4.       Background 

Since more than 150 years ago, Rudolf Virchow proposed the relationship between immune function and cancer when he observed the prevalence of leukocytes in tumors [1], and subsequent Friedrich Fehleisen and William Corley reported the scientific attempt to determine the nature and limits of the treatment for sarcoma by inoculation with erysipelas in late19th century [2], immunotherapy towards cancer is a continuous standing subject important enough all the time. Especially recent regulatory approval of ipilimumab (Yervoy; Bristol-Myers Squibb), pembrolizumab (Keytruda; Merck) and novolumab (Opidivo; Bristol-Myers Squibb) et al, and the dendritic cell therapy sipuleucel-T (Provenge; Dendreon) as well as the Chimeric Antigen Receptors (CAR)-T therapy tisagenlecleucel-T (Kymriah; Novartis) evince that the immune system could be modulated to provide a reproducible benefit for patients suffering cancer [3-6].

These clinical successes for cancer treatment implies the interpretation how immune metabolism supports many aspects of cellular function and how metabolic reprogramming can drive cell differentiation and fate [7-10]. However, it is not yet clear to what degree immunotherapy will benefits patients with other types of cancer besides the authorized indications such as melanoma, acute lymphoblastic leukemia, synovial sarcoma, and lung cancer et al. [11-13]. In this review, we combs through the metabolism of Tumor-Infiltrating Lymphocytes (TILs) for the scientific strategies to improve the therapy outcomes by analyzing metabolic configuration resulted from the heterogeneous rate of metabolic conversion in the cellular network. 

4.1.  Principles of Immuno-Metabolism Modulation to Treat Human Cancer 

Cancer immunotherapy is proposed on the clinical observations and idea that, as a result of unique mutations or protein expression patterns, tumor cells with neoantigen may be recognized and eliminated by the immune system with discriminating between self and non-self and a high degree of specificity [14,15]. Besides the other several forms currently under try in immunotherapy [16], present therapeutic approaches that seek to utilize T cells to paralyze tumors can mainly be divided into three categories: 

1)       Genetic modification of T cells to confer the ability to specifically recognize malignant cells, as exemplified the CARs and tumor antigen-specific T Cell Receptors (TCRs) transduction therapy [17,18],

2)       Isolation of tumor-specific T cells from existing tumor masses, followed by ex vivo expansion and reinfusion into the patient, known as Adoptive Cell Transfer (ACT) of TILs [19,20],

3)       Treatment of patients with agents designed to activate tumor-specific T cell in situ [21-23], actually involved partly in the immune metabolism modulation. 

Immuno-metabolism, the interplay between immunology and metabolism, especially focuses on the emerging role of the intracellular metabolic machinery in the regulation of an immune response. The insights of intracellular metabolic pathways controlling immune responses helps us understand the bio-energetic requirements of T cell differentiation, cell-fate decision and function of T cells [24,25]. Metabolic changes such as nutrition deprivation or overload could dictate the characteristics of the T cell compartment and subsequent downstream immune reactions [26]. Current methods for generating T cell products for adoptive immunotherapy have the pitfall of driving cells toward terminal differentiation and senescence [27,28]. Approaches that are informed by a deepened knowledge the metabolic requirements for the optimal function of anti-tumor T cells are likely to be the subject of intense work in the field of immune metabolism [29]. 

4.2.  TILs in Tumor Microenvironment 

Tumors are now recognized as structures of multiple cell types [30]. Emerging evidence indicates that to effectively control cancer, we need to consider carcinogenesis and tumor progression not as a cell autonomous, cancer-cell centered condition, but rather as disease involving complex heterotypic multi-cellular interactions within a newly formed tissue [31]. In addition to malignant cells, the tumor microenvironment also includes nonmalignant cells including TILs, endothelial cells, pericytes, smooth muscle cells, fibroblasts, carcinoma-associated fibroblasts, neutrophils, basophils, mast cells, B lymphocytes, natural killer cells and Antigen Presenting Cells (APC) such as macrophages and dendritic cells [32], secreted proteins, and blood vessels that surround and support the growth of the tumor. The structure and composition of the tumor microenvironment varies among different types of cancers and between patients [33]. Furthermore, even among patients with the same type of cancer, the frequency, distribution and function of immune cells may differ, and this inter-individual variability has been showing to affect patient outcome [34,35]. Tumors biopsies from patients diagnosed with B-cell lymphomas are typically characterized by a high prevalence of T cells with the tumor microenvironment, with T cells comprising up to 50% of the intratumoral cells [36]. 

Tumor microenvironment is characterized by a consistent reduction in oxygen and blood-borne nutrients that significantly affects the metabolism of distinct cell subsets [37]. It is increasingly appreciated that as the neoplasm initiates and progresses, the surrounding microenvironment coevolving through continuous paracrine communication and supporting carcinogenesis is activated [38]. The cancer cells can themselves manipulate the microenvironment by skewing the differentiation of infiltrated T cells, attracting regulatory T cells or suppressive monocytes, or secreting immunosuppressive cytokines [39,40].

TILs can be divided into two groups: those expressing CD4 and those expressing CD8. Those cells are both responsible for the performance of anti-cancer activity. However, the type of CD8⁺ cells is named as cytotoxic T cells in which expressing the FasL in the surface and they have the capability to kill and disrupt the target cells by secreting interferon gamma (IFN-γ), granzyme B and perforin which endow cytotoxic and apoptotic activity for those cells [41]. In the other hand, CD4⁺ T cells can enhance the production of antibodies by neighboring B cells, and promote an effective immune response through the production of cytokines such as IL-2 and chemokines [42]. The patients having the higher amount of TILs were shown that the tend to exhibit better prognosis in various types of solid cancer including breast, lung, colon, pancreatic and hematologic malignancies et al. [43-45]. However, the quantity and activity of TILs can be affected by several factors encompassing the trafficking hurdle, the amount of cytokines or chemokines which are released by the tumor microenvironment, the behavior of interactions between TCR and MHC molecule, as well as the metabolic configuration [46,47].

4.3.  Increasing Quantity of T Cells Trafficked and Infiltrated into Tumors 

Regardless of ACT therapy and CAR-T, it is obvious that pharmacological enhancement of quantity of T cells recruited, trafficked and infiltrated into tumors can provide solutions for the optimization of therapeutic option in cancer immunotherapy. Compared to the B-cell Non-Hodgkin Lymphoma (NHL) in which there is abundant immune cells in secondary lymphoid organs, infiltration of immune cells is more limited in solid tumors [48]. Therefore, the goal in this segment is to induce more population of T cells. The detailed illustration for naïve T cell migrating through specialized endothelium of secondary lymphoid and ways as well as the mechanisms to enhance T cells trafficking into tumors by infiltrating through post-capillary venules into the target tissue to their antigenic site have been covered in other reviews [49-53], and therefore we will not discuss in detail in this review. 

4.4.  Favoring the Functionality of Existed TILs at the Tumor Site 

Experiencing many potential obstacles to reach the tumor site, TILs encountered metabolic alterations within the tumor mass in which also limit T cell functions. Restoring and favoring the anti-tumor functionality of TILs could render the significant improvement of therapeutic effectiveness. 

Metabolism which is integrated into every cellular process and fate decision controls T-cell lineage choices. In regard of this aspect, Ericka L.Pearce from Max Planck Institute in Germany and Nicholas P. Restifo from National Institute of Health in USA et al. as the representatives proposed their excellent opinions in the recent several intensive published reviews [10,54,55]. Manipulation of immunometabolism has emerged from these related studies. Simply stated, different immune cell functions are associated with distinct metabolic configurations. For example, resting immune cells utilize energetically efficient processes such as the Tricarboxylic Acid (TCA), linked to the generation of ATP via Oxidative Phosphorylation (OXPHOS), while activated T cells use aerobic glycolysis [56]. Novel metabolic features and metabolism-regulated function in TILs have recently been identified and could be targeted therapeutically by modification of system metabolism [57]. 

As described in the reviews [58-60], adaption of specific metabolic phenotype is critical for TILs immune functions. A key metabolic regulator of infiltrated T cells is Mammalian Target of Rapamycin (mTOR), which integrates immune and metabolic cues, such as antigens via PI3K-Akt signaling and nutrients via amino acid sensing, and as an intracellular kinase phosphorylates downstream targets to mediate mRNA translation and protein degradation [61]. To be deserved to mention, rapamycin, the inhibitor of mTOR and as an immunosuppressive drug, was effective during both the expansion and contraction phases of the T cell response. During the expansion phase rapamycin increased the number of memory precursors and during the contraction phase which means effector to memory transition it accelerated the memory T cell differentiation program [62]. Proteins associated with mitochondrial OXPHOS and glutamine metabolism were significantly increased following rapamycin treatment, indicating selectivity of the role of mTORC1 in T cell metabolism. [63]. This mTOR pathway directs T cell metabolic reprogramming and ultimately acquisition of effect function, as reported that pharmacological or siRNA targeting of mTORC1 can increase memory CD+ T cell response in tumor models [64]. It also needs to keep in mind that the use of mTOR to modulate metabolism in TILs in right doses of its inhibitor, treatment timing, and/or tumor characteristics [65,66] to avoid the contrary outcomes. 

Another regulator adenosine Monophosphate-Activated Protein Kinase (AMPK)’s roles which counterbalances mTOR in T lymphocytes energy metabolism homeostasis have also been revealed in recent years [67,68]. AMPK is an evolutionarily conserved serine/theronine kinsase that modulate the cellular response to an energy challenge. AMPK activation is regulated by the cellular AMP/ATP ratio and by upstream kinases [69]. Once activated by stimuli like muscle contraction, hypoxia, metabolic poisoning, inflammation or sepsis, the kinase switches on ATP-producing catabolic pathways and switches off ATP-consuming anabolic process, via both direct phosphorylation of regulatory proteins such as PFK2 involved in glycolysis and Acetyl-CoA carboxylase involved in fatty acid synthesis and indirect effects on gene expression [69]. Differentiation of naïve T cells to effector cells and subsequent long-term memory cells is tightly associated with changes in their energy metabolic activity, and therefore fine-tuning of metabolism by AMPK could modulate TILs functions [70]. 

Together with other regulators such as PIM2 and AKT in Phosphatidylinsitol 3-Kinase (PI3K) pathway for TILs functionality [71], metabolism thus represents a key node of regulation for T cell fate decision. Sufficient and sometimes distinct fuels are required throughout their functional states as naïve, effector or memory T cells. T cells can enter into alternative, dysfunctional states when these needs are not met, including energy referred as metabolic inert and exhaustion meaning metabolic sufficient. Future illustration of how metabolic fitness underlies healthy and dysfunctional T cells has the potential to discover novel therapeutic modalities for the immunotherapy [72,73].

4.5.  Enhancing the Persistence of Activated TILs 

Activated TILs could exhibit anti-tumor effect via producing effector molecules and inducing tumor cell apoptosis. However, persistence is the paramount intrinsic quality for activated TILs with anti-tumor engagement. As recorded by high-resolution 4D imaging [74], Cytotoxic T Lymphocytes (CTLs) use polarized secretion accompanied with temporal order of events to rapidly destroy tumor cells. In the adoptive transfer of TILs setting, T cells in human patients that mount effective anti-tumor responses are characterized by increased persistence and survival in vivo [75]. Cells with early differentiation states, including naïve T cells, stem central memory T cells, and central memory T cells, are more efficacious in treating established vascularized melanoma than terminally differentiated effector memory T cells or effector T cells, suggesting that acquisition of full effector function in vitro paradoxically compromise the in vivo antitumor efficacy of adoptively transferred CD8⁺ T cells [76]. Interestingly, positive correlations between the expression of markers along with telomere length in T cells and the magnitude of response against tumors have been found [77].

Recent emerging evidences demonstrated that metabolic interventions can provide a therapeutic strategy to enhance lifespan of TILs [78]. Usually, increased metabolic activity alterations drive rapid proliferation of activated T lymphocytes but can also results in decreased cell persistence and increased susceptibility to cell death [79]. The condition is similar as the adage saying “you can’t have your cake and eat it”. Several attempts have been conducted to promote increased longevity of TILs. Genetic deletion of Atg5 or Atg7 playing roles in catabolic autophagy results in impaired memory cell formation and long-term survival of CD8⁺ cells [80]. In addition, inhibition of mTORC1 and T cell glycolysis, as well as activation of AMPK signaling pathway, can promote TILs persistence in vivo besides the functionality mentioned above [81-83]. It has been indicated that T cells that take up low levels of glucose are metabolically fit for long-term survival and memory cells formation [82]. In further, the role fatty acid oxidation and mitochondrial metabolism in the promotion of T cell persistence and longevity also been explored. By measuring upon treatment of cells with the uncoupled agent FCCP, increased mitochondrial Spare Respiratory Capacity (SRC) is revealed to exhibit increased longevity and persistence of T cells [84]. However, stabilization of Hypoxia Inducible Factor 1α (HIF1α) through the loss of tumor suppressor Von Hipple-Lindau (VHL) results in T cells with increased in vivo persistence, simultaneously along with elevated glycolysis and no increase in SRC [85]. Meanwhile, T cells with increased mitochondrial membrane potential (Δφ), which is indicative of high rates of glycolysis and basal mitochondrial oxidative metabolism and associated with increased Reactive Oxygen Species (ROS) production and DNA damage, have reduced persistence and self-renewal capacity compared with cell exhibiting low Δφ [86]. Take together, moderate metabolic activity other than high rates of metabolic activity benefits the persistence and longevity of TILs, and overall rate of cellular metabolic activity, rather that of specific pathways is deserved to be systemically considered to enhancing the persistence of activated TILs.

4.6.  Overcoming the Suppressive Constraint Over TILs 

Unfortunately, TILs as the potential soldier fighting with the enemy cancer is kidnapped in the tumor environment. Many suppressive constraints imposed on TILs release full potentiality. The success of CTLA and PD-1 antibody treatment underlying the mechanism of removal of the inhibitory signal proved this concept [3-6]. Meanwhile, TILs face a fierce competition environment for nutrients availability. For in-depth discussion, we recommend several recent reviews [87]. 

Simply stated, suppression of tumor-specific T cells is orchestrated by the activity of a variety of stromal myeloid and lymphoid cells, and these suppressive mechanisms are often inducible. The more the tumor comes under attack, the more counter-regulatory mechanism may be induced. For example, T cells attempting to activate in the tumor microenvironment may encounter constitutive expressions of negative regulators such as PD-1, CTLA4, Indoleamine 2,3-Dioxygenase (IDO) in the tumor cells. Myeloid-Derived Suppressor Cells (MDSC) in the tumor may generate immunosuppressive nitric oxide, arginase-1 or ROS. Tumor associated microphages may produce TGFß and VEGF, which can be inhibitory for both TILs and Dendritic Cells (DCs) [88,89]. Activated Tregs which derived from CD4⁺ T cells can produce IL-10 and TGFß, which may directly suppress CD8⁺ T cells. Tregs may also inhibit expression of co-stimulatory ligands CD80 and CD86 on local DCs, thus rendering them ineffective and tolerating antigen-presenting cells. As effector T cells attempt to activate, their production of IFNγ and other pro-inflammatory cytokines such as IL-2 may actively up-regulate expression of IDO and PD-L1 by DCs, thus eliciting counter-regulatory suppression. Many tumor cells may also up-regulate numerous inhibitory molecules such as IDO, PD-Ll, when responding to IFNγ [90]. Furthermore, the metabolites of Cyclooxygenase (COX2) which is over-expressed in some tumors such as colorectal cancer due to the hypoxic tumor microenvironment, primarily prostaglandin E₂, have direct effects on immune-mediated tumor escape and enhance the activity of multiple immune suppressor cells, including tumor associated macrophages, Treg cells and MDSCs [91]. Therefore, it deserved to emphasize that IDO1 and COX2 inhibitors, as well as Mitogen-Activated Protein Kinase (MAPK) inhibition and cholesterol modulation, could extend the possibilities for overcoming the suppressive constraint over TILs [92-95]. 

It is naturally wondered that the combination approaches which weaken as much as possible the demonstrated suppressive factors imposed over TILs would works or not. For this aspect in further discussion, see several recent reviews [95-98, 2017]. In fact, the recent clinical trials present that PL-1 and/or CTLA-4 antibody combined with other agents based on distinct mechanisms benefits significantly the patients, though the ability of immuno-oncology to meet its full potential will depend on overcoming development challenges, including the need for clear strategies to determine optimal dose and scheduling for combination approaches [99]. It is expected that combination therapy would dominate the immunotherapy against cancer in the coming years. 

5.       Perspective 

There are a lot of things to do in the coming days in the field of immune metabolism against cancer. Firstly, the mechanism that shapes the weakness of TILs in the hostile tumor microenvironment need to be completely demonstrated in light of the edging technology such as eryo-electron microscopy and immune photonics. Secondly, the quantificational metabolic flux analysis in the distinct system levels including organelle mitochondrial, T cell itself, and tumor tissue need to be strengthened. These investigations could provide systemically the cues of metabolic configuration. Targeting these metabolic configurations by specific anaplerotic reaction can effectively modulate the metabolic direction of tendency in the disturbed equilibrium. In this analysis, mining big data from -omics modalities in real-world patients’ samples could facilitate this practice. Once the nature of metabolic configuration was captured when TILs become exhausted, negative amplification of TILs metabolic equilibriums towards functionality for anti-tumor activity could be practical. Thirdly, the need of more precise predictive biomarker in immunotherapy for TILs reprogramming remains pressing. Challenge accompanied with personalized immunotherapy includes demonstrating large-scale efficacy as well as cost-effective implementation of such strategies in the clinical setting. Improving immunotherapy should incorporate the genetic and non-genetic predictors of response. Establishment of such a paradigm will reveal individual immunotherapeutic susceptibilities and help tailor immunotherapy to specific genetic profiles. Last but not least, detailed clinical evaluations, such as relationship between human T cell differentiation status and anti-tumor efficacy, need to be conducted in clinical trial settings. 

6.       Conclusion

As said by Mark Twain, part of the secret of success in life is to eat what you like and let the food fight it out inside. Simply stated, we are what we consume in the process of eating and respiration. The similar principle holds true at the cellular level to support and reinvigorate TILs functionality, durability and longevity. Deepened perceiving of TILs metabolism, including the origin, development, and transition between Naïve, activated and exhausted/memory phase, till apoptosis, oncolysis and/or pyroptosis, could identify the appropriate strategies targeting its metabolic program for immunotherapy. The breakthrough success of several mAbs for PD-1 et al. leads the immunotherapy as a continuous hot topic. It is not wonder that study on immune metabolism will be of paramount important. Thus it need cross-disciplinary experts including biologist, informatician and the clinical physician working integrally to usher the new era of immunotherapy which could be the first choice to eradicate cancer. 

7.       Author’s Contributions 

LN, ZSS, KYY, LXL contributed to the conceptualization and writing of the draft. WLY guided as the director and revised carefully the manuscript. All authors read and approved the final manuscript. 

8.       Acknowledgements 

The related experimental exploration and corresponding author of this review were supported from the National Science Foundation of China for the grant (81673689). 

9.       Competing Interests 

The authors declare that they have no competing interests.

1.       Balkwill F, Mantovani A (2001) Inflammation and cancer: back to Virchow? Lancet 357: 539-545.

2.       Coley WB (1891) Contribution to the knowledge of sarcoma. Ann Surg 14: 199-220.

3.       Mandal R, Chan TA (2016) Personalized oncology meets immunology: the path toward precision immunotherapy. Cancer Discov 6: 1-12.

4.       Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, et al. (2012) Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 366: 2443-2454.

5.       Maude SL, Frey N, Shaw PA, Aplenc R, Barrett DM, et al. (2014) Chimeric antigen receptor T cells for sustained remissions in leukemia. N Engl J Med 371: 1507-1517.

6.       Callahan MK, Postow MA, Wolchok JD (2016) Targeting T cell co-receptors for cancer therapy. Immunity 44: 1069-1078.

7.       Cairns RA, Harris IS, Mak TW (2011) Regulation of cancer cell metabolism. Nat Rev Cancer 11: 85-95.

8.       Ghesquière B, Wong BW, Kuchnio A, Carmeliet P (2014) Metabolism of stromal and immune cells in health and disease. Nature 511: 167-176.

9.       Chang CH, Pearce EL (2016) Emerging concepts of T cell metabolism as a target of immunotherapy. Nat Immunol 17: 364-368.

10.    Sukumar M, Kishton RJ, Restifo NP (2017) Metabolic reprogramming of anti-tumor immunity. Curr opin in immunol 46: 14-22.

11.    Chen DS, Mellman I (2013) Oncology meets immunology: The cancer-immunity cycle. Immunity 39: 1-10.

12.    Loo K, Daud AI (2017) Inhibitors of cytotoxic T lymphocyte antigen 4 and programmed death 1/programmed death 1 ligand for metastatic melanoma, dual versus monotherapy- summary of advances and future direction for studying these drugs. Cancer J 23: 3-9.

13.    Malhotra J, Jabbour SK, Aisner J (2017) Current state of immunotherapy for non-small cell lung cancer. Trans Lung Cancer Res 6: 196-211.

14.    Chen DS, Mellman I (2017) Elements of cancer immunity and the cancer-immune set point. Nature 541: 321-330.

15.    Martin-Liberal J, Hierro C, Ochoa de Olza M, Rodon J (2017) Immuno-Oncology: The third paradigm in early drug development. Target Oncol 12: 125-138.

16.    Kourie HR, Awada G, Awada A (2017) The second wave of immune checkpoint inhibitor tsunami: advance, challenges and perspectives. Immunotherapy 9: 647-657.

17.    Kim S, Moon EK (2017) Development of novel avenues to overcome challenges facing CAR T cells. Transl Res 187: 22-31.

18.    Singh NK, Riley TP, Baker SCB, Borrman T, Weng Z, et al. (2017) Emerging concepts in TCR specificity: rationalizing and (Maybe) predicting outcomes. J Immunol 199: 2203-2213.

19.    Baruch EN, Berg AL, Besser MJ, Schachter J, Markel G (2017) Adoptive T cell therapy: An overview of obstacles and opportunities. Cancer 123: 2154-2162.

20.    Merhavi-Shoham E, Itzhaki O, Markel G, Schachter J, Besser MJ (2017) Adoptive Cell Therapy for Metastatic Melanoma. Cancer J 23: 48-53.

21.    Ho CY, Lo TW, Leung KN, Fung KP, Choy YM (2000) The immunostimulating activities of anti-tumor polysaccharide from K1 capsular (polysaccharide) antigen isolated from Klebsiella pneumoniae. Immunopharmacology 46: 1-13.

22.    Sánchez JA, Alfonso A, Rodriguez I, Alonso E, Cifuentes JM, et al. (2016) Spongionella secondary metabolites, promising modulators of immune response through CD147 receptor modulation. Front Immunol 7: 452.

23.    Schaller TH, Batich KA, Suryadevara CM, Desai R, Sampson JH (2017) Chemokines as adjuvants for immunotherapy: implications for immune activation with CCL3. Expert Rev Clin Immunol 5: 1-12.

24.    Bird L (2017) Immunometabolism: NK cell subset expands with your waistline. Nat Rev Immunol 17: 530-531.

25.    Ho PC, Hess C (2017) Editorial: Immunometabolic Regulations in Adaptive and Innate Immune cells shapes and re-directs host immunity. Front Immunol 8: 852.

26.    Maddocks ODK, Athineos D, Cheung EC, Lee P, Zhang T, et al. (2017) Modulating the therapeutic response of tumours to dietary serine and glycine starvation. Nature 544: 372-376.

27.    Barsov EV (2011) Telomerase and primary T cells: biology and immortalization for adoptive immunotherapy. Immunotherapy 3: 407-421.

28.    Schietinger A, Philip M, Krisnawan VE, Chiu EY, Delrow JJ, et al. (2016) Tumor-specific T cell dysfunction is a dynamic antigen-driven differentiation program initiated early during tumorigenesis. Immunity 45: 389-401.

29.    Dugnani E, Pasquale V, Bordignon C, Canu A, Piemonti L, et al. (2017) Integrating T cell metabolism in cancer immunotherapy. Cancer Lett 411: 12-18.

30.    Maley CC, Aktipis A, Graham TA, Sottoriva A, Boddy AM, et al. (2017) Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 17: 605-619.

31.    Yang CC, Burg KJ (2015) Designing a tunable 3D heterocellular breast cancer tissue test system. J Tissue Eng Regen Med 9: 310-314.

32.    Coussens LM, Werb Z (2002) Inflammation and cancer. Nature 420: 860-867.

33.    Novikova MV, Khromova NV, Kopnin PB (2017) Components of the hepatocellular carcinoma microenvironment and their role in tumor progression. Biochemistry (Mosc) 82: 861-873.

34.    Casadevall D, Clavé S, Taus Á, Hardy-Werbin M, Rocha P, et al. (2017) Heterogeneity of Tumor and Immune Cell PD-L1 Expression and Lymphocyte Counts in Surgical NSCLC Samples. Clin Lung Cancer 18: 682-691.

35.    Shi L, Zhang Y, Feng L, Wang L, Rong W, Wu F, et al. (2017) Multi-omics study revealing the complexity and spatial heterogeneity of tumor-infiltrating lymphocytes in primary liver carcinoma. Oncotarget 8: 348: 44-57.

36.    Dave SS, Wright G, Tan B, Rosenwald A, Gascoyne RD, et al. (2004) Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med 351: 2159-2169.

37.    Chang CH, Qiu J, O'Sullivan D, Buck MD, Noguchi T, et al. (2015) Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162: 1229-1241.

38.    Gupta S, Roy A, Dwarakanath BS (2017) Metabolic cooperation and competition in the tumor microenvironment: implications for therapy. Front Oncol 7: 68.

39.    Lo Presti E, Dieli F, Meraviglia S (2014) Tumor-infiltrating γδ T lymphocytes: pathogenic role, clinical significance, and differential programming in the tumor microenvironment. Front Immunol 5: 607.

40.    Garnelo M, Tan A, Her Z, Yeong J, Lim CJ, et al. (2017) Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma. Gut 66: 342-351.

41.    Schiavoni G, Mattei F, Gabriele L (2013) Type I interferons as stimulators of DC-mediated cross-priming: impact on anti-tumor response. Front Immunol 4: 483.

42.    Seder RA, Paul WE (1994) Acquisition of lymphokine-producing phenotype by CD4+ T cells. Annu Rev Immunol 12: 635-673.

43.    Pagès F, Berger A, Camus M, Sanchez-Cabo F, Costes A, et al. (2005) Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med 353: 2654-2666.

44.    Senovilla L, Vacchelli E, Galon J, Adjemian S, Eggermont A, et al. (2012) Trial watch: Prognostic and predictive value of the immune infiltrate in cancer. Oncoimmunology 1: 1323-1343.

45.    Nejati R, Goldstein JB, Halperin DM, Wang H, Hejazi N, et al. (2017) Prognostic Significance of Tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma treated with neoadjuvant chemotherapy. Pancreas 46: 1180-1187.

46.    Mempel TR, Bauer CA (2009) Intravital imaging of CD8+ T cell function in cancer. Clin Exp Metastasis 26: 311-327.

47.    Miller A, Nagy C, Knapp B, Laengle J, Ponweiser E, et al. (2017) Exploring Metabolic configurations of single cells within complex tissue microenvironments. Cell Metab 26: 788-800.

48.    Ansell SM, Vonderheide RH (2013) Cellular composition of the tumor microenvironment. Am Soc Clin Oncol Educ Book.

49.    Marelli-Berg FM, Fu H, Vianello F, Tokoyoda K, Hamann A (2010) Memory T-cell trafficking: new directions for busy commuters. Immunology 130: 158-165.

50.    Bellone M, Calcinotto A (2013) Ways to enhance lymphocyte trafficking into tumors and fitness of tumor infiltrating lymphocytes. Front Oncol 3: 231.

51.    Oelkrug C, Ramage JM (2014) Enhancement of T cell recruitment and infiltration into tumours. Clin Exp Immunol 178: 1-8.

52.    Sharma RK, Chheda ZS, Jala VR, Haribabu B (2015) Regulation of cytotoxic T-Lymphocyte trafficking to tumors by chemoattractants: implications for immunotherapy. Expert Rev Vaccines 14: 537-549.

53.    Sackstein R, Schatton T, Barthel SR (2017) T-lymphocyte homing: an underappreciated yet critical hurdle for successful cancer immunotherapy. Lab Invest 97: 669-697.

54.    Buck MD, Sowell RT, Kaech SM, Pearce EL (2017) Metabolic instruction of immunity. Cell 169: 570-586.

55.    Kishton RJ, Sukumar M, Restifo NP (2017) Metabolic regulation of T cell longevity and function in tumor immunotherapy. Cell Metab 26: 94-109.

56.    Chang CH, Curtis JD, Maggi LB Jr, Faubert B, Villarino AV, et al. (2013) Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153: 1239-1251.

57.    Norata GD, Caligiuri G, Chavakis T, Matarese G, Netea MG, et al. (2015) The cellular and molecular basis of translational immunometabolism. Immunity 43: 421-434.

58.    Reiser J, Banerjee A (2016) Effector, memory, and dysfunctional CD8(+) T cell fates in the antitumor immune response. J Immunol Res.

59.    Savas P, Salgado R, Denkert C, Sotiriou C, Darcy PK, et al. (2016) Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat Rev Clin Oncol 13: 228-241.

60.    Zito Marino F, Ascierto PA, Rossi G, Staibano S, Montella M, et al. (2017) Are tumor-infiltrating lymphocytes protagonists or background actors in patient selection for cancer immunotherapy? Expert Opin Biol Ther 17: 735-746.

61.    Powell JD, Pollizzi KN, Heikamp EB, Horton MR (2012) Regulation of immune responses by mTOR. Annu Rev Immunol 30: 39-68.

62.    Araki K, Turner AP, Shaffer VO, Gangappa S, Keller SA, et al. (2009) mTOR regulates memory CD8 T-cell differentiation. Nature 460: 108-112.

63.    Hukelmann JL, Anderson KE, Sinclair LV, Grzes KM, Murillo AB, et al. (2016) The cytotoxic T cell proteome and its shaping by the kinase mTOR. Nat Immunol 17: 104-112.

64.    Berezhnoy A, Castro I, Levay A, Malek TR, Gilboa E (2014) Aptamer-targeted inhibition of mTOR in T cells enhances antitumor immunity. J Clin Invest 124: 188-197.

65.    Gomez-Pinillos A, Ferrari AC (2012) mTOR signaling pathway and mTOR inhibitors in cancer therapy. Hematol Oncol Clin North Am 26: 483-505.

66.    Zaytseva YY, Valentino JD, Gulhati P, Evers BM (2012) Cancer Lett 319: 1-7.

67.    Andris F, Leo O (2015) AMPK in lymphocyte metabolism and function. Int Rev Immunol 34: 67-81.

68.    Herzig S, Shaw RJ (2017) AMPK: guardian of metabolism and mitochondrial homeostasis. Nat Rev Mol Cell Biol 19: 121-135.

69.    Hardie DG (2004) AMP-activated protein kinase: the guardian of cardiac energy status. J Clin Invest 114: 465-468.

70.    Li W, Saud SM, Young MR, Chen G, Hua B (2015) Targeting AMPK for cancer prevention and treatment. Oncotarget 6: 7365-7378.

71.    Fox CJ, Hammerman PS, Thompson CB (2005) Fuel feeds function: energy metabolism and the T-cell response. Nat Rev Immunol 5: 844-852.

72.    Delgoffe GM, Powell JD (2015) Feeding an army: The metabolism of T cells in activation, anergy, and exhaustion. Mol Immunol 68: 492-496.

73.    Zhou P, Shaffer DR, Alvarez Arias DA, Nakazaki Y, Pos W, et al. (2014) In vivo discovery of immunotherapy targets in the tumour microenvironment. Nature 506: 52-57.

74.    Ritter AT, Asano Y, Stinchcombe JC, Dieckmann NM, Chen BC, et al. (2015) Actin depletion initiates events leading to granule secretion at the immunological synapse. Immunity 42: 864-876.

75.    Gattinoni L, Lugli E, Ji Y, Pos Z, Paulos CM, et al. (2011) A human memory T cell subset with stem cell-like properties. Nat Med 17: 1290-1297.

76.    Gattinoni L, Klebanoff CA, Palmer DC, Wrzesinski C, Kerstann K, et al. (2005) Acquisition of full effector function in vitro paradoxically impairs the in vivo antitumor efficacy of adoptively transferred CD8+ T cells. J Clin Invest 115: 1616-1626.

77.    Rosenberg SA (2015) CCR 20th Anniversary Commentary: Autologous T cells-the ultimate personalized drug for the immunotherapy of human cancer. Clin Cancer Res 21: 5409-5411.

78.    Chang CH, Pearce EL (2016) Emerging concepts of T cell metabolism as a target of immunotherapy. Nat Immunol 17: 364-368.

79.    Verbist KC, Guy CS, Milasta S, Liedmann S, Kamiński MM, et al. (2016) Metabolic maintenance of cell asymmetry following division in activated T lymphocytes. Nature 532: 389-393.

80.    Xu X, Araki K, Li S, Han JH, Ye L, et al. (2014) Autophagy is essential for effector CD8(+) T cell survival and memory formation. Nat Immunol 15: 1152-1161.

81.    Araki K, Turner AP, Shaffer VO, Gangappa S, Keller SA, et al. (2009) mTOR regulates memory CD8 T-cell differentiation. Nature 460: 108-112.

82.    Sukumar M, Liu J, Ji Y, Subramanian M, Crompton JG, et al. (2013) Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function. J Clin Invest 123: 4479-4488.

83.    Blagih J, Coulombe F, Vincent EE, Dupuy F, Galicia-Vázquez G, et al. (2016) The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo. Immunity 42: 41-54.

84.    van der Windt GJ, Everts B, Chang CH, Curtis JD, Freitas TC, et al. (2012) Mitochondrial respiratory capacity is a critical regulator of CD8+ T cell memory development. Immunity 36: 68-78.

85.    Bollinger T, Bollinger A, Gies S, Feldhoff L, Solbach W, et al. (2016) Transcription regulates HIF-1α expression in CD4(+) T cells. Immunol Cell Biol 94: 109-113.

86.    Sukumar M, Liu J, Mehta GU, Patel SJ, Roychoudhuri R, et al. (2016) Mitochondrial membrane potential identifies cells with enhanced stemness for cellular therapy. Cell Metab 23: 63-76.

87.    Munn DH, Bronte V (2016) Immune suppressive mechanisms in the tumor microenvironment. Curr Opin Immunol 39: 1-6.

88.    Boissonnas A, Licata F, Poupel L, Jacquelin S, Fetler L, et al. (2013) CD8+ tumor-infiltrating T cells are trapped in the tumor-dendritic cell network. Neoplasia 15: 85-94.

89.    Michaud HA, Eliaou JF, Lafont V, Bonnefoy N, Gros L (2014) Tumor antigen-targeting monoclonal antibody-based immunotherapy: Orchestrating combined strategies for the development of long-term antitumor immunity. Oncoimmunology 3: e955684.

90.    Shalapour S, Karin M (2015) Immunity, inflammation, and cancer: an eternal fight between good and evil. J Clin Invest 125: 3347-3355.

91.    Obermajer N, Muthuswamy R, Lesnock J, Edwards RP, Kalinski P (2011) Positive feedback between PGE2 and COX2 redirects the differentiation of human dendritic cells toward stable myeloid-derived suppressor cells. Blood 118: 5498-5505.

92.    Liu X, Shin N, Koblish HK, Yang G, Wang Q, et al. (2010) Selective inhibition of IDO1 effectively regulates mediators of antitumor immunity. Blood 115: 3520-3530.

93.    Wang D, DuBois RN (2013) The role of anti-inflammatory drugs in colorectal cancer. Annu Rev Med 64: 131-144.

94.    Dushyanthen S, Teo ZL, Caramia F, Savas P, Mintoff CP, et al. (2017) Agonist immunotherapy restores T cell function following MEK inhibition improving efficacy in breast cancer. Nat Commun 8: 606.

95.    Yang W, Bai Y, Xiong Y, Zhang J, Chen S, et al. (2016) Potentiating the antitumour response of CD8(+) T cells by modulating cholesterol metabolism. Nature 531: 651-655.

96.    Dhanak D, Edwards JP, Nguyen A, Tummino PJ (2017) Small-Molecule Targets in Immuno-Oncology. Cell Chem Biol 2: 1148-1160.

97.    Wilson AL, Plebanski M, Stephens AN (2017) New trends in anti-cancer therapy: combining conventional chemotherapeutics with novel immunomodulators. Curr Med Chem.

98.    Ernstoff MS, Gandhi S, Pandey M, Puzanov I, Grivas P, et al. (2017) Challenges faced when identifying patients for combination immunotherapy. Future Oncol 13: 1607-1618.

99.    Sheng J, Srivastava S, Sanghavi K, Lu Z, Schmidt BJ, et al. (2017) Clinical pharmacology considerations for the development of immune checkpoint inhibitors. J Clin Pharmacol 57: S26-S42.