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Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth

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  • Corresponding author: nielsenj@chalmers.se
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    1. ■ Simplified application of enzyme usage constraints to genome-scale metabolic models with the software GECKO Light.
    2. ■ Estimation of maximum metabolite influx into tumors at varying levels of hypoxia.
    3. ■ Simulations of tumor metabolism explain "glutamine addiction" in cancers.
    4. ■ Metabolic collaboration between cell types in the tumor microenvironment does not increase tumor growth.
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  • [1] Forster, J.C., Harriss-Phillips, W.M., Douglass, M.J., et al. (2017). A review of the development of tumor vasculature and its effects on the tumor microenvironment. Hypoxia 5: 21–32. https://doi.org/10.2147/HP.S133231.

    View in Article CrossRef Google Scholar

    [2] Nagy, J.A., Chang, S.-H., Dvorak, A.M., et al. (2009). Why are tumour blood vessels abnormal and why is it important to know? Br. J. Cancer 100: 865–869. https://doi.org/10.1038/sj.bjc. 6604929.

    View in Article CrossRef Google Scholar

    [3] Jain, R.K., Martin, J.D., Chauhan, V.P., et al. (2020). 8 - Tumor Microenvironment: Vascular and Extravascular Compartment. In Abeloff’s Clinical Oncology, Sixth Edition, J.E. Niederhuber, J.O. Armitage, and M.B. Kastan, et al., eds. (Elsevier), pp. 108–126.e7. https://doi.org/10.1016/B978-0-323-47674-4.00008-6.

    View in Article Google Scholar

    [4] Sefidgar, M., Soltani, M., Raahemifar, K., et al. (2014). Effect of tumor shape, size, and tissue transport properties on drug delivery to solid tumors. J. Biol. Eng. 8: 12. https://doi.org/10.1186/1754-1611-8-12.

    View in Article CrossRef Google Scholar

    [5] Busk, M., Overgaard, J., and Horsman, M.R. (2020). Imaging of Tumor Hypoxia for Radiotherapy: Current Status and Future Directions. Semin. Nucl. Med. 50: 562–583. https://doi.org/10.1053/j.semnuclmed.2020.05.003.

    View in Article CrossRef Google Scholar

    [6] Liberti, M.V., and Locasale, J.W. (2016). The Warburg Effect: How Does it Benefit Cancer Cells? Trends Biochem. Sci. 41: 211–218. https://doi.org/10.1016/j.tibs.2015.12.001.

    View in Article CrossRef Google Scholar

    [7] Cammann, C., Rath, A., Reichl, U., et al. (2016). Early changes in the metabolic profile of activated CD8+ T cells. BMC Cell Biol. 17: 28. https://doi.org/10.1186/s12860-016-0104-x.

    View in Article CrossRef Google Scholar

    [8] Wise, D.R., and Thompson, C.B. (2010). Glutamine Addiction: A New Therapeutic Target in Cancer. Trends Biochem. Sci. 35: 427–433. https://doi.org/10.1016/j.tibs.2010.05.003.

    View in Article CrossRef Google Scholar

    [9] Nilsson, A., Björnson, E., Flockhart, M., et al. (2019). Complex I is bypassed during high intensity exercise. Nat. Commun. 10: 5072. https://doi.org/10.1038/s41467-019-12934-8.

    View in Article CrossRef Google Scholar

    [10] Martinez-Outschoorn, U.E., Lisanti, M.P., and Sotgia, F. (2014). Catabolic cancer-associated fibroblasts transfer energy and biomass to anabolic cancer cells, fueling tumor growth. Semin. Cancer Biol. 25: 47–60. https://doi.org/10.1016/j.semcancer.2014.01.005.

    View in Article CrossRef Google Scholar

    [11] Pavlides, S., Whitaker-Menezes, D., Castello-Cros, R., et al. (2009). The reverse Warburg effect: Aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle 8: 3984–4001. https://doi.org/10.4161/cc.8.23.10238.

    View in Article CrossRef Google Scholar

    [12] Zhao, H., Yang, L., Baddour, J., et al. (2016). Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism. Elife 5: e10250. https://doi.org/10.7554/eLife.10250.

    View in Article CrossRef Google Scholar

    [13] Avagliano, A., Granato, G., Ruocco, M.R., et al. (2018). Metabolic Reprogramming of Cancer Associated Fibroblasts: The Slavery of Stromal Fibroblasts. BioMed Res. Int. 2018: 6075403. https://doi.org/10.1155/2018/6075403.

    View in Article CrossRef Google Scholar

    [14] Hui, S., Ghergurovich, J.M., Morscher, R.J., et al. (2017). Glucose feeds the TCA cycle via circulating lactate. Nature 551: 115–118. https://doi.org/10.1038/nature24057.

    View in Article CrossRef Google Scholar

    [15] Orth, J.D., Thiele, I., and Palsson, B.Ø. (2010). What is flux balance analysis? Nat. Biotechnol. 28: 245–248. https://doi.org/10.1038/nbt.1614.

    View in Article CrossRef Google Scholar

    [16] Lewis, J.E., Forshaw, T.E., Boothman, D.A., et al. (2021). Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors. Cell Syst. 12: 68–81.e11. https://doi.org/10.1016/j.cels.2020.12.001.

    View in Article CrossRef Google Scholar

    [17] Wang, H., Robinson, J.L., Kocabas, P., et al. (2021). Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc. Natl. Acad. Sci. USA 118: e2102344118. https://doi.org/10.1073/pnas.2102344118.

    View in Article CrossRef Google Scholar

    [18] Sánchez, B.J., Zhang, C., Nilsson, A., et al. (2017). Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol. Syst. Biol. 13: 935. https://doi.org/10.15252/msb.20167411.

    View in Article CrossRef Google Scholar

    [19] Robinson, J.L., Kocabaş, P., Wang, H., et al. (2020). An atlas of human metabolism. Sci. Signal. 13: eaaz1482. https://doi.org/10.1126/scisignal.aaz1482.

    View in Article CrossRef Google Scholar

    [20] Opdam, S., Richelle, A., Kellman, B., et al. (2017). A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models. Cell Syst. 4: 318–329.e6. https://doi.org/10.1016/j.cels.2017.01.010.

    View in Article CrossRef Google Scholar

    [21] Kilburn, D.G., Lilly, M.D., and Webb, F.C. (1969). The Energetics of Mammalian Cell Growth. J. Cell Sci. 4: 645–654.

    View in Article CrossRef Google Scholar

    [22] Domenzain, I., Sánchez, B., Anton, M., et al. (2021). Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Preprint at bioRxiv. https://doi.org/10.1101/2021.03.05.433259.

    View in Article CrossRef Google Scholar

    [23] Chang, A., Jeske, L., Ulbrich, S., et al. (2021). BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49: D498–D508. https://doi.org/10.1093/nar/gkaa1025.

    View in Article CrossRef Google Scholar

    [24] Liu, D., Chalkidou, A., Landau, D.B., et al. (2014). Interstitial diffusion and the relationship between compartment modelling and multi-scale spatial-temporal modelling of 18 F-FLT tumour uptake dynamics. Phys. Med. Biol. 59: 5175–5202. https://doi.org/10.1088/0031-9155/59/17/5175.

    View in Article CrossRef Google Scholar

    [25] Harada, S., Hirayama, A., Chan, Q., et al. (2018). Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry. PLoS One 13: e0191230. https://doi.org/10.1371/journal.pone.0191230.

    View in Article CrossRef Google Scholar

    [26] Hoogenboezem, E.N., and Duvall, C.L. (2018). Harnessing Albumin as a Carrier for Cancer Therapies. Adv. Drug Deliv. Rev. 130: 73–89. https://doi.org/10.1016/j.addr.2018.07.011.

    View in Article CrossRef Google Scholar

    [27] Quehenberger, O., Armando, A.M., Brown, A.H., et al. (2010). Lipidomics reveals a remarkable diversity of lipids in human plasma1[S]. J. Lipid Res. 51: 3299–3305. https://doi.org/10.1194/jlr.M009449.

    View in Article CrossRef Google Scholar

    [28] Siggaard-andersen, O., Gøthgen, I.H., Wimberley, P.D., et al. (1990). The oxygen status of the arterial blood revised: Relevant oxygen parameters for monitoring the arterial oxygen availability. Scand. J. Clin. Lab. Invest. 203: 17–28. https://doi.org/10.3109/00365519009087488.

    View in Article CrossRef Google Scholar

    [29] Zhang, X., Li, C.-G., Ye, C.-H., et al. (2001). Determination of Molecular Self-Diffusion Coefficient Using Multiple Spin-Echo NMR Spectroscopy with Removal of Convection and Background Gradient Artifacts. Anal. Chem. 73: 3528–3534. https://doi.org/10.1021/ac0101104.

    View in Article CrossRef Google Scholar

    [30] Chary, S.R., and Jain, R.K. (1989). Direct measurement of interstitial convection and diffusion of albumin in normal and neoplastic tissues by fluorescence photobleaching. Proc. Natl. Acad. Sci. USA 86: 5385–5389. https://doi.org/10.1073/pnas.86.14.5385.

    View in Article CrossRef Google Scholar

    [31] Goldstick, T.K., Ciuryla, V.T., and Zuckerman, L. (1976). Diffusion of oxygen in plasma and blood. Adv. Exp. Med. Biol. 75: 183–190. https://doi.org/10.1007/978-1-4684-3273-2_23.

    View in Article CrossRef Google Scholar

    [32] Valencia, D.P., and González, F.J. (2012). Estimation of diffusion coefficients by using a linear correlation between the diffusion coefficient and molecular weight. J. Electroanal. Chem. 681: 121–126. https://doi.org/10.1016/j.jelechem.2012.06.013.

    View in Article CrossRef Google Scholar

    [33] Fan, J., Kamphorst, J.J., Mathew, R., et al. (2013). Glutamine-driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxia. Mol. Syst. Biol. 9: 712. https://doi.org/10.1038/msb.2013.65.

    View in Article CrossRef Google Scholar

    [34] Seidlitz, E.P., Sharma, M.K., Saikali, Z., et al. (2009). Cancer cell lines release glutamate into the extracellular environment. Clin. Exp. Metastasis 26: 781–787. https://doi.org/10.1007/s10585-009-9277-4.

    View in Article CrossRef Google Scholar

    [35] Nilsson, A., Haanstra, J.R., Engqvist, M., et al. (2020). Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis. Proc. Natl. Acad. Sci. USA 117: 10294–10304. https://doi.org/10.1073/pnas.1919250117.

    View in Article CrossRef Google Scholar

    [36] Chinopoulos, C., and Seyfried, T.N. (2018). Mitochondrial Substrate-Level Phosphorylation as Energy Source for Glioblastoma: Review and Hypothesis. ASN Neuro 10: 1759091418818261. https://doi.org/10.1177/1759091418818261.

    View in Article CrossRef Google Scholar

    [37] Wang, Y., Bai, C., Ruan, Y., et al. (2019). Coordinative metabolism of glutamine carbon and nitrogen in proliferating cancer cells under hypoxia. Nat. Commun. 10: 201. https://doi.org/10.1038/s41467-018-08033-9.

    View in Article CrossRef Google Scholar

    [38] Corbet, C., and Feron, O. (2017). Tumour acidosis: from the passenger to the driver’s seat. Nat. Rev. Cancer 17: 577–593. https://doi.org/10.1038/nrc.2017.77.

    View in Article CrossRef Google Scholar

    [39] Jain, M., Nilsson, R., Sharma, S., et al. (2012). Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336: 1040–1044. https://doi.org/10.1126/science.1218595.

    View in Article CrossRef Google Scholar

    [40] Hollinshead, K.E.R., Parker, S.J., Eapen, V.V., et al. (2020). Respiratory Supercomplexes Promote Mitochondrial Efficiency and Growth in Severely Hypoxic Pancreatic Cancer. Cell Rep. 33: 108231. https://doi.org/10.1016/j.celrep.2020.108231.

    View in Article CrossRef Google Scholar

    [41] Ma, G., Zhang, Z., Li, P., et al. (2022). Reprogramming of glutamine metabolism and its impact on immune response in the tumor microenvironment. Cell Commun. Signal. 20: 114. https://doi.org/10.1186/s12964-022-00909-0.

    View in Article CrossRef Google Scholar

    [42] Tanner, J.J., Fendt, S.-M., and Becker, D.F. (2018). The Proline Cycle As a Potential Cancer Therapy Target. Biochemistry 57: 3433–3444. https://doi.org/10.1021/acs.biochem.8b00215.

    View in Article CrossRef Google Scholar

    [43] Moxley, M.A., and Becker, D.F. (2012). Rapid Reaction Kinetics of Proline Dehydrogenase in the Multifunctional Proline Utilization A Protein. Biochemistry 51: 511–520. https://doi.org/10.1021/bi201603f.

    View in Article CrossRef Google Scholar

    [44] Quinlan, C.L., Orr, A.L., Perevoshchikova, I.V., et al. (2012). Mitochondrial Complex Ⅱ Can Generate Reactive Oxygen Species at High Rates in Both the Forward and Reverse Reactions. J. Biol. Chem. 287: 27255–27264. https://doi.org/10.1074/jbc.M112.374629.

    View in Article CrossRef Google Scholar

    [45] Spinelli, J.B., Rosen, P.C., Sprenger, H.-G., et al. (2021). Fumarate is a terminal electron acceptor in the mammalian electron transport chain. Science 374: 1227–1237. https://doi.org/10.1126/science.abi7495.

    View in Article CrossRef Google Scholar

    [46] Tedeschi, P.M., Markert, E.K., Gounder, M., et al. (2013). Contribution of serine, folate and glycine metabolism to the ATP, NADPH and purine requirements of cancer cells. Cell Death Dis. 4: e877. https://doi.org/10.1038/cddis.2013.393.

    View in Article CrossRef Google Scholar

    [47] Jung, J.G., and Le, A. (2021). Targeting Metabolic Cross Talk Between Cancer Cells and Cancer-Associated Fibroblasts. In The Heterogeneity of Cancer Metabolism Advances in Experimental Medicine and Biology, A. Le, ed. (Springer International Publishing), pp. 205–214. https://doi.org/10.1007/978-3-030-65768-0_15.

    View in Article Google Scholar

    [48] Sousa, C.M., Biancur, D.E., Wang, X., et al. (2016). Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 536: 479–483. https://doi.org/10.1038/nature19084.

    View in Article CrossRef Google Scholar

    [49] Capparelli, C., Guido, C., Whitaker-Menezes, D., et al. (2012). Autophagy and senescence in cancer-associated fibroblasts metabolically supports tumor growth and metastasis, via glycolysis and ketone production. Cell Cycle 11: 2285–2302. https://doi.org/10.4161/cc.20718.

    View in Article CrossRef Google Scholar

    [50] Sonveaux, P., Végran, F., Schroeder, T., et al. (2008). Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. J. Clin. Invest. 118: 3930–3942. https://doi.org/10.1172/JCI36843.

    View in Article CrossRef Google Scholar

    [51] de la Cruz-López, K.G., Castro-Muñoz, L.J., Reyes-Hernández, D.O., et al. (2019). Lactate in the Regulation of Tumor Microenvironment and Therapeutic Approaches. Front. Oncol. 9: 1143. https://doi.org/10.3389/fonc.2019.01143.

    View in Article CrossRef Google Scholar

    [52] Ohl, K., and Tenbrock, K. (2018). Reactive Oxygen Species as Regulators of MDSC-Mediated Immune Suppression. Front. Immunol. 9: 2499.

    View in Article Google Scholar

    [53] Vitale, I., Manic, G., Coussens, L.M., et al. (2019). Macrophages and Metabolism in the Tumor Microenvironment. Cell Metabol. 30: 36–50. https://doi.org/10.1016/j.cmet.2019.06.001.

    View in Article CrossRef Google Scholar

    [54] Lin, Y., Xu, J., and Lan, H. (2019). Tumor-associated macrophages in tumor metastasis: biological roles and clinical therapeutic applications. J. Hematol. Oncol. 12: 76. https://doi.org/10.1186/s13045-019-0760-3.

    View in Article CrossRef Google Scholar

    [55] Guido, C., Whitaker-Menezes, D., Capparelli, C., et al. (2012). Metabolic reprogramming of cancer-associated fibroblasts by TGF-b drives tumor growth: connecting TGF-b signaling with “Warburg-like” cancer metabolism and L-lactate production. Cell Cycle 11: 3019–3035. https://doi.org/10.4161/cc.21384.

    View in Article CrossRef Google Scholar

    [56] Faubert, B., Li, K.Y., Cai, L., et al. (2017). Lactate Metabolism in Human Lung Tumors. Cell 171: 358–371.e9. https://doi.org/10.1016/j.cell.2017.09.019.

    View in Article CrossRef Google Scholar

    [57] Hosios, A.M., Hecht, V.C., Danai, L.V., et al. (2016). Amino acids rather than glucose account for the majority of cell mass in proliferating mammalian cells. Dev. Cell 36: 540–549. https://doi.org/10.1016/j.devcel.2016.02.012.

    View in Article CrossRef Google Scholar

    [58] Wei, Z., Liu, X., Cheng, C., et al. (2020). Metabolism of Amino Acids in Cancer. Front. Cell Dev. Biol. 8: 603837. https://doi.org/10.3389/fcell.2020.603837.

    View in Article CrossRef Google Scholar

    [59] Yang, L., Venneti, S., and Nagrath, D. (2017). Glutaminolysis: A Hallmark of Cancer Metabolism. Annu. Rev. Biomed. Eng. 19: 163–194. https://doi.org/10.1146/annurev-bioeng-071516-044546.

    View in Article CrossRef Google Scholar

    [60] Notarangelo, G., Spinelli, J.B., Perez, E.M., et al. (2022). Oncometabolite d -2HG alters T cell metabolism to impair CD8 + T cell function. Science 377: 1519–1529. https://doi.org/10.1126/science.abj5104.

    View in Article CrossRef Google Scholar

    [61] Yang, L., Achreja, A., Yeung, T.-L., et al. (2016). Targeting Stromal Glutamine Synthetase in Tumors Disrupts Tumor Microenvironment-Regulated Cancer Cell Growth. Cell Metabol. 24: 685–700. https://doi.org/10.1016/j.cmet.2016.10.011.

    View in Article CrossRef Google Scholar

  • Cite this article:

    Johan Gustafsson, Fariba Roshanzamir, Anders Hagnestål, Sagar M. Patel, Oseeyi I. Daudu, Donald F. Becker, Jonathan L. Robinson, Jens Nielsen. Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth[J]. The Innovation, 2024, 5(2). https://doi.org/10.1016/j.xinn.2024.100583
    Johan Gustafsson, Fariba Roshanzamir, Anders Hagnestål, Sagar M. Patel, Oseeyi I. Daudu, Donald F. Becker, Jonathan L. Robinson, Jens Nielsen. Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth[J]. The Innovation, 2024, 5(2). https://doi.org/10.1016/j.xinn.2024.100583

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