Metabolic Cancer-Macrophage Crosstalk in the Tumor Microenvironment

Goede, Kyra de; Driessen, Amber J. M.; Bossche, Jan Van den

doi:10.3390/biology9110380

Cited by 22 publications

(31 citation statements)

References 126 publications

(165 reference statements)

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“…As cancer cells themselves are typically dependent on glucose, they consume most glucose from the surrounding microenvironment and administrate glycolysis to supply rapidly growing energy requirements. Consequently, TAMs domestically shift toward OXPHOS and FAO metabolism and exhibit functions primarily similar to M2 macrophages in a poor glucose TME to maintain their immunosuppressive roles ( 61 , 62 ). Wenes et al.…”

Section: Glucose Metabolism Signature Of Tumor-associated Macrophagesmentioning

confidence: 99%

Glucose Metabolism: The Metabolic Signature of Tumor Associated Macrophage

et al. 2021

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Macrophages exist in most tissues of the body, where they perform various functions at the same time equilibrating with other cells to maintain immune responses in numerous diseases including cancer. Recently, emerging investigations revealed that metabolism profiles control macrophage phenotypes and functions, and in turn, polarization can trigger metabolic shifts in macrophages. Those findings implicate a special role of metabolism in tumor-associated macrophages (TAMs) because of the sophisticated microenvironment in cancer. Glucose is the major energy source of cells, especially for TAMs. However, the complicated association between TAMs and their glucose metabolism is still unclearly illustrated. Here, we review the recent advances in macrophage and glucose metabolism within the tumor microenvironment, and the significant transformations that occur in TAMs during the tumor progression. Additionally, we have also outlined the potential implications for macrophage-based therapies in cancer targeting TAMs.

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Section: Glucose Metabolism Signature Of Tumor-associated Macrophagesmentioning

confidence: 99%

Glucose Metabolism: The Metabolic Signature of Tumor Associated Macrophage

et al. 2021

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“…With the use of the ScFBA algorithm developed by Damiani et al [ 191 ], it was possible to model a heterogeneous cancer cell population and represent the metabolite exchange between different cell types. Because TAMs can be induced by oncometabolites that activate in macrophages’ cancer-promoting pathways, reversing the polarization of protumoral TAMs is likely to be an effective strategy against cancer [ 197 ]. In this direction, a new avenue constitutes to interconvert pro-inflammatory M1 (with the ability to metabolize arginine to nitric oxide) to wound-healing M2 (with the ability to metabolize arginine to ornithine) [ 198 ] populations of macrophages by using approaches such as depletion of TAMs, reprogramming of TAMs toward M1 polarized macrophages, acting over the recruitment of monocytes into the tumor or inhibiting the endothelial–mesenchymal transition [ 199 ].…”

Section: Discussionmentioning

confidence: 99%

Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment

Frades

Foguet

Cascante

et al. 2021

Cancers

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The tumor’s physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.

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“…Competition for nutrients between cancer cells and immune cells, such as tumor-associated macrophages (TAMs), arises due to hypoxic regions within the tumor. Oncometabolites such as lactate, but also succinate and 2-HG, interact with TAMs to alter their phenotype and enhance tumor progression [103].…”

Section: Lactatementioning

confidence: 99%

Metabolic Rewiring and the Characterization of Oncometabolites

Beyoğlu

Idle

2021

Cancers

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The study of low-molecular-weight metabolites that exist in cells and organisms is known as metabolomics and is often conducted using mass spectrometry laboratory platforms. Definition of oncometabolites in the context of the metabolic phenotype of cancer cells has been accomplished through metabolomics. Oncometabolites result from mutations in cancer cell genes or from hypoxia-driven enzyme promiscuity. As a result, normal metabolites accumulate in cancer cells to unusually high concentrations or, alternatively, unusual metabolites are produced. The typical oncometabolites fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate inhibit 2-oxoglutarate-dependent dioxygenases, such as histone demethylases and HIF prolyl-4-hydroxylases, together with DNA cytosine demethylases. As a result of the cancer cell acquiring this new metabolic phenotype, major changes in gene transcription occur and the modification of the epigenetic landscape of the cell promotes proliferation and progression of cancers. Stabilization of HIF1α through inhibition of HIF prolyl-4-hydroxylases by oncometabolites such as fumarate and succinate leads to a pseudohypoxic state that promotes inflammation, angiogenesis and metastasis. Metabolomics has additionally been employed to define the metabolic phenotype of cancer cells and patient biofluids in the search for cancer biomarkers. These efforts have led to the uncovering of the putative oncometabolites sarcosine, glycine, lactate, kynurenine, methylglyoxal, hypotaurine and (2R,3S)-dihydroxybutanoate, for which further research is required.

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Metabolic Cancer-Macrophage Crosstalk in the Tumor Microenvironment

Cited by 22 publications

References 126 publications

Glucose Metabolism: The Metabolic Signature of Tumor Associated Macrophage

Glucose Metabolism: The Metabolic Signature of Tumor Associated Macrophage

Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment

Metabolic Rewiring and the Characterization of Oncometabolites

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