E. Luo, Samuel Doré & Logan A. Walsh

  4. Departments of Psychology and Computer Science, University of Toronto, Toronto, Ontario, Canada

    Morteza Rezanejad

  5. Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada

    Ryan R. Yang

  6. Department of Pathology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

    Katherine D. Lach, Marianne S. M. Issac, Marie-Christine Guiot & Pierre O. Fiset

  7. Department of Biochemistry, McGill University, Montreal, Quebec, Canada

    Dan Moldoveanu, Ian R. Watson & Lawrence Kazak

  8. Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada

    Roni F. Rayes, Jonathan D. Spicer & Daniela F. Quail

  9. Montreal Neurological Institute, McGill University Health Centre, Montreal, Quebec, Canada

    Marie-Christine Guiot

  10. Department of Surgery, McGill University Health Centre, Montreal, Quebec, Canada

    Jonathan D. Spicer

  11. Department of Medicine, Weill Cornell Medical College, New York, New York, USA

    Andrew J. Dannenberg

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  1. Sheri A. C. McDowell
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Contributions

S.A.C.M., L.A.W. and D.F.Q. designed the study, reviewed the data and wrote the paper. A.J.D. provided essential insight throughout the study and contributed neutrophil RNA-seq data. S.A.C.M. oversaw and performed all experiments and data analysis. R.B.E.L., E.K., M.R., R.R.Y. and B.F. provided bioinformatics support. R.B.E.L., A.A., S.D., N.C.B., V.B., B.S., R.F.R. and L.K. provided experimental support and/or optimized protocols. L.J.M.P., D.M., Y.W. and I.R.W. optimized the IMC antibody panel. K.D.L., M.S.M.I., R.F.R., M-C.G., P.O.F. and J.D.S. provided human tissue samples, built the patient tissue microarray and provided pathology/clinical insight. All authors reviewed and approved the paper.

Corresponding authors

Correspondence to Logan A. Walsh or Daniela F. Quail.

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Competing interests

P.O.F. has received honoraria and consultancy fees from Amgen Canada, AstraZeneca Canada, Bristol Myers Squibb, EMD Serono, Hoffmann-La Roche, Merck Canada, Pfizer Canada and Roche Canada. J.D.S. has received consultancy fees for BMS, Merck, Amgen, AstraZeneca, Protalix and TransHit Bio, and grant funding from AstraZeneca, CLS Therapeutics, Merck and Hoffman La Roche. All other authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Luisa Iruela-Arispe, Elena Piskounova and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Obesity is associated with enhanced lung metastasis in multiple genetic models.

a, Weight curves for C57BL6 MMTV-PyMT mice enrolled on LF or HF diet from 5-25 weeks of age. LF, n = 7 mice; HF, n = 9 mice; mean ± SEM. b, Spider-plot showing primary tumor growth for trial shown in a, measured by digital caliper. c, Quantification of metastatic burden in lung from MMTV-PyMT mice at end point. LF, n = 18 lung sections; HF, n = 31 lung sections; Box = median ± interquartile range, whiskers = min-max, all datapoints shown; two-tailed Mann–Whitney test. d, Control conditions for TEM assays using serum gradients. Upper chamber of the Transwell contains 0% serum in all cases. Bottom conditions include: 2% heat-inactivated (H.I.) FBS (n = 4 Transwells), a dose-response from 0%-10% FBS (2%, n = 8 Transwells, 0,5,10%, n = 4 Transwells), 2% serum isolated from LF or HF mice (n = 4 Transwells), and 2% serum isolated from LF or HF and subject to H.I. (n = 4 Transwells); mean ± SEM; two-tailed Student’s t-test. Transwells represent individual experimental replicates with similar results; mouse serum was obtained from different mice. e, Average weight of the leptin-deficient model of genetic-induced obesity (GIO). Wild type (WT) or B6.Cg-Lepob (ob/ob) mice on a C57BL6 background were raised on a standard rodent diet until 10 weeks of age. WT, n = 10 mice; ob/ob, n = 9 mice; mean ± SEM; two-tailed Student’s t-test. f, Schematic illustration of the in vivo extravasation assay corresponding to g-i. WT or ob/ob mice were injected via tail vein with syngeneic breast cancer cells labelled with a green fluorescent CellTracker, and extravasation was quantified in lung after 48h via fluorescence microscopy. g, Quantification of lung vascular density in the in vivo extravasation assay. Vascular density is quantified as CD31+ cells as a percentage of total DAPI+ cells. WT, n = 10 mice; ob/ob, n = 9 mice; mean ± SEM; two-tailed Student’s t-test. h, Fluorescence quantification of Py230 cancer cell extravasation in lung tissue. Extravasation is quantified as the ratio of cancer cells outside the vessels (CellTracker+ CD31-) versus inside the vessels (CellTracker+ CD31+). WT, n = 10 mice; ob/ob, n = 9 mice; mean ± SEM; two-tailed Student’s t-test. i, Representative immunofluorescence image for data in h.

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Extended Data Fig. 2 JAM1 is sufficient to regulate vascular permeability.

a, Schematic illustration of the in vitro TransEndothelial Electrical Resistance (TEER) assay. b, Schematic illustration of the in vitro dextran–FITC permeability assay. c, Representative immunofluorescence image for Fig. 2l, showing JAM1+ CD31+ cells in LF (n = n = 4 mice) and HF (n = 7 mice) lung tissue. d, Representative immunofluorescence images for Fig. 2l showing adhesion protein staining in CD31+ cells in LF (n = 4 mice) and HF lung tissue (N-cadherin, n = 4 mice; all others, n = 5 mice). e, Top: Western blot analysis confirming knockdown of VCAM1 protein in HMEC endothelial cells via infection with shRNA targeting VCAM1. Knockdown was confirmed in 2 independent blots. Bottom: TEM quantification in which monolayers were formed with either shSCR or shVCAM1 HMEC endothelial cells prior to assessing Py230 breast cancer transmigration. n = 4 Transwells per group representing individual experimental replicates; mean ± SEM; two-tailed Student’s t-test. f, Representative western blot confirming knockdown of JAM1 protein in HMEC endothelial cells after infection with 2 shRNA constructs. Knockdown was confirmed in 2 independent blots. g, Quantification of TEER across HMEC monolayers genetically modified to express either a JAM1 shRNA (shJAM11, n = 5 Transwells; shJAM12, n = 4 Transwells), a VCAM1 shRNA (shVCAM1; n = 4 Transwells) or a scramble control shRNA (shSCR). Mean ± SEM; two-tailed Student’s t-test. Transwells represent individual experimental replicates with similar results. h, Schematic illustration of the in vitro TEM assay corresponding to Fig. 2o, in which HMEC monolayers genetically modified to express a JAM1 shRNA (shJAM11) or a scramble control shRNA (shSCR) were formed prior to assessing Py230 breast cancer transmigration.

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Extended Data Fig. 3 Neutrophil depletion in vivo.

a, Schematic illustration of a modified in vitro TEM assay, in which endothelial cells are pre-treated with neutrophil conditioned media (nCM) generated from bone marrow-derived neutrophils from LF or HF mice. b, Quantification of control TEM assay where HMEC endothelial cells are pre-treated for 24 hours with serum-free media (SFM), or nCM from either LF or HF mice. PyMT cells are then seeded in the upper chamber, and a 0-2% FBS gradient is used to stimulate TEM. n = 4 Transwells per condition representing experimental replicates using nCM from individual mice; mean ± SEM; two-tailed Student’s t-test. c, Schematic of trial design for in vivo 48h extravasation assay with antibody-based neutrophil depletion using ?Ly6G/?Rat or an IgG control. d, Flow cytometry confirmation that neutrophils are effectively depleted in response to ?Ly6G/?Rat treatment, corresponding to experiments in Fig. 3h,i.

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Extended Data Fig. 4 Obesity is associated with enhanced ROS in neutrophils.

a, Flow cytometry gating strategy for neutrophils, monocytes, and macrophages in lung. Gates were first drawn around CD45+ CD11b+ cells (not shown), followed by gating according to Ly6G and Ly6C as shown. CD45+ CD11b+ Ly6G+ Ly6Clo neutrophils were used for RNA-seq, corresponding to Fig. 4a-d. b, Weights of LF (n = 3 mice) and HF (n = 4 mice) mice used for lung neutrophil RNA-sequencing. Mean ± SEM. c, Neutrophil CellROX flow cytometry plot, corresponding to Fig. 4e. d, Quantification of CellROX+ neutrophils (CD45+ CD11b+ Ly6G+ Ly6Clo) in lungs from LF or HF B16F10 primary tumor-bearing mice. n = 8 mice per condition; mean ± SEM; two-tailed Mann-Whitney test. e, Quantification of CellROX+ monocytes (CD45+ CD11b+ Ly6G- Ly6Chi) in lungs from LF or HF Py230 primary tumor-bearing mice. LF, n = 5 mice; HF, n = 7 mice; mean ± SEM; two-tailed Mann-Whitney test. f, Quantification of CellROX+ macrophages (CD45+ CD11b+ Ly6G- Ly6C-) in lungs from LF or HF Py230 primary tumor-bearing mice. LF, n = 5 mice; HF, n = 7 mice; mean ± SEM; two-tailed Mann-Whitney test. g, Quantification of CellROX+ monocytes (CD45+ CD11b+ Ly6G- Ly6Chi) in lungs from LF or HF B16F10 primary tumor-bearing mice. n = 8 mice per condition; mean ± SEM; two-tailed Mann-Whitney test. h, Quantification of CellROX+ macrophages (CD45+ CD11b+ Ly6G- Ly6C-) in lungs from LF or HF B16F10 primary tumor-bearing mice. n = 8 mice per condition; mean ± SEM; two-tailed Student’s t-test. i, Quantification of SOD activity (U/ml) using bone marrow neutrophils isolated from WT or ob/ob mice. LF, n = 3 mice; HF, n = 4 mice; mean ± SEM; two-tailed Mann-Whitney test. j, Quantification of SOD activity (U/ml) using serum isolated from WT or ob/ob mice. n = 5 mice per condition; mean ± SEM; two-tailed Student’s t-test. k, Quantification of MPO activity (mU/ml) using bone-marrow neutrophils isolated from LF or HF mice. n = 4 mice per condition; mean ± SEM; two-tailed Student’s t-test. l, Quantification of hypochlorite concentration in bone marrow neutrophils isolated from LF or HF mice harbouring E0771 mammary tumors (tumor-bearing; TB). LF, n = 6 mice; HF, n = 4 mice; mean ± SEM; two-tailed Student’s t-test.

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Extended Data Fig. 5 Catalase reduces breast cancer extravasation to lung in obese hosts.

a, Representative immunofluorescence image for data shown in Fig. 5c. LF + veh, n = 5 mice; LF + cat, n = 4 mice; HF + veh, n = 5 mice; HF + cat, n = 4 mice. b, Schematic illustration of the in vivo extravasation assay corresponding to Fig. 5d. LF or HF mice were injected via tail vein with syngeneic breast cancer cells labelled with a green fluorescent CellTracker, treated with either vitamin E or vehicle and extravasation was quantified in lung after 48h via fluorescence microscopy. c, Representative immunofluorescence image for data shown in Fig. 5f. LF + veh, n = 6 mice; LF + cat, n = 4 mice; HF + veh, n = 8 mice; HF + cat, n = 8 mice. d, Schematic illustration of the in vivo extravasation assay corresponding to Fig. 5g,h. WT or ob/ob mice were injected via tail vein with syngeneic breast cancer cells labelled with a green fluorescent CellTracker, treated with either catalase or vehicle and extravasation was quantified in lung after 48h via fluorescence microscopy. e, Representative immunofluorescence image for data shown in Fig. 5g. WT + veh, n = 9 mice; WT + cat, n = 7 mice; ob/ob + veh, n = 7 mice; ob/ob + cat, n = 4 mice. f, Quantification of TEER across an HMEC monolayer genetically modified to express an shRNA against catalase (shCAT), versus a scramble control (shSCR). n = 4 Transwells per condition representing individual experimental replicates with similar results; mean ± SEM; two-tailed Student’s t-test.

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Extended Data Fig. 6 Obesity increases MMP9 from neutrophils.

a, Cytokine array from lung neutrophil conditioned media (nCM) obtained from LF or HF mice. MMP9 and LCN2 were the top two differences observed. b, Outline of 111 cytokine array membrane targets, corresponding to a. c, Immunofluorescence quantification of Py230 cancer cell extravasation in lung tissue from LF or HF mice treated with DNase1 or vehicle. LF + veh, n = 5 mice; LF + DNase1, n = n = 4 mice; HF + veh, n = 7 mice; HF + DNase1, n = 6 mice; mean ± SEM; One-way ANOVA with Bonferroni multiple comparisons test. d, Representative immunofluorescence image for data shown in c. e, Immunofluorescence quantification of MPO and H3cit in lung tissue from obese (HF) mice treated with DNase1 or vehicle. n = 4 mice per group; mean ± SEM; two-tailed Student’s t-test. f, RNA-seq differentially expressed genes in HF (n = 4 mice) relative to LF (n = 3 mice) lung neutrophils, showing representative genes relevant to cell cycle progression during metastasis. Data are displayed as log2fold HF vs LF with standard error.

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Extended Data Fig. 7 Diet-induced weight changes between WT and Nos2−/− mice.

a, Weight curves comparing wild type (WT) and Nos2−/− mice enrolled on LF, HF or SRD diet from 5-20 weeks of age (15 weeks total). LF WT, n = 10 mice; HF WT, n = 10 mice; HF Nos2−/− n = 5 mice; SRD WT, n = 10 mice; SRD Nos2−/−, n = 10 mice; mean ± SEM.

Extended Data Fig. 8 Imaging mass cytometry (IMC) allows for single cell and spatial analysis.

a, Patient information (BMI and primary cancer type) for samples analyzed by IMC. Patients with an asterisk indicate those used for immunofluorescence validation. Patients with BMI<25.9 were assigned to the BMIlow group (ave BMI=24), and patients with a BMI>26 were assigned to the BMIhigh group (ave BMI=30; black line). b, IMC antibody panel information. c, Schematic of IMC staining workflow. Tissue sections were stained with antibody panel (as in b), and subject to CyTOF acquisition using a Hyperion imaging system. d, Immunofluorescence quantification of JAM1+ PyMT tumor cells in lung tissue from WT or ob/ob mice. WT, n = 10 mice; ob/ob, n = 8 mice; mean ± SEM; two-tailed Student’s t-test. e, Quantification of TEM of PyMT breast cancer cells infected with an shRNA against Jam1 (shJAM1) or scramble control (shSCR). n = 4 Transwells per group representing individual experimental replicates; mean ± SEM; two-tailed Student’s T test. f, Quantification of the average number of total CD163- CD68+ M1-like macrophages in BMIlow (n = 8 patients) and BMIhigh (n = 14 patients) lung metastasis samples. Box = median ± interquartile range, whiskers = min-max, all datapoints shown; two-tailed Mann–Whitney test.

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Supplementary information

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McDowell, S.A.C., Luo, R.B.E., Arabzadeh, A. et al. Neutrophil oxidative stress mediates obesity-associated vascular dysfunction and metastatic transmigration. Nat Cancer 2, 545–562 (2021). https://doi.org/10.1038/s43018-021-00194-9

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