Systemic CD4 Immunity and PD-L1/PD-1 Blockade Immunotherapy: History
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Contributor:
David Escors
,
Ana Bocanegra
,
Luisa Chocarro
,
Ester Blanco
,
Sergio Piñeiro-Hermida
,
Maider Garnica
,
Leticia Fernandez-Rubio
,
Ruth Vera
,
Hugo Arasanz
,
Grazyna Kochan

PD-L1/PD-1 blockade immunotherapy has changed the therapeutic approaches for the treatment of many cancers. Nevertheless, the mechanisms underlying its efficacy or treatment failure are still unclear. Proficient systemic immunity seems to be a prerequisite for efficacy, as recently shown in patients and in mouse models. It is widely accepted that expansion of anti-tumor CD8 T cell populations is principally responsible for anti-tumor responses. In contrast, the role of CD4 T cells has been less studied. 

  • T lymphocytes
  • immune checkpoint
  • biomarker

1. CD4 T Cells and Anti-Tumor Immunity

Anti-cancer immune responses start by the capture and processing of tumor-associated antigens (TAA) by antigen-presenting cells such as dendritic cells (DCs) [1]. TAAs can be varied in nature, and they range from, for example, viral proteins, overexpressed proteins, embryonic antigens and neoantigens. Hence, the nature of such TAAs can affect therapeutic activities and toxicities, as extensively revised in [1]. These TAAs are released by cancer cells dying either by immunogenic cell death or following attack by natural killer cells (NK) through the establishment of an initial inflammatory response. TAA-loaded DCs migrate to secondary lymphoid organs such as lymph nodes, where they prime both CD4 and CD8 T cells specific for these TAAs. As CD8 T cells possess strong cytotoxic activities, this T cell subset has been classically considered as the main effector in anti-tumor immunity through direct tumor killing [2][3][4][5][6]. Following antigen presentation, CD8 T cells expand exponentially in peripheral blood and differentiate into cytotoxic T cells (CTLs). These primed T cells infiltrate tumors where they recognise cancer cells bearing TAAs and exert their cytotoxic activities [4][7][8][9][10]. It is believed that this process of TAA recognition and initial immunological attack over tumors takes place right at the beginning. Thus, most cancers that progress end up evading this initial immunological attack by reducing their immunogenicity through immunological editing. This escape mechanism leads to the selection of poorly immunogenic cancer cell variants which down-modulate MHC molecules, or express immunosuppressive molecules [11][12][13][14][15][16][17].
The CD4 T cell contribution to anti-cancer immunity has been much less studied. Even so, the evidence supporting their anti-tumor capacities is compelling, principally by regulating innate and adaptive immunity [18][19]. Indeed, their importance is highlighted by the study of immunoedited cancer cells, in which mutations in MHC-II-restricted neoantigens are more potently selected during tumorigenesis [20][21][22]. This reflects the significant contribution of CD4 T cells in immunosurveillance.
CD4 T cells differentiate into several subsets with different regulatory roles. This diversity of CD4 T cell subsets reflects the variety of immune responses they regulate [23]. CD4 T cells differentiate during antigen presentation towards different subtypes depending on the cytokine milieu. During antigen presentation, mostly at peripheral lymph nodes, antigen-presenting cells such as DCs present antigen peptides complexed to MHC-II molecules. These MHC-II-peptide complexes are recognised by naïve CD4 T cells. In this process, CD4 T cells receive three signals; the first through their TCR, the second the integration of positive and negative co-stimulation. The third one is called cytokine priming, and consists on stimulation by cytokines produced by antigen presenting cells and those present in the microenvironment. The specific cytokines determine the CD4 T helper (Th) subtype that will be activated. The major Th subtypes are Th1, Th2, Th17 and inducible regulatory T cells (Tregs).
Classically, the CD4 Th1 subtype is associated to anti-tumor immunity. Th1 cells act in concert with antigen presenting cells for CD8 T cell priming and differentiation towards CTLs in a process called T cell licensing [24][25]. During this process, Th1 cells and APCs produce pro-inflammatory cytokines such as IFNγ and IL-12 [26][27][28]. Th1 cells are also responsible for DC licensing through engagement of CD40L with CD40 on the DC surface [29][30][31]. During this process, DCs mature by up-regulating the co-stimulatory molecules CD80, CD86 and CD40L, together with concomitant IL-12 and IL-15 secretion for cytokine priming [32][33][34][35][36][37]. Activated DCs prime naïve CD8 T cells towards CTLs and memory phenotypes. CD8 T cells acquire CTL effector functions through co-stimulation by CD27 with CD70 via CD40-CD40L signaling between APCs and CD8 T cells [18][38][39]. Indeed, CD8 T cell priming in the absence of CD4 T fails to fully activate CTLs leading to limited expansion of anergic CD8 T cells with dysfunctional phenotypes, and lack of CD8 T cell memory [18][25][40][41][42]. The activity of CD4 Th1 cells potentiates anti-tumor responses from NK and M1-type macrophages, which further promotes tumor killing causing the release of more TAAs for T cell priming [43][44]. CD4 T helper cells can also differentiate into Th2 and Th17 subtypes, characterized by expression of cytokines such as IL10 and IL4 for the former, and IL-6, IFN-γ and IL-17 for the latter [45][46][47][48][49]. These T helper subtypes are generally associated with tumor progression, although this may be context-specific. For example, CD4 Th2 cells are required for long-term memory responses, while Th17 cells can induce potent inflammation that can amplify anti-tumor immunity [50][51][52][53][54][55]. Regulatory CD4 Tregs consist of at least three main subtypes, natural Tregs, inducible Tregs, and Tr1 CD4 T cells that are involved in maintaining central and peripheral tolerance. These CD4 T cells present potent immunosuppressive activities by several mechanisms, ranging from cell-to-cell contacts and expression of immunosuppressive cytokines [56][57][58][59][60][61][62][63]. In certain conditions, CD4 T cells can also acquire direct cytotoxicity through production of IFN-γ and TNF-α, expression of FasL and TRIAL, and cytotoxic granules [64][65][66].

2. CD4 T Cell Differentiation Phenotypes according to Effector Functions

Upon antigen presentation, both CD8 and CD4 T cells expand exponentially and differentiate into effector phenotypes, including CTLs and the different subsets of T helper cells. These T cells are short-lived, but a small pool survives as long-lived memory subsets after antigen clearance. These memory T cells can last decades and are critical for recall responses. Memory T cells need less requirements for activation and mobilization following antigen presentation compared to naïve T cells. Following antigen re-encounter, memory T cells undergo fast activation and expansion, leading to stronger effector T cells [67][68][69][70]. Different T cell differentiation phenotypes can be readily distinguished in humans by evaluating the expression profiles of CD62L and CD45RA. CD62L+ CD45RA+ naïve T cells migrate out of the thymus towards secondary lymphoid organs by the expression of CD62L [70]. Memory T cells can be differentiated into two main types based on their location and migration patterns: Memory T cells residing in secondary lymphoid organs are represented by central memory subsets, while those migrating into sites of inflammation are termed effector memory T cells. As such, memory T cells lose CD45RA expression, which allows these subsets to move between secondary lymphoid organs. Effector memory T cells further lose CD62L expression, as these cells will remain tissue-resident and upregulate chemokine and cytokine receptors required for chemotaxis towards sites of inflammation. From the effector memory pool, T cells can then re-express CD45RA (effector memory cells that re-express RA, or EMRA), which is also a marker of terminal differentiation. EMRA cells end up accumulating during the lifetime of the individual [71].
In addition to this classification, human T cell differentiation can also be studied based on CD27/CD28 expression profiles. Thus, poorly differentiated T cells which includes naïve and central memory phenotypes co-express both markers. Then, T cells progressively lose first CD27 expression, and then CD28, leading to highly differentiated T cells which englobe effector memory and EMRA T cells [72][73][74][75][76]. CD27- and CD28- CD4 T cells are considered senescent T cells in humans.

3. Systemic CD4 Immunity as a Biomarker of Response to PD-L1/PD-1 Blockade Immunotherapy

Biomarkers of response to immunotherapies have typically been looked for within the tumor, or by evaluating tumor infiltration by immune cells. This is also the case for PD-L1/PD-1 blockade. For the latter case, the assessment of PD-L1 expression in tumor biopsies is the most well-established. Nevertheless, other markers including neoantigen expression, mutational status, DNA repair proteins and infiltration with TILs and immunosuppressive cells have been used. PD-L1 expression in tumors is the most extensively used, but its reliability could be a function of the tumor type, and also of the specific PD-L1/PD-1 blockade drug used [77][78][79][80][81]. Tumor mutational burden and transcriptomic analyses of TILs can also have predictive value in patients undergoing PD-L1/PD-1 blockade [82][83][84][85]. It is evident that a major drawback in implementing these biomarkers in clinical practice is, apart from their price, the unavailability of sufficient material from tumor biopsies in many cases. A second issue is whether a sample represents the tumor heterogeneity found in patients. Indeed, PD-L1/PD-1 blockade is carried out systemically and will have a broad impact in the immune system. These global effects will probably contribute to clinical responses, and systemic immune biomarkers could be an inexpensive way forward for their clinical use [10][72][86][87][88][89][90][91][92]. Therefore, immune profiling of immune cells in peripheral blood samples could be a promising non-invasive procedure to evaluate biomarkers of response in immunotherapies. Profiling studies in combination with tumor sampling could improve patient stratification for immunotherapies. For example, early expansion of PD-1+ CD8 T cells in peripheral blood by flow cytometry correlated with clinical efficacy, which was confirmed in patients with thymic epithelial tumors [10][90]. In the first study, T cell expansion was evaluated by assessing proliferation through the expression of Ki67 after the first week of treatment. The predictive value of this functional immunological biomarker was validated in two independent cohorts of NSCLC patients. Similarly, genome-wide sequencing of peripheral T cell populations uncovered early clonal expansion that was associated with clinical responses in NSCLC patients undergoing PD-1 blockade [93]. This phenomenon seems to be common to many tumor types, for example in metastatic melanoma patients treated with PD-1 blockers [94]. High-dimensional techniques for the analysis of multiple populations can increase the accuracy for the identification of cellular biomarkers of response. For example, the identification of a CCR7-CD27-CD8 T cell subset that expanded systemically after PD-1 blockade in melanoma patients [95]. All these data clearly indicate that quantification of proliferating CD8 T cell populations following PD-L1/PD-1 blockade may be suitable biomarkers of response, or at least as a biomarker for real-time monitoring of responses in patients using a non-invasive technique. These biomarkers have the advantage of helping the clinician in decision-making during the early onset of immunotherapies. However, they may not be useful for the early identification of hyperprogressors [96]. Therefore, quantification of CD8 T cell subsets before the start of immunotherapies may not be sufficient for the identification of responders and non-responders. Other high-dimensional techniques with the power of identifying multiple immune cell populations have been applied for the identification of predictive biomarkers. For example, mass cytometry (CYTOF) which has been applied for the analysis of peripheral blood populations in metastatic melanoma patients undergoing PD-1 blockade before treatment initiation. One pioneering study showed that elevated CD14+ CD16- HLA-DRhigh monocytes before applying immunotherapy correlated with a significant increase in progression free survival (PFS) [97].  The application of multi-parametric flow cytometry highlighted the elevation of PD-1, PD-L1 and PD-L1 in T cells as biomarkers of worse survival [98]. However, this might not apply to myeloid cell populations, in which elevation of PD-L1 correlate with efficacy of PD-L1 blockade with atezolizumab in NSCLC patients with PD-L1-negative tumors [86]. Nevertheless, none of the above immune cell-based biomarkers have been fully validated in prospective studies. In addition, some of the high-dimensional technologies such as CYTOF are difficult to standardize and apply into routine clinical practice.
Work from independent research groups is demonstrating the potential of quantifying CD4 T cell subsets in peripheral blood to predict the efficacy of PD-L1/PD-1 blockade immunotherapies. In a small-scale study in metastatic melanoma patients, elevation of central memory T cells was associated with prolonged survival [99]. A higher ratio of central memory versus effector T cell subsets correlated with benefit from PD-1 blockade in a small cohort of NSCLC patients [100]. Two prospective studies, independently showed that evaluating the dynamics of CD4 T cell populations in peripheral blood can predict clinical outcome in patients treated with PD-L1/PD-1 blockade immunotherapy [72][88][89][92]. Both studies independently found similar quantitative thresholds for evaluation of CD4 memory T cells in peripheral blood before the start of immunotherapies. CD4 T cells exhibited a CD27-CD28low phenotype, which included both enriched central and effector memory CD4 T cell subsets [72]. Response rates of about 50% were observed in patients with more than 40% of the T cells with this biomarker phenotype. All patients with percentages below this threshold were non-responders and with a significant increase in risk for developing hyperprogression [72][101]. Kagamu and collaborators independently found a population of CD4 T cells (CD62Llow effector memory subset) with similar threshold values [88][89]. Elevation of this subset and reduction in Tregs were significantly represented in responders to PD-1 blockade immunotherapy. A high ratio between effector memory CD4 T cells and Treg cells could identify responders from progressors.
Other immunological biomarkers have also been studied in many cancer types and for a variety of treatments, including immunotherapies and PD-L1/PD-1 blockade. These include absolute and relative neutrophil and lymphocyte numbers, and the classical neutrophil-to-lymphocyte ratio (NLR), amongst others [102][103][104][105]. These markers have been shown to have prognostic rather than predictive power. This is a subtle difference, but whereas a prognostic marker is treatment-independent, a truly predictive biomarker is one specifically associated with a particular therapy. However, the quantification of these biomarkers is not standardized, and their practical value might be limited [87]. Modifications to the classical NLR have been introduced to incorporate other immune populations, especially other myeloid cells apart from neutrophils. For example, the derived NLR (dNLR) that together with quantification of lactate dehydrogenase is used for the calculation of the lung immune prognostic index for PD-L1/PD-1 blockade [106]. However, all these classical prognostic markers are based on standard clinical blood analyses that do not discriminate the variety of immune cell types and their activation status in peripheral blood [87]. To achieve this, high-dimensional techniques, such as multiparametric flow cytometry, can provide more accurate quantification of different cell types, leading to better correlations with clinical outcomes. Using these techniques, the prognostic value of cell populations such as monocytes, neutrophils and other granulocytes have been confirmed in NSCLC patients undergoing PD-L1/PD-1 blockade immunotherapies [87][91].

This entry is adapted from the peer-reviewed paper 10.3390/ijms232113241

References

  1. Escors, D. Tumour immunogenicity, antigen presentation and immunological barriers in cancer immunotherapy. New J. Sci. 2014, 2014, 734515.
  2. Horton, B.L.; Morgan, D.M.; Momin, N.; Zagorulya, M.; Torres-Mejia, E.; Bhandarkar, V.; Wittrup, K.D.; Love, J.C.; Spranger, S. Lack of CD8(+) T cell effector differentiation during priming mediates checkpoint blockade resistance in non-small cell lung cancer. Sci. Immunol. 2021, 6, eabi8800.
  3. Yamauchi, T.; Hoki, T.; Oba, T.; Saito, H.; Attwood, K.; Sabel, M.S.; Chang, A.E.; Odunsi, K.; Ito, F. CX3CR1-CD8+ T cells are critical in antitumor efficacy but functionally suppressed in the tumor microenvironment. JCI Insight 2020, 5, e133920.
  4. Han, J.; Duan, J.; Bai, H.; Wang, Y.; Wan, R.; Wang, X.; Chen, S.; Tian, Y.; Wang, D.; Fei, K.; et al. TCR Repertoire Diversity of Peripheral PD-1(+)CD8(+) T Cells Predicts Clinical Outcomes after Immunotherapy in Patients with Non-Small Cell Lung Cancer. Cancer Immunol. Res. 2020, 8, 146–154.
  5. Fairfax, B.P.; Taylor, C.A.; Watson, R.A.; Nassiri, I.; Danielli, S.; Fang, H.; Mahe, E.A.; Cooper, R.; Woodcock, V.; Traill, Z.; et al. Peripheral CD8(+) T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat. Med. 2020, 26, 193–199.
  6. Jadhav, R.R.; Im, S.J.; Hu, B.; Hashimoto, M.; Li, P.; Lin, J.X.; Leonard, W.J.; Greenleaf, W.J.; Ahmed, R.; Goronzy, J.J. Epigenetic signature of PD-1+ TCF1+ CD8 T cells that act as resource cells during chronic viral infection and respond to PD-1 blockade. Proc. Natl. Acad. Sci. USA 2019, 116, 14113–14118.
  7. Karwacz, K.; Bricogne, C.; Macdonald, D.; Arce, F.; Bennett, C.L.; Collins, M.; Escors, D. PD-L1 co-stimulation contributes to ligand-induced T cell receptor down-modulation on CD8(+) T cells. EMBO Mol. Med. 2011, 3, 581–592.
  8. Karwacz, K.; Arce, F.; Bricogne, C.; Kochan, G.; Escors, D. PD-L1 co-stimulation, ligand-induced TCR down-modulation and anti-tumor immunotherapy. Oncoimmunology 2012, 1, 86–88.
  9. Fehlings, M.; Jhunjhunwala, S.; Kowanetz, M.; O’Gorman, W.E.; Hegde, P.S.; Sumatoh, H.; Lee, B.H.; Nardin, A.; Becht, E.; Flynn, S.; et al. Late-differentiated effector neoantigen-specific CD8+ T cells are enriched in peripheral blood of non-small cell lung carcinoma patients responding to atezolizumab treatment. J. Immunother. Cancer 2019, 7, 249.
  10. Kamphorst, A.O.; Pillai, R.N.; Yang, S.; Nasti, T.H.; Akondy, R.S.; Wieland, A.; Sica, G.L.; Yu, K.; Koenig, L.; Patel, N.T.; et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc. Natl. Acad. Sci. USA 2017, 114, 4993–4998.
  11. O’Donnell, J.S.; Teng, M.W.L.; Smyth, M.J. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat. Rev. Clin. Oncol. 2019, 16, 151–167.
  12. Schreiber, R.D.; Old, L.J.; Smyth, M.J. Cancer immunoediting: Integrating immunity’s roles in cancer suppression and promotion. Science 2011, 331, 1565–1570.
  13. Sengupta, N.; MacFie, T.S.; MacDonald, T.T.; Pennington, D.; Silver, A.R. Cancer immunoediting and “spontaneous” tumor regression. Pathol. Res. Pract. 2010, 206, 1–8.
  14. Dunn, G.P.; Koebel, C.M.; Schreiber, R.D. Interferons, immunity and cancer immunoediting. Nat. Rev. Immunol. 2006, 6, 836–848.
  15. Smyth, M.J. Type I interferon and cancer immunoediting. Nat. Immunol. 2005, 6, 646–648.
  16. Dunn, G.P.; Bruce, A.T.; Sheehan, K.C.; Shankaran, V.; Uppaluri, R.; Bui, J.D.; Diamond, M.S.; Koebel, C.M.; Arthur, C.; White, J.M.; et al. A critical function for type I interferons in cancer immunoediting. Nat. Immunol. 2005, 6, 722–729.
  17. Teng, M.W.; Galon, J.; Fridman, W.H.; Smyth, M.J. From mice to humans: Developments in cancer immunoediting. J. Clin. Investig. 2015, 125, 3338–3346.
  18. Ahrends, T.; Busselaar, J.; Severson, T.M.; Babala, N.; de Vries, E.; Bovens, A.; Wessels, L.; van Leeuwen, F.; Borst, J. CD4(+) T cell help creates memory CD8(+) T cells with innate and help-independent recall capacities. Nat. Commun. 2019, 10, 5531.
  19. Borst, J.; Ahrends, T.; Babala, N.; Melief, C.J.M.; Kastenmuller, W. CD4(+) T cell help in cancer immunology and immunotherapy. Nat. Rev. Immunol. 2018, 18, 635–647.
  20. Marty Pyke, R.; Thompson, W.K.; Salem, R.M.; Font-Burgada, J.; Zanetti, M.; Carter, H. Evolutionary Pressure against MHC Class II Binding Cancer Mutations. Cell 2018, 175, 416–428.e413.
  21. Kreiter, S.; Vormehr, M.; van de Roemer, N.; Diken, M.; Lower, M.; Diekmann, J.; Boegel, S.; Schrors, B.; Vascotto, F.; Castle, J.C.; et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature 2015, 520, 692–696.
  22. Topalian, S.L. MHC class II restricted tumor antigens and the role of CD4+ T cells in cancer immunotherapy. Curr. Opin. Immunol. 1994, 6, 741–745.
  23. Editorial: Gene cloning: One milestone on a very long road. Lancet 1976, 1, 893.
  24. Smith, C.M.; Wilson, N.S.; Waithman, J.; Villadangos, J.A.; Carbone, F.R.; Heath, W.R.; Belz, G.T. Cognate CD4(+) T cell licensing of dendritic cells in CD8(+) T cell immunity. Nat. Immunol. 2004, 5, 1143–1148.
  25. Janssen, E.M.; Lemmens, E.E.; Wolfe, T.; Christen, U.; von Herrath, M.G.; Schoenberger, S.P. CD4+ T cells are required for secondary expansion and memory in CD8+ T lymphocytes. Nature 2003, 421, 852–856.
  26. Bos, R.; Sherman, L.A. CD4+ T-cell help in the tumor milieu is required for recruitment and cytolytic function of CD8+ T lymphocytes. Cancer Res. 2010, 70, 8368–8377.
  27. Wong, S.B.; Bos, R.; Sherman, L.A. Tumor-specific CD4+ T cells render the tumor environment permissive for infiltration by low-avidity CD8+ T cells. J. Immunol. 2008, 180, 3122–3131.
  28. Bennett, S.R.; Carbone, F.R.; Karamalis, F.; Miller, J.F.; Heath, W.R. Induction of a CD8+ cytotoxic T lymphocyte response by cross-priming requires cognate CD4+ T cell help. J. Exp. Med. 1997, 186, 65–70.
  29. Ridge, J.P.; Di Rosa, F.; Matzinger, P. A conditioned dendritic cell can be a temporal bridge between a CD4+ T-helper and a T-killer cell. Nature 1998, 393, 474–478.
  30. Schoenberger, S.P.; Toes, R.E.; van der Voort, E.I.; Offringa, R.; Melief, C.J. T-cell help for cytotoxic T lymphocytes is mediated by CD40-CD40L interactions. Nature 1998, 393, 480–483.
  31. Nesbeth, Y.C.; Martinez, D.G.; Toraya, S.; Scarlett, U.K.; Cubillos-Ruiz, J.R.; Rutkowski, M.R.; Conejo-Garcia, J.R. CD4+ T cells elicit host immune responses to MHC class II-negative ovarian cancer through CCL5 secretion and CD40-mediated licensing of dendritic cells. J. Immunol. 2010, 184, 5654–5662.
  32. Greyer, M.; Whitney, P.G.; Stock, A.T.; Davey, G.M.; Tebartz, C.; Bachem, A.; Mintern, J.D.; Strugnell, R.A.; Turner, S.J.; Gebhardt, T.; et al. T Cell Help Amplifies Innate Signals in CD8(+) DCs for Optimal CD8(+) T Cell Priming. Cell Rep. 2016, 14, 586–597.
  33. Mescher, M.F.; Curtsinger, J.M.; Agarwal, P.; Casey, K.A.; Gerner, M.; Hammerbeck, C.D.; Popescu, F.; Xiao, Z. Signals required for programming effector and memory development by CD8+ T cells. Immunol. Rev. 2006, 211, 81–92.
  34. Curtsinger, J.M.; Lins, D.C.; Mescher, M.F. Signal 3 determines tolerance versus full activation of naive CD8 T cells: Dissociating proliferation and development of effector function. J. Exp. Med. 2003, 197, 1141–1151.
  35. Curtsinger, J.M.; Johnson, C.M.; Mescher, M.F. CD8 T cell clonal expansion and development of effector function require prolonged exposure to antigen, costimulation, and signal 3 cytokine. J. Immunol. 2003, 171, 5165–5171.
  36. Liechtenstein, T.; Perez-Janices, N.; Blanco-Luquin, I.; Schwarze, J.; Dufait, I.; Lanna, A.; De Ridder, M.; Guerrero-Setas, D.; Breckpot, K.; Escors, D. Anti-melanoma vaccines engineered to simultaneously modulate cytokine priming and silence PD-L1 characterized using ex vivo myeloid-derived suppressor cells as a readout of therapeutic efficacy. Oncoimmunology 2014, 3, e29178.
  37. Escors, D.; Lopes, L.; Lin, R.; Hiscott, J.; Akira, S.; Davis, R.J.; Collins, M.K. Targeting dendritic cell signalling to regulate the response to immunisation. Blood 2008, 111, 3050–3061.
  38. Ahrends, T.; Babala, N.; Xiao, Y.; Yagita, H.; van Eenennaam, H.; Borst, J. CD27 Agonism Plus PD-1 Blockade Recapitulates CD4+ T-cell Help in Therapeutic Anticancer Vaccination. Cancer Res. 2016, 76, 2921–2931.
  39. van de Ven, K.; Borst, J. Targeting the T-cell co-stimulatory CD27/CD70 pathway in cancer immunotherapy: Rationale and potential. Immunotherapy 2015, 7, 655–667.
  40. Laidlaw, B.J.; Craft, J.E.; Kaech, S.M. The multifaceted role of CD4(+) T cells in CD8(+) T cell memory. Nat. Rev. Immunol. 2016, 16, 102–111.
  41. Shedlock, D.J.; Shen, H. Requirement for CD4 T cell help in generating functional CD8 T cell memory. Science 2003, 300, 337–339.
  42. Sun, J.C.; Bevan, M.J. Defective CD8 T cell memory following acute infection without CD4 T cell help. Science 2003, 300, 339–342.
  43. Eisel, D.; Das, K.; Dickes, E.; Konig, R.; Osen, W.; Eichmuller, S.B. Cognate Interaction With CD4(+) T Cells Instructs Tumor-Associated Macrophages to Acquire M1-Like Phenotype. Front. Immunol. 2019, 10, 219.
  44. Shklovskaya, E.; Terry, A.M.; Guy, T.V.; Buckley, A.; Bolton, H.A.; Zhu, E.; Holst, J.; de St. Groth, B.F. Tumour-specific CD4 T cells eradicate melanoma via indirect recognition of tumour-derived antigen. Immunol. Cell Biol. 2016, 94, 593–603.
  45. Yosef, N.; Shalek, A.K.; Gaublomme, J.T.; Jin, H.; Lee, Y.; Awasthi, A.; Wu, C.; Karwacz, K.; Xiao, S.; Jorgolli, M.; et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature 2013, 496, 461–468.
  46. Obermajer, N.; Wong, J.L.; Edwards, R.P.; Chen, K.; Scott, M.; Khader, S.; Kolls, J.K.; Odunsi, K.; Billiar, T.R.; Kalinski, P. Induction and stability of human Th17 cells require endogenous NOS2 and cGMP-dependent NO signaling. J. Exp. Med. 2013, 210, 1433–1445.
  47. Lambrecht, B.N.; De Veerman, M.; Coyle, A.J.; Gutierrez-Ramos, J.C.; Thielemans, K.; Pauwels, R.A. Myeloid dendritic cells induce Th2 responses to inhaled antigen, leading to eosinophilic airway inflammation. J. Clin. Investig. 2000, 106, 551–559.
  48. Moser, M. Regulation of Th1/Th2 development by antigen-presenting cells in vivo. Immunobiology 2001, 204, 551–557.
  49. Diehl, S.; Rincon, M. The two faces of IL-6 on Th1/Th2 differentiation. Mol. Immunol. 2002, 39, 531–536.
  50. Lorvik, K.B.; Hammarstrom, C.; Fauskanger, M.; Haabeth, O.A.; Zangani, M.; Haraldsen, G.; Bogen, B.; Corthay, A. Adoptive Transfer of Tumor-Specific Th2 Cells Eradicates Tumors by Triggering an In Situ Inflammatory Immune Response. Cancer Res. 2016, 76, 6864–6876.
  51. Kryczek, I.; Wei, S.; Zou, L.; Altuwaijri, S.; Szeliga, W.; Kolls, J.; Chang, A.; Zou, W. Cutting edge: Th17 and regulatory T cell dynamics and the regulation by IL-2 in the tumor microenvironment. J. Immunol. 2007, 178, 6730–6733.
  52. Kryczek, I.; Banerjee, M.; Cheng, P.; Vatan, L.; Szeliga, W.; Wei, S.; Huang, E.; Finlayson, E.; Simeone, D.; Welling, T.H.; et al. Phenotype, distribution, generation, and functional and clinical relevance of Th17 cells in the human tumor environments. Blood 2009, 114, 1141–1149.
  53. Wilke, C.M.; Bishop, K.; Fox, D.; Zou, W. Deciphering the role of Th17 cells in human disease. Trends Immunol. 2011, 32, 603–611.
  54. Martin-Orozco, N.; Muranski, P.; Chung, Y.; Yang, X.O.; Yamazaki, T.; Lu, S.; Hwu, P.; Restifo, N.P.; Overwijk, W.W.; Dong, C. T helper 17 cells promote cytotoxic T cell activation in tumor immunity. Immunity 2009, 31, 787–798.
  55. Muranski, P.; Boni, A.; Antony, P.A.; Cassard, L.; Irvine, K.R.; Kaiser, A.; Paulos, C.M.; Palmer, D.C.; Touloukian, C.E.; Ptak, K.; et al. Tumor-specific Th17-polarized cells eradicate large established melanoma. Blood 2008, 112, 362–373.
  56. Sasada, T.; Kimura, M.; Yoshida, Y.; Kanai, M.; Takabayashi, A. CD4+CD25+ regulatory T cells in patients with gastrointestinal malignancies: Possible involvement of regulatory T cells in disease progression. Cancer 2003, 98, 1089–1099.
  57. Curiel, T.J.; Coukos, G.; Zou, L.; Alvarez, X.; Cheng, P.; Mottram, P.; Evdemon-Hogan, M.; Conejo-Garcia, J.R.; Zhang, L.; Burow, M.; et al. Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat. Med. 2004, 10, 942–949.
  58. Sato, E.; Olson, S.H.; Ahn, J.; Bundy, B.; Nishikawa, H.; Qian, F.; Jungbluth, A.A.; Frosina, D.; Gnjatic, S.; Ambrosone, C.; et al. Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc. Natl. Acad. Sci. USA 2005, 102, 18538–18543.
  59. Bates, G.J.; Fox, S.B.; Han, C.; Leek, R.D.; Garcia, J.F.; Harris, A.L.; Banham, A.H. Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2006, 24, 5373–5380.
  60. Larmonier, N.; Marron, M.; Zeng, Y.; Cantrell, J.; Romanoski, A.; Sepassi, M.; Thompson, S.; Chen, X.; Andreansky, S.; Katsanis, E. Tumor-derived CD4(+)CD25(+) regulatory T cell suppression of dendritic cell function involves TGF-beta and IL-10. Cancer Immunol. Immunother. CII 2007, 56, 48–59.
  61. Jarnicki, A.G.; Lysaght, J.; Todryk, S.; Mills, K.H. Suppression of antitumor immunity by IL-10 and TGF-beta-producing T cells infiltrating the growing tumor: Influence of tumor environment on the induction of CD4+ and CD8+ regulatory T cells. J. Immunol. 2006, 177, 896–904.
  62. Liu, V.C.; Wong, L.Y.; Jang, T.; Shah, A.H.; Park, I.; Yang, X.; Zhang, Q.; Lonning, S.; Teicher, B.A.; Lee, C. Tumor evasion of the immune system by converting CD4+CD25- T cells into CD4+CD25+ T regulatory cells: Role of tumor-derived TGF-beta. J. Immunol. 2007, 178, 2883–2892.
  63. Arce, F.; Breckpot, K.; Stephenson, H.; Karwacz, K.; Ehrenstein, M.R.; Collins, M.; Escors, D. Selective ERK activation differentiates mouse and human tolerogenic dendritic cells, expands antigen-specific regulatory T cells, and suppresses experimental inflammatory arthritis. Arthritis Rheum. 2011, 63, 84–95.
  64. Hung, K.; Hayashi, R.; Lafond-Walker, A.; Lowenstein, C.; Pardoll, D.; Levitsky, H. The central role of CD4(+) T cells in the antitumor immune response. J. Exp. Med. 1998, 188, 2357–2368.
  65. Pardoll, D.M.; Topalian, S.L. The role of CD4+ T cell responses in antitumor immunity. Curr. Opin. Immunol. 1998, 10, 588–594.
  66. Quezada, S.A.; Simpson, T.R.; Peggs, K.S.; Merghoub, T.; Vider, J.; Fan, X.; Blasberg, R.; Yagita, H.; Muranski, P.; Antony, P.A.; et al. Tumor-reactive CD4(+) T cells develop cytotoxic activity and eradicate large established melanoma after transfer into lymphopenic hosts. J. Exp. Med. 2010, 207, 637–650.
  67. McKinstry, K.K.; Strutt, T.M.; Swain, S.L. Regulation of CD4+ T-cell contraction during pathogen challenge. Immunol. Rev. 2010, 236, 110–124.
  68. Strutt, T.M.; McKinstry, K.K.; Kuang, Y.; Bradley, L.M.; Swain, S.L. Memory CD4+ T-cell-mediated protection depends on secondary effectors that are distinct from and superior to primary effectors. Proc. Natl. Acad. Sci. USA 2012, 109, E2551–E2560.
  69. Taylor, J.J.; Jenkins, M.K. CD4+ memory T cell survival. Curr. Opin. Immunol. 2011, 23, 319–323.
  70. Sallusto, F.; Lenig, D.; Forster, R.; Lipp, M.; Lanzavecchia, A. Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 1999, 401, 708–712.
  71. Mahnke, Y.D.; Brodie, T.M.; Sallusto, F.; Roederer, M.; Lugli, E. The who’s who of T-cell differentiation: Human memory T-cell subsets. Eur. J. Immunol. 2013, 43, 2797–2809.
  72. Zuazo, M.; Arasanz, H.; Fernandez-Hinojal, G.; Garcia-Granda, M.J.; Gato, M.; Bocanegra, A.; Martinez, M.; Hernandez, B.; Teijeira, L.; Morilla, I.; et al. Functional systemic CD4 immunity is required for clinical responses to PD-L1/PD-1 blockade therapy. EMBO Mol. Med. 2019, 11, e10293.
  73. Henson, S.M.; Lanna, A.; Riddell, N.E.; Franzese, O.; Macaulay, R.; Griffiths, S.J.; Puleston, D.J.; Watson, A.S.; Simon, A.K.; Tooze, S.A.; et al. p38 signaling inhibits mTORC1-independent autophagy in senescent human CD8(+) T cells. J. Clin. Investig. 2014, 124, 4004–4016.
  74. Lanna, A.; Gomes, D.C.; Muller-Durovic, B.; McDonnell, T.; Escors, D.; Gilroy, D.W.; Lee, J.H.; Karin, M.; Akbar, A.N. A sestrin-dependent Erk-Jnk-p38 MAPK activation complex inhibits immunity during aging. Nat. Immunol. 2017, 18, 354–363.
  75. Lanna, A.; Henson, S.M.; Escors, D.; Akbar, A.N. The kinase p38 activated by the metabolic regulator AMPK and scaffold TAB1 drives the senescence of human T cells. Nat. Immunol. 2014, 15, 965–972.
  76. Zuazo, M.; Arasanz, H.; Fernandez-Hinojal, G.; Gato-Canas, M.; Hernandez-Marin, B.; Martinez-Aguillo, M.; Lecumberri, M.J.; Fernadez de Lascoiti, A.; Teijeira, L.; Vera, R.; et al. Highly differentiated CD4 T cells Unequivocally Identify Primary Resistance and Risk of Hyperprogression to PD-L1/PD-1 Immune Checkpoint Blockade in Lung Cancer. bioRxiv 2018.
  77. Escors, D.; Gato-Canas, M.; Zuazo, M.; Arasanz, H.; Garcia-Granda, M.J.; Vera, R.; Kochan, G. The intracellular signalosome of PD-L1 in cancer cells. Signal Transduct. Target. Ther. 2018, 3, 26.
  78. Juneja, V.R.; McGuire, K.A.; Manguso, R.T.; LaFleur, M.W.; Collins, N.; Haining, W.N.; Freeman, G.J.; Sharpe, A.H. PD-L1 on tumor cells is sufficient for immune evasion in immunogenic tumors and inhibits CD8 T cell cytotoxicity. J. Exp. Med. 2017, 214, 895–904.
  79. Kleinovink, J.W.; Marijt, K.A.; Schoonderwoerd, M.J.A.; van Hall, T.; Ossendorp, F.; Fransen, M.F. PD-L1 expression on malignant cells is no prerequisite for checkpoint therapy. Oncoimmunology 2017, 6, e1294299.
  80. Grigg, C.; Rizvi, N.A. PD-L1 biomarker testing for non-small cell lung cancer: Truth or fiction? J. Immunother. Cancer 2016, 4, 48.
  81. Patel, S.P.; Kurzrock, R. PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy. Mol. Cancer Ther. 2015, 14, 847–856.
  82. Cristescu, R.; Mogg, R.; Ayers, M.; Albright, A.; Murphy, E.; Yearley, J.; Sher, X.; Liu, X.Q.; Lu, H.; Nebozhyn, M.; et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 2018, 362, eaax1384.
  83. Rizvi, N.A.; Hellmann, M.D.; Snyder, A.; Kvistborg, P.; Makarov, V.; Havel, J.J.; Lee, W.; Yuan, J.; Wong, P.; Ho, T.S.; et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015, 348, 124–128.
  84. Thommen, D.S.; Koelzer, V.H.; Herzig, P.; Roller, A.; Trefny, M.; Dimeloe, S.; Kiialainen, A.; Hanhart, J.; Schill, C.; Hess, C.; et al. A transcriptionally and functionally distinct PD-1(+) CD8(+) T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 2018, 24, 994–1004.
  85. Prat, A.; Navarro, A.; Pare, L.; Reguart, N.; Galvan, P.; Pascual, T.; Martinez, A.; Nuciforo, P.; Comerma, L.; Alos, L.; et al. Immune-Related Gene Expression Profiling After PD-1 Blockade in Non-Small Cell Lung Carcinoma, Head and Neck Squamous Cell Carcinoma, and Melanoma. Cancer Res. 2017, 77, 3540–3550.
  86. Bocanegra, A.; Fernandez-Hinojal, G.; Zuazo-Ibarra, M.; Arasanz, H.; Garcia-Granda, M.J.; Hernandez, C.; Ibanez, M.; Hernandez-Marin, B.; Martinez-Aguillo, M.; Lecumberri, M.J.; et al. PD-L1 Expression in Systemic Immune Cell Populations as a Potential Predictive Biomarker of Responses to PD-L1/PD-1 Blockade Therapy in Lung Cancer. Int. J. Mol. Sci. 2019, 20, 1631.
  87. Bocanegra, A.; Fernandez, G.; Ajona, D.; Arasanz, H.; Blanco, E.; Zuazo, M.; Chocarro, L.; Pineiro-Hermida, S.; Morente, P.; Fernandez, L.; et al. Potent clinical predictive and systemic adjuvant therapeutic value of plasma fractalkine in PD-L1/PD-1 blockade immunotherapy for lung cancer. medRxiv 2022.
  88. Zuazo, M.; Arasanz, H.; Bocanegra, A.; Chocarro, L.; Vera, R.; Escors, D.; Kagamu, H.; Kochan, G. Systemic CD4 immunity: A powerful clinical biomarker for PD-L1/PD-1 immunotherapy. EMBO Mol. Med. 2020, 12, e12706.
  89. Kagamu, H.; Kitano, S.; Yamaguchi, O.; Yoshimura, K.; Horimoto, K.; Kitazawa, M.; Fukui, K.; Shiono, A.; Mouri, A.; Nishihara, F.; et al. CD4(+) T-cell Immunity in the Peripheral Blood Correlates with Response to Anti-PD-1 Therapy. Cancer Immunol. Res. 2020, 8, 334–344.
  90. Kim, K.H.; Cho, J.; Ku, B.M.; Koh, J.; Sun, J.M.; Lee, S.H.; Ahn, J.S.; Cheon, J.; Min, Y.J.; Park, S.H.; et al. The First-week Proliferative Response of Peripheral Blood PD-1(+)CD8(+) T Cells Predicts the Response to Anti-PD-1 Therapy in Solid Tumors. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2019, 25, 2144–2154.
  91. Arasanz, H.; Bocanegra, A.I.; Morilla, I.; Fernandez-Irigoyen, J.; Martinez-Aguillo, M.; Teijeira, L.; Garnica, M.; Blanco, E.; Chocarro, L.; Ausin, K.; et al. Circulating Low Density Neutrophils Are Associated with Resistance to First Line Anti-PD1/PDL1 Immunotherapy in Non-Small Cell Lung Cancer. Cancers 2022, 14, 3846.
  92. Zuazo, M.; Arasanz, H.; Bocanegra, A.; Fernandez, G.; Chocarro, L.; Vera, R.; Kochan, G.; Escors, D. Systemic CD4 Immunity as a Key Contributor to PD-L1/PD-1 Blockade Immunotherapy Efficacy. Front. Immunol. 2020, 11, 586907.
  93. Olugbile, S.; Kiyotani, K.; Inoue, H.; Park, J.; Hoffman, P.; Szeto, L.; Patel, J.; Vokes, E.; Nakamura, Y. P3.02c-058 In-Depth Molecular Characterization of T Cell Clonal Expansion Induced by Anti-PD1 Therapy in NSCLC. J. Thorac. Oncol. Off. Publ. Int. Assoc. Study Lung Cancer 2017, 12, S1310.
  94. Huang, A.C.; Postow, M.A.; Orlowski, R.J.; Mick, R.; Bengsch, B.; Manne, S.; Xu, W.; Harmon, S.; Giles, J.R.; Wenz, B.; et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 2017, 545, 60–65.
  95. Valpione, S.; Galvani, E.; Tweedy, J.; Mundra, P.A.; Banyard, A.; Middlehurst, P.; Barry, J.; Mills, S.; Salih, Z.; Weightman, J.; et al. Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy. Nat. Cancer 2020, 1, 210–221.
  96. Arasanz, H.; Zuazo, M.; Bocanegra, A.; Chocarro, L.; Blanco, E.; Martinez, M.; Morilla, I.; Fernandez, G.; Teijeira, L.; Morente, P.; et al. Hyperprogressive Disease: Main Features and Key Controversies. Int. J. Mol. Sci. 2021, 22, 3736.
  97. Krieg, C.; Nowicka, M.; Guglietta, S.; Schindler, S.; Hartmann, F.J.; Weber, L.M.; Dummer, R.; Robinson, M.D.; Levesque, M.P.; Becher, B. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 2018, 24, 144–153.
  98. Arrieta, O.; Montes-Servin, E.; Hernandez-Martinez, J.M.; Cardona, A.F.; Casas-Ruiz, E.; Crispin, J.C.; Motola, D.; Flores-Estrada, D.; Barrera, L. Expression of PD-1/PD-L1 and PD-L2 in peripheral T-cells from non-small cell lung cancer patients. Oncotarget 2017, 8, 101994–102005.
  99. Takeuchi, Y.; Tanemura, A.; Tada, Y.; Katayama, I.; Kumanogoh, A.; Nishikawa, H. Clinical response to PD-1 blockade correlates with a sub-fraction of peripheral central memory CD4+ T cells in patients with malignant melanoma. Int. Immunol. 2018, 30, 13–22.
  100. Manjarrez-Orduno, N.; Menard, L.C.; Kansal, S.; Fischer, P.; Kakrecha, B.; Jiang, C.; Cunningham, M.; Greenawalt, D.; Patel, V.; Yang, M.; et al. Circulating T Cell Subpopulations Correlate with Immune Responses at the Tumor Site and Clinical Response to PD1 Inhibition in Non-Small Cell Lung Cancer. Front. Immunol. 2018, 9, 1613.
  101. Arasanz, H.; Zuazo, M.; Bocanegra, A.; Gato, M.; Martinez-Aguillo, M.; Morilla, I.; Fernandez, G.; Hernandez, B.; Lopez, P.; Alberdi, N.; et al. Early detection of hyperprogressive disease in non-small cell lung cancer by monitoring of systemic T cell dynamics. Cancers 2020, 12, 344.
  102. Tanizaki, J.; Haratani, K.; Hayashi, H.; Chiba, Y.; Nakamura, Y.; Yonesaka, K.; Kudo, K.; Kaneda, H.; Hasegawa, Y.; Tanaka, K.; et al. Peripheral Blood Biomarkers Associated with Clinical Outcome in Non-Small Cell Lung Cancer Patients Treated with Nivolumab. J. Thorac. Oncol. Off. Publ. Int. Assoc. Study Lung Cancer 2018, 13, 97–105.
  103. Bagley, S.J.; Kothari, S.; Aggarwal, C.; Bauml, J.M.; Alley, E.W.; Evans, T.L.; Kosteva, J.A.; Ciunci, C.A.; Gabriel, P.E.; Thompson, J.C.; et al. Pretreatment neutrophil-to-lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell lung cancer. Lung Cancer 2017, 106, 1–7.
  104. Bilen, M.A.; Dutcher, G.M.A.; Liu, Y.; Ravindranathan, D.; Kissick, H.T.; Carthon, B.C.; Kucuk, O.; Harris, W.B.; Master, V.A. Association Between Pretreatment Neutrophil-to-Lymphocyte Ratio and Outcome of Patients With Metastatic Renal-Cell Carcinoma Treated With Nivolumab. Clin. Genitourin. Cancer 2018, 16, e563–e575.
  105. Jiang, T.; Qiao, M.; Zhao, C.; Li, X.; Gao, G.; Su, C.; Ren, S.; Zhou, C. Pretreatment neutrophil-to-lymphocyte ratio is associated with outcome of advanced-stage cancer patients treated with immunotherapy: A meta-analysis. Cancer Immunol. Immunother. CII 2018, 67, 713–727.
  106. Mezquita, L.; Auclin, E.; Ferrara, R.; Charrier, M.; Remon, J.; Planchard, D.; Ponce, S.; Ares, L.P.; Leroy, L.; Audigier-Valette, C.; et al. Association of the Lung Immune Prognostic Index with Immune Checkpoint Inhibitor Outcomes in Patients with Advanced Non-Small Cell Lung Cancer. JAMA Oncol. 2018, 4, 351–357.
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