Abstract
Cancer cells characteristically consume glucose through Warburg metabolism1, a process that forms the basis of tumour imaging by positron emission tomography (PET). Tumour-infiltrating immune cells also rely on glucose, and impaired immune cell metabolism in the tumour microenvironment (TME) contributes to immune evasion by tumour cells2,3,4. However, whether the metabolism of immune cells is dysregulated in the TME by cell-intrinsic programs or by competition with cancer cells for limited nutrients remains unclear. Here we used PET tracers to measure the access to and uptake of glucose and glutamine by specific cell subsets in the TME. Notably, myeloid cells had the greatest capacity to take up intratumoral glucose, followed by T cells and cancer cells, across a range of cancer models. By contrast, cancer cells showed the highest uptake of glutamine. This distinct nutrient partitioning was programmed in a cell-intrinsic manner through mTORC1 signalling and the expression of genes related to the metabolism of glucose and glutamine. Inhibiting glutamine uptake enhanced glucose uptake across tumour-resident cell types, showing that glutamine metabolism suppresses glucose uptake without glucose being a limiting factor in the TME. Thus, cell-intrinsic programs drive the preferential acquisition of glucose and glutamine by immune and cancer cells, respectively. Cell-selective partitioning of these nutrients could be exploited to develop therapies and imaging strategies to enhance or monitor the metabolic programs and activities of specific cell populations in the TME.
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Data availabilty
All data will be made available upon reasonable request to J.C.R. or W.K.R. Tumour mRNA transcript data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) with accession number GSE165223. These data are also found in Supplementary Table 4. Source data are provided with this paper.
Code availability
The code used to support tumour mRNA transcript analysis has been previously published (see Methods references) and will be made available upon request to J.C.R. or W.K.R.
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Acknowledgements
We thank members of the J.C.R. and W.K.R. laboratories for their constructive input; the J. Balko, Y. Kim and P. Hurley laboratories for the use of their tumour dissociators; the Center for Small Animal Imaging at the Vanderbilt University Institute of Imaging Science (VUIIS) for support with PET–CT imaging; and the VUMC Radiochemistry core for synthesis and handling of radioactive material. This work was supported by F30 CA239367 (M.Z.M.), F30 CA247202 (B.I.R.), F30 DK120149 (R.E.B.), R01 CA217987 (J.C.R.), NIH T32 GM007753 (B.T.D.), R01 DK105550 (J.C.R.), AHA 20PRE35080073 (A.A.), VA Merit 1I01BX001426 (C.S.W.), Crohn’s and Colitis Foundation 623541 (C.S.W.), the American Association for Cancer Research (B.I.R. and W.K.R.), T32 GM007347 (M.Z.M., B.I.R., R.E.B., A.A., A.S. and C.S.W.), K12 CA090625 (K.E.B. and W.K.R.), K00 CA234920 (J.E.B.) and the Vanderbilt-Incyte Alliance (J.C.R. and W.K.R.). The Vanderbilt VANTAGE Core, including P. Baker, provided technical assistance for this work. VANTAGE is supported in part by a CTSA Grant (5UL1 RR024975-03), the Vanderbilt Ingram Cancer Center (P30 CA68485), the Vanderbilt Vision Center (P30 EY08126) and the NIH/NCRR (G20 RR030956). For the MC38 THY1.1 cells, we thank the cell and genome engineering core of the Vanderbilt O’Brien Kidney Center (P30 DK114809). Flow-sorting experiments were performed in the VUMC Flow Cytometry Shared Resource by D. K. Flaherty and B. K. Matlock and were supported by the Vanderbilt Ingram Cancer Center (P30 CA68485) and the Vanderbilt Digestive Disease Research Center (P30 DK058404). This work was supported by grant 1S10OD019963-01A1 for the GE TRACERlab FX2 N and Comecer Hotcell, housed in the VUIIS Radiochemistry Core to synthesize 18F-Gln. The Inveon microPET was funded by NIH 1S10 OD016245. We acknowledge the Translational Pathology Shared Resource supported by NCI/NIH Cancer Center Support Grant 5P30 CA68485-19 and the Vanderbilt Mouse Metabolic Phenotyping Center Grant 2 U24 DK059637-16, as well as the Shared Instrumentation Grant for the Leica Bond RX MicroArrayer (S10 OD023475-01A1) and the VA shared equipment grant for the LCM (IS1BX003154). Figs. 1d, 4k, Extended Data Figs. 2d, 3f were created with Biorender.com.
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Authors and Affiliations
Contributions
B.I.R., M.Z.M., J.C.R. and W.K.R. conceived and designed the study and composed the manuscript. B.I.R., R.A.H. and K.L.Y. collected tumour interstitial fluid from patients with ccRCC. K.E.B. provided clinical expertise and samples for interstitial fluid analysis. A. Ali, A.M., B.T.D., C.A.L. and M.G.V.H. performed, analysed and provided expertise for metabolite analysis of tumour interstitial fluid. B.I.R., M.Z.M. and M.M.W. conducted 18F nutrient uptake and extracellular flux experiments. A.S.C. and H.C.M. provided expertise to develop 18F nutrient uptake assays. F.X. and M.N.T. injected and handled mice for 18F nutrient uptake assays, and performed and provided expertise for PET imaging and autoradiography. T.H. and W.D.M. performed and provided expertise for intrarenal Renca experiments. R.W.J. and V.M.T. generated and provided expertise for PyMT GEMM tumours. R.E.B. and C.S.W. generated and provided expertise for AOM/DSS CRC tumours. B.I.R., R.T.O. and M.H.W. generated the pTZeo-EL-THY1.1 transposon construct and engineered MC38 cells using this transposon system. B.I.R., M.Z.M. and A.S. performed in vivo 2-NBDG studies. J.E.B. provided expertise in characterizing TAMs. A.R.P. provided expertise in flow sorting for mRNA transcript analysis. B.I.R. and M.Z.M. performed extracellular flux and mRNA transcript experiments. F.M.M. and E.F.M. performed and provided expertise in cell staining for light microscopy. A.C. provided expertise for and performed animal study monitoring. E.F.M. performed light microscopy and pathological examination of MC38 tumours. A. Abraham conducted transcriptomic analysis. B.I.R. and M.Z.M. analysed all data generated in this study. J.C.R. and W.K.R. obtained funding for this study.
Corresponding authors
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Competing interests
J.C.R. has held stock equity in Sitryx and within the past two years has received unrelated research support, travel and honoraria from Sitryx, Caribou, Nirogy, Kadmon, Calithera, Tempest, Merck, Mitobridge and Pfizer. Within the past two years, W.K.R. has received unrelated clinical research support from Bristol-Meyers Squib, Merck, Pfizer, Peloton, Calithera and Incyte. H.C.M. holds a patent for V9302 (WO 2018/107173 Al). M.G.V.H. is a founder of Auron Therapeutics and is a member of the Scientific Advisory Board for Agios Pharmaceuticals, Aeglea Biotherapeutics and iTeos Therapeutics.
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Extended data figures and tables
Extended Data Fig. 1 Supporting data for Fig. 1.
a–f, Fraction purity, viability and yield for MC38 (a; n = 5 mice), CT26 (b; n = 4 mice), and Renca (c; n = 4 mice) subcutaneous tumours; intrarenal Renca tumours (d; n = 3 mice); AOM/DSS CRC tumours (e; n = 6 mice for tumours, n = 11 mice for spleens); and spontaneous PyMT GEMM tumours (f; n = 3 mice). g, Representative flow cytometry analysis of PyMT and AOM/DSS CRC whole tumour, CD45+ immune cell and EPCAM+ cancer cell fractions gated on live cells. Each data point represents a biological replicate; data are mean ± s.e.m. Data are representative studies performed independently at least twice.
Extended Data Fig. 2 Validation of in vivo cellular FDG uptake assay.
a, Intravenous (IV) anti-CD45 PE staining of leukocytes from designated tissues gated on live CD45+ cells. b, Demonstration of dynamic range of 18F quantification using serially diluted in vivo FDG-labelled splenocytes. c, Correlation plots of CPM per million live cells versus cell viability, cells counted and tumour mass across multiple tumour cell populations. Only the CD45− and other CD45+ simple linear regressions had slopes significantly different than 0 for tumour mass (n = 10 mice). d, FDG-labelled digest supernatant from in vivo labelled MC38 tumours was applied to FDG-naive MC38 tumour single-cell suspensions to determine the contribution of ex vivo background FDG uptake to the final signal. e, Cellular FDG avidity in designated ex vivo and in vivo labelled MC38 tumour cell populations (n = 4 mice/group). f, Cellular FDG avidity in designated tumour cell fractions from MC38 THY1.1 tumours (n = 2 mice). g, Proportion of CD45+ and THY1.1+ cells, cell viability and live-cell yield from MC38 THY1.1 tumours (n = 2 mice for tumours; n = 5 mice for spleens). h, Representative flow cytometry analysis of MC38 THY1.1 tumour fractions. Each data point represents a biological replicate; data are mean ± s.e.m. Data in b, d–h are from a representative study performed independently at least twice.
Extended Data Fig. 3 In vivo 2-NBDG uptake does not mirror FDG uptake.
a, Representative histograms of in vivo 2-NBDG uptake in splenic and MC38 tumour cell subsets. b, MFI of in vivo 2-NBDG uptake across spleen and MC38 tumour cells (n = 3 mice). c, d, Representative histograms of 2-NBDG uptake in vivo in splenic CD4+ (c) and CD8+ (d) T cells. e, 2-NBDG staining in splenic CD4+ and CD8+ subsets (n = 3 mice). CM, central memory T cell; EM, effector memory T cell; N, naive T cell; Tconv, conventional CD4+ T cell; Treg, regulatory CD4+ T cell. f, Schema for 2-NBDG and FDG co-injection experiment. g, Representative histogram of 2-NBDGhi and 2-NBDGlo populations collected through flow sorting. h, Per-cell FDG avidity of flow-sorted 2-NBDGlo versus 2-NBDGho splenic T cells (n = 3 mice). Each data point represents a biological replicate; data are mean ± s.e.m. Data are from representative studies performed independently at least twice. P values were calculated using the Brown–Forsythe and Welch ANOVA with Dunnett’s T3 for multiple comparison tests (b, e), two-tailed Welch’s t-test for CD4+ comparisons (e) or a paired t-test (h); *P < 0.05, **P < 0.01, ***P < 0.001; exact P values are provided in the Source Data.
Extended Data Fig. 4 Spatial organization of immune cells in subcutaneous MC38 tumours.
Representative micrographs of H&E and indicated IHC stains of subcutaneous MC38 tumours. Arrows indicate positive cells on faint CD11B stain. The centre column is a low-power overview (scale bars, 200 μm). Insets demonstrate high-power images from central (left) and peripheral (right) tumour locations (scale bars, 20 μm). Images are representative of five biological replicates.
Extended Data Fig. 5 Tumour model characterizations by flow cytometry.
a–g, Spleen and tumour CD45+ immune cell populations from MC38 (a; n = 3 mice), CT26 (b; n = 4 mice) and Renca (c; n = 4 mice) subcutaneous tumours; intrarenal Renca tumours (d; n = 3 mice); spontaneous PyMT GEMM tumours (e; n = 3 mice); AOM/DSS CRC tumours (f; n = 6 mice for tumours; n = 11 mice for spleens); and MC38 subcutaneous tumours grown in Rag1−/− mice (g; (n = 6 mice). DC, dendritic cell; NK cell: natural killer cell, PMN-MDSC, polymorphonuclear myeloid-derived suppressor cell. h, Gating strategy for immune cell identification using lymphocyte and myeloid-focused antibody panels. Each data point represents a biological replicate; data are mean ± s.e.m. Data in a–f are representative of independent experiments performed at least twice.
Extended Data Fig. 6 Supporting data for Fig. 2.
a, b, Fraction composition, viability and live-cell yield from MC38 tumour fractions isolated using CD4+/CD8+ microbeads (n = 3 mice for tumours; n = 4 mice for spleens) (a) and CD11B+ microbeads (n = 4 mice) (b). c, d, Cellular FDG avidity in designated CT26 tumour cell fractions using CD4+/CD8+ microbeads (n = 5 mice for whole spleens; n = 3 mice for spleen fraction, other CD45+ and whole tumours; n = 4 mice for all others) (c) and CD11B+ microbeads (n = 5 mice for spleen fraction; n = 3 mice for whole tumours; n = 4 mice for all others) (d). e, f, Fraction composition, viability and live-cell yield from MC38 tumour fractions isolated using GR1+ microbeads (e) and F4/80+ microbeads (f) (n = 4 mice). g, Cellular FDG avidity in designated MC38 tumour cell fractions from Rag1−/− mice (n = 6 mice). h, Cellular FDG avidity in MC38 tumour cell fractions using CD11B+ and CD11C+ microbeads (n = 9 mice for whole spleens; n = 5 mice for spleen fraction; n = 10 mice for all others). i, Fraction composition of CD11C+ purification (n = 9 mice for whole spleens; n = 5 mice for spleen fraction, n = 10 mice for all others). j, Representative flow cytometry illustrating CD103 and LY6C staining of cDC (CD45+CD11B−CD11c+MHCII+ cells) from MC38 tumour and spleen. Each data point represents a biological replicate; data are mean ± s.e.m. Data are representative of independent experiments performed at least twice. Data in h are from two independent experiments. P values were calculated using Welch’s two-tailed t-test; *P < 0.05. **P < 0.01, ***P < 0.001; exact P values are provided in the Source Data.
Extended Data Fig. 7 Supporting data for Fig. 3.
a, pS6 levels in CT26 tumour populations (n = 5 mice). b, MC38 tumour mass at study end-point with rapamycin (n = 20 mice for vehicle; n = 19 mice for rapamycin). c, Metabolite concentrations in tumour interstitial fluid and matched plasma from MC38-tumour-bearing mice treated with rapamycin or vehicle (n = 5 mice except for lactate and glutamine plasma and TIF vehicle, n = 4 mice). d, Immune cell infiltration of MC38 tumours from mice treated with rapamycin or vehicle (n = 15 mice for vehicle; n = 19 mice for rapamycin). Statistical significance between rapamycin and vehicle treatment for individual populations is indicated. A significant decrease in total CD45+ cell infiltration is noted. e, f, Flow cytometry quantification of Ki67 positivity (e) and cell size (forward scatter, FSC) (f) from MC38 tumour populations in mice treated with rapamycin or vehicle (n = 4 mice for vehicle; n = 5 mice for rapamycin). g–k, MC38 tumour CD3+CD8A+ T cell phenotypes from rapamycin- or vehicle-treated mice for effector memory phenotype (g), ex vivo IFNγ production (h), PD1 and TIM3 expression (i), LAG3 expression (j) (n = 5 mice per group) and ratio of CD8+ T cells to CD4+FOXP3+ Treg (k) (n = 15 mice for vehicle; n = 19 mice for rapamycin). l, % M2-like TAMs (CD11CloCD206hi) in MC38 tumours from mice treated with rapamycin or vehicle (n = 15 mice for vehicle; n = 19 mice for rapamycin). m, n, Myeloid suppression assay representative histogram of CD8A+ OT-I T cell dilution of CellTrace Violet (CTV), indicative of proliferation (m), and quantification of division index (n) for MC38 tumour myeloid cells isolated using CD11B+ microbeads from rapamycin- and vehicle-treated mice (n = 5 mice per group). Each data point represents a biological replicate; data are mean ± s.e.m. Data in a, e–j are representative of independent experiments performed at least twice; b, d, k–l display data merged from four independent experiments. P values were calculated using the Brown-Forsythe and Welch ANOVA with Dunnett’s T3 for multiple comparison tests (a) or Welch’s two-tailed t-test (b–l, n); *P < 0.05. **P < 0.01, ***P < 0.001; exact P values are provided in the Source Data.
Extended Data Fig. 8 Metabolic transcriptional signatures of MC38 tumour cell populations.
a, Cell sorting scheme of MC38 tumour cell populations used for mRNA transcript analyses. b, Clustering analysis heat map of differentially expressed metabolic genes from MC38 tumour cell populations. Selected genes are annotated. c, Reactome gene set enrichment analysis for genes most highly expressed in each MC38 tumour population. Significantly enriched gene sets are shown and coloured according to metabolic pathway. OXPHOS, oxidative phosphorylation; TCA, tricarboxylic acid cycle.
Extended Data Fig. 9 Effect of rapamycin on metabolic markers of MC38 tumour cell populations.
a–e, Heat maps of significantly altered metabolic genes between rapamycin- and vehicle-treated MC38 tumour cell populations for the indicated metabolic pathways. White spaces indicate non-significant changes with rapamycin treatment for that gene and tumour cell population. Genes were grouped and classified manually. (n = 3 per group, except n = 2 for rapamycin-treated M-MDSCs and CD4+ T cells). AA, amino acid; FAO, fatty acid oxidation; NT, nucleotide; PPP, pentose phosphate pathway; PTGS, prostaglandin synthases; reg, regulatory genes; RNR, ribonucleotide reductase; SLCs, solute carrier proteins. f, Flow cytometry quantification of GLUT1 expression in MC38 tumour populations from mice treated with rapamycin or vehicle (n = 4 mice for vehicle; n = 5 mice for rapamycin). Each data point represents a biological replicate; data are mean ± s.e.m; exact P values are provided in the Source Data.
Extended Data Fig. 10 Supporting data for Fig. 4.
a, b, Representative histograms (a) and quantification (b) for ex vivo staining of C16 BODIPY by indicated MC38 tumour cell populations from tumour single-cell suspensions (n = 5 mice). c, Per cent contribution to total tumour C16 BODIPY signal from indicated tumour cell populations (n = 5 mice). d–f, Cellular 18F-Gln avidity in designated tumour cell fractions in CT26 (d; n = 4 mice for spleen; n = 3 mice for tumour) and Renca (e; n = 5 mice) subcutaneous tumours and AOM/DSS spontaneous tumours (f; n = 4 mice). g, MC38 tumour mass from mice treated with V9302 or DMSO (n = 13 mice for V9302; n = 12 mice for DMSO). h, Immune cell infiltration of MC38 tumours from mice treated with V9302 or DMSO (n = 13 mice for V9302; n = 12 mice for DMSO). Statistical significance between V9302 and DMSO treatment in distinct populations is indicated. There is no significant change in total CD45+ cell infiltration (n = 13 mice for V9302; n = 12 mice for DMSO). i, j, Representative plot (i) and abundance (j) of MC38 M2-like TAMs from mice treated with V9302 or DMSO (n = 13 mice for V9302; n = 12 mice for DMSO). Each data point represents a biological replicate; data are mean ± s.e.m. Data are representative of at least two independent experiments; g–j are data combined from two experiments. P values were calculated using the Brown-Forsythe and Welch ANOVA with Dunnett’s T3 for multiple comparison tests (b, c) or Welch’s two-tailed t-test (d–j); *P < 0.05, **P < 0.01, ***P < 0.001; exact P values are provided in the Source Data.
Supplementary information
Supplementary Figures
Additional Flow Cytometry Gating. Gating schemes for Figure 3, and extended data figures 3/7.
Supplementary Table 1
ccRCC Patient Characteristics.
Supplementary Table 2
mRNA transcript data from flow-sorted MC38 tumour populations. TAM=Tumour Associated Macrophages (CD11b+ Ly6C/Glo F4/80+), M-MDSC= Monocytic-Myeloid Derived Suppressor Cell (CD11b+ Ly6C+), Wh tumour=unfractionated whole tumour.
Supplementary Table 3
Genes most highly expressed in distinct MC38 tumour populations.
Supplementary Table 4
Effect of rapamycin on MC38 tumour cell mRNA transcripts. Includes gene selected for metabolic clustering in Figure 3I.
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Reinfeld, B.I., Madden, M.Z., Wolf, M.M. et al. Cell-programmed nutrient partitioning in the tumour microenvironment. Nature 593, 282–288 (2021). https://doi.org/10.1038/s41586-021-03442-1
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DOI: https://doi.org/10.1038/s41586-021-03442-1
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