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Metabolic Heterogeneity of Tumors

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Abstract—Currently, much attention in oncology is devoted to the issues of tumor heterogeneity, which creates serious problems in the diagnosis and therapy of malignant neoplasms. Intertumoral and intratumoral differences relate to various characteristics and aspects of the vital activity of tumor cells, including cellular metabolism. This review provides general information about the tumor metabolic heterogeneity with a focus on energy metabolism, its causes, mechanisms and research methods. Among the methods, fluorescence lifetime imaging is described in more detail as a new promising method for observing metabolic heterogeneity at the cellular level. The review demonstrates the importance of studying the features of tumor metabolism and identifying intra- and intertumoral metabolic differences.

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REFERENCES

  1. Viale A., Corti D., Draetta G.F. 2015. Tumors and mitochondrial respiration: A neglected connection. Cancer Res. 75, 3687.

    Article  CAS  Google Scholar 

  2. Bensinger S.J., Christofk H.R. 2012. New aspects of the Warburg effect in cancer cell biology. Semin. Cell Dev. Biol. 23, 352–361.

    Article  CAS  PubMed  Google Scholar 

  3. Solaini G., Sgarbi G., Baracca A. 2011. Oxidative phosphorylation in cancer cells. Biochim. Biophys. Acta, Bioenerg. 1807, 534–542.

    Article  CAS  Google Scholar 

  4. Cluntun A.A., Lukey M.J., Cerione R.A., Locasale J.W. 2017. Glutamine metabolism in cancer: Understanding the heterogeneity. Trends Cancer. 3, 169–180.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cao Y. 2019. Adipocyte and lipid metabolism in cancer drug resistance. J. Clin. Invest. 129, 3006–3017.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gerashchenko T.S., Denisov E.V., Litvyakov N.V., Zavyalova M.V., Vtorushin S.V., Tsyganov M.M., Perelmuter V.M., Cherdyntseva N.V. 2013. Intratumoral heterogeneity: Nature and biological significance. Biochemistry (Moscow). 78, 1531–1549.

    Google Scholar 

  7. Nassar A., Radhakrishnan A., Cabrero I.A., Cotsonis G.A., Cohen C. 2010. Intratumoral heterogeneity of immunohistochemical marker expression in breast carcinoma: A tissue microarray-based study. Appl. Immunohistochem. Mol. Morphol. 18, 433–441.

    Article  CAS  PubMed  Google Scholar 

  8. Somasundaram R., Villanueva J., Herlyn M. 2012. Intratumoral heterogeneity as a therapy resistance mechanism. Adv. Pharmacol. 65, 335–359.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fisher R., Pusztai L., Swanton C. 2013. Cancer heterogeneity: Implications for targeted therapeutics. Br. J. Cancer. 108, 479–585.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Prasetyanti P.R., Medema J.P. 2017. Intra-tumor heterogeneity from a cancer stem cell perspective. Mol. Cancer. 16, 41.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Marusyk A., Polyak K. 2010. Tumor heterogeneity: Causes and consequences. Biochim. Biophys. Acta, Rev. Cancer. 1805, 105–117.

    CAS  Google Scholar 

  12. McGranahan N., Swanton C. 2017. Clonal heterogeneity and tumor evolution: Past, present, and the future. Cell. 168, 613–628.

    Article  CAS  PubMed  Google Scholar 

  13. Allis C.D., Jenuwein T. 2016. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500.

    Article  CAS  PubMed  Google Scholar 

  14. Kim J., DeBerardinis R.J. 2019. Mechanisms and implications of metabolic heterogeneity in cancer. Cell Metabolism. 30, 434–446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chekhun V.F., Sherban S.D., Savtsova Z.D. 2012. Tumor heterogeneity is a dynamic state. Onkologiya. 14, 4–12.

    Google Scholar 

  16. Hanahan D., Weinberg R.A. 2011. Hallmarks of cancer: The next generation. Cell. 144, 646–674.

    Article  CAS  PubMed  Google Scholar 

  17. Clevers H. 2011. The cancer stem cell: Premises, promises and challenges. Nat. Med. 17, 313–319.

    Article  CAS  PubMed  Google Scholar 

  18. Murata M. 2018. Inflammation and cancer. Environ. Healthcare Prev. Med. 23, 50.

    Article  Google Scholar 

  19. Xiao Z., Dai Z., Locasale J.W. 2019. Metabolic landscape of the tumor microenvironment at single cell resolution. Nat. Commun. 10, 3763.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Balkwill F., Mantovani A. 2001. Inflammation and cancer: Back to Virchow? Lancet. 357, 539–545.

    Article  CAS  PubMed  Google Scholar 

  21. Korkaya H., Kim G.I., Davis A., Malik F., Henry N.L., Ithimakin S., Quraishi A.A., Tawakkol N., D’Angelo R., Paulson A.K., Chung S., Luther T., Paholak H.J., Liu S., Hassan K.A., Zen Q., Clouthier S.G., Wicha M.S. 2012. Activation of an IL6 inflammatory loop mediates trastuzumab resistance in HER2+ breast cancer by expanding the cancer stem cell population. Mol. Cell. 47, 570–584.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Brosalov V.M., Sudapina A.R., Mikulyak N.I. 2020. Clonal evolution of breast cancer: Current perspectives on a longstanding theory (literature review). Izv. Vyssh. Uchebn. Zaved., Povolzh. Reg., Med. Nauki. 2, 109–119. 2, 109–119.

  23. Michor F., Polyak K. 2010. The origins and implications of intratumor heterogeneity. Cancer Prevention Res. 3, 1361–1364.

    Article  Google Scholar 

  24. Warmoes M.O., Locasale J.W. 2014. Heterogeneity of glycolysis in cancers and therapeutic opportunities. Biochem. Pharmacol. 92, 12–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Diaz-Ruiz R., Rigoulet M., Devin A. 2011. The Warburg and Crabtree effects: On the origin of cancer cell energy metabolism and of yeast glucose repression. Biochim. Biophys. Acta, Bioenergetics. 1807, 568–576.

    Article  CAS  Google Scholar 

  26. Vander Heiden M.G., Cantley L.C., Thompson C.B. 2009. Understanding the Warburg effect: The metabolic requirements of cell proliferation. Science. 324, 1029–1033.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Stern R., Shuster S., Neudecker B.A., Formby B. 2002. Lactate stimulates fibroblast expression of hyaluronan and CD44: The Warburg effect revisited. Exp. Cell Res. 276, 24–31.

    Article  CAS  PubMed  Google Scholar 

  28. Sonveaux P., Copetti T., De Saedeleer C.J., Végran F., Verrax J., Kennedy K.M., Moon E.J., Dhup S., Danhier P., Frérart F., Gallez B., Ribeiro A., Michiels C., Dewhirst M.W., Feron O. 2012. Targeting the lactate transporter MCT1 in endothelial cells inhibits lactate-induced HIF-1 activation and tumor angiogenesis. PLoS One. 7, e33418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cairns R.A., Harris I.S., Mak T.W. 2011. Regulation of cancer cell metabolism. Nat. Rev. Cancer. 11, 85–95.

    Article  CAS  PubMed  Google Scholar 

  30. Lunt S.Y., Vander Heiden M.G. 2011. Aerobic glycolysis: Meeting the metabolic requirements of cell proliferation. Annu. Rev. Cell Dev. Biol. 27, 441–464.

    Article  CAS  PubMed  Google Scholar 

  31. Cantor J.R., Sabatini D.M. 2012. Cancer cell metabolism: One hallmark, many faces. Cancer Discov. 2, 881–898.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Smolková K., Bellance N., Scandurra F., Génot E., Gnaiger E., Plecitá-Hlavatá L., Ježek P., Rossignol R. 2010. Mitochondrial bioenergetic adaptations of breast cancer cells to aglycemia and hypoxia. J. Bioenerg. Biomembr. 42, 55–67.

    Article  PubMed  Google Scholar 

  33. Pavlova N.N., Thompson C.B. 2016. The emerging hallmarks of cancer metabolism. Cell Metabolism. 23, 27–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sengupta D., Pratx G. 2016. Imaging metabolic heterogeneity in cancer. Mol. Cancer. 15, 4.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Sancho P., Barneda D., Heeschen C. 2016. Hallmarks of cancer stem cell metabolism. Br. J. Cancer. 114, 1305–1312.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ganapathy-Kanniappan S. 2018. Molecular intricacies of aerobic glycolysis in cancer: Current insights into the classic metabolic phenotype. Crit. Rev. Biochem. Mol. Biol. 53, 667–682.

    Article  CAS  PubMed  Google Scholar 

  37. Seth Nanda C., Venkateswaran S.V., Patani, N., Yuneva M. 2020. Defining a metabolic landscape of tumours: Genome meets metabolism. Br. J. Cancer. 122, 136–149.

    Article  CAS  PubMed  Google Scholar 

  38. Harami-Papp H., Pongor L.S., Munkacsy G., Horvath G., Nagy A.M., Ambrus A., Hauser P., Szabo A., Tretter L., Gyorffy, B. 2016. TP53 mutation hits energy metabolism and increases glycolysis in breast cancer. Oncotarget7, 67183–67195.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Zhang X., Wang J., Zhuang J., Liu C., Gao C., Li H., Ma X., Li J., Sun C. 2021. A novel glycolysis-related four-mRNA signature for predicting the survival of patients with breast cancer. Front. Genet. 12, 606937.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Farhadi P., Yarani R., Valipour E., Kiani S., Hoseinkhani Z., Mansouri K. 2022. Cell line-directed breast cancer research based on glucose metabolism status. Biomed. Pharmacotherapy. 146, 112526.

    Article  CAS  Google Scholar 

  41. The Cancer Genome Atlas Research Network 2014. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 511, 543–550.

    Article  PubMed Central  Google Scholar 

  42. Romero R., Sayin V.I., Davidson S.M., Bauer M.R., Singh S.X., LeBoeuf S.E., Karakousi T.R., Ellis D.C., Bhutkar A., Sánchez-Rivera F.J., Subbaraj L., Martinez B., Bronson R.T., Prigge J.R., Schmidt E.E., Thomas C.J., Goparaju C., Davies A., Dolgalev I., Heguy A., Allaj V., Poirier J.T., Moreira A.L., Rudin C.M., Pass H.I., Vander Heiden M.G., Jacks T., Papagiannakopoulos T. 2017. Keap1 loss promotes Kras-driven lung cancer and results in dependence on glutaminolysis. Nat. Med. 23, 1362–1368.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yuneva M.O., Fan T.W., Allen T.D., Higashi R.M., Ferraris D.V., Tsukamoto T., Matés J.M., Alonso F.J., Wang C., Seo Y., Chen X., Bishop J.M. 2012. The metabolic profile of tumors depends on both the responsible genetic lesion and tissue type. Cell Metabolism. 15, 157–170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Patra S., Elahi N., Armorer A., Arunachalam S., Omala J., Hamid I., Ashton A.W., Joyce D., Jiao X., Pestell R.G. 2021. Mechanisms governing metabolic heterogeneity in breast cancer and other tumors. Front. Oncol. 11, 700629.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Goel A., Mathupala S.P., Pedersen P.L. 2003. Glucose metabolism in cancer. J. Biol. Chem. 278, 15333–15340.

    Article  CAS  PubMed  Google Scholar 

  46. Chen M., Zhang J., Li N., Qian Z., Zhu M., Li Q., Zheng J., Wang X., Shi G. 2011. Promoter hypermethylation mediated downregulation of FBP1 in human hepatocellular carcinoma and colon cancer. PLoS One. 6, e25564.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jose C., Bellance N., Rossignol R. 2011. Choosing between glycolysis and oxidative phosphorylation: A tumor’s dilemma? Biochim. Biophys. Acta, Bioenergetics. 1807, 552–561.

    Article  CAS  Google Scholar 

  48. Hill R.P., De Jaeger K., Jang A., Cairns R. 2008. pH, hypoxia and metastasis. Novartis Found. Symp. 240, 154–168.

    Article  Google Scholar 

  49. Masson N., Ratcliffe P.J. 2014. Hypoxia signaling pathways in cancer metabolism: The importance of co-selecting interconnected physiological pathways. Cancer Metab. 2, 3.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Muz B., de la Puente P., Azab F., Azab A.K. 2015. The role of hypoxia in cancer progression, angiogenesis, metastasis, and resistance to therapy. Hypoxia. 3, 83–92.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kierans S.J., Taylor C.T. 2021. Regulation of glycolysis by the hypoxia-inducible factor (HIF): Implications for cellular physiology. J. Physiol. 599, 23–37.

    Article  CAS  PubMed  Google Scholar 

  52. Korbecki J., Simińska D., Gąssowska-Dobrowolska M., Listos J., Gutowska I., Chlubek D., Baranowska-Bosiacka I. 2021. Chronic and cycling hypoxia: Drivers of cancer chronic inflammation through HIF-1 and NF-κB activation: A review of the molecular mechanisms. Int. J. Mol. Sci. 22, 10701.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Vogelstein B., Kinzler K.W. 2004. Cancer genes and the pathways they control. Nat. Med. 10, 789–799.

    Article  CAS  PubMed  Google Scholar 

  54. Wieman H.L., Wofford J.A., Rathmell J.C. 2007. Cytokine stimulation promotes glucose uptake via phosphatidylinositol-3 kinase/Akt regulation of Glut1 activity and trafficking. MBoC. 18, 1437–1446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wise D.R., DeBerardinis R.J., Mancuso A., Sayed N., Zhang X.Y., Pfeiffer H.K., Nissim I., Daikhin E., Yudkoff M., McMahon S.B., Thompson C.B. 2008. Myc regulates a transcriptional program that stimulates mitochondrial glutaminolysis and leads to glutamine addiction. Proc. Natl. Acad. Sci. U. S. A. 105, 18782–18787.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Gao P., Tchernyshyov I., Chang T.C., Lee Y.S., Kita K., Ochi T., Zeller K.I., De Marzo A.M., Van Eyk J.E., Mendell J.T., Dang C.V. 2009. c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature. 458, 762–765.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Reynolds M.R., Lane A.N., Robertson B., Kemp S., Liu Y., Hill B.G., Dean D.C., Clem B.F. 2014. Control of glutamine metabolism by the tumor suppressor Rb. Oncogene. 33, 556–566.

    Article  CAS  PubMed  Google Scholar 

  58. Méndez-Lucas A., Lin W., Driscoll P.C., Legrave N., Novellasdemunt L., Xie C., Charles M., Wilson Z., Jones N.P., Rayport S., Rodríguez-Justo M., Li V., MacRae J.I., Hay N., Chen X., Yuneva M. 2020. Identifying strategies to target the metabolic flexibility of tumours. Nat. Metabolism. 2, 335–350.

    Article  Google Scholar 

  59. Yoshida G.J. 2015. Metabolic reprogramming: The emerging concept and associated therapeutic strategies. J. Exp. Clin. Cancer Res. 34, 111.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Sonveaux P., Végran F., Schroeder T., Wergin M.C., Verrax J., Rabbani Z.N., De Saedeleer C.J., Kennedy K.M., Diepart C., Jordan B.F., Kelley M.J., Gallez B., Wahl M.L., Feron O., Dewhirst M.W. 2008. Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. J. Clin. Invest. 118 (12), 3930–3942.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Meshcheryakova O.V., Churova M.V., Nemova N.N. 2010. Mitochondrial lactate-oxidizing complex and its importance for maintaining cell energy homeostasis. In Sovremennye problemy fiziologii i biokhimii vodnykh organizmov: sbornik nauchnykh statei (Modern Problems of Physiology and Biochemistry of Aquatic Organisms: A Collection of Scientific Articles). Petrozavodsk: Karel. Nauchn. Tsentr Ross. Akad. Nauk, 163–171.

  62. Stine Z.E., Walton Z.E., Altman B.J., Hsieh A.L., Dang C.V. 2015. MYC, metabolism, and cancer. Cancer Discovery 5, 1024–1039.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Gordan J.D., Thompson C.B., Simon M.C. 2007. HIF and c-Myc: Sibling rivals for control of cancer cell metabolism and proliferation. Cancer Cell. 12, 108–113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Pavlides S., Whitaker-Menezes D., Castello-Cros R., Flomenberg N., Witkiewicz A.K., Frank P.G., Casimiro M.C., Wang C., Fortina P., Addya S., Pestell R.G., Martinez-Outschoorn U.E., Sotgia F., Lisanti M.P. 2009. The reverse Warburg effect: Aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle. 8, 3984–4001.

    Article  CAS  PubMed  Google Scholar 

  65. Dong C., Yuan T., Wu Y., Wang Y., Fan T.W., Miriyala S., Lin Y., Yao J., Shi J., Kang T., Lorkiewicz P., St Clair D., Hung M.C., Evers B.M., Zhou B.P. 2013. Loss of FBP1 by snail-mediated repression provides metabolic advantages in basal-like breast cancer. Cancer Cell. 23, 316–331.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Shen Y.-A., Wang C.-Y., Hsieh Y.-T., Chen Y.-J., Wei Y.-H. 2015. Metabolic reprogramming orchestrates cancer stem cell properties in nasopharyngeal carcinoma. Cell Cycle. 14, 86–98.

    Article  PubMed  Google Scholar 

  67. Chen C.-L., Uthaya Kumar D.B., Punj V., Xu J., Sher L., Tahara S.M., Hess S., Machida K. 2016. Metabolically reprograms tumor-initiating stem-like cells through tumorigenic changes in oxidative phosphorylation and fatty acid metabolism. Cell Metab. 23, 206–219.

    Article  CAS  PubMed  Google Scholar 

  68. Ye X.-Q., Li Q., Wang G.-H., Sun F.-F., Huang G.-J., Bian X.-W., Yu S.-C., Qian G.-S. 2011. Mitochondrial and energy metabolism-related properties as novel indicators of lung cancer stem cells. Int. J. Cancer. 129, 820–831.

    Article  CAS  PubMed  Google Scholar 

  69. Janiszewska M., Suvà M.L., Riggi N., Houtkooper R.H., Auwerx J., Clément-Schatlo V., Radovanovic I., Rheinbay E., Provero P., Stamenkovic I. 2012. Imp2 controls oxidative phosphorylation and is crucial for preserving glioblastoma cancer stem cells. Genes Dev. 26, 1926–1944.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Badr C.E., Silver D.J., Siebzehnrubl F.A., Deleyrolle L.P. 2020. Metabolic heterogeneity and adaptability in brain tumors. Cell. Mol. Life Sci. 77, 5101–5119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Shah A.T., Diggins K.E., Walsh A.J., Irish J.M., Skala M.C. 2015. In vivo autofluorescence imaging of tumor heterogeneity in response to treatment. Neoplasia. 17, 862–870.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Haffner M.C., Zwart W., Roudier M.P., True L.D., Nelson W.G., Epstein J.I., De Marzo A.M., Nelson P.S., Yegnasubramanian S. 2021. Genomic and phenotypic heterogeneity in prostate cancer. Nat. Rev. Urol. 18, 79–92.

    Article  PubMed  Google Scholar 

  73. Tlostanova M.S. 2014. The effectiveness of positron emission tomography with 18F-fluorodeoxyglucose, 11C-methionine and 82Rb-chloride in the differential diagnosis of tumor and some inflammatory lung diseases. Sovrem. Tekhnol. Med. 6, 45–50.

    Google Scholar 

  74. Ivashchenko I.M., Shnyakin P.G., Kataeva A.A., Pavlova I.S., Grigoryan K.V., Shirvanyan M.A. 2018. Possibilities of positron emission tomography in the diagnosis of malignant brain tumors (literature review). Mire Nauchn. Otkrytii. 10, 72–87.

    Google Scholar 

  75. Shen B., Huang T., Sun Y., Jin Z., Li X.F. 2017. Revisit 18F-fluorodeoxyglucose oncology positron emission tomography: “Systems molecular imaging” of glucose metabolism. Oncotarget. 8 (26), 43536–43542.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Yao Y., Li Y.-M., He Z.-X., Civelek A.C., Li X.-F. 2021. Likely common role of hypoxia in driving 18F-FDG uptake in cancer, myocardial ischemia, inflammation and infection. Cancer Biother. Radiopharm. 36, 624–631.

    CAS  PubMed  Google Scholar 

  77. Kaira K., Shimizu K., Kitahara S., Yajima T., Atsumi J., Kosaka T., Ohtaki Y., Higuchi T., Oyama T., Asao T., Mogi A. 2018. 2-Deoxy-2-[fluorine-18] fluoro-d-glucose uptake on positron emission tomography is associated with programmed death ligand-1 expression in patients with pulmonary adenocarcinoma. Eur. J. Cancer. 101, 181–190.

    Article  CAS  PubMed  Google Scholar 

  78. Li X.-F., Du Y., Ma Y., Postel G.C., Civelek A.C. 2014. 18F-fluorodeoxyglucose uptake and tumor hypoxia: Revisit 18F-fluorodeoxyglucose in oncology application. Transl. Oncol. 7, 240–247.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Skvortsova T.Yu., Savintceva Z.I., Zakhs D.V., Tyurin R.V., Gurchin A.F., Kholyavin A.I., Trofimova T.N. 2021. Comparison of amino acid radiotracers L-[methyl-11C]methionine and O-2-[18F]fluoroethyl-L-tyrosine for PET/CT imaging of cerebral gliomas. Diagn. Radiol. Radiother. 12, 49–58.

    Article  Google Scholar 

  80. Mittra E.S., Koglin N., Mosci C., Kumar M., Hoehne A., Keu K.V., Iagaru A. H., Mueller A., Berndt M., Bullich S., Friebe M., Schmitt-Willich H., Gekeler V., Fels L.M., Bacher-Stier C., Moon D.H., Chin F.T., Stephens A.W., Dinkelborg L.M., Gambhir S.S. 2016. Pilot preclinical and clinical evaluation of (4S)-4-(3-[18F]fluoropropyl)-L-glutamate (18F-FSPG. for PET/CT imaging of intracranial malignancies. PLoS One. 11, e0148628.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Horiguchi K., Tosaka M., Higuchi T., Arisaka Y., Sugawara K., Hirato J., Yokoo H., Tsushima Y., Yoshimoto Y. 2017. Clinical value of fluorine-18α-methyltyrosine PET in patients with gliomas: Comparison with fluorine-18 fluorodeoxyglucose PET. EJNMMI Res. 7, 50.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Tepede A.A., Welch J., Lee M., Mandl A., Agarwal S.K., Nilubol N., Patel D., Cochran C., Simonds W.F., Weinstein L.S., Jha A., Millo C., Pacak K., Blau J.E. 2020. 18F-FDOPA PET/CT accurately identifies MEN1-associated pheochromocytoma. Endocrinol. Diabetes Metab. Case Rep. 2020, 19-0156.

    PubMed  PubMed Central  Google Scholar 

  83. Crippa F., Alessi A., Serafini G.L. 2012. PET with radiolabeled amino acid. Q. J. Nucl. Med. Mol. Imaging. 56, 151–162.

    CAS  PubMed  Google Scholar 

  84. Bakunovich A.V., Sinitsyn V.E., Mershina E.A. 2014. Clinical application of proton magnetic resonance spectroscopy in tumors of the brain and adjacent tissues. Vestn. Rentgenol. Radiol. 1, 39–50.

    Google Scholar 

  85. Gillies R.J., Morse D.L. 2005. In vivo magnetic resonance spectroscopy in cancer. Annu. Rev. Biomed. Eng. 7, 287–326.

    Article  CAS  PubMed  Google Scholar 

  86. Nguyen M.L., Willows B., Khan R., Chi A., Kim L., Nour S.G., Sroka T., Kerr C., Godinez J., Mills M., Karlsson U., Altdorfer G., Nguyen N.P., Jendrasiak G. 2014. The potential role of magnetic resonance spectroscopy in image-guided radiotherapy. Front. Oncol. 4, 91.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Peter S.B., Nandhan V.R. 2022. 31-Phosphorus magnetic resonance spectroscopy in evaluation of glioma and metastases in 3T MRI. Indian J. Radiol. Imaging. 31, 873–881.

    PubMed  PubMed Central  Google Scholar 

  88. Mishkovsky M., Gusyatiner O., Lanz B., Cudalbu C., Vassallo I., Hamou M.F., Bloch J., Comment A., Gruetter R., Hegi M.E. 2021. Hyperpolarized 13C-glucose magnetic resonance highlights reduced aerobic glycolysis in vivo in infiltrative glioblastoma. Sci. Rep. 11, 5771.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Gallagher F.A., Woitek R., McLean M.A., Gill A.B., Manzano Garcia R., Provenzano E., Riemer F., Kaggie J., Chhabra A., Ursprung S., Grist J.T., Daniels C.J., Zaccagna F., Laurent M.C., Locke M., Hilborne S., Frary A., Torheim T., Boursnell C., Schiller A., Patterson I., Slough R., Carmo B., Kane J., Biggs H., Harrison E., Deen S.S., Patterson A., Lanz T., Kingsbury Z., Ross M., Basu B., Baird R., Lomas D.J., Sala E., Wason J., Rueda O.M., Chin S.F., Wilkinson I.B., Graves M.J., Abraham J.E., Gilbert F.J., Caldas C., Brindle K.M. 2020. Imaging breast cancer using hyperpolarized carbon-13 MRI. Proc. Natl. Acad. Sci. U. S. A. 117, 2092–2098.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Tang S., Meng M.V., Slater J.B., Gordon J.W., Vigneron D.B., Stohr B.A., Larson P.E.Z., Wang Z.J. 2021. Metabolic imaging with hyperpolarized 13C pyruvate magnetic resonance imaging in patients with renal tumors—initial experience. Cancer. 127, 2693–2704.

    Article  CAS  PubMed  Google Scholar 

  91. Nam A.S., Chaligne R., Landau D.A. 2021. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat. Rev. Genet. 22, 3–18.

    Article  CAS  PubMed  Google Scholar 

  92. Zeng D., Ye Z., Shen R., Yu G., Wu J., Xiong Y., Zhou R., Qiu W., Huang N., Sun L., Li X., Bin J., Liao Y., Shi M., Liao W. 2021. IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front. Immunol. 12, 687975.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Ding S., Chen X., Shen K. 2020. Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy. Cancer Commun. 40, 329–344.

    Article  Google Scholar 

  94. Lei Y., Tang R., Xu J., Wang W., Zhang B., Liu J., Yu X., Shi S. 2021. Applications of single-cell sequencing in cancer research: Progress and perspectives. J. Hematol. Oncol. 14, 91.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Yu T.J., Ma D., Liu Y.Y., Xiao Y., Gong Y., Jiang Y.Z., Shao Z.M., Hu X., Di G.H. 2021. Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in human breast cancers. Mol. Ther. 29, 2350–2365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Xiao Z., Dai Z., Locasale J.W. 2019. Metabolic landscape of the tumor microenvironment at single cell resolution. Nat. Commun.10, 3763.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Kou W., Zhao N., Zhao L., Yin Z., Zhang M.C., Zhang L., Song J., Wang Y., Qiao C., Li H. 2022. Single-cell characterization revealed hypoxia-induced metabolic reprogramming of gastric cancer. Heliyon. 8 (11), e11866.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Yu Q., Jiang M., Wu L. 2022. Spatial transcriptomics technology in cancer research. Front. Oncol. 12, 1019111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Wu L., Yan J., Bai Y., Chen F., Xu J., Zou X., Huang A., Hou L., Zhong Y., Jing Z., Zhou X., Sun H., Cheng M., Ji Y., Luo R., Li Q., Wu L., Wang P., Guo D., Huang W., Lei J., Liao S., Li Y., Jiang Z., Yao N., Yu Y., Li Y., Liu F., Zhang M., Yang H., Yang S., Xu X., Liu L., Wang X., Wang J., Fan J., Liu S., Yang X., Chen A., Zhou J. 2021. Spatially-resolved transcriptomics analyses of invasive fronts in solid tumors. bioRxiv. 2021, 10.

    Google Scholar 

  100. Lv J., Shi Q., Han Y., Li W., Liu H., Zhang J., Niu C., Gao G., Fu Y., Zhi R., Wu K., Li S., Gu F., Fu L. 2021. Spatial transcriptomics reveals gene expression characteristics in invasive micropapillary carcinoma of the breast. Cell Death Dis. 12 (12), 1095.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Wu Y., Yang S., Ma J., Chen Z., Song G., Rao D., Cheng Y., Huang S., Liu Y., Jiang S., Liu J., Huang X., Wang X., Qiu S., Xu J., Xi R., Bai F., Zhou J., Fan J., Zhang X., Gao Q. 2022. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov. 12 (1), 134–153.

    Article  CAS  PubMed  Google Scholar 

  102. Glycolysis and Carbohydrates Assay Kits. In Assay Genie. https://www.assaygenie.com/glycolysis-and-carbohydrates-assay-kits. Accessed September 2, 2022.

  103. Leippe D., Sobol M., Vidugiris G., Cali J.J., Vidugiriene J. 2017. Bioluminescent assays for glucose and glutamine metabolism: High-throughput screening for changes in extracellular and intracellular metabolites. SLAS Discov. 22, 366–377.

    Article  CAS  PubMed  Google Scholar 

  104. Huang S.-L., Huang Z.-C., Zhang C.-J., Xie J., Lei S.-S., Wu Y.-Q., Fan P.-Z. 2022. LncRNA SNHG5 promotes the glycolysis and proliferation of breast cancer cell through regulating BACH1 via targeting miR-299. Breast Cancer. 29, 65–76.

    Article  PubMed  Google Scholar 

  105. Qin Y., Zheng Y., Huang C., Li Y., Gu M., Wu Q. 2021. Downregulation of miR-181b-5p inhibits the viability, migration, and glycolysis of gallbladder cancer by upregulating PDHX under hypoxia. Front. Oncol. 11, 683725.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Zhang K., Hu H., Xu J., Qiu L., Chen H., Jiang X., Jiang Y. 2020. Circ_0001421 facilitates glycolysis and lung cancer development by regulating miR-4677-3p/CDCA3. Diagn. Pathol. 15, 133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Zhang Y., Gao M., Zhu M., Li H., Ma T., Wu C. 2022. Isobavachalcone induces cell death through multiple pathways in human breast cancer MCF-7 cells. J. Southern Med. Univ. 42, 878–885.

    CAS  Google Scholar 

  108. Cargill K.R., Stewart C.A., Park E.M., Ramkumar K., Gay C.M., Cardnell R.J., Wang Q., Diao L., Shen L., Fan Y.H., Chan W.K., Lorenzi P.L., Oliver T.G., Wang J., Byers L.A. 2021. Targeting MYC-enhanced glycolysis for the treatment of small cell lung cancer. Cancer Metab. 9, 33.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Mazurkiewicz J., Simiczyjew A., Dratkiewicz E., Pietraszek-Gremplewicz K., Majkowski M., Kot M., Ziętek M., Matkowski R., Nowak D. 2022. Melanoma cells with diverse invasive potential differentially induce the activation of normal human fibroblasts. Cell Commun. Signal. 20, 63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Lang L., Wang F., Ding Z., Zhao X., Loveless R., Xie J., Shay C., Qiu P., Ke Y., Saba N.F., Teng Y. 2021. Blockade of glutamine-dependent cell survival augments antitumor efficacy of CPI-613 in head and neck cancer. J. Exp. Clin. Cancer Res. 40, 393.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Almouhanna F., Blagojevic B., Can S., Ghanem A., Wölfl S. 2021. Pharmacological activation of pyruvate kinase M2 reprograms glycolysis leading to TXNIP depletion and AMPK activation in breast cancer cells. Cancer Metab. 9, 5.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Zhou X., Mehta S., Zhang J. 2020. Genetically encodable fluorescent and bioluminescent biosensors light up signaling networks. Trends Biochem. Sci. 45, 889–905.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Zhang Z., Cheng X., Zhao Y., Yang Y. 2020. Lighting up live-cell and in vivo central carbon metabolism with genetically encoded fluorescent sensors. Annu. Rev. Analyt. Chem. (Palo Alto, Calif.). 13, 293–314.

    Article  CAS  Google Scholar 

  114. Shirmanova M.V., Druzhkova I.N., Lukina M.M., Matlashov M.E., Belousov V.V., Snopova L.B., Prodanetz N.N., Dudenkova V.V., Lukyanov S.A., Zagaynova E.V. 2015. Intracellular pH imaging in cancer cells in vitro and tumors in vivo using the new genetically encoded sensor SypHer2. Biochim. Biophys. Acta. 1850, 1905–1911.

    Article  CAS  PubMed  Google Scholar 

  115. Shimolina L., Potekhina E., Druzhkova I., Lukina M., Dudenkova V., Belousov V., Shcheslavskiy V., Zagaynova E., Shirmanova M. 2022. Fluorescence lifetime-based pH mapping of tumors in vivo using genetically encoded sensor SypHerRed. Biophys. J. 121, 1156–1165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Parshina Y.P., Komarova A.D., Bochkarev L.N., Kovylina T.A., Plekhanov A.A., Klapshina L.G., Konev A.N., Mozherov A.M., Shchechkin I.D., Sirotkina M.A., Shcheslavskiy V.I., Shirmanova M.V. 2022. Simultaneous probing of metabolism and oxygenation of tumors in vivo using FLIM of NAD(P)H and PLIM of a new polymeric Ir(III) oxygen sensor. Int. J. Mol. Sci. 23, 10263.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Solomatina A.I., Su S., Lukina M.M., Dudenkova V.V., Shcheslavskiy V.I., Wu C., Chelushkin P.S., Chou P., Koshevoy I.O., Tunik S.P. 2018. Water-soluble cyclometalated platinum(II) and iridium(III) complexes: Synthesis, tuning of the photophysical properties, and in vitro and in vivo phosphorescence lifetime imaging. RSC Adv. 8, 17224–17236.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Díaz-García C.M., Lahmann C., Martínez-François J.R., Li B., Koveal D., Nathwani N., Rahman M., Keller J.P., Marvin J.S., Looger L.L., Yellen G. 2019. Quantitative in vivo imaging of neuronal glucose concentrations with a genetically encoded fluorescence lifetime sensor. J. Neurosci. Res. 97, 946–960.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Babichenko I.I. 2008. New methods of immunohistochemical diagnosis of tumor growth. Vestn. RUDN, Ser. Med. 4, 94–99.

    Google Scholar 

  120. Fack F., Tardito S., Hochart G., Oudin A., Zheng L., Fritah S., Golebiewska A., Nazarov P.V., Bernard A., Hau A.C., Keunen O., Leenders W., Lund-Johansen M., Stauber J., Gottlieb E., Bjerkvig R., Niclou S.P. 2017. Altered metabolic landscape in IDH-mutant gliomas affects phospholipid, energy, and oxidative stress pathways. EMBO Mol. Med. 9, 1681–1695.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Garrett M., Sperry J., Braas D., Yan W., Le T.M., Mottahedeh J., Ludwig K., Eskin A., Qin Y., Levy R., Breunig J.J., Pajonk F., Graeber T.G., Radu C.G., Christofk H., Prins R.M., Lai A., Liau L.M., Coppola G., Kornblum H.I. 2018. Metabolic characterization of isocitrate dehydrogenase (IDH) mutant and IDH wildtype gliomaspheres uncovers cell type-specific vulnerabilities. Cancer Metab. 6, 4.

    Article  PubMed  PubMed Central  Google Scholar 

  122. Meijer T.W.H., Looijen-Salamo, M.G., Lok J., van den Heuvel M., Tops B., Kaanders J.H.A.M., Span P.N., Bussink J. 2019. Glucose and glutamine metabolism in relation to mutational status in NSCLC histological subtypes. Thoracic Cancer. 10, 2289–2299.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Pinheiro C., Garcia E.A., Morais-Santos F., Scapulatempo-Neto C., Mafra A., Steenbergen R.D., Boccardo E., Villa L.L., Baltazar F., Longatto-Filho A. 2014. Lactate transporters and vascular factors in HPV-induced squamous cell carcinoma of the uterine cervix. BMC Cancer. 14, 751.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Mikkilineni L., Whitaker-Menezes D., Domingo-Vidal M., Sprandio J., Avena P., Cotzia P., Dulau-Florea A., Gong J., Uppal G., Zhan T., Leiby B., Lin Z., Pro B., Sotgia F., Lisanti M.P., Martinez-Outschoorn U. 2017. Hodgkin lymphoma: A complex metabolic ecosystem with glycolytic reprogramming of the tumor microenvironment. Semin. Oncol. 44, 218–225.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Gooptu M., Whitaker-Menezes D., Sprandio J., Domingo-Vidal M., Lin Z., Uppal G., Gong J., Fratamico R., Leiby B., Dulau-Florea A., Caro J., Martinez-Outschoorn U. 2017. Mitochondrial and glycolytic metabolic compartmentalization in diffuse large B-cell lymphoma. Semin. Oncol. 44, 204–217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Georgakoudi I., Quinn K.P. 2012. Optical imaging using endogenous contrast to assess metabolic state. A-nnu. Rev. Biomed. Eng. 14, 351–367.

    Article  CAS  Google Scholar 

  127. Shirmanova M.V., Shcheslavskiy V.I., Lukina M.M., Becker W., Zagaynova E.V. 2020. Exploring tumor metabolism with time-resolved fluorescence methods: From single cells to a whole tumor. In Multimodal Optical Diagnostics of Cancer. Tuchin V.V., Popp J., Zakharov V., Eds. Cham: Springer, 133–155.

    Google Scholar 

  128. Chance B., Schoener B., Oshino R., Itshak F., Nakase Y. 1979. Oxidation−reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals. J. Biol. Chem. 254, 4764–4771.

    Article  CAS  PubMed  Google Scholar 

  129. Skala M.C., Riching K.M., Gendron-Fitzpatrick A., Eickhoff J., Eliceiri K.W., White J.G., Ramanujam N. 2007. In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia. Proc. Natl. Acad. Sci. U. S. A. 104, 19494–19499.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Chorvat D., Chorvatova A. 2009. Multi-wavelength fluorescence lifetime spectroscopy: A new approach to the study of endogenous fluorescence in living cells and tissues. Laser Phys. Lett. 6, 175–193.

    Article  CAS  Google Scholar 

  131. Lakowicz J.R., Szmacinski H., Nowaczyk K., Johnson M.L. 1992. Fluorescence lifetime imaging of free and protein-bound NADH. Proc. Natl. Acad. Sci. U. S. A. 89, 1271–1275.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Blacker T.S., Mann Z.F., Gale J.E., Ziegler M., Bain A.J., Szabadkai G., Duchen M.R. 2014. Separating NADH and NAD(P)H fluorescence in live cells and tissues using FLIM. Nat. Commun. 5, 3936.

    Article  CAS  PubMed  Google Scholar 

  133. Kalinina S., Freymueller C., Naskar N., von Einem B., Reess K., Sroka R., Rueck A. 2021. Bioenergetic alterations of metabolic redox coenzymes as NADH, FAD and FMN by means of fluorescence lifetime imaging techniques. Int. J. Mol. Sci. 22, 5952.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Berezin M.Y., Achilefu S. 2010. Fluorescence lifetime measurements and biological imaging. Chem. Rev. 110, 2641–2684.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Druzhkova I.N., Shirmanova M.V., Lukina M.M., Dudenkova V.V., Mishina N.M., Zagaynova E.V. 2016. The metabolic interaction of cancer cells and fibroblasts—coupling between NAD(P)H and FAD, intracellular pH and hydrogen peroxide. Cell Cycle. 15, 1257–1266.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Shirmanova M.V., Gorbachev D.A., Sarkisyan K.S., Parnes A.P., Gavrina A.I., Polozova A.V., Kovaleva T.F., Snopova L.B., Dudenkova V.V., Zagaynova E.V., Lukyanov K.A. 2021. FUCCI-Red: A single-color cell cycle indicator for fluorescence lifetime imaging. Cell. Mol. Life Sci. 78, 3467–3476.

    Article  CAS  PubMed  Google Scholar 

  137. Lukina M.M., Dudenkova V.V., Ignatova N.I., Druzhkova I.N., Shimolina L.E., Zagaynova E.V., Shirmanova M.V. 2018. Metabolic cofactors NAD(P)H and FAD as potential indicators of cancer cell response to chemotherapy with paclitaxel. Biochim. Biophys. Acta, Gen. Subj. 1862, 1693–1700.

    Article  CAS  PubMed  Google Scholar 

  138. Shirshin E.A., Shirmanova M.V., Gayer A.V., Lukina M.M., Nikonova E.E., Yakimov B.P., Budylin G.S., Dudenkova V.V., Ignatova N.I., Komarov D.V., Yakovlev V.V., Becker W., Zagaynova E.V., Shcheslavskiy V.I., Scully M.O. 2022. Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity. Proc. Natl. Acad. Sci. U. S. A. 119, e2118241119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Yuzhakova D., Kiseleva E., Shirmanova M., Shcheslavskiy V., Sachkova D., Snopova L., Bederi-na E., Lukina M., Dudenkova V., Yusubalieva G., Belovezhets T., Matvienko D., Baklaushev V. 2022. Highly invasive fluorescent/bioluminescent patient-derived orthotopic model of glioblastoma in mice. Front. Oncol. 12, 897839.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Sharick J.T., Jeffery J.J., Karim M.R., Walsh C.M., Esbona K., Cook R.S., Skala M.C. 2019. Cellular metabolic heterogeneity in vivo is recapitulated in tumor organoids. Neoplasia. 21, 615–626.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Walsh A.J., Castellanos J.A., Nagathihalli N.S., Merchant N.B., Skala M.C. 2016. Optical imaging of drug-induced metabolism changes in murine and human pancreatic cancer organoids reveals heterogeneous drug response. Pancreas. 45, 863–869.

    Article  CAS  PubMed  Google Scholar 

  142. Walsh A.J., Cook R.S., Sanders M.E., Aurisicchio L., Ciliberto G., Arteaga C.L., Skala M.C. 2014. Quantitative optical imaging of primary tumor organoid metab-olism predicts drug response in breast cancer. Cancer Res. 74, 5184–5194.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Sharick J.T., Walsh C.M., Sprackling C.M., Pasch C.A., Pham D.L., Esbona K., Choudhary A., Garcia-Valera R., Burkard M.E., McGregor S.M., Matkowskyj K.A., Parikh A.F., Meszoely I.M., Kelley M.C., Tsai S., Deming D.A., Skala M.C. 2020. Metabolic heterogeneity in patient tumor-derived organoids by primary site and drug treatment. Front. Oncol. 10, 553.

    Article  PubMed  PubMed Central  Google Scholar 

  144. Gillette A.A., Babiarz C.P., VanDommelen A.R., Pasch C.A., Clipson L., Matkowskyj K.A., Deming D.A., Skala M.C. 2021. Autofluorescence imaging of treatment response in neuroendocrine tumor organoids. Cancers. 13, 1873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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ACKNOWLEDGMENTS

The authors would like to thank the employees of the PRMU I.N. Druzhkova, A.M. Mozherov, and V.I. Shcheslavsky for help in the experiments and useful discussions, and the NOKOD oncologist V.M. Terekhov for providing clinical material.

Funding

The work was supported by the Russian Science Foundation (agreement no. 22-64-00057).

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Abbreviations: FLIM, Fluorescence Lifetime Imaging; 18F-FDG, 18F-fluorodeoxyglucose; IHC, immunohistochemistry; MRS, magnetic resonance spectroscopy; MRI, magnetic resonance imaging; TSC, tumor stem cells; PET, positron emission tomography.

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Shirmanova, M.V., Sinyushkina, S.D. & Komarova, A.D. Metabolic Heterogeneity of Tumors. Mol Biol 57, 1125–1142 (2023). https://doi.org/10.1134/S002689332306016X

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