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Review
. 2021 Feb 2;10(2):304.
doi: 10.3390/cells10020304.

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Jessica Karta  1 , Ysaline Bossicard  1 , Konstantinos Kotzamanis  1 , Helmut Dolznig  2 , Elisabeth Letellier  1
Affiliations
Review

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Jessica Karta et al. Cells. .

Abstract

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. This review aims to highlight the metabolic landscape of tumor cells and CAFs, including their recently identified subtypes, in different tumor types. In addition, we discuss various in vitro and in vivo metabolic techniques as well as different in silico computational tools that can be used to identify and characterize CAF-tumor cell interactions. Finally, we provide our view on how mapping the complex metabolic networks of stromal-tumor metabolism will help in finding novel metabolic _targets for cancer treatment.

Keywords: CAF-tumor cross-talk; cancer; cancer-associated fibroblasts (CAFs); in silico modeling; metabolomics’ measurement techniques; personalized metabolic drugs; tumor metabolism.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
CAF heterogeneity. The origin of cancer-associated fibroblasts (CAFs) remains unclear. Several potential progenitors have been identified including normal fibroblasts, cancer-associated adipocytes, mesenchymal or hematopoietic stem cells, epithelial cells, and endothelial cells. Such heterogeneity in progenitors suggest that CAFs consist of several populations. Today, two principal CAF subtypes have been established: Inflammatory fibroblasts (iCAF) that secrete high levels of cytokines, and myofibroblasts (myCAF) that secrete extracellular matrix components. Additionally, new putative subtypes have been suggested such as antigen-presenting CAFs (apCAF) that express major histocompatibility complex (MHC) class II genes and could be responsible for CD4+ T-cells deactivation, and meflin-expressing CAFs (meflin-CAF), which were found to reduce tumor progression.
Figure 2
Figure 2
Future perspective on mapping the metabolic CAF–tumor cells interactions. The interaction of CAF and cancer cells lead to metabolic reprogramming in tumors, majorly in glucose, amino acids and lipid metabolism. Reprogrammed CAFs increase several amino acids (glutamine, kynurenine, alanine, asparagine, proline), lipid (LPC) and lactate secretions which fuels malignant glucose cancer metabolism, supporting tumor growth. CAFs-derived exosomes also contribute in driving carcinogenesis by supplying glucose and glutamine to cancer cells. ROS, produced by CAFs or tumor cells, become an important mediator in CAFs–tumor cells’ metabolic interactions further supporting cancer malignancy. Yet, there is no therapy that specifically _targets CAFs’ metabolism most probably due to the high CAF heterogeneity. In silico approaches may help in unravelling the mechanistic insights of CAF-tumor crosstalk, ultimately identifying tailored CAF-_targeted therapies. Glc (Glucose), Gln (Glutamine), GS (Glutamine synthetase), FAK (Focal adhesion kinase), MAPK (mitogen-activated protein kinases), ECM (Extracellular matrix), ROS (Reactive oxygen species), CCR (Chemokine receptor), TDO2 (Tryptophan 2,3-Dioxygenase), Cav-1 (Caveolin-1), FASN (Fatty acid synthase), LPC (Lysophophatidylcholine), HIF1a (Hypoxia-inducible factor 1-alpha), ATF4 (Activating Transcription Factor 4), MCT (Monocarboxylate Transporter), FATP1 (Fatty acid transport protein 1).
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