Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Mar 15.
Published in final edited form as: Cancer Res. 2017 Feb 28;77(6):1245–1249. doi: 10.1158/0008-5472.CAN-16-1647

Interplay Between Immune Checkpoint Proteins and Cellular Metabolism

Sangbin Lim 1,*, Joshua B Phillips 1,*, Luciana Madeira da Silva 1, Ming Zhou 2, Oystein Fodstad 3, Laurie B Owen 1, Ming Tan 1
PMCID: PMC5354977  NIHMSID: NIHMS845079  PMID: 28246276

Abstract

With the recent successes in immuno-oncology, renewed interest in the role of immune checkpoint modulators such as the B7 family proteins has escalated. The immune checkpoint proteins play a crucial role in the regulation of cellular immunity; however, their contribution to other aspects of cancer biology remains unclear. Accumulating evidence indicate that immune checkpoint proteins can regulate metabolic energetics of the tumor, the tumor microenvironment, and the tumor-specific immune response, leading to metabolic reprogramming of both malignant cells and immune cells involved in mounting and sustaining this response. Immune cell metabolism impacts the activation status of immune cells and ultimately the immune response in cancer. Tumor cells may deplete nutrients that immune cells require for optimal generation, expansion, and function. They may also generate toxic metabolites in the microenvironment or induce conserved inhibitory pathways that impair immune function and thus inhibit anti-tumor responses. In this review, we will discuss how cancer cells with altered expression of immune checkpoint proteins can potently inhibit immune function through the alteration of cellular and microenvironmental metabolism, providing a new perspective on the interplay between these pathways and offering a potential therapeutic intervention strategy in treatment of malignant disease.

Introduction

Cancer cells differ from normal cells in the metabolic machinery used to support cell proliferation and survival. This dysregulated metabolism has been regarded a hallmark of cancer. Energy production in most cancer cells shows a distinct that is dependent more on aerobic glycolysis (also known as Warburg effect) instead of mitochondrial oxidative phosphorylation (OXPHOS) (1). Although glycolysis is a physiological response to hypoxia in normal tissues, cancer cells constitutively take up glucose and produce lactate regardless of oxygen availability (2). This increase in glycolytic flux allows rapid production of ATP and intermediates of the glycolytic pathways to fulfill the metabolic demands of proliferating cells. Cancer cells also show an increase in biosynthetic pathways leading to the production of macromolecules required for growth and proliferation (3). Reactive oxygen species (ROS) are a diverse class of radical species that can have different roles depending on their concentration. In cancer cells, the same oncogenic mutations that promote altered metabolism and oncogenic signaling also result in high rate of ROS production (4). Although the precise molecular mechanisms underlying dysregulated cellular metabolism in cancer cells are not completely understood, emerging evidence shows that this altered metabolism contributes to enhanced cancer cell proliferation, survival, drug resistance and invasion/metastasis.

The immune system plays a crucial role in the protection of the animal or human against pathogens and cancers. It is typically divided into two categories: innate immunity and adaptive immunity. Adaptive immunity refers to a complex, antigen-specific immune response. Once an antigen is processed and then recognized, the adaptive immune system produces a plethora of immune cells specifically attacking that particular antigen, and creates “memory” that makes future responses to that antigen more efficient. When the immune system mounts a cell-mediated response to foreign antigens, the adaptive immunity recruits a variety of effectors including CD8+ cytotoxic T cells and CD4+ helper T cells (5). Three signals are required for T cell activation (5,6). First, T cell receptors must engage specific peptides presented by major histocompatibility complexes (MHCs) on antigen presenting cells (APCs) or the cancer cell. Second, specific receptors on T cells must bind ligands expressed on APCs or cancer cells to prevent anergy, which refers to failure to mount the response against an antigen. Third, signals provided by cytokines play a critical role in regulating the strength and type of immune responses. There are multiple co-stimulatory or co-inhibitory interactions among APCs and T cells (Figure 1A, Supplementary Table S1) (6), providing a key checkpoint in the regulation of T cell immunity and maintenance of immune homeostasis. These co-stimulatory or co-inhibitory proteins either can turn up a signal (co-stimulatory) or turn down a signal (co-inhibitory) in the immune system, and are referred to as immune checkpoint proteins.

Figure 1.

Figure 1

The interplay between immune checkpoint proteins and cellular metabolism. A. Immune checkpoint proteins can affect cellular metabolism and immune cell activation by 1) receptor/ligand ligation on cancer cells and immune cells, 2) nutrients competition, and 3) cancer cell produced metabolites. B. Impact of immune checkpoint proteins on glycolytic signaling pathway. These signaling processes can suppress the immune cells leading to escaping immune surveillance and consequently promote cancer progression.

Immune cells, such as lymphocytes, also use Warburg effect like glycolytic pathways to accommodate their bioenergetics needs. Quiescent naïve T cells rely on OXPHOS and use glucose, amino acids and fatty acids for basal cellular needs. During the process of activation, these cells reprogram themselves to glycolytic metabolism to adapt to the increased energy requirements (7,8). Intriguingly, several recent studies have shown that the aberrant expression of immune checkpoint proteins is associated with tumor cell metabolic changes. The crosstalk between these immune checkpoint proteins and cellular metabolism may have a profound impact on cancer cell evasion from the immune system.

Immune checkpoint proteins

Immune checkpoint proteins regulate the immune response in order to maintain self-tolerance and prevent excessive inflammatory reactions. Many different ligands/receptors are involved in the coordinated actions of an effective and efficient immune response (Supplementary Table S1). Several unique classes of checkpoint proteins exist, among these are the tumor necrosis factor receptor superfamily (9) and B7 family (10), which can influence immune cells differently based on their expression patterns. Cancer cells can disrupt the immune response through the overexpression of inhibitory molecules like PD-L1 (Figure 1A) (11) or the loss of expression of stimulatory molecules like CD40L (12). Tumors can evade immune surveillance even in the presence of tumor antigens because the immune cells may not receive adequate signals for activation and proliferation or are suppressed by inhibitory checkpoint proteins (13).

Suppressing immune inhibitory checkpoint proteins can enhance immune responses, prevent cancer progression, and improve patient survival (14). The goal of effective immunotherapy is to activate the patient’s own immune system to eliminate cancer with high selectivity, low toxicity, and durable responses that can recognize neo-antigens arising in evolving tumors (15). Several immune checkpoint proteins were shown to be dysregulated in tumors and immune cells and contribute to immune evasion. Blockade of these inhibitory checkpoint proteins has been intensely pursued in recent years as a strategy to enhance T cell infiltration and effector functions in cancer. Blocking antibodies against the T cell co-inhibitory receptors or ligands, such as CTLA-4, PD-1, and PD-L1, have shown promising efficacy in advanced melanoma, non-small cell lung carcinoma, renal cell carcinoma, bladder cancer, and lymphoma (1618).

Metabolic interplay between tumor and immune cells

Along with direct interaction between tumor and immune cells, an indirect mechanism of immune response modulation has been investigated. Several recent studies reported that the metabolic interplay between tumor and immune cells in the tumor microenvironment plays an important role in immune response regulation (19,20). Nutrient competition is one such metabolic mechanism involved in tumor immune evasion (Figure 1A). In growing tumors, deprivation of environmental nutrients, such as glucose or amino acids, by rapid proliferation, high rates of glycolysis or overexpression of tumor specific molecules can inhibit T cell functions (21). Activation of T cells and their effector functions relies on activation of distinct signaling pathways leading to metabolic reprograming. Metabolic reprograming in T cells increases energy demand and is crucial for the triggering of effector T cell activation. A decreased level of nutrients within the tumor microenvironment can lead to T cell “anergy” or dormancy to spare energy or to preferentially activate autophagy as a survival mechanism to counteract nutrient insufficiency (22). Chang et al. described a competition between tumor cells and tumor infiltrating T lymphocytes (TILs) for glucose within the tumor niche that this can drive cancer progression through metabolic competition (20). In this study, tumor PD-L1 expression promoted glycolysis and Akt/mTOR activation in tumor cells while suppressing mTOR activity in T cells through glucose competition. Checkpoint blockade with anti-PD-L1 antibodies inhibited tumor progression and glucose uptake in tumor cells and increased mTOR activity and glucose uptake of T cells (Figure 1B). Remarkably, two other checkpoint-blockade antibodies, anti-PD-1 and anti-CTLA-4, were also shown to cause changes in extracellular glucose concentrations, although the mechanisms underlying these metabolic changes are still not fully understood (20). Akt/mTOR activation is known to promote glycolysis via enhanced HIF-1α activity, which additionally sustains fatty acid and protein synthesis to support malignant cell survival. Rao et al. found that in solid tumors mTOR activation of TILs tuned a balance between effector and memory CD8+ T cells by regulating expression of a T cell-associated transcription factor, T-bet (23). They also showed that the immune checkpoint molecule B7-1, induced mTOR kinase activity in naive CD8+ T cells via PI3K and STAT4 pathways. Blocking mTOR activity by rapamycin reversed IL-12-induced effector functions consistent with T-bet loss. Consequently, mTOR signaling has an important role in metabolic reprograming and nutrient competition in both tumors and immune cells, and immune checkpoints including PD-L1 and B7-1 can mediate metabolic reprograming through mTOR signal pathway. Further, PD-1 intrinsically expressed in melanoma cells, but not lymphocytes, has also been shown to upregulate Akt/mTOR signaling in cancer cells, with the phosphorylation status of two intracellular motifs of PD-1, ITIM and ITSM, being critical for this modulatory role (24).

Recently, B7-H3 has been reported to play to regulate cancer cell metabolism (Figure 1B). Lim et al. reported that expression of B7-H3 metabolically reprograms cancer cells by increases HIF-1α activity, glucose uptake, and lactate production in breast cancer cells (25). They showed that B7-H3 enhanced glycolysis in tumor cells through upregulating HIF-1α and its target proteins, LDHA and PDK1, while knocking down B7-H3 suppressed glycolysis and tumor growth in vitro and in a breast cancer xenograft mouse model. Nunes-Xavier et al. also showed that B7-H3 activated Akt/mTOR pathway and increased glycolytic capacity in breast cancer cells (26) and observed that B7-H3 confers resistance to mTOR inhibitors. These reports raise the possibility that tumor cell expressed immune check point proteins, such as B7-H3, can promote glycolysis in tumor cells and cause nutrient competition between tumor cells and immune cells through metabolic reprograming in tumor microenvironment.

In addition to nutrients competition, accumulation of toxic metabolites in tumor microenvironment can also impact the interplay between tumor cells and immune cells (Figure 1A). The byproduct of aerobic glycolysis, lactate, is secreted from tumor cells and accumulates in the microenvironment resulting in local acidification known as acidosis. Acidosis in the tumor environment can suppress proliferation and cytokine production in cytotoxic T cells (CTLs) and limit CTL anti-tumor activity (27). The tumor microenvironment is characterized by a consistent reduction in oxygen resulting in hypoxia-induced upregulation of HIF-1α and the expression of PD-L1 in tumors, leading to inhibition of T cell-mediated cytotoxicity and immune escape (28). Taken together, experimental evidence indicates that acidosis induced by hypoxia and HIF-α activation can result in the upregulation of immune checkpoint proteins such as PD-L1 that further contribute to the inhibition of T cell antitumor responses.

Similar to glucose metabolism, amino acid metabolism can play a regulatory role in T cell activation. For example, tumor indoleamine 2, 3-dioxygenase (IDO), an enzyme which converts tryptophan to kynurenine and generates nicotinamide adenine dinucleotide (NAD), has been shown to deplete the essential amino acid, tryptophan, in the microenvironment resulting in T cell inhibition (21). Another amino acid, arginine, is tightly regulated by two enzymes, nitric oxide synthase (NOS) and arginase (ARG). While ARG hydrolyzes arginine to ornithine and urea, NOS oxidizes arginine to citrulline and nitric oxide (NO). Several reports have documented that the inducible isoform of NOS (iNOS) is highly expressed and ARG activity is upregulated in diverse cancer types and can be regulated by acidosis (29). A large amount of NO generated by both enzymes, is capable of either promoting or inhibiting tumor progression and metastasis depending on the concentration, duration of exposure, and cellular sensitivity to NO (30). Peroxynitrite is one of reactive nitrogen species (RNS) produced by the reaction between NO and reactive oxygen species within the tumor, and has been shown to induce apoptosis in T cells (31). As a consequence of high RNS production, the tumor microenvironment may become unsuitable for T cell activation, expansion, and effector function. Indeed, numerous reports indicate that peroxynitrite negatively affects T cell immunity. The connection between immune checkpoint protein regulation and amino acid metabolism has not been well elucidated, but deserves further study.

Lipid metabolism has also been implicated in immune response regulation. Recently, Yang et al. showed that cholesterol metabolism can modulate CD8+ T cell antitumor activity (32). Acetyl-CoA Acetyltransferase 1 (ACAT1) is a cholesterol esterification enzyme that converts free cholesterol to cholesteryl esters for storage. The inhibition of ACAT1 led to enhanced effector function and proliferation of CD8+ T cells due to increase in plasma membrane cholesterol levels. ACAT1-deficient CD8+ T cells showed reduction of tumor progression and metastasis in vivo. A combination treatment of ACAT1 inhibitor and anti-PD-1 antibody improved antitumor efficacy. Importantly, recent work by Patsoukis et al (33) showed that PD-1 prevents effector T cell development by inhibiting glycolytic reprogramming and promoting fatty acid oxidation (FAO) of activated CD4+ T cells endogenous lipids through overexpression of carnitine palmitoyltransferase I (CPT1A), a rate-limiting step enzyme in the mitochondria responsible for β-oxidation of long chain fatty acids. Their data also revealed that increased FAO was accompanied by upregulation of the major triacylglycerol hydrolase ATGL.

Intriguingly, Le et al. found that patients with mismatch repair-defects showed a clinical benefit of immune checkpoint blockade with pembrolizumab (34). When treated with the PD-1 antibody, patients with mismatch repair-deficient cancers showed a 40%–71% objective response rate while mismatch repair proficient patients were unresponsive to the same treatment regimen. Additionally, patients with a mismatch repair deficiency showed longer progression free survival intervals. Since DNA damage response has been shown to regulate metabolic homeostasis (35), we speculate that there is a possibility that a deficiency in DNA damage repair may cause dysregulation of cellular metabolism including oxidative stress, mutations in metabolic genes, and activation of metabolic signaling pathways, to create a favorable microenvironment benefitting immune checkpoint manipulation of the anti-tumor immune response.

Concluding remarks and future directions

In summary, we have discussed how cancer cells with dysregulated expression of immune checkpoint proteins inhibit immune cells by altering cellular and microenvironmental metabolism. Several lines of evidence suggest the metabolic interplay between tumor and immune cells plays an important role in immune response regulation. The evidence raises a strong argument that therapeutic limitations may exist in the absence of a combinational strategy to address both aspects (36). For example, immune cells can be activated by targeting an overexpressed inhibitory checkpoint protein present on cancer cells, but the immune cells may not be able to sustain their energetic needs without a metabolically favorable environment. Currently, multiple antibodies have been developed against immune checkpoint proteins (37), and a few promising metabolic targets in cancer have been identified (38). A combinational strategy such as drug-antibody conjugates (39) or nanoparticles (40) could be specifically designed to target a checkpoint protein overexpressed in cancer cells like B7-H3 (41) which would inhibit immune suppression, and target delivery of a metabolic drug into the tumor cells. Future studies should address the mechanistic connection between immune checkpoint proteins and the metabolism of cancer and immune cells which may not be independent variables as we once thought. Unanswered and important questions remain including: What is the underlying mechanism of metabolic reprogramming in cancer cells and immune cells by immune checkpoint proteins? Is there a therapeutic window to exploit metabolic interventions in immuno-oncology? How can the metabolic contributions mediated through checkpoint proteins be targeted to maximize anti-tumor effects? Do specific nutrients or metabolites affect immune responses more than others (activation, anergy, death)? How do metabolic changes in normal tissues and the tumor niche impact the development and maintenance of a favorable immune response? Does metabolic function of the tumor or immune cells contribute to the outcome of immune checkpoint therapy? Answering these questions will expand our understanding of the interplay between metabolic regulation and the immune response, potentially providing new avenues to improve cancer immunotherapy.

Supplementary Material

1

Acknowledgments

We would like to thank Dr. Yu-quan Wei and Dr. Guangyong Peng for critically reading our manuscript. We are grateful to the support from The Vincent F. Kilborn, Jr. Cancer Research Foundation (M. Tan), NIH Grant R01CA149646 (M. Tan), and NSF of China, No. 81328019 (M. Zhou and M. Tan).

Footnotes

Conflict of interest statement: None of the authors have a conflict of interest to declare.

References

  • 1.Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324(5930):1029–33. doi: 10.1126/science.1160809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Lunt SY, Vander Heiden MG. Aerobic glycolysis: meeting the metabolic requirements of cell proliferation. Annual review of cell and developmental biology. 2011;27:441–64. doi: 10.1146/annurev-cellbio-092910-154237. [DOI] [PubMed] [Google Scholar]
  • 3.DeBerardinis RJ, Mancuso A, Daikhin E, Nissim I, Yudkoff M, Wehrli S, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(49):19345–50. doi: 10.1073/pnas.0709747104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Vaughn AE, Deshmukh M. Glucose metabolism inhibits apoptosis in neurons and cancer cells by redox inactivation of cytochrome c. Nature cell biology. 2008;10(12):1477–83. doi: 10.1038/ncb1807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Pennock ND, White JT, Cross EW, Cheney EE, Tamburini BA, Kedl RM. T cell responses: naive to memory and everything in between. Advances in physiology education. 2013;37(4):273–83. doi: 10.1152/advan.00066.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Chen L, Flies DB. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nature reviews Immunology. 2013;13(4):227–42. doi: 10.1038/nri3405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Pearce EL, Pearce EJ. Metabolic pathways in immune cell activation and quiescence. Immunity. 2013;38(4):633–43. doi: 10.1016/j.immuni.2013.04.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.O’Sullivan D, Pearce EL. Targeting T cell metabolism for therapy. Trends in immunology. 2015;36(2):71–80. doi: 10.1016/j.it.2014.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Locksley RM, Killeen N, Lenardo MJ. The TNF and TNF receptor superfamilies: integrating mammalian biology. Cell. 2001;104(4):487–501. doi: 10.1016/s0092-8674(01)00237-9. [DOI] [PubMed] [Google Scholar]
  • 10.Collins M, Ling V, Carreno BM. The B7 family of immune-regulatory ligands. Genome biology. 2005;6(6):223. doi: 10.1186/gb-2005-6-6-223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Patel SP, Kurzrock R. PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy. Molecular cancer therapeutics. 2015;14(4):847–56. doi: 10.1158/1535-7163.MCT-14-0983. [DOI] [PubMed] [Google Scholar]
  • 12.Dimberg J, Hugander A, Wagsater D. Expression of CD137 and CD137 ligand in colorectal cancer patients. Oncology reports. 2006;15(5):1197–200. [PubMed] [Google Scholar]
  • 13.Whiteside TL. Immune suppression in cancer: effects on immune cells, mechanisms and future therapeutic intervention. Seminars in cancer biology. 2006;16(1):3–15. doi: 10.1016/j.semcancer.2005.07.008. [DOI] [PubMed] [Google Scholar]
  • 14.Postow MA, Callahan MK, Wolchok JD. Immune Checkpoint Blockade in Cancer Therapy. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2015;33(17):1974–82. doi: 10.1200/JCO.2014.59.4358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. doi: 10.1016/j.cell.2011.02.013. [DOI] [PubMed] [Google Scholar]
  • 16.Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, et al. Improved survival with ipilimumab in patients with metastatic melanoma. The New England journal of medicine. 2010;363(8):711–23. doi: 10.1056/NEJMoa1003466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Wolchok JD, Neyns B, Linette G, Negrier S, Lutzky J, Thomas L, et al. Ipilimumab monotherapy in patients with pretreated advanced melanoma: a randomised, double-blind, multicentre, phase 2, dose-ranging study. The Lancet Oncology. 2010;11(2):155–64. doi: 10.1016/S1470-2045(09)70334-1. [DOI] [PubMed] [Google Scholar]
  • 18.Voena C, Chiarle R. Advances in cancer immunology and cancer immunotherapy. Discovery medicine. 2016;21(114):125–33. [PubMed] [Google Scholar]
  • 19.Ghesquiere B, Wong BW, Kuchnio A, Carmeliet P. Metabolism of stromal and immune cells in health and disease. Nature. 2014;511(7508):167–76. doi: 10.1038/nature13312. [DOI] [PubMed] [Google Scholar]
  • 20.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(6):1229–41. doi: 10.1016/j.cell.2015.08.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Munn DH, Mellor AL. Indoleamine 2,3 dioxygenase and metabolic control of immune responses. Trends in immunology. 2013;34(3):137–43. doi: 10.1016/j.it.2012.10.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Zheng Y, Delgoffe GM, Meyer CF, Chan W, Powell JD. Anergic T cells are metabolically anergic. Journal of immunology. 2009;183(10):6095–101. doi: 10.4049/jimmunol.0803510. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Rao RR, Li Q, Odunsi K, Shrikant PA. The mTOR kinase determines effector versus memory CD8+ T cell fate by regulating the expression of transcription factors T-bet and Eomesodermin. Immunity. 2010;32(1):67–78. doi: 10.1016/j.immuni.2009.10.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kleffel S, Posch C, Barthel SR, Mueller H, Schlapbach C, Guenova E, et al. Melanoma Cell-Intrinsic PD-1 Receptor Functions Promote Tumor Growth. Cell. 2015;162(6):1242–56. doi: 10.1016/j.cell.2015.08.052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Lim S, Liu H, Madeira da Silva L, Arora R, Liu Z, Phillips JB, et al. Immunoregulatory Protein B7-H3 Reprograms Glucose Metabolism in Cancer Cells by ROS-Mediated Stabilization of HIF1α. Cancer Res. 2016;76(8):2231–42. doi: 10.1158/0008-5472.CAN-15-1538. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Nunes-Xavier CE, Karlsen KF, Tekle C, Pedersen C, Oyjord T, Hongisto V, et al. Decreased expression of B7-H3 reduces the glycolytic capacity and sensitizes breast cancer cells to AKT/mTOR inhibitors. Oncotarget. 2016;7(6):6891–901. doi: 10.18632/oncotarget.6902. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Fischer K, Hoffmann P, Voelkl S, Meidenbauer N, Ammer J, Edinger M, et al. Inhibitory effect of tumor cell-derived lactic acid on human T cells. Blood. 2007;109(9):3812–9. doi: 10.1182/blood-2006-07-035972. [DOI] [PubMed] [Google Scholar]
  • 28.Barsoum IB, Smallwood CA, Siemens DR, Graham CH. A mechanism of hypoxia-mediated escape from adaptive immunity in cancer cells. Cancer Res. 2014;74(3):665–74. doi: 10.1158/0008-5472.CAN-13-0992. [DOI] [PubMed] [Google Scholar]
  • 29.Cederbaum SD, Yu H, Grody WW, Kern RM, Yoo P, Iyer RK. Arginases I and II: do their functions overlap? Molecular genetics and metabolism. 2004;81(Suppl 1):S38–44. doi: 10.1016/j.ymgme.2003.10.012. [DOI] [PubMed] [Google Scholar]
  • 30.Choudhari SK, Chaudhary M, Bagde S, Gadbail AR, Joshi V. Nitric oxide and cancer: a review. World journal of surgical oncology. 2013;11:118. doi: 10.1186/1477-7819-11-118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Brito C, Naviliat M, Tiscornia AC, Vuillier F, Gualco G, Dighiero G, et al. Peroxynitrite inhibits T lymphocyte activation and proliferation by promoting impairment of tyrosine phosphorylation and peroxynitrite-driven apoptotic death. Journal of immunology. 1999;162(6):3356–66. [PubMed] [Google Scholar]
  • 32.Yang W, Bai Y, Xiong Y, Zhang J, Chen S, Zheng X, et al. Potentiating the antitumour response of CD8(+) T cells by modulating cholesterol metabolism. Nature. 2016;531(7596):651–5. doi: 10.1038/nature17412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Patsoukis N, Bardhan K, Chatterjee P, Sari D, Liu B, Bell LN, et al. PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nature communications. 2015;6:6692. doi: 10.1038/ncomms7692. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. The New England journal of medicine. 2015;372(26):2509–20. doi: 10.1056/NEJMoa1500596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Shimizu I, Yoshida Y, Suda M, Minamino T. DNA damage response and metabolic disease. Cell metabolism. 2014;20(6):967–77. doi: 10.1016/j.cmet.2014.10.008. [DOI] [PubMed] [Google Scholar]
  • 36.Sharma P, Allison JP. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell. 2015;161(2):205–14. doi: 10.1016/j.cell.2015.03.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nature reviews Cancer. 2012;12(4):252–64. doi: 10.1038/nrc3239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Zhao Y, Butler EB, Tan M. Targeting cellular metabolism to improve cancer therapeutics. Cell death & disease. 2013;4:e532. doi: 10.1038/cddis.2013.60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Sievers EL, Senter PD. Antibody-drug conjugates in cancer therapy. Annual review of medicine. 2013;64:15–29. doi: 10.1146/annurev-med-050311-201823. [DOI] [PubMed] [Google Scholar]
  • 40.Brigger I, Dubernet C, Couvreur P. Nanoparticles in cancer therapy and diagnosis. Advanced drug delivery reviews. 2002;54(5):631–51. doi: 10.1016/s0169-409x(02)00044-3. [DOI] [PubMed] [Google Scholar]
  • 41.Loo D, Alderson RF, Chen FZ, Huang L, Zhang W, Gorlatov S, et al. Development of an Fc-enhanced anti-B7-H3 monoclonal antibody with potent antitumor activity. Clinical cancer research : an official journal of the American Association for Cancer Research. 2012;18(14):3834–45. doi: 10.1158/1078-0432.CCR-12-0715. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

RESOURCES