Tumor Cells and Cancer-Associated Fibroblasts: Comparison
Please note this is a comparison between Version 2 by Vivi Li and Version 1 by Elisabeth Letellier.

Metabolism is considered to be the core of all cellular activity. Thus, extensive studies of metabolic processes are ongoing in various fields of biology, including cancer research. Cancer cells are known to adapt their metabolism to sustain high proliferation rates and survive in unfavorable environments with low oxygen and nutrient concentrations. Hence, targeting cancer cell metabolism is a promising therapeutic strategy in cancer research. However, cancers consist not only of genetically altered tumor cells but are interwoven with endothelial cells, immune cells and fibroblasts, which together with the extracellular matrix (ECM) constitute the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are linked to poor prognosis in different cancer types, are one important component of the TME. CAFs play a significant role in reprogramming the metabolic landscape of tumor cells, but how, and in what manner, this interaction takes place remains rather unclear.

  • cancer
  • cancer-associated fibroblasts (CAFs)
  • CAF-tumor metabolic cross-talk
  • tumor metabolism
  • metabolomics’ measurement techniques
  • in silico modeling
  • personalized metabolic drugs
Please wait, diff process is still running!

References

  1. Dhom, G. The cancer cell and the connective tissue: A historical review. Pathologe 1994, 15, 271–278.
  2. Shiga, K.; Hara, M.; Nagasaki, T.; Sato, T.; Takahashi, H.; Takeyama, H. Cancer-Associated Fibroblasts: Their Characteristics and Their Roles in Tumor Growth. Cancers 2015, 7, 2443–2458.
  3. Dvorak, H.F. Tumors: Wounds that do not heal-redux. Cancer Immunol. Res. 2015, 3, 1–11.
  4. Xing, F.; Saidou, J.; Watabe, K. Cancer associated fibroblasts (CAFs) in tumor microenvironment. Front. Biosci. 2010, 15, 166–179.
  5. Romero, I.L.; Mukherjee, A.; Kenny, H.A.; Litchfield, L.M.; Lengyel, E. Molecular pathways: Trafficking of metabolic resources in the tumor microenvironment. Clin. Cancer Res. 2015, 21, 680–686.
  6. LeBleu, V.S.; Kalluri, R. A peek into cancer-associated fibroblasts: Origins, functions and translational impact. DMM Dis. Model. Mech. 2018, 11.
  7. Li, Z.; Zhang, J.; Zhou, J.; Lu, L.; Wang, H.; Zhang, G.; Wan, G.; Cai, S.; Du, J. Nodal Facilitates Differentiation of Fibroblasts to Cancer-Associated Fibroblasts that Support Tumor Growth in Melanoma and Colorectal Cancer. Cells 2019, 8, 538.
  8. Santolla, M.F.; Vivacqua, A.; Lappano, R.; Rigiracciolo, D.C.; Cirillo, F.; Galli, G.R.; Talia, M.; Brunetti, G.; Miglietta, A.M.; Belfiore, A.; et al. GPER Mediates a Feedforward FGF2/FGFR1 Paracrine Activation Coupling CAFs to Cancer Cells Toward Breast Tumor Progression. Cells 2019, 8, 223.
  9. Eiro, N.; Gonzalez, L.O.; Fraile, M.; Cid, S.; Schneider, J.; Vizoso, F.J. Breast cancer tumor stroma: Cellular components, phenotypic heterogeneity, intercellular communication, prognostic implications and therapeutic opportunities. Cancers 2019, 11, 664.
  10. Sahai, E.; Astsaturov, I.; Cukierman, E.; DeNardo, D.G.; Egeblad, M.; Evans, R.M.; Fearon, D.; Greten, F.R.; Hingorani, S.R.; Hunter, T.; et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer 2020, 20, 174–186.
  11. Chen, X.; Song, E. Turning foes to friends: Targeting cancer-associated fibroblasts. Nat. Rev. Drug Discov. 2019, 18, 99–115.
  12. Almeida-Porada, G.; Atala, A.J.; Porada, C.D. Therapeutic Mesenchymal Stromal Cells for Immunotherapy and for Gene and Drug Delivery. Mol. Ther. Methods Clin. Dev. 2020, 16, 204–224.
  13. Sage, E.K.; Thakrar, R.M.; Janes, S.M. Genetically modified mesenchymal stromal cells in cancer therapy. Cytotherapy 2016, 18, 1435–1445.
  14. Hmadcha, A.; Martin-Montalvo, A.; Gauthier, B.R.; Soria, B.; Capilla-Gonzalez, V. Therapeutic Potential of Mesenchymal Stem Cells for Cancer Therapy. Front. Bioeng. Biotechnol. 2020, 8, 1–13.
  15. Barrett, R.; Puré, E. Cancer-associated fibroblasts: Key determinants of tumor immunity and immunotherapy. Curr. Opin. Immunol. 2020, 64, 80–87.
  16. Salimifard, S.; Masjedi, A.; Hojjat-Farsangi, M.; Ghalamfarsa, G.; Irandoust, M.; Azizi, G.; Mohammadi, H.; Keramati, M.R.; Jadidi-Niaragh, F. Cancer associated fibroblasts as novel promising therapeutic targets in breast cancer. Pathol. Res. Pract. 2020, 216, 152915.
  17. Bhattacharya, S.D.; Mi, Z.; Talbot, L.J.; Guo, H.; Kuo, P.C. Human mesenchymal stem cell and epithelial hepatic carcinoma cell lines in admixture: Concurrent stimulation of cancer-associated fibroblasts and epithelial-to-mesenchymal transition markers. Surgery 2012, 152, 449–454.
  18. Hossen, M.N.; Rao, G.; Dey, A.; Robertson, J.D.; Bhattacharya, R.; Mukherjee, P. Gold Nanoparticle Transforms Activated Cancer-Associated Fibroblasts to Quiescence. ACS Appl. Mater. Interfaces 2019, 11, 26060–26068.
  19. Auciello, F.R.; Bulusu, V.; Oon, C.; Tait-Mulder, J.; Berry, M.; Bhattacharyya, S.; Tumanov, S.; Allen-Petersen, B.L.; Link, J.; Kendsersky, N.D.; et al. A stromal lysolipid–autotaxin signaling axis promotes pancreatic tumor progression. Cancer Discov. 2019, 9, 617–627.
  20. Alt, E.; Welte, G.; Li, J.; Hennessy, B.T.; Devarajan, E.; Krishnappa, S.; Pinilla, S.; Droll, L.; Jotzu, C. Adipose tissue-derived stem cells differentiate into carcinoma-associated fibroblast-like cells under the influence of tumor-derived factors. Anal. Cell. Pathol. 2010, 33, 61–79.
  21. McDonald, L.T.; LaRue, A.C. Hematopoietic stem cell derived carcinoma-associated fibroblasts: A novel origin. Int. J. Clin. Exp. Pathol. 2012, 5, 863–873.
  22. Augsten, M. Cancer-Associated Fibroblasts as Another Polarized Cell Type of the Tumor Microenvironment. Front. Oncol. 2014, 4, 1–8.
  23. Biffi, G.; Oni, T.E.; Spielman, B.; Hao, Y.; Elyada, E.; Park, Y.; Preall, J.; Tuveson, D.A. Il1-induced Jak/STAT signaling is antagonized by TGFβ to shape CAF heterogeneity in pancreatic ductal adenocarcinoma. Cancer Discov. 2019, 9, 282–301.
  24. Kieffer, Y.; Hocine, H.R.; Gentric, G.; Pelon, F.; Bernard, C.; Bourachot, B.; Lameiras, S.; Albergante, L.; Bonneau, C.; Guyard, A.; et al. Single-Cell Analysis Reveals Fibroblast Clusters Linked to Immunotherapy Resistance in Cancer. Cancer Discov. 2020, 10, 1330–1351.
  25. Elyada, E.; Bolisetty, M.; Laise, P.; Flynn, W.F.; Courtois, E.T.; Burkhart, R.A.; Teinor, J.A.; Belleau, P.; Biffi, G.; Lucito, M.S.; et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov. 2019, 9, 1102–1123.
  26. Mizutani, Y.; Kobayashi, H.; Iida, T.; Asai, N.; Masamune, A.; Hara, A.; Esaki, N.; Ushida, K.; Mii, S.; Shiraki, Y.; et al. Meflin-positive cancer-associated fibroblasts inhibit pancreatic carcinogenesis. Cancer Res. 2019, 79, 5367–5381.
  27. Reina-Campos, M.; Moscat, J.; Diaz-Meco, M. Metabolism shapes the tumor microenvironment. Curr. Opin. Cell Biol. 2017, 48, 47–53.
  28. Ocaña, M.C.; Martínez-Poveda, B.; Quesada, A.R.; Medina, M.Á. Metabolism within the tumor microenvironment and its implication on cancer progression: An ongoing therapeutic target. Med. Res. Rev. 2019, 39, 70–113.
  29. Warburg, O.; Wind, F.; Negelein, E. The metabolism of tumors in the body. J. Gen. Physiol. 1927, 8, 519–530.
  30. Fadaka, A.; Ajiboye, B.; Ojo, O.; Adewale, O.; Olayide, I.; Emuowhochere, R. Biology of glucose metabolization in cancer cells. J. Oncol. Sci. 2017, 3, 45–51.
  31. Gatenby, R.A.; Gillies, R.J. Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer 2004, 4, 891–899.
  32. Courtney, K.D.; Bezwada, D.; Mashimo, T.; Pichumani, K.; Vemireddy, V.; Funk, A.M.; Wimberly, J.; McNeil, S.S.; Kapur, P.; Lotan, Y.; et al. Isotope Tracing of Human Clear Cell Renal Cell Carcinomas Demonstrates Suppressed Glucose Oxidation In Vivo. Cell Metab. 2018, 28, 793–800.e2.
  33. Sanderson, S.M.; Locasale, J.W. Revisiting the Warburg Effect: Some Tumors Hold Their Breath. Cell Metab. 2018, 28, 669–670.
  34. 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.; et al. The reverse Warburg effect: Aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle 2009, 8, 3984–4001.
  35. Knudsen, E.S.; Balaji, U.; Freinkman, E.; McCue, P.; Witkiewicz, A.K. Unique metabolic features of pancreatic cancer stroma: Relevance to the tumor compartment, prognosis, and invasive potential. Oncotarget 2016, 7, 78396–78411.
  36. Blomme, A.; Van Simaeys, G.; Doumont, G.; Costanza, B.; Bellier, J.; Otaka, Y.; Sherer, F.; Lovinfosse, P.; Boutry, S.; Palacios, A.P.; et al. Murine stroma adopts a human-like metabolic phenotype in the PDX model of colorectal cancer and liver metastases. Oncogene 2018, 37, 1237–1250.
  37. Bertero, T.; Oldham, W.M.; Grasset, E.M.; Bourget, I.; Boulter, E.; Pisano, S.; Hofman, P.; Bellvert, F.; Meneguzzi, G.; Bulavin, D.V.; et al. Tumor-Stroma Mechanics Coordinate Amino Acid Availability to Sustain Tumor Growth and Malignancy. Cell Metab. 2019, 29, 124–140.e10.
  38. Liu, Q.-P.; Luo, Q.; Deng, B.; Ju, Y.; Song, G.-B. Stiffer Matrix Accelerates Migration of Hepatocellular Carcinoma Cells through Enhanced Aerobic Glycolysis Via the MAPK-YAP Signaling. Cancers 2020, 12, 490.
  39. Santinon, G.; Brian, I.; Pocaterra, A.; Romani, P.; Franzolin, E.; Rampazzo, C.; Bicciato, S.; Dupont, S. d NTP metabolism links mechanical cues and YAP / TAZ to cell growth and oncogene-induced senescence. EMBO J. 2018, 37, 1–16.
  40. Mah, E.J.; Lefebvre, A.E.Y.T.; McGahey, G.E.; Yee, A.F.; Digman, M.A. Collagen density modulates triple-negative breast cancer cell metabolism through adhesion-mediated contractility. Sci. Rep. 2018, 8, 1–11.
  41. Park, J.S.; Burckhardt, C.J.; Lazcano, R.; Solis, L.M.; Isogai, T.; Li, L.; Chen, C.S.; Gao, B.; Minna, J.D.; Bachoo, R.; et al. Mechanical regulation of glycolysis via cytoskeleton architecture. Nature 2020, 578, 621–626.
  42. Choi, B.H.; Coloff, J.L. The diverse functions of non-essential amino acids in cancer. Cancers 2019, 11, 675.
  43. Lieu, E.L.; Nguyen, T.; Rhyne, S.; Kim, J. Amino acids in cancer. Exp. Mol. Med. 2020, 52, 15–30.
  44. Xie, H.; Simon, M.C. Oxygen availability and metabolic reprogramming in cancer. J. Biol. Chem. 2017, 292, 16825–16832.
  45. Sanford-Crane, H.; Abrego, J.; Sherman, M.H. Fibroblasts as modulators of local and systemic cancer metabolism. Cancers 2019, 11, 619.
  46. Lopes-Coelho, F.; André, S.; Félix, A.; Serpa, J. Breast cancer metabolic cross-talk: Fibroblasts are hubs and breast cancer cells are gatherers of lipids. Mol. Cell. Endocrinol. 2018, 462, 93–106.
  47. Yang, L.; Achreja, A.; Yeung, T.L.; Mangala, L.S.; Jiang, D.; Han, C.; Baddour, J.; Marini, J.C.; Ni, J.; Nakahara, R.; et al. Targeting Stromal Glutamine Synthetase in Tumors Disrupts Tumor Microenvironment-Regulated Cancer Cell Growth. Cell Metab. 2016, 24, 685–700.
  48. Mestre-Farrera, A.; Bruch-Oms, M.; Peña, R.; Rodríguez-Morató, J.; Alba-Castellón, L.; Comerma, L.; Quintela-Fandino, M.; Duñach, M.; Baulida, J.; Pozo, Ó.J.; et al. Glutamine-directed migration of cancer-activated fibroblasts facilitates epithelial tumor invasion. Cancer Res. 2020.
  49. Zhao, H.; Yang, L.; Baddour, J.; Achreja, A.; Bernard, V.; Moss, T.; Marini, J.C.; Tudawe, T.; Seviour, E.G.; San Lucas, F.A.; et al. Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism. Elife 2016, 5.
  50. Ko, C.W.; Qu, J.; Black, D.D.; Tso, P. Regulation of intestinal lipid metabolism: Current concepts and relevance to disease. Nat. Rev. Gastroenterol. Hepatol. 2020, 17, 169–183.
  51. Munir, R.; Lisec, J.; Swinnen, J.V.; Zaidi, N. Lipid metabolism in cancer cells under metabolic stress. Br. J. Cancer 2019, 120, 1090–1098.
  52. Koundouros, N.; Poulogiannis, G. Reprogramming of fatty acid metabolism in cancer. Br. J. Cancer 2020, 122, 4–22.
  53. Kamphorst, J.J.; Cross, J.R.; Fan, J.; De Stanchina, E.; Mathew, R.; White, E.P.; Thompson, C.B.; Rabinowitz, J.D. Hypoxic and Ras-transformed cells support growth by scavenging unsaturated fatty acids from lysophospholipids. Proc. Natl. Acad. Sci. USA 2013, 110, 8882–8887.
  54. Radhakrishnan, R.; Ha, J.H.; Jayaraman, M.; Liu, J.; Moxley, K.M.; Isidoro, C.; Sood, A.K.; Song, Y.S.; Dhanasekaran, D.N. Ovarian cancer cell-derived lysophosphatidic acid induces glycolytic shift and cancer-associated fibroblast-phenotype in normal and peritumoral fibroblasts. Cancer Lett. 2019, 442, 464–474.
  55. Gong, J.; Lin, Y.; Zhang, H.; Liu, C.; Cheng, Z.; Yang, X.; Zhang, J.; Xiao, Y.; Sang, N.; Qian, X.; et al. Reprogramming of lipid metabolism in cancer-associated fibroblasts potentiates migration of colorectal cancer cells. Cell Death Dis. 2020, 11.
  56. Romani, P.; Brian, I.; Santinon, G.; Pocaterra, A.; Audano, M.; Pedretti, S.; Mathieu, S.; Forcato, M.; Bicciato, S.; Manneville, J.B.; et al. Extracellular matrix mechanical cues regulate lipid metabolism through Lipin-1 and SREBP. Nat. Cell Biol. 2019, 21, 338–347.
  57. Boulter, E.; Estrach, S.; Tissot, F.S.; Hennrich, M.L.; Tosello, L.; Cailleteau, L.; de la Ballina, L.R.; Pisano, S.; Gavin, A.C.; Féral, C.C. Cell metabolism regulates integrin mechanosensing via an SLC3A2-dependent sphingolipid biosynthesis pathway. Nat. Commun. 2018, 9.
  58. De Jaeghere, E.A.; Denys, H.G.; De Wever, O. Fibroblasts Fuel Immune Escape in the Tumor Microenvironment. Trends Cancer 2019, 5, 704–723.
  59. Chen, J.Y.; Li, C.F.; Kuo, C.C.; Tsai, K.K.; Hou, M.F.; Hung, W.C. Cancer/stroma interplay via cyclooxygenase-2 and indoleamine 2,3-dioxygenase promotes breast cancer progression. Breast Cancer Res. 2014, 16, 1–14.
  60. Cheng, J.; Deng, Y.; Yi, H.; Wang, G.; Fu, B.; Chen, W.; Liu, W.; Tai, Y.; Peng, Y.; Zhang, Q. Hepatic carcinoma-associated fibroblasts induce IDO-producing regulatory dendritic cells through IL-6-mediated STAT3 activation. Oncogenesis 2016, 5, e198.
  61. Hsu, Y.L.; Hung, J.Y.; Chiang, S.Y.; Jian, S.F.; Wu, C.Y.; Lin, Y.S.; Tsai, Y.M.; Chou, S.H.; Tsai, M.J.; Kuo, P.L. Lung cancer-derived galectin-1 contributes to cancer associated fibroblast-mediated cancer progression and immune suppression through TDO2/kynurenine axis. Oncotarget 2016, 7, 27584–27598.
  62. Ino, Y.; Yamazaki-Itoh, R.; Oguro, S.; Shimada, K.; Kosuge, T.; Zavada, J.; Kanai, Y.; Hiraoka, N. Arginase II Expressed in Cancer-Associated Fibroblasts Indicates Tissue Hypoxia and Predicts Poor Outcome in Patients with Pancreatic Cancer. PLoS ONE 2013, 8, e55146.
  63. Monteran, L.; Erez, N. The dark side of fibroblasts: Cancer-associated fibroblasts as mediators of immunosuppression in the tumor microenvironment. Front. Immunol. 2019, 10, 1835.
  64. Korkmaz, B.; Horwitz, M.S.; Jenne, D.E.; Gauthier, F. Neutrophil Elastase, Proteinase 3, and Cathepsin G as Therapeutic Targets in Human Diseases. Pharmacol. Rev. 2010, 62, 726–759.
  65. Ren, G.; Zhao, X.; Wang, Y.; Zhang, X.; Chen, X.; Xu, C.; Yuan, Z.R.; Roberts, A.I.; Zhang, L.; Zheng, B.; et al. CCR2-dependent recruitment of macrophages by tumor-educated mesenchymal stromal cells promotes tumor development and is mimicked by TNFα. Cell Stem Cell 2012, 11, 812–824.
  66. Zhu, Q.; Zhang, X.; Zhang, L.; Li, W.; Wu, H.; Yuan, X.; Mao, F.; Wang, M.; Zhu, W.; Qian, H.; et al. The IL-6-STAT3 axis mediates a reciprocal crosstalk between cancer-derived mesenchymal stem cells and neutrophils to synergistically prompt gastric cancer progression. Cell Death Dis. 2014, 5, e1295.
  67. Cheng, Y.; Li, H.; Deng, Y.; Tai, Y.; Zeng, K.; Zhang, Y.; Liu, W.; Zhang, Q.; Yang, Y. Cancer-associated fibroblasts induce PDL1+ neutrophils through the IL6-STAT3 pathway that foster immune suppression in hepatocellular carcinoma. Cell Death Dis. 2018, 9, 422.
  68. Zhang, A.; Qian, Y.; Ye, Z.; Chen, H.; Xie, H.; Zhou, L.; Shen, Y.; Zheng, S. Cancer-associated fibroblasts promote M2 polarization of macrophages in pancreatic ductal adenocarcinoma. Cancer Med. 2017, 6, 463–470.
  69. Xiang, H.; Ramil, C.P.; Hai, J.; Zhang, C.; Wang, H.; Watkins, A.A.; Afshar, R.; Georgiev, P.; Sze, M.A.; Song, X.S.; et al. Cancer-Associated Fibroblasts Promote Immunosuppression by Inducing ROS-Generating Monocytic MDSCs in Lung Squamous Cell Carcinoma. Cancer Immunol. Res. 2020, 8, 436–450.
  70. Sampson, N.; Brunner, E.; Weber, A.; Puhr, M.; Schäfer, G.; Szyndralewiez, C.; Klocker, H. Inhibition of Nox4-dependent ROS signaling attenuates prostate fibroblast activation and abrogates stromal-mediated protumorigenic interactions. Int. J. Cancer 2018, 143, 383–395.
  71. Ippolito, L.; Morandi, A.; Taddei, M.L.; Parri, M.; Comito, G.; Iscaro, A.; Raspollini, M.R.; Magherini, F.; Rapizzi, E.; Masquelier, J.; et al. Cancer-associated fibroblasts promote prostate cancer malignancy via metabolic rewiring and mitochondrial transfer. Oncogene 2019, 38, 5339–5355.
  72. Kim, Y.M.; Heyman, H.M. Mass spectrometry-based metabolomics. In Methods in Molecular Biology; Humana Press Inc.: Totowa, NJ, USA, 2018; Volume 1775, pp. 107–118.
  73. Fiehn, O. Metabolomics—The link between genotypes and phenotypes. Plant Mol. Biol. 2002, 48, 155–171.
  74. Jang, C.; Chen, L.; Rabinowitz, J.D. Metabolomics and Isotope Tracing. Cell 2018, 173, 822–837.
  75. Parri, M.; Ippolito, L.; Cirri, P.; Ramazzotti, M.; Chiarugi, P. Metabolic cell communication within tumour microenvironment: Models, methods and perspectives. Curr. Opin. Biotechnol. 2020, 63, 210–219.
  76. Lagziel, S.; Lee, W.D.; Shlomi, T. Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches. BMC Biol. 2019, 17, 1–11.
  77. Bantscheff, M.; Schirle, M.; Sweetman, G.; Rick, J.; Kuster, B. Quantitative mass spectrometry in proteomics: A critical review. Anal. Bioanal. Chem. 2007, 389, 1017–1031.
  78. Antoniewicz, M.R. A guide to 13C metabolic flux analysis for the cancer biologist. Exp. Mol. Med. 2018, 50, 19.
  79. Faubert, B.; Deberardinis, R.J. Analyzing tumor metabolism in vivo. Annu. Rev. Cancer Biol. 2017, 1, 99–117.
  80. Elia, I.; Fendt, S.M. In vivo cancer metabolism is defined by the nutrient microenvironment. Transl. Cancer Res. 2016, 5, S1284–S1287.
  81. Fernández-García, J.; Altea-Manzano, P.; Pranzini, E.; Fendt, S.M. Stable Isotopes for Tracing Mammalian-Cell Metabolism In Vivo. Trends Biochem. Sci. 2020, 45, 185–201.
  82. Application Brief Agilent Technologies. Measuring Glycolysis and Oxidative Metabolism in Cancer Cells. Available online: https://www.agilent.com (accessed on 19 August 2020).
  83. Plitzko, B.; Loesgen, S. Measurement of Oxygen Consumption Rate (OCR) and Extracellular Acidification Rate (ECAR) in Culture Cells for Assessment of the Energy Metabolism. Bio-Protocol 2018, 8.
  84. Qureshi-Baig, K.; Ullmann, P.; Haan, S.; Letellier, E. Tumor-Initiating Cells: A criTICal review of isolation approaches and new challenges in targeting strategies. Mol. Cancer 2017, 16, 1–16.
  85. Russell, S.; Wojtkowiak, J.; Neilson, A.; Gillies, R.J. Metabolic Profiling of healthy and cancerous tissues in 2D and 3D. Sci. Rep. 2017, 7, 1–11.
  86. Demircioglu, F.; Wang, J.; Candido, J.; Costa, A.S.H.; Casado, P.; de Luxan Delgado, B.; Reynolds, L.E.; Gomez-Escudero, J.; Newport, E.; Rajeeve, V.; et al. Cancer associated fibroblast FAK regulates malignant cell metabolism. Nat. Commun. 2020, 11, 1290.
  87. Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2006, 2, 62.
  88. Ji, Z.; Yan, K.; Li, W.; Hu, H.; Zhu, X. Mathematical and Computational Modeling in Complex Biological Systems. BioMed Res. Int. 2017, 2017, 5958321.
  89. Alm, E.; Arkin, A.P. Biological networks. Curr. Opin. Struct. Biol. 2003, 13, 193–202.
  90. Hadjicharalambous, M.; Wijeratne, P.A.; Vavourakis, V. From tumour perfusion to drug delivery and clinical translation of in silico cancer models. Methods 2020.
  91. Werner, H.M.J.; Mills, G.B.; Ram, P.T. Cancer systems biology: A peek into the future of patient care? Nat. Rev. Clin. Oncol. 2014, 11, 167–176.
  92. Thiele, I.; Swainston, N.; Fleming, R.M.T.; Hoppe, A.; Sahoo, S.; Aurich, M.K.; Haraldsdottir, H.; Mo, M.L.; Rolfsson, O.; Stobbe, M.D.; et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 2013, 31, 419–425.
  93. Blais, E.M.; Rawls, K.D.; Dougherty, B.V.; Li, Z.I.; Kolling, G.L.; Ye, P.; Wallqvist, A.; Papin, J.A. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nat. Commun. 2017, 8.
  94. Robinson, J.L.; Kocabaş, P.; Wang, H.; Cholley, P.E.; Cook, D.; Nilsson, A.; Anton, M.; Ferreira, R.; Domenzain, I.; Billa, V.; et al. An atlas of human metabolism. Sci. Signal. 2020, 13, eaaz1482.
  95. Thiele, I.; Palsson, B. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 2010, 5, 93–121.
  96. Angione, C. Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BioMed Res. Int. 2019, 2019, 1–16.
  97. Patil, K.R.; Åkesson, M.; Nielsen, J. Use of genome-scale microbial models for metabolic engineering. Curr. Opin. Biotechnol. 2004, 15, 64–69.
  98. Granata, I.; Troiano, E.; Sangiovanni, M.; Guarracino, M.R. Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer. BMC Bioinform. 2019, 20, 162.
  99. Schultz, A.; Mehta, S.; Hu, C.W.; Hoff, F.W.; Horton, T.M.; Kornblau, S.M.; Qutub, A.A. Identifying Cancer Specific Metabolic Signatures Using Constraint-Based Models. In Proceedings of the Biocomputing 2017, Kohala Coast, HI, USA, 4–8 January 2017; World Scientific: Singapore, 2017; Volume 22, pp. 485–496.
  100. Aurich, M.K.; Fleming, R.M.T.; Thiele, I. MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models. Front. Physiol. 2016, 7.
  101. Wegrzyn, A.B.; Herzog, K.; Gerding, A.; Kwiatkowski, M.; Wolters, J.C.; Dolga, A.M.; van Lint, A.E.M.; Wanders, R.J.A.; Waterham, H.R.; Bakker, B.M. Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease. FEBS J. 2020, 287, 5096–5113.
  102. Zur, H.; Ruppin, E.; Shlomi, T. iMAT: An integrative metabolic analysis tool. Bioinformatics 2010, 26, 3140–3142.
  103. Agren, R.; Bordel, S.; Mardinoglu, A.; Pornputtapong, N.; Nookaew, I.; Nielsen, J. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput. Biol. 2012, 8.
  104. Wang, Y.; Eddy, J.A.; Price, N.D. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 2012, 6, 153.
  105. Vlassis, N.; Pacheco, M.P.; Sauter, T. Fast Reconstruction of Compact Context-Specific Metabolic Network Models. PLoS Comput. Biol. 2014, 10, e1003424.
  106. Pacheco, M.P.; John, E.; Kaoma, T.; Heinäniemi, M.; Nicot, N.; Vallar, L.; Bueb, J.L.; Sinkkonen, L.; Sauter, T. Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network. BMC Genom. 2015, 16, 809.
  107. Pacheco, M.P.; Bintener, T.; Ternes, D.; Kulms, D.; Haan, S.; Letellier, E.; Sauter, T. Identifying and targeting cancer-specific metabolism with network-based drug target prediction. EBioMedicine 2019, 43, 98–106.
  108. Bintener, T.; Pacheco, M.P.; Sauter, T. Towards the routine use of in silico screenings for drug discovery using metabolic modelling. Biochem. Soc. Trans. 2020, 48, 955–969.
  109. Heinken, A.; Thiele, I. Systems biology of host-microbe metabolomics. Wiley Interdiscip. Rev. Syst. Biol. Med. 2015, 7, 195–219.
  110. Lewis, N.E.; Schramm, G.; Bordbar, A.; Schellenberger, J.; Andersen, M.P.; Cheng, J.K.; Patel, N.; Yee, A.; Lewis, R.A.; Eils, R.; et al. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat. Biotechnol. 2010, 28, 1279–1285.
  111. Capuani, F.; De Martino, D.; Marinari, E.; De Martino, A. Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle. Sci. Rep. 2015, 5, 11880.
  112. Shan, M.; Dai, D.; Vudem, A.; Varner, J.D.; Stroock, A.D. Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors. PLoS Comput. Biol. 2018, 14, e1006584.
  113. Damiani, C.; Maspero, D.; Di Filippo, M.; Colombo, R.; Pescini, D.; Graudenzi, A.; Westerhoff, H.V.; Alberghina, L.; Vanoni, M.; Mauri, G. Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLoS Comput. Biol. 2019, 15, e1006733.
More
Video Production Service