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Tumor-associated macrophages restrict CD8+ T cell function through collagen deposition and metabolic reprogramming of the breast cancer microenvironment

Nature Cancer (2024)Cite this article

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Abstract

Tumor progression is accompanied by fibrosis, a condition of excessive extracellular matrix accumulation, which is associated with diminished antitumor immune infiltration. Here we demonstrate that tumor-associated macrophages (TAMs) respond to the stiffened fibrotic tumor microenvironment (TME) by initiating a collagen biosynthesis program directed by transforming growth factor-β. A collateral effect of this programming is an untenable metabolic milieu for productive CD8+ T cell antitumor responses, as collagen-synthesizing macrophages consume environmental arginine, synthesize proline and secrete ornithine that compromises CD8+ T cell function in female breast cancer. Thus, a stiff and fibrotic TME may impede antitumor immunity not only by direct physical exclusion of CD8+ T cells but also through secondary effects of a mechano-metabolic programming of TAMs, which creates an inhospitable metabolic milieu for CD8+ T cells to respond to anticancer immunotherapies.

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Fig. 1: Tumor fibrosis and ECM stiffening promote a collagen ECM-synthetic phenotype in myeloid cells.
Fig. 2: Immune-rich tumors have less ECM-synthetic myeloid cells.
Fig. 3: ECM-synthetic TAMs affect tumor growth and T cell composition.
Fig. 4: Arginine metabolism in ECM-synthetic myeloid cells affects tumor ECM and T cell abundance.
Fig. 5: TGFβ1 and ECM stiffness regulate arginine-to-ornithine metabolism in myeloid cells.
Fig. 6: Ornithine alters CTL function.
Fig. 7: Ornithine impairs anti-PD-1 therapy in stiff-collagen PyMT tumor models.

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Data availability

RNAseq data described here have been deposited in Gene Expression Omnibus and is publicly available (referenced accession numbers: GSE157290 and GSE184398), and data for the breast invasive carcinoma, glioblastoma multiforme, kidney renal clear cell carcinoma, lung adenocarcinoma and pancreatic adenocarcinoma projects were acquired from TCGA repository (https://portal.gdc.cancer.gov/). Source data for Figs. 1b–d, 3f,h,i,j, 4a–d,h,j, 5e,g,h, 6b–f,h and Fig. 7b–f,i and Extended Data Figs. 1a,b,d, 2, 3a,d,e, 4a,c–i, 5a,e,f,g and 6b have been provided as source data files. Minimally processed metabolomics datasets are provided in Supplementary Table 2. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Open-source R scripts for metabolomics analysis can be found at https://rdrr.io/github/graeberlab-ucla/MetabR/.

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Acknowledgements

We thank E. Benedetti and E. Reznik for access to the Cancer Atlas of Metabolic Profiles datasets. This work was supported by 1F32CA236156-01A1, 5T32CA108462-15 and the Sandler Program for Breakthrough Biomedical Research (postdoctoral independence award) to K.M.T.; R35 CA242447-01A1, R01CA192914 and R01CA222508-01 to V.M.W.; and the National Institutes of Health Shared Instrumentation Grant S10 OD016387. Quantitative analysis of amino acids in interstitial fluid was performed by H. Shah and R. AminiTabrizi at the University of Chicago Comprehensive Cancer Center Metabolomics Platform, which receives financial support from the University of Chicago Comprehensive Cancer Center Support Grant (P30-CA014599).

Author information

Authors and Affiliations

  1. Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA

    Kevin M. Tharp, Ori Maller, Greg A. Timblin, Connor Stashko, Mary-Kate Hayward, Ilona Berestjuk & Valerie M. Weaver

  2. Department of Pathology, University of California San Francisco, San Francisco, CA, USA

    Kelly Kersten, Bushra Samad & Alexis J. Combes

  3. ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA

    Kelly Kersten, Bushra Samad & Alexis J. Combes

  4. Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland

    Fernando P. Canale & Roger Geiger

  5. Department of Surgery, University of Michigan, Ann Arbor, MI, USA

    Rosa E. Menjivar & Marina Pasca di Magliano

  6. UCLA Metabolomics Center, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA

    Johanna ten Hoeve

  7. UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA

    Bushra Samad & Alexis J. Combes

  8. Department of Pathology, Western General Hospital, NHS Lothian, Edinburgh, UK

    Alastrair J. Ironside

  9. Department of Cell and Developmental Biology, Cancer Biology Program, University of Michigan, Ann Arbor, MI, USA

    Marina Pasca di Magliano

  10. Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA

    Alexander Muir

  11. Department of Medicine, University of California San Francisco, San Francisco, CA, USA

    Alexis J. Combes

  12. Department of Bioengineering and Therapeutic Sciences and Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, and The Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA

    Valerie M. Weaver

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Contributions

Conceptualization: K.M.T. and V.M.W. Methodology: K.M.T. Investigation: K.M.T., K.K., O.M., G.A.T., A.M., C.S., J.t.H., F.P.C., I.B. R.E.M. and M.-K.H. Formal analysis: K.M.T., K.K., A.M., C.S., B.S., A.J.C., J.t.H., M.-K.H. and A.J.I. Data curation: K.M.T. Funding acquisition: V.M.W. and K.M.T. Project administration: K.M.T. Software: C.S. Supervision: V.M.W. Validation: K.M.T., K.K., G.A.T. and C.S. Visualization: K.M.T. Writing—original draft: K.M.T. Writing—review and editing: K.M.T., V.M.W., K.K., M-K.H., M.P.d.M., R.G., G.A.T. and A.J.C.

Corresponding author

Correspondence to Valerie M. Weaver.

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The authors declare no competing interests.

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Nature Cancer thanks Costas Lyssiotis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Tumor progression is associated with ECM synthetic myeloid programming.

a. H&E-stained histological sections of 8 week and 11 week FVB/N PyMT mammary tumors, quantification of the grade of stromal fibrosis (n = 10 or 9 mice). b. Quantification of pathological assessment for area of hyperplasia, DCIS with early invasion (DCIS) and advanced invasion (invasion) within H&E-stained histological sections of 8 week and 11 week FVB/N PyMT mammary tumors (n = 10 or 9 mice). c. Representative second-harmonic generation (SHG) images of collagen fibers in 11 week old FVB/N PyMT mammary tumors, treated with anti-CSF1 blocking antibody or IgG control weekly from 4 weeks of age until 11 weeks of age (n = 3 mice), (scale bar: 100 µm). d. Relative expression of macrophage polarization-associated gene expression of TAMs derived from 11 week old FVB/N PyMT mammary tumors, relative to TAMs derived from 8 week old PyMT mammary tumors, (n = 5 mice). e. Representative immunofluorescence microscopy of Collagen VI (white) and DNA (blue) of 11 week old FVB/N PyMT mammary tumors treated with or without LOX-inhibition, representative of the effect observed in 2 independent experiments (n = 4 mice) (scale bar: 100 µm). f. Representative immunofluorescence microscopy of F4/80 (red) and DNA (blue) in 11 week old FVB/N PyMT mammary tumors, representative localization of every tumor-stroma border assessed (n = >10 mice from > 10 independent experiments) (scale bar: 40 µm). g. Representative immunofluorescence microscopy of Collagen VI (white) and DNA (blue) of BMDMs treated with or without 5 ng/mL IL4 and 1 ng/mL TGFβ1 on fibroblast synthesized ECM surfaces for 24 h, representative of the effect observed in 3 independent experiments (scale bar: 40 µm). Data shown represent ± SEM via two-tailed unpaired Student t test (b).

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Extended Data Fig. 2 ECM-synthetic stage II and IIIA tumors are associated with poor survival.

Kaplan-Meier survival curves of 2506 stage II and IIIA breast tumors, stratified for the top and bottom quartile expression level of the genes comprising the top GO category identified in d (Cox Proportional Hazard model: p = 0.0364, z = 2.092432; LogRank: p = 0.0346).

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Extended Data Fig. 3 TGFβ1 signaling and production is mechanosensitive.

a. Relative gene expression of IL4-polarized BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces treated with 0, 0.1, 1, or 10 ng/mL TGFβ1 for 4 h, qPCR-ΔΔCT normalized to BMDMs treated without IL4 (housekeeping gene: 18 s), (n = 3 independent experiments). b. Representative immunofluorescence microscopy for RETNLA (red) and DNA (blue) of IL4-polarized BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces, treated with 1 ng/mL TGFβ1 with or without 10 µg/mL TGFβ1-blocking antibody (1D.11) for 24 h, representative of the effect observed in 3 independent experiments (scale bar: 20 µm). c. Representative immunofluorescence microscopy for phosphorylated-SMAD2/3 (red) and DNA (blue) of IL4-polarized BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces, treated with 1 ng/mL TGFβ1 with or without 10 µg/mL TGFβ1-blocking antibody (1D.11) for 24 h, representative of the effect observed in 3 independent experiments (scale bar: 40 µm). d. Active TGFβ1 in culture medium after 48 h of culture of IL4-polarized BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces. (n = 4 independent experiments) e. Relative luminescent intensity of TGFβ1-reporter (PAI-1 Luc) expressing mink lung epithelial cells88 when cultured for 24 h in 1:1 conditioned media from IL4-polarized BMDMs cultured on soft (400 Pa) or stiff (60k Pa), (n = 9, 3 independent experiments of 3 technical replicates). f. Representative immunofluorescence microscopy of f4/80 (red), and DNA (blue) of stiff collagen orthotopic C57BL/6J PyMT mammary after 3 weeks of growth in Tgfbr2MyeKO or control animals, (n = 3 mice) (scale bar: 100 µm). Data shown represent ± SEM via two-tailed unpaired Student t test (d-e).

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Extended Data Fig. 4 Arginine, TGFβ1, and ECM stiffness affect myeloid ECM synthesis and metabolism.

a. Relative arginine concentrations of stiff collagen orthotopic C57BL/6J PyMT mammary tumors in Tgfbr2MyeKO or control animals for 3 weeks, LC-MS analysis, (n = 7 or 6 mice). b. Representative immunofluorescence microscopy of Collagen VI (orange) and DNA (blue) of IL4-polarized WT or Arg1KO BMDMs cultured with or without 0.4 mM arginine on stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces treated with 1 ng/mL TGFβ1 for 24 h, representative of the effect observed in 3 independent experiments (scale bar:40 µm). c. Relative hydroxyproline (Pro-OH) concentration in BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces with or without 1 ng/mL TGFβ1 for 24 h, measured via LC-MS, (n = 3 independent experiments). d. Fractional contributions (all isotopologues, m + 5 or m + 3) of 13C derived from 13C5-glutamine in BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces in medium containing 12C5-glutamine, treated with 1 ng/mL TGFβ1 for 22 h, swapped for fresh medium containing 13C5-glutamine for 2 h, BMDMs were harvested and measured via LC-MS, (n = 3 independent experiments). e. Fractional contributions (all isotopologues, m + 5 or m + 3) of 13C derived from 13C5-glutamine in WT or Tgfbr2MyeKO BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces in medium containing 12C5-glutamine, treated with or without 1 ng/mL TGFβ1 for 22 h, swapped for fresh medium containing 13C5-glutamine for 2 h, BMDMs were harvested and measured via LC-MS, (n = 3 independent experiments). f. Cellular respirometry of BMDMs cultured with or without 1 ng/mL TGFβ1 for 24 h or 100 ng/mL LPS for 1 h along with sequential additions via injection ports of Oligomycin [1 µM final], FCCP [1 µM final], and Antimycin A/Rotenone [1 µM final] during respirometry measurements, measured with an SeahorseXF24, (n = 5 wells, repeated 3 times) g-i. Relative g. NADH h. NAD i. NADH/NAD ratio in BMDMs cultured on soft (400 Pa) or stiff (60k Pa) collagen I-coated polyacrylamide hydrogel surfaces with or without 1 ng/mL TGFβ1 for 24 h, measured via LC-MS, (n = 3). Data shown represent ± SEM via two-tailed unpaired Student t test (a, d, and f) or one-way ANOVA with Tukey test for multiple comparisons (c, e, g-i) and **P < 0.01 via, ns indicates statistically not significant.

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Extended Data Fig. 5 Arginine improves CTL tumor infiltration.

a. LC-MS based metabolomics of medium after 3 h of culture of Arg-ECN or ECN metabolizing a modified M9 medium, lacking ammonia, supplemented with ornithine [3 mM], (n = 3 independent experiments). b. Using the BRCA1 dataset from the Cancer Atlas of Metabolic Profiles42 we compared the levels of arginine and proline in 61 breast tumor to 47 normal adjacent tissues. c. Graphical description of the experimental setup for c-d. d. Tumor mass after 3 weeks of growth in C57BL/6J mice gavaged daily with 2 g/kg glycine, ornithine, arginine, or water, (n = 8 mice) e. Relative serum metabolites derived from retro-orbital isolated blood from mice containing PyMT tumors after 3 weeks of growth in C57BL/6J mice gavaged daily with 2 g/kg glycine, ornithine, arginine, or water (100 µL), 4 h prior to isolation of blood/serum, measured with LC-MS (n = 8 mice pooled and measured as 2 technical replicates). f. Quantitation of cleaved-caspase 3+ %-area per field view of tumor border zone of 3 week old tumors from C57BL/6J mice gavaged daily with 2 g/kg glycine, ornithine, arginine, or water (n = 5 mice). g. Quantitation of CD8+ %-area per field view from the core of 3 week old tumors from C57BL/6J mice gavaged daily with 2 g/kg glycine, ornithine, arginine, or water (n = 4 mice). h. Representative thesholded-area mask for CD8+ from the core of 3 week old tumors from C57BL/6J mice gavaged daily with 2 g/kg glycine, ornithine, arginine, or water (n = 4 mice) (scale bar: 100 µm). i. Representative immunofluorescence microscopy for CD8+ from the core of 3 week old tumors from C57BL/6J mice gavaged daily with 2 g/kg glycine, ornithine, arginine, or water (n = 4 mice) (scale bar: 100 µm). j. Representative immunofluorescence microscopy of CD8+ morphologies observed in orthotopic PyMT mammary tumors in Tgfbr2MyeKO mice gavaged daily with 2 g/kg ornithine (n = 5 mice) (scale bar: 10 µm). Data shown represent ± SEM via two-tailed unpaired Student t test (a) or one-way ANOVA with Tukey test for multiple comparisons (c-e, & f-g).

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Extended Data Fig. 6 Ornithine alters CTL metabolism.

a. Heat map of relative metabolite levels of CD3/CD28-activated CD8+ CTLs cultured for 24 h in medium containing a molar ratio of 1:1 [0.5 mM], 3:1 [1.5 mM], and 9:1 [4.5 mM] ornithine:arginine, LC-MS analysis. (n = 3 independent experiments). b. Graphical description of the experimental setup and fractional contribution of 13C5-ornithine to intracellular ornithine in CD3/CD28-activated CD8+ CTLs cultured for 24 h with and without 1 mM Arginine (12C) present in medium, LC-MS analysis, (n = 3 independent experiments). c. Heat map of differentially abundant metabolite levels in b, LC-MS analysis. (n = 3 independent experiments). d. Representative immunofluorescence microscopy of OVA-PyMT tumor cells challenged with GFP+-OTI CTLs (green) for 24 h in medium containing a molar ratio of 1:1 [0.5 mM] or 3:1 [1.5 mM] ornithine:arginine, cleaved-caspase 3 (red) and DNA (blue), representative of the effect observed in 4 independent experiments (scale bar: 40 µm).

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Supplementary information

Supplementary Information

Supplementary Fig. 1: flow and RNAseq gating strategy.

Reporting Summary

Supplementary Data 1

TCGA case list used.

Supplementary Tables

Supplementary Table 1: qPCR primers. Supplementary Table 2: raw/minimally processed LC–MS datasets.

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Tharp, K.M., Kersten, K., Maller, O. et al. Tumor-associated macrophages restrict CD8+ T cell function through collagen deposition and metabolic reprogramming of the breast cancer microenvironment. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00775-4

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