Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 3237 2022-05-12 14:09:00 |
2 format -108 word(s) 3129 2022-05-13 03:36:21 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Walchli, S.; Forcados, C.; Joaquina, S.; , . Metabolism in Chimeric Antigen Receptor T Cells. Encyclopedia. Available online: (accessed on 18 April 2024).
Walchli S, Forcados C, Joaquina S,  . Metabolism in Chimeric Antigen Receptor T Cells. Encyclopedia. Available at: Accessed April 18, 2024.
Walchli, Sebastien, Christopher Forcados, Sandy Joaquina,  . "Metabolism in Chimeric Antigen Receptor T Cells" Encyclopedia, (accessed April 18, 2024).
Walchli, S., Forcados, C., Joaquina, S., & , . (2022, May 12). Metabolism in Chimeric Antigen Receptor T Cells. In Encyclopedia.
Walchli, Sebastien, et al. "Metabolism in Chimeric Antigen Receptor T Cells." Encyclopedia. Web. 12 May, 2022.
Metabolism in Chimeric Antigen Receptor T Cells

The manufacture of efficacious chimeric antigen receptor (CAR) T cells represents a major challenge in cellular therapy. An important aspect of their quality concerns energy production and consumption, known as metabolism. T cells tend to adopt diverse metabolic profiles depending on their differentiation state and their stimulation level. It is therefore expected that the introduction of a synthetic molecule such as CAR, activating endogenous signaling pathways, will affect metabolism. In addition, upon patient treatment, the tumor microenvironment might influence the CAR T cell metabolism by compromising the energy resources. The access to novel technology with higher throughput and reduced cost has led to an increased interest in studying metabolism. Indeed, methods to quantify glycolysis and mitochondrial respiration have been available for decades but were rarely applied in the context of CAR T cell therapy before the release of the Seahorse XF apparatus. 

metabolism CAR T cells

1. Introduction

T cells modified with a chimeric antigen receptor (CAR) have demonstrated remarkable clinical efficacy in several B-cell malignancies. However, this strategy has shown less promising results against solid tumors, where heterogeneity, an immunosuppressive microenvironment, and low antigen specificities remain major barriers to effective and safe CAR therapy. A solution resides in optimizing the CAR T cell fitness, which involves a proper understanding of their metabolism.
Metabolism plays a pivotal role in many cellular processes through the maintenance of survival, adaptation (fitness), and specialized functions. Different techniques have been developed to study the cellular respiratory profile through the detection and analysis of metabolic markers. In 1960, the Clark electrodes were used to measure the concentration of glucose and oxygen by electrochemistry, using a platinum catalytic surface [1][2][3]. The respiratory function can now be analyzed at high resolution with fluorescence microscopy by measurement of the mitochondrial calcium, mitochondrial membrane potential, pH, and NAD(P)H autofluorescence. Real-time fluorescence resonance energy transfer (FRET) is also used to estimate the mitochondrial or glucose flux in single live cells [4][5]. Other strategies include the tracking of specific metabolic markers by stable isotopes and intra- or extracellular metabolic sensors [6] using flow cytometry and mass cytometry [7][8][9]. Although a variety of assays exist to assess metabolism, they are usually destructive and/or lack real-time measurements. Furthermore, most of these assays lack in throughput capacity. Recent technologies have emerged to address these issues, among them are Seahorse XF by Agilent and O2k by Oroboros. Both allow for the assessment of the mitochondrial respiration of live cells or isolated mitochondria. Their respective advantages and limitations are further discussed by Horan et al. [10].

2. Study of Metabolism in CAR T Cells

It is therefore important to benchmark a CAR T cell metabolic assessment method by harmonizing the protocols in order to capture the impact of metabolism for future CAR T cell therapies. The studies involving Seahorse analyzers and CAR T cells were mainly performed using the Mito Stress Kit, whereby mitochondrial respiration is challenged with OXPHOS inhibitors. Briefly, specific agonists or antagonists of the ETC are sequentially administered throughout the experiment, and the variations in OCR and ECAR are measured. The measurements are operated using fluorescent sensors fitted in a bio-cartridge [11]. First, oligomycin, an inhibitor of ATP synthase [12], decreases mitochondrial respiration and therefore oxygen consumption. Second, carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) uncouples the mitochondrial proton gradient necessary for ATP production, which induces maximum oxygen consumption through complex IV. Finally, Rotenone and Antimycin A, which are, respectively, complex I and III inhibitors, are injected, inducing a mitochondrial respiration blockade [13][14][15]. Altogether, the drug-induced variations inform on the key mitochondrial parameters through OCR variations (such as basal respiration, ATP-synthesis linked respiration, and proton leak, which can inform on mitochondria damage or be used as a mechanism to regulate mitochondrial ATP production) and the spare respiratory capacity (SRC). The latter is of great importance as it characterizes the cell’s ability to meet an energetic challenge and therefore reflects specific T cell functions, such as activation, proliferation, or differentiation. In addition, ECAR measures the rate of extracellular acidification, which mainly comes from the accumulation of lactic acid in the medium (glycolytic pathway) [16]. Among the studies reported, some used the Seahorse technology to explore the influence of CAR signaling tails (4-1BB vs. CD28) [17][18], while others focused on comparing CARs with either different designs or targets [19][20][21][22][23], the additional secretory functions [24][25], the combination with PD-1/PD-L1 blockage [26], the polarization of the T cells [24][27], or the influence of co-expressing enzymes alongside the CARs in T cells [28][29].

3. The Influence of the Signaling Domain

The articles comparing the CD28 and 4-1BB co-stimulatory domains used either a mesothelin CAR or a CD19 CAR [17][18]. They observed differences between 4-1BB CARs and CD28 CARs in terms of basal OCR and SRC. In the study of Kawalekar et al. [17], CARs with a 4-1BB co-stimulatory domain showed a higher basal OCR and SRC than the CD28 CAR. Interestingly, extensive investigation of the candidate genes of the oxidative or glycolytic pathways showed a differential expression between the 4-1BB and CD28 CAR T cells that was consistent with the Seahorse assay results. Similarly, the ratio of mitochondrial mass to cell mass and the expression levels of the mitochondrial genes (encoded by either the nuclear or the mitochondrial genome) were increased in CAR T cells with 4-1BB co-stimulation. The observed difference in SRC strongly hints at a reprogramming of the transcriptional networks by the 4-1BB CAR, leading to enhanced mitochondrial biogenesis and oxidative metabolism. The researchers showed that the co-stimulation domain used in the CAR design strongly impacts the metabolic profiles and lineage fate of the T cells. While Liu and colleagues [18] observed a lower basal OCR with 4-1BB CARs than with CD28 CARs, the SRC was also higher with 4-1BB CARs. In their study, CARs bearing the two different co-stimulatory domains were used to validate and model exhausted tumor-infiltrating lymphocytes (TILs) in human hepatocellular carcinoma (HCC). Notably, to gain insights into the mechanism leading to these metabolic differences, the researchers measured the expression of candidate genes but focused on glycolytic and lipid metabolism. They showed that adding a 4-1BB signaling domain to the CAR makes the CAR T cells downregulate the expression of several genes involved in glycolysis and upregulate the genes associated with mitochondrial FAO. They also confirmed that the surface expression of CD137 is a marker of exhausted T cells (Tex) with superior effector functions and proliferation potential. Moreover, CD137+ Tex had a higher fatty acid-binding protein 5 (FABP5) expression. Inhibiting the FABP5 expression and the mitochondrial FAO impaired the anti-apoptotic and proliferative capacities of CD137-enriched Tex [18]. Both teams demonstrated that CAR T cells possessing the 4-1BB domain were more likely to use the OXPHOS pathway, which characterizes memory T cells, thus supporting central memory differentiation and T cell persistence. In both studies, the experimental conditions were different with respect to the number of cells and the concentrations of the drugs used. In fact, Kawalekar et al. [17] used both Seahorse XF24 and XF96 with 1 × 106 cells/well, while Liu et al. [18] used 1.5 × 105 cells/well. Agilent advises a cell monolayer in order to avoid hypoxia, which could affect the metabolism of the cells. As such, 1 × 106 T cells in a 96-well plate might be too dense, suggesting that the results obtained might be influenced by stressful culture conditions. In addition, the second study also used Etomoxir, which is a fatty acid oxidation inhibitor (FAO) [30][31], without further explanation. It should also be noted that these studies did not show results from untransduced (mock) controls (T cells without CARs), which would have provided an overview of the basal metabolic conditions of the T cells. This would have strengthened the conclusions made.

4. The Influence of the CAR Design

The pioneer work of W. Li et al. [19] showed that the blocking of CAR downmodulation by inhibiting its ubiquitination affected lysosomal degradation while promoting the recycling of internalized CARs to the cell surface. Inhibiting CAR ubiquitination also led to enhanced endosomal signaling promoting oxidative phosphorylation and memory T cell differentiation, leading to better in vivo persistence. Importantly, the researchers based their initial conclusions on observations of a higher OCR and SRC for ubiquitination-deficient CD19 CAR T cells, which they further confirmed with additional techniques. The second study was focused on the tonic signaling of CARs and, in particular, Treg CARs [22]. The researchers observed an exhaustion profile for Tregs expressing CARs prone to tonic signaling. Although stable and suppressive in vitro, the cells failed to maintain in vivo function in a xenogeneic model of graft-versus-host disease (GvHD). Indeed, Lamarche and colleagues [22] obtained a higher OCR and ECAR, and a lower SRC, for tonic-signaling CARs, which led them to the conclusion that tonic CAR T cells preferentially exploited a glycolytic pathway. Importantly, these results complemented transcriptome analysis showing changes in metabolic pathways and a phenotypic analysis demonstrating an exhaustion profile. Moreover, they were obtained using different T cells donors, strengthening their conclusions. Lastly, Hirabayashi et al. [23] compared a dual CAR approach with a classical CD28 second-generation CAR. Their dual CAR consisted of a polycistronic vector expressing both the second generation GD2 CAR (GD2.28ζ) and a B7-H3-4-1BB (B7-H3.BB) receptor. This CAR design allows for rapid antitumor effects in vivo, protects from tumor re-challenge, and prevents tumor escape resulting from low antigen density. Moreover, it retains both the functions of the induction of glycolysis and the preservation of OXPHOS. They used an XF24 apparatus with both the Cell Mito stress and the Cell Glycolytic stress kits to assess the metabolic profile of CAR T cells and compared the values obtained at days 0, 1, and 5 post-CAR activation, using 5 × 105 cells in a 24-well plate. The GD2.28ζ/B7-H3.BB dual CAR demonstrated higher glycolytic and OXPHOS activity than GD2.28ζ. Their assay was strengthened by the use of three independent experiments.

5. Polarization of CAR T Cells

The metabolic changes associated with CAR T cell polarization were also the focus of several recent studies. For instance, Liu et al. [24] examined the metabolism of Th1- and Th9-polarized T cells using Seahorse XF technology and showed that Th9 featured higher OCR and SRC than Th1. Furthermore, Th9-polarized CAR T cells secreted IL-9, expressed lower levels of exhaustion markers, maintained strong proliferative capacities, and had a central memory phenotype and a greater antitumor activity in vivo than Th1-polarized CAR T cells. These findings may not only broaden understanding of T cell polarization at the metabolic level but may also open the way for the production of a CAR T cell product adapted to solid tumors. However, it should be noted that the basal measurement of the OCR value was outside the range defined by Agilent for the XF24 instrument (600 pmol/min for 50–400 pmol/min). On the same line, Sabatino et al. [27] focused on characterizing CD19 CAR in CD8+ T cells enriched in the TSCM subset. These CD19 CAR CD8+ TSCM cells exhibited an enhanced metabolic fitness and had a robust and long-lasting antitumor effect in an acute lymphoblastic leukemia xenograft model. They observed across five different donors that CD19 CAR TSCM cells displayed a higher SRC than CD19 CAR T cells without TSCM enrichment. As previously mentioned, memory T cells favor OXPHOS while effector T cells favor glycolysis; therefore, memory T cells have a higher SRC compared to effector T cells.

6. Effect of the Combination of CAR T Cells and Combinatorial Designs

A set of studies also examined the T cell metabolic outcome following various stimulations. Serganova et al. [26] compared naïve, PHA-stimulated, and anti-human prostate-specific membrane antigen (PSMA) CAR T cells, while looking at the kinetics of OCR and ECAR up to 15 days after T cell isolation. The aim of their study was to assess the combination of CAR targeting PSMA with a PD-1/PD-L1 blockade. They observed that the PD-1/PD-L1 blockade resulted in a short-term enhanced response, suggesting other immunomodulatory mechanisms restricting CAR T cells in their prostate cancer model. The researchers observed that naïve T cells featured slightly increased basal OCR and ECAR at day 2, which declined over time. Conversely, CAR T cells showed only a modest increase in basal and maximal OCR, starting from day 8, and remained constant until day 15. Concerning basal ECAR variation, the same observation was made following PHA treatment, suggesting that unspecific and specific stimulation yielded similar levels of ECAR. These data can be considered strong because they were the means of values obtained from different donors. In addition to the Mito Stress Kit, these last two studies [26][27] also used the Glycolysis Stress Test Kit. Apart from studying the CAR influence on the T cell phenotype, other researchers have focused upon capturing the direct metabolic impact of introducing a modified/enhanced CAR construct in comparison to a conventional one. In the work of Zhang et al. [25], CD19 CAR T cells co-expressing or not a soluble PD-1 receptor (sPD-1) were analyzed. The co-expressing T cells led to a reduced tumor burden and prolonged survival in an in vivo tumor model of high PD-L1 expression, compared to conventional second-generation CAR T cells. Here, the researchers obtained similar levels of OCR in all conditions (mock, CD19 CAR, and CD19 CAR-sPD-1)—with or without a 48 h co-culture with target tumor cells—and observed that the OCR of the CD19 CAR-sPD-1 T cells was slightly higher, and the ECAR lower, compared to the CD19 CAR T cells. In conclusion, the CD19 CAR-sPD-1 T cells showed similar levels of OXPHOS, but reduced glycolysis, compared to the CD19 CAR T cells, regardless of the presence of tumor cells. Here again, the researchers used different complementary experiments to draw conclusions on the metabolism of CAR T cells. In particular, they performed RNA sequencing and showed that their CD19-sPD1 CAR T cells were less differentiated and less exhausted and activated less glycolytic pathway-related genes. Researchers have recently presented an approach to improving the efficacy of CD19 CAR T cells by combining CD19 CAR with an anti-IgKappa (IGK) CAR, called Kz19BB [20]. Researchers studied the metabolism of the T cells expressing different CARs and observed a similar metabolic profile for the anti-IgK CAR and the Kz-19BB CAR when stimulated with a specific antigen (kappa light chain). An inverted construct, 19z-KBB CAR, showed a similar metabolic profile to the CD19 CAR, demonstrating that the metabolic changes observed were solely due to the CD3z-containing construct. These results were obtained with one donor and corroborate additional in vitro results. Elsewhere, another group has tested a different combination of targeting units against GD2 and B7H3 antigens. There, the researchers used the synthetic Notch system (CAR GD2-B7H3) [21] to promote resistance to exhaustion and improve the metabolic fitness of the expressing cells. Briefly, this strategy was based on the transcriptional control of the B7-H3 CAR by a GD2 SynNotch CAR. This SynNotch-gated CAR T cell controlled the tumor burden both in vitro and in vivo. Moreover, the fatal neurotoxicity reported using the GD2 CAR T cell was linked to the use of the CD28 co-stimulation domain as this was not observed when a 4-1BB co-stimulation domain was used. With this arrangement, GD2-B7H3 T cells showed a similar rate of oxygen consumption to that of the non-transduced cells, and a higher SRC compared to conventional B7H3 CAR after 48 h of co-culture with target cells. This indicated a greater capacity for oxidative metabolism. This was confirmed by Gene Set Enrichment Analysis, which demonstrated a significant increase in the transcription of glycolytic exhaustion genes in conventional CAR-T B7H3 cells compared to GD2-B7H3 cells.

7. CAR Modified to Enhance T Cell Resistance to TME

Finally, two recent studies [28][29] evaluated the impact of the co-expression of enzymes affecting metabolism, alongside CARs, in order to generate constructs equipped to fight a strong TME. The enzymes used, Lactobacillus brevis NADH Oxidase (LbNOX) [28] and D-2-hydroxyglutarate dehydrogenase (D2HGDH) [29], were expected to provide resistance to the TME by affecting mitochondrial respiration. Co-expression of LbNOX with a mesothelin-specific CD28ζ CAR (CAR_Nox) induced higher levels of baseline OCR compared to a CD28ζ CAR co-expressed with GFP (CAR_GFP), reaching levels similar to those of untransduced T cells. The addition of lactate increased the OCR levels for the CAR_Nox. Subsequent treatment with Rotenone and Antimycin A did not impair the CAR_Nox T cells OCR as much as the OCR of untransduced and CAR_GFP T cells, validating the LbNOX activity in vitro. In addition to the inclusion of an untransduced T cell control, it specified the use of three donors, strengthening the validity of the observations. Therefore, co-expressing LbNox with the CAR in T cells resulted in enhanced oxygen and lactate consumption, as well as increased pyruvate production and resiliency to lactate dehydrogenase inhibition but did not confer an increased antitumor efficiency in vivo. The second study was more promising; Yang et al. [29] tested CD19 CAR T cells with a D2HGDH knocked-out (KO) or overexpressed (OE) backgrounds. D2HGDH is a mitochondrial protein shown to catabolize a component present at high levels in the TME, D-2-hydroxyglutarate (D2HG). In the D2HGDH-OE background, CAR-T cells showed a significant decrease in basal and maximal OCR, which corroborates their flow cytometric observation of an expanded effector memory subset in D2HGDH-OE CAR-T cells. This was further complemented by an in vivo assessment of D2HGDH-OE CAR-T cells, which showed increased tumor control and persistence. Accordingly, the opposite was observed in the KO background. Thus, affecting metabolism seems to represent a reliable route to resisting the TME.
Together, these studies used the Seahorse technology to investigate the differentiation and stimulation states of CAR T cells. Overall, when higher OCR rates were detected, it reflected the involvement of OXPHOS, which is associated with a memory T cell phenotype and therefore a more persistent state. In contrast, a high ECAR associated with a preference toward glycolysis and was associated with an effector T cell phenotype. The ECAR value was not taken into account in all the studies. The ECAR values complement the OCR values, allowing an overview of both the OXPHOS and the glycolytic pathways. Another key point is the influence of the co-stimulatory domain on the differentiation state of transduced T cells. Notably, a CD28 co-stimulatory domain favors an effector memory phenotype, while 4-1BB favors a central memory phenotype. On a technical note, the majority of the articles did not indicate the number of technical replicates (Agilent recommends doing six) and the number of biological replicates (or the number of donors used). These are important aspects of the analysis since variation between individuals can sometimes be greater than the effect of the drug in one individual. Furthermore, non-transduced T cells were not always used as a control. As such, intra- and inter-study comparisons were difficult to capture; indeed, some baselines were drawn using mock T cells while others were made with irrelevant CAR-expressing T cells.


  1. Clark, L.C.; Wolf, R.; Granger, D.; Taylor, Z. Continuous Recording of Blood Oxygen Tensions by Polarography. J. Appl. Physiol. 1953, 6, 189–193.
  2. Wang, J. Electrochemical Glucose Biosensors. Chem. Rev. 2008, 108, 814–825.
  3. Clark, L.C., Jr.; Lyons, C. Electrode Systems for Continuous Monitoring in Cardiovascular Surgery. Ann. N. Y. Acad. Sci. 1962, 102, 29–45.
  4. Bittner, C.; Loaiza, A.; Ruminot, I.; Larenas, V.; Sotelo-Hitschfe, T.; Gutiérrez, R.; Córdova, A.; Valdebenito, R.; Frommer, W.; Barros, L.F. High Resolution Measurement of the Glycolytic Rate. Front. Neuroenergetics 2010, 2, 26.
  5. Takanaga, H.; Chaudhuri, B.; Frommer, W.B. GLUT1 and GLUT9 as the Major Contributors to Glucose Influx in HEPG2 Cells Identified by a High Sensitivity Intramolecular FRET Glucose Sensor. Biochim. Biophys. Acta 2008, 1778, 1091–1099.
  6. Kang, Y.P.; Ward, N.P.; DeNicola, G.M. Recent Advances in Cancer Metabolism: A Technological Perspective. Exp. Mol. Med. 2018, 50, 1–16.
  7. Rumsey, W.; Vanderkooi, J.; Wilson, D. Imaging of Phosphorescence: A Novel Method for Measuring Oxygen Distribution in Perfused Tissue. Science 1988, 241, 1649–1651.
  8. Dmitriev, R.I.; Papkovsky, D.B. Optical Probes and Techniques for O2 Measurement in Live Cells and Tissue. Cell Mol. Life Sci. 2012, 69, 2025–2039.
  9. Argüello, R.J.; Combes, A.J.; Char, R.; Gigan, J.-P.; Baaziz, A.I.; Bousiquot, E.; Camosseto, V.; Samad, B.; Tsui, J.; Yan, P.; et al. SCENITH: A Flow Cytometry Based Method to Functionally Profile Energy Metabolism with Single Cell Resolution. Cell Metab. 2020, 32, 1063–1075.e7.
  10. Horan, M.P.; Pichaud, N.; Ballard, J.W.O. Review: Quantifying Mitochondrial Dysfunction in Complex Diseases of Aging. J. Gerontol. Ser. A 2012, 67, 1022–1035.
  11. Ferrick, D.A.; Neilson, A.; Beeson, C. Advances in Measuring Cellular Bioenergetics Using Extracellular Flux. Drug Discov. Today 2008, 13, 268–274.
  12. Lardy, H.A.; Johnson, D.; McMurray, W.C. Antibiotics as Tools for Metabolic Studies. I. A Survey of Toxic Antibiotics in Respiratory, Phosphorylative and Glycolytic Systems. Arch. Biochem. Biophys. 1958, 78, 587–597.
  13. Öberg, K.E. The Site of the Action of Rotenone in the Respiratory Chain. Exp. Cell Res. 1961, 24, 163–164.
  14. Kim, H.; Esser, L.; Hossain, M.B.; Xia, D.; Yu, C.-A.; Rizo, J.; Van Der Helm, D.; Deisenhofer, J. Structure of Antimycin A1, a Specific Electron Transfer Inhibitor of Ubiquinol−Cytochrome c Oxidoreductase. Available online: (accessed on 7 September 2021).
  15. Baur, J.R.; Workman, M. Influence of Carbonyl Cyanide Phenylhydrazone Derivatives on the Respiration Rate of Banana Pulp Tissue. Nature 1964, 201, 612.
  16. Wu, M.; Neilson, A.; Swift, A.L.; Moran, R.; Tamagnine, J.; Parslow, D.; Armistead, S.; Lemire, K.; Orrell, J.; Teich, J.; et al. Multiparameter Metabolic Analysis Reveals a Close Link between Attenuated Mitochondrial Bioenergetic Function and Enhanced Glycolysis Dependency in Human Tumor Cells. Am. J. Physiol. Cell Physiol. 2007, 292, C125–C136.
  17. Kawalekar, O.U.; O’Connor, R.S.; Fraietta, J.A.; Guo, L.; McGettigan, S.E.; Posey, A.D.; Patel, P.R.; Guedan, S.; Scholler, J.; Keith, B.; et al. Distinct Signaling of Coreceptors Regulates Specific Metabolism Pathways and Impacts Memory Development in CAR T Cells. Immunity 2016, 44, 380–390.
  18. Liu, F.; Liu, W.; Zhou, S.; Yang, C.; Tian, M.; Jia, G.; Wang, H.; Zhu, B.; Feng, M.; Lu, Y.; et al. Identification of FABP5 as an Immunometabolic Marker in Human Hepatocellular Carcinoma. J. Immunother. Cancer 2020, 8, e000501.
  19. Li, W.; Qiu, S.; Chen, J.; Jiang, S.; Chen, W.; Jiang, J.; Wang, F.; Si, W.; Shu, Y.; Wei, P.; et al. Chimeric Antigen Receptor Designed to Prevent Ubiquitination and Downregulation Showed Durable Antitumor Efficacy. Immunity 2020, 53, 456–470.e6.
  20. Köksal, H.; Dillard, P.; Juzeniene, A.; Kvalheim, G.; Smeland, E.B.; Myklebust, J.H.; Inderberg, E.M.; Wälchli, S. Combinatorial CAR Design Improves Target Restriction. J. Biol. Chem. 2020, 100116.
  21. Moghimi, B.; Muthugounder, S.; Jambon, S.; Tibbetts, R.; Hung, L.; Bassiri, H.; Hogarty, M.D.; Barrett, D.M.; Shimada, H.; Asgharzadeh, S. Preclinical Assessment of the Efficacy and Specificity of GD2-B7H3 SynNotch CAR-T in Metastatic Neuroblastoma. Nat. Commun. 2021, 12, 511.
  22. Lamarche, C.; Novakovsky, G.E.; Qi, C.N.; Weber, E.W.; Mackall, C.L.; Levings, M.K. Repeated Stimulation or Tonic-Signaling Chimeric Antigen Receptors Drive Regulatory T Cell Exhaustion. bioRxiv 2020.
  23. Hirabayashi, K.; Du, H.; Xu, Y.; Shou, P.; Zhou, X.; Fucá, G.; Landoni, E.; Sun, C.; Chen, Y.; Savoldo, B.; et al. Dual-Targeting CAR-T Cells with Optimal Co-Stimulation and Metabolic Fitness Enhance Antitumor Activity and Prevent Escape in Solid Tumors. Nat. Cancer 2021, 2, 904–918.
  24. Liu, L.; Bi, E.; Ma, X.; Xiong, W.; Qian, J.; Ye, L.; Su, P.; Wang, Q.; Xiao, L.; Yang, M.; et al. Enhanced CAR-T Activity against Established Tumors by Polarizing Human T Cells to Secrete Interleukin-9. Nat. Commun. 2020, 11, 5920.
  25. Zhang, A.; Sun, Y.; Wang, S.; Du, J.; Gao, X.; Yuan, Y.; Zhao, L.; Yang, Y.; Xu, L.; Lei, Y.; et al. Secretion of Human Soluble Programmed Cell Death Protein 1 by Chimeric Antigen Receptor-Modified T Cells Enhances Anti-Tumor Efficacy. Cytotherapy 2020, 22, 734–743.
  26. Serganova, I.; Moroz, E.; Cohen, I.; Moroz, M.; Mane, M.; Zurita, J.; Shenker, L.; Ponomarev, V.; Blasberg, R. Enhancement of PSMA-Directed CAR Adoptive Immunotherapy by PD-1/PD-L1 Blockade. Mol. Ther. Oncolytics 2017, 4, 41–54.
  27. Sabatino, M.; Hu, J.; Sommariva, M.; Gautam, S.; Fellowes, V.; Hocker, J.D.; Dougherty, S.; Qin, H.; Klebanoff, C.A.; Fry, T.J.; et al. Generation of Clinical-Grade CD19-Specific CAR-Modified CD8+ Memory Stem Cells for the Treatment of Human B-Cell Malignancies. Blood 2016, 128, 519–528.
  28. Garcia-Canaveras, J.C.; Heo, D.; Trefely, S.; Leferovich, J.; Xu, C.; Philipson, B.I.; Ghassemi, S.; Milone, M.C.; Moon, E.K.; Snyder, N.W.; et al. CAR T-Cells Depend on the Coupling of NADH Oxidation with ATP Production. Cells 2021, 10, 2334.
  29. Yang, Q.; Hao, J.; Chi, M.; Wang, Y.; Li, J.; Huang, J.; Zhang, J.; Zhang, M.; Lu, J.; Zhou, S.; et al. D2HGDH-Mediated D2HG Catabolism Enhances the Anti-Tumor Activities of CAR-T Cells in an Immunosuppressive Microenvironment. Mol. Ther. 2022.
  30. Choi, B.K.; Lee, D.Y.; Lee, D.G.; Kim, Y.H.; Kim, S.-H.; Oh, H.S.; Han, C.; Kwon, B.S. 4-1BB Signaling Activates Glucose and Fatty Acid Metabolism to Enhance CD8+ T Cell Proliferation. Cell Mol. Immunol. 2017, 14, 748–757.
  31. Lopaschuk, G.D.; Wall, S.R.; Olley, P.M.; Davies, N.J. Etomoxir, a Carnitine Palmitoyltransferase I Inhibitor, Protects Hearts from Fatty Acid-Induced Ischemic Injury Independent of Changes in Long Chain Acylcarnitine. Circ. Res. 1988, 63, 1036–1043.
Subjects: Immunology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , ,
View Times: 393
Revisions: 2 times (View History)
Update Date: 13 May 2022