The success of immune-checkpoint blockade and chimeric antigen receptor (CAR) T cell therapies has established the remarkable capacity of the immune system to fight cancer. Over the past several years, it has become clear that immune cell responses to cancer are critically dependent upon metabolic programs that are specific to both immune cell type and function. Metabolic features of cancer cells and the tumor microenvironment impose constraints on immune cell metabolism that can favor immunosuppressive phenotypes and block antitumor responses. Advances in both preclinical and clinical studies have demonstrated that metabolic interventions can dramatically enhance the efficacy of immune-based therapies for cancer. As such, understanding the metabolic requirements of immune cells in the tumor microenvironment, as well as the limitations imposed therein, can have significant benefits for informing both current practice and future research in cancer immunotherapy.

In order to support the tremendous growth that characterizes tumors, cancer cells engage unique metabolic programs (1). These programs not only serve to facilitate unbridled proliferation but also facilitate the ability of cancer to evade immune responses. The specialized metabolic programs used by cancer create a hypoxic, acidic, nutrient-depleted tumor microenvironment (TME), which presents a prodigious hurdle for effective antitumor immunity. The TME is well suited for immune cells that suppress effector function and thus promote tumor immune evasion. As such, therapeutically targeting metabolic pathways affords the unique opportunity to not only inhibit tumor growth, but also alter the TME in order to enhance the efficacy of immunotherapy. As our understanding of immune metabolism has increased, it has become apparent that targeting metabolism can also have the added bonus of directly enhancing antitumor immune responses. There are several in-depth reviews delineating the metabolic pathways of both cancer and anticancer immune cells (2–5). In this review, at the crossroads of immunometabolism and immunotherapy, we seek to bring to light, for the cancer immunotherapist, the multiple different facets whereby targeting metabolism can potentially enhance the efficacy of cancer immunotherapy.

The extraordinary potential of endogenous antitumor immune responses to treat cancer has been revealed by checkpoint blockade in a number of different tumor types (6). That is, by simply inhibiting an inhibitory pathway (blocking the negative signal delivered by PD-1 to T cells), a patient's own tumor-specific T cells can eliminate their cancer. However, although remarkable, this single-agent therapy is only effective in a limited number of patients, in part consequent to the fact that tumor growth continues to outpace the rate of the immune response. Thus, it stands to reason that slowing down tumor growth and decreasing the tumor mass could enhance the efficacy of immunotherapy.

To this end, targeting tumor metabolism represents a powerful means to inhibit tumor growth. Cancer cells reprogram their metabolism to promote anabolic pathways and growth (7–10). In order to rapidly proliferate, cancer cells require proteins for growth, lipids for creating new membranes, and nucleic acids to support transcription and translation. For somatic cells, mitochondrial oxidation of nutrients, including glucose, amino acids, and fatty acids through the tricarboxylic acid (TCA) cycle, is used as an efficient means of generating ATP. However, because of their anabolic state, cancer cells reprogram their metabolism by upregulating the lactate-forming glycolysis called Warburg physiology (11, 12). This refers to using glycolysis to generate ATP even in the presence of oxygen, which allows for more rapid metabolism of glucose and regeneration of NAD+. Glycolytic intermediates enter other essential pathways, such as the pentose phosphate pathway, the one-carbon pathway, and the hexosamine biosynthesis pathway, all of which support high levels of cellular growth and proliferation. These pathways are readily inhibited by therapeutic agents. Studies also demonstrate that some cancers may use alternative fuels for energy generation, including lactate and branch-chained amino acids (13–15). Interestingly, many traditional chemotherapies such as methotrexate, 6-mercaptopurine (6-MP), and 5-fluorouracil (5-FU) are in fact metabolic inhibitors (16). Along these lines, the combination of carboplatin and pemetrexed, along with anti–PD-1, has demonstrated efficacy for the treatment of lung cancer (17). Although the efficacy in these trials is typically presented as a function of combination chemotherapy and immunotherapy, it is instructive to understand that pemetrexed is fundamentally a metabolic inhibitor that suppresses folate metabolism, as well as purine and pyrimidine synthesis.

Targeting metabolism to directly inhibit cancer cell growth and proliferation is a straightforward approach to enhance the efficacy of immunotherapy. However, as mentioned above, tumor metabolism also profoundly influences the TME. The high metabolic activity of cancer cells, in addition to a disorganized, dysfunctional vasculature, can drive hypoxia and nutrient depletion in the TME, leading to competition for oxygen and nutrients between cells within the TME, including cancer and immune cells (18–20). For example, robust glucose uptake and glycolysis in cancer cells is associated with enhanced infiltration of immune-suppressive myeloid-derived suppressor cells (MDSC) and decreased antitumor immune responses among tumor-infiltrating lymphocytes (TIL; refs. 18–22). Targeting cancer cell glycolysis has been shown to preserve antitumor T-cell function and improve response to checkpoint immunotherapy (23). The Cancer Genome Atlas data demonstrate decreased immune responses in tumors with high expression of hexokinase 2, the rate-limiting enzyme of glycolysis (20). Amino acids may also be the subject of metabolic competition between cancer and immune cells. A report demonstrates that high methionine uptake in cancer cells can lead to epigenetic reprogramming of antitumor T cells and impaired antitumor function (24). Similarly, it has been reported that ovarian cancers can dampen T-cell glycolysis and effector function through microRNA-mediated suppression of the methyltransferase, EZH2 (25). Another study reports that TME hypoxia can induce T-cell exhaustion, specifically through dysregulated mitochondrial dynamics in the context of T-cell receptor (TCR) and PD-1 signaling (26). In addition to depriving immune cells of necessary nutrients, tumor metabolism also leads to the production of immunosuppressive metabolites, such as lactic acid (27), reactive oxygen species (ROS; ref. 28), kynurenine (29), polyamines (30–34), adenosine (35–40), and cholesterol (41), all of which suppress antitumor immunity. Thus, targeting tumor metabolism can enhance immunotherapy by creating a TME that is more hospitable to the antitumor immune response (Fig. 1, Immune suppression). To this end, trials are currently under way that seek to enhance immunotherapy by blocking the production of adenosine by the ectonucleotidase CD73, as well as blocking the adenosine receptor, A2aR. Likewise, in spite of initial disappointing trials, there still remains interest in preventing the depletion of tryptophan and the production of kynurenine by inhibiting IDO1. In this regard, a study has identified interleukin-4–induced-1 (IL4I1) as a critical activator of aryl hydrocarbon receptor (AHR) activity through IDO1-independent generation of indole metabolites and kynurenic acid (42). As such, this may explain clinical trial failures of IDO1-specific inhibitors for immunotherapy.

Figure 1.

Metabolic intervention has pleiotropic effects on tumor immunology. The metabolic programs of cancer cells function as immune checkpoints within the TME through a number of mechanisms, including depletion of oxygen and nutrients, generation of toxic metabolites (e.g., acid, adenosine, and lactate, polyamines), and the production of aberrant extracellular matrix (ECM; left, Immune suppression). These characteristics favor immune-suppressive phenotypes, including regulatory T cells, MDSCs, and tumor-associated macrophages, and suppress antitumor effector responses of T cells and natural killer cells. Targeted interventions designed to dismantle cancer cell metabolism can simultaneously suppress cancer cell growth, decrease the production of ECM components, limit nutrient deprivation, curtail generation of toxic metabolites, and reprogram the immune response, favoring T-cell persistence and inflammatory myeloid infiltration (right, Immune promotion). CAR-T, chimeric antigen receptor T cell; CC, cancer cell; iMφ, inflammatory macrophage; TAM, tumor-associated macrophage; Teff, effector T cell; Tmem, long-lived memory T cell; Treg, regulatory T cell.

Figure 1.

Metabolic intervention has pleiotropic effects on tumor immunology. The metabolic programs of cancer cells function as immune checkpoints within the TME through a number of mechanisms, including depletion of oxygen and nutrients, generation of toxic metabolites (e.g., acid, adenosine, and lactate, polyamines), and the production of aberrant extracellular matrix (ECM; left, Immune suppression). These characteristics favor immune-suppressive phenotypes, including regulatory T cells, MDSCs, and tumor-associated macrophages, and suppress antitumor effector responses of T cells and natural killer cells. Targeted interventions designed to dismantle cancer cell metabolism can simultaneously suppress cancer cell growth, decrease the production of ECM components, limit nutrient deprivation, curtail generation of toxic metabolites, and reprogram the immune response, favoring T-cell persistence and inflammatory myeloid infiltration (right, Immune promotion). CAR-T, chimeric antigen receptor T cell; CC, cancer cell; iMφ, inflammatory macrophage; TAM, tumor-associated macrophage; Teff, effector T cell; Tmem, long-lived memory T cell; Treg, regulatory T cell.

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Another strategy is to alter the TME by directly inhibiting tumor metabolism. For example, inhibition of glutamine metabolism leads to a dramatic decrease in hypoxia, acidosis, and lactate production, as well as enhanced availability of nutrients in the TME for immune cells (43). Such an approach has the benefit of both inhibiting tumor growth and altering the TME. Interestingly, blocking glutamine metabolism also has the added benefit of leading to decreased kynurenine production by inhibiting the expression of IDO (44). It has also been demonstrated that T cell–generated IFNγ induces tumor cell lipid peroxidation and ferroptosis (45). This cytotoxic effect can be pharmacologically enhanced through inhibition of the glutamate–cystine antiporter system, leading to improved efficacy of checkpoint blockade. Finally, some tumors evade immune destruction by physically blocking infiltration of immune cells through the elaboration of extracellular matrix (ECM). This is particularly evident in pancreatic cancer (46). The production and elaboration of the ECM is metabolically demanding. Thus, targeting metabolism has the potential to overcome resistance to immunotherapy by making tumors accessible to immune cells. For example, a particular tumor may be classified as resistant to anti–PD-1. However, it is possible that anti–PD-1 is actually able to successfully unleash tumor-specific T cells, but these cells cannot infiltrate into the tumor. Thus, inhibition of the generation of ECM through metabolic therapy might readily convert such anti–PD-1–resistant tumors into susceptible tumors. Likewise, such a strategy might serve to enhance the efficacy of adoptive cellular therapy (ACT).

Myeloid-derived cells make up a considerable proportion of the cells in the TME, contributing up to 40% of the mass of a tumor in some cancers (47). Many of these cells play important roles in promoting tumor immune evasion. In addition to expressing the immunosuppressive ligands PD-L1 and PD-L2, tumor-associated macrophages (TAM) also express immunosuppressive metabolic enzymes such as arginase-1, as well as IDO (30–33, 48, 49). Notably, the metabolism of TAMs is distinct and resembles the metabolic programming of M2 macrophages (50, 51). For example, similar to T-regulatory cells (Treg), M2 macrophages rely more on fatty acid oxidation (FAO) and oxidative phosphorylation (OXPHOS) and are more reliant on glutamine metabolism than inflammatory M1 macrophages (50, 51). Studies have uncovered a distinct role for lipid uptake, accumulation, and oxidation, which are critical for TAM polarization and immunosuppressive activity (52). As such, blockade of lipid uptake or FAO suppresses the protumor activity of TAMs in mouse models. Similar to the M2-like immunosuppressive TAMs, tumor associated immunosuppressive MDSCs also possess distinct metabolic programs. Blocking glycolysis or glutamine metabolism has been shown to inhibit the expansion and function of these suppressive cells in the TME (44, 53). Indeed, blocking glutamine metabolism with 6-Diazo-5-oxo-l-norleucine (DON) not only inhibited MDSC accumulation in tumors but also promoted the generation and function of inflammatory M1 macrophages. Thus, metabolic therapy has the potential to inhibit tumor growth, inhibit the generation and function of suppressive TAMs/MDSCs, and promote the accumulation of inflammatory M1 macrophages (Fig. 1, Immune promotion).

The discussion thus far has emphasized the concept that targeting metabolism can inhibit tumor growth AND also positively influence the antitumor response by conditioning the TME and inhibiting the generation and function of suppressive cells. Effector CD4+ and CD8+ T cells are critical executors of the antitumor immune response. Upon activation, in the context of costimulation, these cells reprogram their metabolism to support their own prodigious growth and anabolic function (54). Early studies highlight the similarities in metabolic programming between activated, proliferating T cells and cancer cells (55–60), whereby activated T cells increase glycolysis even in the presence of oxygen (known as Warburg physiology). However, it is important to note that increased TCA cycle metabolism and OXPHOS are also instrumental (55–60). The similarity between tumor metabolism and activated T-cell metabolism raises the concern that targeting tumor metabolism might in fact inhibit antitumor T cells. As it turns out, this is not necessarily the case, as work has demonstrated that targeting tumor metabolism can simultaneously enhance antitumor T cells (43, 44). Indeed, in spite of their metabolic similarities, it has become clear that it is possible to differentially target cancer growth and the anticancer immune response. Although many cancer cells can be rigid in their metabolic programs, T cells can be plastic. It has been shown that acetate metabolism can overcome glucose restriction in CD8+ T-effector cells (61). Similarly, although blockade of glutamine metabolism can inhibit tumor growth, T cells can overcome this blockade by acquiring carbon via acetate metabolism (43).

Nonetheless, in spite of these alternative pathways, similar to cancer cells, targeting T-cell metabolism can inhibit proliferation and clonal expansion. Triple combination therapy using the glycolysis inhibitor 2-deoxyglucose (2-DG), glutamine inhibitor DON, and mitochondrial inhibitor metformin can inhibit activated T-cell proliferation and cytokine production (62), which has been shown to be an effective approach to preventing allograph rejection. Despite these dramatic suppressive effects through the simultaneous blockade of three independent metabolic pathways, targeted inhibition of any single pathway may actually enhance critical attributes, such as effector response upon rechallenge and resistance to activation-induced cell death. Blocking glycolysis with the inhibitor 2-DG will mitigate clonal expansion. However, it has been shown that 2-DG can also condition T cells to become more robust long-lived memory cells (63). To this end, inhibiting mTOR or AKT signaling, both of which will inhibit tumor growth, has been used to enhance the robustness of the antitumor T-cell response (63–65).

Along these lines, tumor immune evasion can take the form of chronic, nonproductive antigen-specific activation. CD8+ T cells in the TME can adopt a state of functional exhaustion, wherein they are poorly proliferative and unable to generate sufficient cytotoxicity against target cancer cells. Interestingly, exhausted T cells can also be defined by their (dysregulated) metabolism. Exhausted T cells can be characterized not just by the upregulation of PD-1 and loss of cytokine production, but also by mTOR signaling in the absence of productive glycolytic function and anabolic processes. To this end, PD-1 signaling inhibits the expression of peroxisome proliferator-activated receptor-γ coactivator 1α (PGC1α), which in turn leads to diminished mitochondrial function and less oxidative capacity compared with normal effector T cells (66, 67). Targeting metabolism can in part reverse this phenotype and restore function.

An important subtype of T cells, called regulatory T cells (Treg), relies on distinct metabolic programs and plays a critical role in dampening antitumor immune responses. This suppressive subset is defined by the FoxP3 transcription factor, which reprograms metabolism toward mitochondrial respiration (OXPHOS) through MYC suppression (68). Unlike antitumor effector T cells, immunosuppressive Tregs adapt to metabolic challenges within the TME, resisting lactate-induced suppression of function and proliferation. Interestingly, work has demonstrated that targeting CD36-mediated lipid metabolism in intratumoral Tregs can disrupt their ability to function in lactate-enriched environments and improve antitumor immune responses (69).

Currently, checkpoint blockade and ACT in the form of chimeric antigen receptor (CAR) T cells represent the two stalwarts of clinically approved cancer immunotherapy. CAR-T cells are approved to treat a number of hematologic malignancies and have shown remarkable efficacy in patients with leukemia and lymphoma with extensive cancer burden. However, the progress of this approach has been stymied by two hurdles. First, it is clear that even after initial responses, lack of persistence of the adoptively transferred cells is a major mechanism of relapse (70). Second, in spite of the successes in hematologic malignancies, CAR-T cell therapy has yet to induce impressive, durable responses in solid tumors (71). Both of these problems have the potential to be overcome with metabolic therapy.

By design, CAR-T cells are generated through ex vivo activation and expansion. This process lends itself to metabolic intervention. For example, expanding T cells in the presence of an inhibitor of glycolysis can promote memory cell generation leading to enhanced persistence and function when the cells are adoptively transferred into tumor-bearing mice (63). In a similar fashion, inhibiting AKT signaling during ex vivo processing can promote the generation of T cells with transcriptional and metabolic profiles associated with enhanced memory (72). Likewise, the inclusion of increased arginine or potassium in the culture media can promote the enhanced generation of long-lived memory cells (73, 74). Other strategies, including inhibition of lactate dehydrogenase (LDH), the critical enzyme in aerobic glycolysis, T-cell sorting based on low mitochondrial membrane potential, and limiting ROS metabolism in T cells, can also enhance the generation of long-lived or stem-like antitumor T cells and enhance adoptive immunotherapy regimens (75–77).

The fact that CAR-T cells are genetically altered creates the opportunity for metabolic reprogramming by genetic means. First, it has been noted that the 4-1BB signaling domain is superior to the CD28 signaling domain in promoting the expansion of central memory T (Tcm) cells with increased mitochondrial biogenesis and oxidative metabolism (78–83). In this case, the design of the CAR itself can reprogram the cells metabolically. However, strategies to genetically engineer T cells further have also been used. For example, overexpression of phosphoenolpyruvate carboxykinase 1 (PCK1), which converts oxaloacetate (OAA) into phosphoenolpyruvate (PEP), has been shown to enhance the efficacy of adoptively transferred T cells (20). Likewise, it has been demonstrated that forced overexpression of PGC1α in donor T cells can promote mitochondrial fitness and prevent exhaustion of adoptively transferred cells (66). Alternatively, pharmacologically promoting mitochondrial fusion and inhibiting mitochondrial fission can lead to superior control of adoptively transferred T cells by enhancing memory generation with increased mitochondrial mass, OXPHOS, and spare respiratory capacity (SRC; ref. 84).

Although ex vivo metabolic interventions are appealing, treating patients either before or during ACT can be an effective strategy as well. The efficacy of even the most potent adoptively transferred cells may be readily thwarted by lack of access to the tumor. Likewise, the potency of antitumor T cells can be mitigated upon tumor infiltration by a hostile TME. To this end, the addition of metabolic therapy to CAR-T therapy might facilitate the success of this modality for solid tumors. Indeed, metabolic therapy can facilitate inhibiting tumor growth and condition the TME to make it more hospitable to antitumor immune responses during the period of cell processing. Treating patients with metabolic therapy post ACT has the potential to enhance the robustness of the adoptively transferred cells. As previously discussed, the inhibition of a number of metabolic targets, such as glutamine metabolism, glycolysis, mTOR, and AKT, not only serves to mitigate tumor growth but also promotes long-lived T-cell memory (63, 72, 73, 85). Thus, for example, the continued treatment with an inhibitor of glutamine metabolism, even after ACT, has the potential to both keep tumor growth in check and promote long-lived memory in the adoptively transferred cells (85).

Undoubtedly, the success of checkpoint blockade and CAR-T therapy has revolutionized the treatment of cancer. Patients with significant tumor burdens can now be cured by endogenous (checkpoint blockade) and exogenous (ACT) antitumor immune responses. However, although the successes have been impressive, it is clear that the task ahead is to build on these successes to enhance the depth of immunotherapy in patients with tumors deemed sensitive and the breadth of immunotherapy to tumors that have not, as of yet, demonstrated robust responses to immunotherapy. The addition of metabolic therapy to both target tumor metabolism and regulate immune metabolism has the potential to accelerate these goals (Fig. 1). Indeed, we view the addition of pemetrexed to immunotherapy for non–small cell lung cancer (NSCLC) as just the beginning. Specifically, targeting metabolism has the potential to enhance the efficacy of immunotherapy in NSCLC, melanoma, renal cell carcinoma, and other cancers for which checkpoint blockade has already been approved. Likewise, for cancers, such as prostate cancer, breast cancer, pancreatic cancer, and others, where immunotherapy has yet to show significant efficacy, targeting metabolism has the potential to both alter the TME and increase immune infiltration, converting these resistant tumors to susceptible ones. For these same reasons, metabolic therapy has the potential to facilitate the expansion of CAR-T therapy to solid tumors, as well as improve the overall efficacy of this approach, by enhancing the persistence of adoptively transferred cells.

R.D. Leone reports grants from NIH during the conduct of the study, other from Corvus (previous support for materials and other financial support to study A2aR inhibitor CPI-444) outside the submitted work, and a patent for Methods for Cancer and Immunotherapy Using Glutamine Analogues, including DON, licensed to Dracen Pharmaceuticals. J.D. Powell reports grants, personal fees, and other from Dracen Pharmaceuticals (scientific founder, consulting, equity), grants, personal fees, and other from Corvus (past sponsored research agreement with institution, consulting fees, and limited amount of equity), grants from AstraZeneca (to institution), Bristol-Myers Squibb (to institution), and Bluebird (past sponsored research agreement), and personal fees from Sitryx (scientific founder and equity) during the conduct of the study, as well as a patent for PCT/US16/44829 pending to Dracen Pharmaceuticals.

This work was supported by the NIH (R01CA226765 and R01CA229451) and The Bloomberg–Kimmel Institute for Cancer Immunotherapy.

1.
DeBerardinis
RJ
,
Chandel
NS
. 
Fundamentals of cancer metabolism
.
Sci Adv
2016
;
2
:
e1600200
.
2.
Leone
RD
,
Powell
JD
. 
Metabolism of immune cells in cancer
.
Nat Rev Cancer
2020
;
20
:
516
31
.
3.
O'Sullivan
D
,
Sanin
DE
,
Pearce
EJ
,
Pearce
EL
. 
Metabolic interventions in the immune response to cancer
.
Nat Rev Immunol
2019
;
19
:
324
35
.
4.
Andrejeva
G
,
Rathmell
JC
. 
Similarities and distinctions of cancer and immune metabolism in inflammation and tumors
.
Cell Metab
2017
;
26
:
49
70
.
5.
Guerra
L
,
Bonetti
L
,
Brenner
D
. 
Metabolic modulation of immunity: a new concept in cancer immunotherapy
.
Cell Rep
2020
;
32
:
107848
.
6.
Darvin
P
,
Toor
SM
,
Sasidharan Nair
V
,
Elkord
E
. 
Immune checkpoint inhibitors: recent progress and potential biomarkers
.
Exp Mol Med
2018
;
50
:
1
11
.
7.
Kim
J
,
DeBerardinis
RJ
. 
Mechanisms and implications of metabolic heterogeneity in cancer
.
Cell Metab
2019
;
30
:
434
46
.
8.
Weinberg
F
,
Hamanaka
R
,
Wheaton
WW
,
Weinberg
S
,
Joseph
J
,
Lopez
M
, et al
Mitochondrial metabolism and ROS generation are essential for Kras-mediated tumorigenicity
.
Proc Natl Acad Sci U S A
2010
;
107
:
8788
93
.
9.
Ma
EH
,
Verway
MJ
,
Johnson
RM
,
Roy
DG
,
Steadman
M
,
Hayes
S
, et al
Metabolic profiling using stable isotope tracing reveals distinct patterns of glucose utilization by physiologically activated CD8(+) T cells
.
Immunity
2019
;
51
:
856
70
.
10.
Chen
PH
,
Cai
L
,
Huffman
K
,
Yang
C
,
Kim
J
,
Faubert
B
, et al
Metabolic diversity in human non-small cell lung cancer cells
.
Mol Cell
2019
;
76
:
838
51
.
11.
Warburg
O
,
Gawehn
K
,
Geissler
AW
. 
[Metabolism of leukocytes]
.
Z Naturforsch B
1958
;
13B
:
515
6
.
12.
Bakker
A
. 
Einige Übereinstimmungen im Stoffwechsel der Carcinomzellen und Exsudatleukocyten
.
Klin Wochenschr
1927
;
6
:
252
4
.
13.
Mashimo
T
,
Pichumani
K
,
Vemireddy
V
,
Hatanpaa
KJ
,
Singh
DK
,
Sirasanagandla
S
, et al
Acetate is a bioenergetic substrate for human glioblastoma and brain metastases
.
Cell
2014
;
159
:
1603
14
.
14.
Hui
S
,
Ghergurovich
JM
,
Morscher
RJ
,
Jang
C
,
Teng
X
,
Lu
W
, et al
Glucose feeds the TCA cycle via circulating lactate
.
Nature
2017
;
551
:
115
8
.
15.
Faubert
B
,
Li
KY
,
Cai
L
,
Hensley
CT
,
Kim
J
,
Zacharias
LG
, et al
Lactate metabolism in human lung tumors
.
Cell
2017
;
171
:
358
71
.
16.
Locasale
JW
. 
Serine, glycine and one-carbon units: cancer metabolism in full circle
.
Nat Rev Cancer
2013
;
13
:
572
83
.
17.
Schaer
DA
,
Geeganage
S
,
Amaladas
N
,
Lu
ZH
,
Rasmussen
ER
,
Sonyi
A
, et al
The folate pathway inhibitor pemetrexed pleiotropically enhances effects of cancer immunotherapy
.
Clin Cancer Res
2019
;
25
:
7175
88
.
18.
Chang
CH
,
Qiu
J
,
O'Sullivan
D
,
Buck
MD
,
Noguchi
T
,
Curtis
JD
, et al
Metabolic competition in the tumor microenvironment is a driver of cancer progression
.
Cell
2015
;
162
:
1229
41
.
19.
Lukey
MJ
,
Katt
WP
,
Cerione
RA
. 
Targeting amino acid metabolism for cancer therapy
.
Drug Discov Today
2017
;
22
:
796
804
.
20.
Ho
PC
,
Bihuniak
JD
,
Macintyre
AN
,
Staron
M
,
Liu
X
,
Amezquita
R
, et al
Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses
.
Cell
2015
;
162
:
1217
28
.
21.
Cascone
T
,
McKenzie
JA
,
Mbofung
RM
,
Punt
S
,
Wang
Z
,
Xu
C
, et al
Increased tumor glycolysis characterizes immune resistance to adoptive T cell therapy
.
Cell Metab
2018
;
27
:
977
87
.
22.
Li
W
,
Tanikawa
T
,
Kryczek
I
,
Xia
H
,
Li
G
,
Wu
Ke
, et al
Aerobic glycolysis controls myeloid-derived suppressor cells and tumor immunity via a specific CEBPB isoform in triple-negative breast cancer
.
Cell Metab
2018
;
28
:
87
103
.
23.
Renner
K
,
Bruss
C
,
Schnell
A
,
Koehl
G
,
Becker
HM
,
Fante
M
, et al
Restricting glycolysis preserves T cell effector functions and augments checkpoint therapy
.
Cell Rep
2019
;
29
:
135
50
.
24.
Bian
Y
,
Li
W
,
Kremer
DM
,
Sajjakulnukit
P
,
Li
S
,
Crespo
J
, et al
Cancer SLC43A2 alters T cell methionine metabolism and histone methylation
.
Nature
2020
;
585
:
277
82
.
25.
Zhao
E
,
Maj
T
,
Kryczek
I
,
Li
W
,
Wu
Ke
,
Zhao
L
, et al
Cancer mediates effector T cell dysfunction by targeting microRNAs and EZH2 via glycolysis restriction
.
Nat Immunol
2016
;
17
:
95
103
.
26.
Yu
YR
,
Imrichova
H
,
Wang
H
,
Chao
T
,
Xiao
Z
,
Gao
M
, et al
Disturbed mitochondrial dynamics in CD8(+) TILs reinforce T cell exhaustion
.
Nat Immunol
2020
;
21
:
1540
51
.
27.
Brand
A
,
Singer
K
,
Koehl
GE
,
Kolitzus
M
,
Schoenhammer
G
,
Thiel
A
, et al
LDHA-associated lactic acid production blunts tumor immunosurveillance by T and NK cells
.
Cell Metab
2016
;
24
:
657
71
.
28.
Liao
Z
,
Chua
D
,
Tan
NS
. 
Reactive oxygen species: a volatile driver of field cancerization and metastasis
.
Mol Cancer
2019
;
18
:
65
.
29.
Labadie
BW
,
Bao
R
,
Luke
JJ
. 
Reimagining IDO pathway inhibition in cancer immunotherapy via downstream focus on the tryptophan-kynurenine-aryl hydrocarbon axis
.
Clin Cancer Res
2019
;
25
:
1462
71
.
30.
Shearer
JD
,
Richards
JR
,
Mills
CD
,
Caldwell
MD
. 
Differential regulation of macrophage arginine metabolism: a proposed role in wound healing
.
Am J Physiol
1997
;
272
:
E181
90
.
31.
Ye
C
,
Geng
Z
,
Dominguez
D
,
Chen
S
,
Fan
J
,
Qin
L
, et al
Targeting ornithine decarboxylase by α-difluoromethylornithine inhibits tumor growth by impairing myeloid-derived suppressor cells
.
J Immunol
2016
;
196
:
915
.
32.
Mills
CD
,
Shearer
J
,
Evans
R
,
Caldwell
MD
. 
Macrophage arginine metabolism and the inhibition or stimulation of cancer
.
J Immunol
1992
;
149
:
2709
14
.
33.
Hayes
CS
,
Shicora
AC
,
Keough
MP
,
Snook
AE
,
Burns
MR
,
Gilmour
SK
. 
Polyamine-blocking therapy reverses immunosuppression in the tumor microenvironment
.
Cancer Immunol Res
2014
;
2
:
274
85
.
34.
Gökmen
SS
,
Aygit
AC
,
Ayhan
MS
,
Yorulmaz
F
,
Gülen
S
. 
Significance of arginase and ornithine in malignant tumors of the human skin
.
J Lab Clin Med
2001
;
137
:
340
4
.
35.
Ferrante
CJ
,
Pinhal-Enfield
G
,
Elson
G
,
Cronstein
BN
,
Hasko
G
,
Outram
S
, et al
The adenosine-dependent angiogenic switch of macrophages to an M2-like phenotype is independent of interleukin-4 receptor alpha (IL-4Ralpha) signaling
.
Inflammation
2013
;
36
:
921
31
.
36.
Leone
RD
,
Lo
YC
,
Powell
JD
. 
A2aR antagonists: next generation checkpoint blockade for cancer immunotherapy
.
Comput Struct Biotechnol J
2015
;
13
:
265
72
.
37.
Leone
RD
,
Emens
LA
. 
Targeting adenosine for cancer immunotherapy
.
J Immunother Cancer
2018
;
6
:
57
.
38.
Waickman
AT
,
Alme
A
,
Senaldi
L
,
Zarek
PE
,
Horton
M
,
Powell
JD
. 
Enhancement of tumor immunotherapy by deletion of the A2A adenosine receptor
.
Cancer Immunol Immunother
2012
;
61
:
917
26
.
39.
Ohta
A
,
Gorelik
E
,
Prasad
SJ
,
Ronchese
F
,
Lukashev
D
,
Wong
MKK
, et al
A2A adenosine receptor protects tumors from antitumor T cells
.
Proc Natl Acad Sci U S A
2006
;
103
:
13132
7
.
40.
Leone
RD
,
Sun
IM
,
Oh
MH
,
Sun
IH
,
Wen
J
,
Englert
J
, et al
Inhibition of the adenosine A2a receptor modulates expression of T cell coinhibitory receptors and improves effector function for enhanced checkpoint blockade and ACT in murine cancer models
.
Cancer Immunol Immunother
2018
;
67
:
1271
84
.
41.
Ma
X
,
Bi
E
,
Lu
Y
,
Su
P
,
Huang
C
,
Liu
L
, et al
Cholesterol induces CD8(+) T cell exhaustion in the tumor microenvironment
.
Cell Metab
2019
;
30
:
143
56
.
42.
Sadik
A
,
Somarribas Patterson
LF
,
Öztürk
S
,
Mohapatra
SR
,
Panitz
V
,
Secker
PF
, et al
IL4I1 is a metabolic immune checkpoint that activates the AHR and promotes tumor progression
.
Cell
2020
;
182
:
1252
70
.
43.
Leone
RD
,
Zhao
L
,
Englert
JM
,
Sun
IM
,
Oh
MH
,
Sun
IH
, et al
Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion
.
Science
2019
;
366
:
1013
21
.
44.
Oh
MH
,
Sun
IH
,
Zhao
L
,
Leone
RD
,
Sun
IM
,
Xu
W
, et al
Targeting glutamine metabolism enhances tumor-specific immunity by modulating suppressive myeloid cells
.
J Clin Invest
2020
;
130
:
3865
84
.
45.
Wang
W
,
Green
M
,
Choi
JE
,
Gijón
M
,
Kennedy
PD
,
Johnson
JK
, et al
CD8+ T cells regulate tumour ferroptosis during cancer immunotherapy
.
Nature
2019
;
569
:
270
4
.
46.
Karamitopoulou
E
. 
Tumour microenvironment of pancreatic cancer: immune landscape is dictated by molecular and histopathological features
.
Br J Cancer
2019
;
121
:
5
14
.
47.
Najac
C
,
Chaumeil
MM
,
Kohanbash
G
,
Guglielmetti
C
,
Gordon
JW
,
Okada
H
, et al
Detection of inflammatory cell function using 13C magnetic resonance spectroscopy of hyperpolarized [6-13C]-arginine
.
Sci Rep
2016
;
6
:
31397
.
48.
Zajac
E
,
Schweighofer
B
,
Kupriyanova
TA
,
Juncker-Jensen
A
,
Minder
P
,
Quigley
JP
, et al
Angiogenic capacity of M1- and M2-polarized macrophages is determined by the levels of TIMP-1 complexed with their secreted proMMP-9
.
Blood
2013
;
122
:
4054
67
.
49.
Liu
H
,
Shen
Z
,
Wang
Z
,
Wang
X
,
Zhang
H
,
Qin
J
, et al
Increased expression of IDO associates with poor postoperative clinical outcome of patients with gastric adenocarcinoma
.
Sci Rep
2016
;
6
:
21319
.
50.
Vats
D
,
Mukundan
L
,
Odegaard
JI
,
Zhang
L
,
Smith
KL
,
Morel
CR
, et al
Oxidative metabolism and PGC-1beta attenuate macrophage-mediated inflammation
.
Cell Metab
2006
;
4
:
13
24
.
51.
Jha
AK
,
Huang
SC-C
,
Sergushichev
A
,
Lampropoulou
V
,
Ivanova
Y
,
Loginicheva
E
, et al
Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization
.
Immunity
2015
;
42
:
419
30
.
52.
Su
P
,
Wang
Q
,
Bi
E
,
Ma
X
,
Liu
L
,
Yang
M
, et al
Enhanced lipid accumulation and metabolism are required for the differentiation and activation of tumor-associated macrophages
.
Cancer Res
2020
;
80
:
1438
50
.
53.
Jian
SL
,
Chen
WW
,
Su
YC
,
Su
YW
,
Chuang
TH
,
Hsu
SC
, et al
Glycolysis regulates the expansion of myeloid-derived suppressor cells in tumor-bearing hosts through prevention of ROS-mediated apoptosis
.
Cell Death Dis
2017
;
8
:
e2779
.
54.
Patel
CH
,
Leone
RD
,
Horton
MR
,
Powell
JD
. 
Targeting metabolism to regulate immune responses in autoimmunity and cancer
.
Nat Rev Drug Discov
2019
;
18
:
669
88
.
55.
Wang
R
,
Dillon
CP
,
Shi
LZ
,
Milasta
S
,
Carter
R
,
Finkelstein
D
, et al
The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation
.
Immunity
2011
;
35
:
871
82
.
56.
Gatza
E
,
Wahl
DR
,
Opipari
AW
,
Sundberg
TB
,
Reddy
P
,
Liu
C
, et al
Manipulating the bioenergetics of alloreactive T cells causes their selective apoptosis and arrests graft-versus-host disease
.
Sci Transl Med
2011
;
3
:
67ra68
.
57.
Chang
CH
,
Curtis
JD
,
Maggi
LB
,
Faubert
B
,
Villarino
AV
,
O'Sullivan
D
, et al
Posttranscriptional control of T cell effector function by aerobic glycolysis
.
Cell
2013
;
153
:
1239
51
.
58.
Sena
LA
,
Li
S
,
Jairaman
A
,
Prakriya
M
,
Ezponda
T
,
Hildeman
DA
, et al
Mitochondria are required for antigen-specific T cell activation through reactive oxygen species signaling
.
Immunity
2013
;
38
:
225
36
.
59.
Frauwirth
KA
,
Riley
JL
,
Harris
MH
,
Parry
RV
,
Rathmell
JC
,
Plas
DR
, et al
The CD28 signaling pathway regulates glucose metabolism
.
Immunity
2002
;
16
:
769
77
.
60.
Menk
AV
,
Scharping
NE
,
Moreci
RS
,
Zeng
X
,
Guy
C
,
Salvatore
S
, et al
Early TCR signaling induces rapid aerobic glycolysis enabling distinct acute T cell effector functions
.
Cell Rep
2018
;
22
:
1509
21
.
61.
Qiu
J
,
Villa
M
,
Sanin
DE
,
Buck
MD
,
O'Sullivan
D
,
Ching
R
, et al
Acetate promotes T cell effector function during glucose restriction
.
Cell Rep
2019
;
27
:
2063
74
.
62.
Lee
CF
,
Lo
YC
,
Cheng
CH
,
Furtmüller
GJ
,
Oh
B
,
Andrade-Oliveira
V
, et al
Preventing allograft rejection by targeting immune metabolism
.
Cell Rep
2015
;
13
:
760
70
.
63.
Sukumar
M
,
Liu
J
,
Ji
Y
,
Subramanian
M
,
Crompton
JG
,
Yu
Z
, et al
Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function
.
J Clin Invest
2013
;
123
:
4479
88
.
64.
Delgoffe
GM
,
Kole
TP
,
Zheng
Y
,
Zarek
PE
,
Matthews
KL
,
Xiao
Bo
, et al
The mTOR kinase differentially regulates effector and regulatory T cell lineage commitment
.
Immunity
2009
;
30
:
832
44
.
65.
Araki
K
,
Turner
AP
,
Shaffer
VO
,
Gangappa
S
,
Keller
SA
,
Bachmann
MF
, et al
mTOR regulates memory CD8 T-cell differentiation
.
Nature
2009
;
460
:
108
12
.
66.
Scharping
NE
,
Menk
AV
,
Moreci
RS
,
Whetstone
RD
,
Dadey
RE
,
Watkins
SC
, et al
The tumor microenvironment represses T cell mitochondrial biogenesis to drive intratumoral T cell metabolic insufficiency and dysfunction
.
Immunity
2016
;
45
:
374
88
.
67.
Bengsch
B
,
Johnson
AL
,
Kurachi
M
,
Odorizzi
PM
,
Pauken
KE
,
Attanasio
J
, et al
Bioenergetic insufficiencies due to metabolic alterations regulated by the inhibitory receptor PD-1 are an early driver of CD8(+) T cell exhaustion
.
Immunity
2016
;
45
:
358
73
.
68.
Angelin
A
,
Gil-de-Gómez
L
,
Dahiya
S
,
Jiao
J
,
Guo
L
,
Levine
MH
, et al
Foxp3 reprograms T cell metabolism to function in low-glucose, high-lactate environments
.
Cell Metab
2017
;
25
:
1282
93
.
69.
Wang
H
,
Franco
F
,
Tsui
YC
,
Xie
X
,
Trefny
MP
,
Zappasodi
R
, et al
CD36-mediated metabolic adaptation supports regulatory T cell survival and function in tumors
.
Nat Immunol
2020
;
21
:
298
308
.
70.
Ghorashian
S
,
Kramer
AM
,
Onuoha
S
,
Wright
G
,
Bartram
J
,
Richardson
R
, et al
Enhanced CAR T cell expansion and prolonged persistence in pediatric patients with ALL treated with a low-affinity CD19 CAR
.
Nat Med
2019
;
25
:
1408
14
.
71.
Martinez
M
,
Moon
EK
. 
CAR T cells for solid tumors: new strategies for finding, infiltrating, and surviving in the tumor microenvironment
.
Front Immunol
2019
;
10
:
128
.
72.
Crompton
JG
,
Sukumar
M
,
Roychoudhuri
R
,
Clever
D
,
Gros
A
,
Eil
RL
, et al
Akt inhibition enhances expansion of potent tumor-specific lymphocytes with memory cell characteristics
.
Cancer Res
2015
;
75
:
296
305
.
73.
Vodnala
SK
,
Eil
R
,
Kishton
RJ
,
Sukumar
M
,
Yamamoto
TN
,
Ha
NH
, et al
T cell stemness and dysfunction in tumors are triggered by a common mechanism
.
Science
2019
;
363
:
eaau0135
.
74.
Geiger
R
,
Rieckmann
JC
,
Wolf
T
,
Basso
C
,
Feng
Y
,
Fuhrer
T
, et al
L-arginine modulates T cell metabolism and enhances survival and anti-tumor activity
.
Cell
2016
;
167
:
829
42
.
75.
Hermans
D
,
Gautam
S
,
García-Cañaveras
JC
,
Gromer
D
,
Mitra
S
,
Spolski
R
, et al
Lactate dehydrogenase inhibition synergizes with IL-21 to promote CD8(+) T cell stemness and antitumor immunity
.
Proc Natl Acad Sci U S A
2020
;
117
:
6047
55
.
76.
Sukumar
M
,
Liu
J
,
Mehta
GU
,
Patel
SJ
,
Roychoudhuri
R
,
Crompton
JG
, et al
Mitochondrial membrane potential identifies cells with enhanced stemness for cellular therapy
.
Cell Metab
2016
;
23
:
63
76
.
77.
Pilipow
K
,
Scamardella
E
,
Puccio
S
,
Gautam
S
,
De Paoli
F
,
Mazza
EMC
, et al
Antioxidant metabolism regulates CD8+ T memory stem cell formation and antitumor immunity
.
JCI insight
2018
;
3
:
e122299
.
78.
Zhao
Z
,
Condomines
M
,
van der Stegen
SJC
,
Perna
F
,
Kloss
CC
,
Gunset
G
, et al
Structural design of engineered costimulation determines tumor rejection kinetics and persistence of CAR T cells
.
Cancer Cell
2015
;
28
:
415
28
.
79.
Milone
MC
,
Fish
JD
,
Carpenito
C
,
Carroll
RG
,
Binder
GK
,
Teachey
D
, et al
Chimeric receptors containing CD137 signal transduction domains mediate enhanced survival of T cells and increased antileukemic efficacy in vivo
.
Mol Ther
2009
;
17
:
1453
64
.
80.
Imai
C
,
Mihara
K
,
Andreansky
M
,
Nicholson
IC
,
Pui
CH
,
Geiger
TL
, et al
Chimeric receptors with 4-1BB signaling capacity provoke potent cytotoxicity against acute lymphoblastic leukemia
.
Leukemia
2004
;
18
:
676
84
.
81.
Finney
HM
,
Akbar
AN
,
Lawson
AD
. 
Activation of resting human primary T cells with chimeric receptors: costimulation from CD28, inducible costimulator, CD134, and CD137 in series with signals from the TCR zeta chain
.
J Immunol
2004
;
172
:
104
13
.
82.
Davila
ML
,
Riviere
I
,
Wang
X
,
Bartido
S
,
Park
J
,
Curran
K
, et al
Efficacy and toxicity management of 19-28z CAR T cell therapy in B cell acute lymphoblastic leukemia
.
Sci Transl Med
2014
;
6
:
224ra225
.
83.
Kawalekar
OU
,
O'Connor
RS
,
Fraietta
JA
,
Guo
L
,
McGettigan
SE
,
Posey
AD
, et al
Distinct signaling of coreceptors regulates specific metabolism pathways and impacts memory development in CAR T cells
.
Immunity
2016
;
44
:
380
90
.
84.
Buck
MD
,
O'Sullivan
D
,
Klein Geltink
RI
,
Curtis
JD
,
Chang
CH
,
Sanin
DE
, et al
Mitochondrial dynamics controls T cell fate through metabolic programming
.
Cell
2016
;
166
:
63
76
.
85.
Nabe
S
,
Yamada
T
,
Suzuki
J
,
Toriyama
K
,
Yasuoka
T
,
Kuwahara
M
, et al
Reinforce the antitumor activity of CD8(+) T cells via glutamine restriction
.
Cancer Sci
2018
;
109
:
3737
50
.