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Modulating the Metabolic Phenotype of Cancer Microenvironment

  • Inês Matias
  • Sérgio Dias
  • Tânia CarvalhoEmail author
Chapter
  • 203 Downloads
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1219)

Abstract

This chapter provides a brief overview of the methods to study and modulate the metabolic phenotype of the tumor microenvironment, including own research work to demonstrate the impact that metabolic shifts in the host have on cancer. Firstly, we briefly discuss the relevance of using animal models to address this topic, and also the importance of acknowledging that animals have diverse metabolic phenotypes according to species, and even with strain, age or sex. We also present original data to highlight the impact that changes in metabolic phenotype of the microenvironment have on tumor progression. Using an acute leukemia mouse xenograft model and high-fat diet we show that a shift in the host metabolic phenotype, induced by high-fat feeding, significantly impacts on tumor progression. The mechanism through which this occurs involves a direct effect of the increased levels of circulating lipoproteins in both tumor and non-neoplastic cells.

Keywords

Murine models Cancer microenvironment Metabolic remodeling Cholesterol impact in cancer progression 

Notes

Acknowledgements

Project funded by Fundação para a Ciência e a Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) through Fundos do Orçamento de Estado (UID/BIM/50005/2019).

References

  1. Alberti KGMM, Zimmet P, Shaw J (2006) Metabolic syndrome – a new world-wide definition. A consensus statement from the international diabetes federation. Diabet Med 23(5):469–480CrossRefGoogle Scholar
  2. Barthold SW (2004) Genetically altered mice: phenotypes, no phenotypes, and faux phenotypes. Genetica 122(1):75–88CrossRefGoogle Scholar
  3. Bergen WG, Mersmann HJ (2018) Comparative aspects of lipid metabolism: impact on contemporary research and use of animal models. J Nutr.  https://doi.org/10.1093/jn/135.11.2499CrossRefGoogle Scholar
  4. Berglund ED, Li CY, Poffenberger G, Ayala JE, Fueger PT, Willis SE, Jewell MM, Powers AC, Wasserman DH (2008) Glucose metabolism in vivo in four commonly used inbred mouse strains. Diabetes.  https://doi.org/10.2337/db07-1615CrossRefGoogle Scholar
  5. Butturini AM, Dorey FJ, Lange BJ, Henry DW, Gaynon PS, Fu C, Franklin J, Siegel SE, Seibel NL, Rogers PC, Sather H, Trigg M, Bleyer WA, Carroll WL (2007) Obesity and outcome in pediatric acute lymphoblastic leukemia. J Clin Oncol.  https://doi.org/10.1200/JCO.2006.07.7792CrossRefGoogle Scholar
  6. Cortes J (2001) Central nervous system involvement in adult acute lymphocytic leukemia. Hematol Oncol Clin North Am.  https://doi.org/10.1016/S0889-8588(05)70203-3CrossRefGoogle Scholar
  7. Demetrius L (2005) Of mice and men. When it comes to studying ageing and the means to slow it down, mice are not just small humans. EMBO Rep 6 Spec No:S39–S44.  https://doi.org/10.1038/sj.embor.7400422
  8. Gomes AL, Carvalho T, Serpa J, Torre C, Dias S (2010) Hypercholesterolemia promotes bone marrow cell mobilization by perturbing the SDF-1:CXCR4 axis. Blood.  https://doi.org/10.1182/blood-2009-08-240580CrossRefGoogle Scholar
  9. Grau G, Portillo N, Almaraz RL, Echebarria A, Adán R, Rodriguez A, Vela A, Astigarraga I, Rica I (2016) Severe hypertriglyceridemia in pediatric oncology patient. Horm Res Paediatr.  https://doi.org/10.1159/000449142
  10. Grubb SC, Bult CJ, Bogue MA (2014) Mouse phenome database. Nucleic Acids Res.  https://doi.org/10.1093/nar/gkt1159CrossRefGoogle Scholar
  11. Harjes U, Bensaad K, Harris AL (2012) Endothelial cell metabolism and implications for cancer therapy. Br J Cancer 107(8):1207–1212CrossRefGoogle Scholar
  12. Kaplan JG, DeSouza TG, Farkash A, Shafran B, Pack D, Rehman F, Fuks J, Portenoy R (1990) Leptomeningeal metastases: comparison of clinical features and laboratory data of solid tumors, lymphomas and leukemias. J Neurooncol.  https://doi.org/10.1007/BF02341153CrossRefGoogle Scholar
  13. Kennedy AJ, Ellacott KLJ, King VL, Hasty AH (2010) Mouse models of the metabolic syndrome. Dis Model Mech.  https://doi.org/10.1242/dmm.003467CrossRefGoogle Scholar
  14. Kouidhi S, Ayed FB, Elgaaied AB (2018) Targeting tumor metabolism: a new challenge to improve immunotherapy. Front Immunol 9:353CrossRefGoogle Scholar
  15. Kuliszkiewicz-Janus M, Małecki R, Mohamed AS (2008) Lipid changes occuring in the course of hematological cancers. Cell Mol Biol Lett.  https://doi.org/10.2478/s11658-008-0014-9
  16. Lee HY, Jeong KH, Choi CS (2014) In-depth metabolic phenotyping of genetically engineered mouse models in obesity and diabetes. Mamm GenomeGoogle Scholar
  17. Martignoni M, Groothuis GMM, de Kanter R (2006) Species differences between mouse, rat, dog, monkey and human CYP-mediated drug metabolism, inhibition and induction. Expert Opin Drug Metab Toxicol.  https://doi.org/10.1517/17425255.2.6.875CrossRefGoogle Scholar
  18. Moschovi M, Trimis G, Apostolakou F, Papassotiriou I, Tzortzatou-Stathopoulou F (2004) Serum lipid alterations in acute lymphoblastic leukemia of childhood. J Pediatr Hematol Oncol.  https://doi.org/10.1097/00043426-200405000-00006CrossRefGoogle Scholar
  19. Muruganandan S, Sinal CJ (2008) Mice as clinically relevant models for the study of cytochrome P450-dependent metabolism. Clin Pharmacol Ther.  https://doi.org/10.1038/clpt.2008.50CrossRefGoogle Scholar
  20. Nandi A (2004) Mouse models of insulin resistance. Physiol Rev.  https://doi.org/10.1152/physrev.00032.2003CrossRefGoogle Scholar
  21. Nayar G, Ejikeme T, Chongsathidkiet P, Elsamadicy AA, Blackwell KL, Clarke JM, Lad SP, Fecci PE, Nayar G, Ejikeme T, Chongsathidkiet P, Elsamadicy AA, Blackwell KL, Clarke JM, Lad SP, Fecci PE, Nayar G, Ejikeme T, Chongsathidkiet P, Elsamadicy AA, Blackwell KL, Clarke JM, Lad SP, Fecci PE (2017) Leptomeningeal disease: current diagnostic and therapeutic strategies. Oncotarget.  https://doi.org/10.18632/oncotarget.20272
  22. Paigen B (1995) Genetics of responsiveness to high-fat and high-cholesterol diets in the mouse. Am J Clin Nutr 62(2):458S–462SCrossRefGoogle Scholar
  23. Picard O, Rolland Y, Poupon MF (1986) Fibroblast-dependent tumorigenicity of cells in nude mice: implication for implantation of metastases. Cancer Res 46(7):3290–3294PubMedGoogle Scholar
  24. Pui CH, Howard SC (2008) Current management and challenges of malignant disease in the CNS in paediatric leukaemia. Lancet Oncol 9(3):257–268CrossRefGoogle Scholar
  25. Rossmeisl M, Rim JS, Koza RA, Kozak LP (2003) Variation in type 2 diabetes – related traits in mouse strains susceptible to diet-induced obesity. Diabetes.  https://doi.org/10.2337/diabetes.52.8.1958CrossRefGoogle Scholar
  26. Rozman J, Klingenspor M, Hrabě de Angelis M (2014) A review of standardized metabolic phenotyping of animal models. Mamm Genome 25(9–10):497–507CrossRefGoogle Scholar
  27. Russell DW (2003) The enzymes, regulation, and genetics of bile acid synthesis. Annu Rev Biochem.  https://doi.org/10.1146/annurev.biochem.72.121801.161712CrossRefGoogle Scholar
  28. Ruzzenente B, Rötig A, Metodiev MD (2016) Mouse models for mitochondrial diseases. Hum Mol Genet 25(R2):R115–R122CrossRefGoogle Scholar
  29. Savage DB (2009) Mouse models of inherited lipodystrophy. Dis Model Mech.  https://doi.org/10.1242/dmm.002907CrossRefGoogle Scholar
  30. Scribano D, Baroni S, Pagano L, Zuppi C, Leone G, Giardina B (1996) Return to normal values of lipid pattern after effective chemotherapy in acute lymphoblastic leukemia. Haematologica 81(4):343–345PubMedGoogle Scholar
  31. Surapaneni UR, Cortes JE, Thomas D, O’Brien S, Giles FJ, Koller C, Faderl S, Kantarjian H (2002) Central nervous system relapse in adults with acute lymphoblastic leukemia. Cancer.  https://doi.org/10.1002/cncr.10265CrossRefGoogle Scholar
  32. Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J, Vardiman J (2017) WHO classification of tumours of haematopoietic and lymphoid tissues. International Agency for Research on Cancer, LyonGoogle Scholar
  33. Zhang Y, Qin C, Yang L, Lu R, Zhao X, Nie G (2018) A comparative genomics study of carbohydrate/glucose metabolic genes: from fish to mammals. BMC Genomics.  https://doi.org/10.1186/s12864-018-4647-4

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Instituto de Medicina Molecular João Lobo Antunes, Universidade de LisboaLisbonPortugal

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