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Review

Pre-Existing Immunity Shapes Cancer Immunotherapy Efficacy

by
Anastasia Xagara
1,
Filippos Koinis
1,2,
Konstantinos Tsapakidis
1,2,
Ioannis Samaras
1,2,
Evangelia Chantzara
1,2,
Konstantina Vasilieva
1,
Alexandros Lazarou
1,2,
Vassilis Georgoulias
3 and
Athanasios Kotsakis
1,2,*
1
Laboratory of Oncology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Mezourlo, 41221 Larissa, Greece
2
Department of Medical Oncology, University General Hospital of Larissa, Mezourlo, 41221 Larissa, Greece
3
1st Department of Medical Oncology, Metropolitan General, 15562 Athens, Greece
*
Author to whom correspondence should be addressed.
Onco 2026, 6(1), 4; https://doi.org/10.3390/onco6010004
Submission received: 4 December 2025 / Revised: 30 December 2025 / Accepted: 2 January 2026 / Published: 7 January 2026
(This article belongs to the Special Issue Liquid Biopsy and Peripheral Immune Status in Cancer Therapy Response)
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Simple Summary

Immunotherapy has significantly improved outcomes for several cancer types by enhancing the ability of the immune system to recognize and eliminate tumor cells. Despite these advances, many patients do not respond or eventually develop resistance. Increasing evidence indicates that the immune status of a patient before treatment, known as pre-existing immunity, is a major determinant of therapeutic success. Immune cells already present within the tumor or in the peripheral blood can influence both tumor progression and response to immune-based therapies. Understanding how these cells are regulated, how they become dysfunctional, and how tumors evade their activity is essential for improving treatment strategies. This review summarizes current knowledge on the role of pre-existing immune cells, particularly T-cell subsets, in shaping responses to immune checkpoint inhibitors and cancer vaccines. These insights may assist in identifying patients who are more likely to benefit and in designing more effective immunotherapy combinations.

Abstract

Immunotherapy has revolutionized the management of patients with cancer. Immune checkpoint inhibition (ICI) is a promising treatment option that targets the molecular mechanisms that cancer cells exploit to prevent immune-mediated elimination. ICI therapy can cause exceptional long-term tumor remissions, in some cases, even after treatment discontinuation. Despite its success, many patients acquire resistance or fail to respond due to immune escape mechanisms mediated by the tumor and its microenvironment. Pre-existing immunity status of individuals seems to play a fundamental role in immunotherapy response and eventually tumor progression, as it orchestrates tumor-immune interactions. Different immune cell subsets, both in the tumor microenvironment and the peripheral blood, are established mediators that contribute to immune escape in various tumor types. Based on these findings, the elucidation of the mechanisms implicated in the regulation of these immune cells has become a priority for investigators focused on improving the efficacy of ICI. This will be essential for identifying responders as well as for developing novel therapeutic modalities to improve clinical outcomes. Herein, we summarize preclinical and clinical evidence proposing a predictive role of pre-existing immunity for clinical responses to immunotherapies.

1. Introduction

Introduction of immune checkpoint inhibitors (ICIs) in daily practice has revolutionized the cancer treatment landscape, providing, in some cases, long-term remissions and prolonged survival [1].
ICIs represent an immunotherapeutic approach that stimulates the host immune system to exert an antitumor immune response and eliminate cancer cells [1]. Immune checkpoints (ICs) comprise a set of inhibitory or activation pathways that regulate immune responses. Under physiological conditions, expression of IC controls self-tolerance and modulates the amplitude and direction of the immune response in order to protect the body’s own tissues from damage [2]. In cancer, IC expression is often dysregulated as a part of immune evasion mechanisms employed by tumors, favoring their prevalence [3].
Currently, nine ICIs, targeting three different inhibitory pathways, have been approved for the treatment of patients with different cancer types, either as monotherapy or as part of combination strategies [4]. Cytotoxic T lymphocyte-associated protein-4 (CTLA-4) is expressed on activated T cells and competes with CD28 for binding to B7 ligand (CD80/CD86) expressed on antigen-presenting cells (APCs) [5]. Programmed cell death protein 1 (PD-1) is transiently expressed on multiple immune cell types, including activated CD8+ T cells, and signals through engagement with its ligand (PD-L1 or PD-L2), expressed, among others, on tumor cells as well [6]. Similarly, Lymphocyte-Activation Gene 3 (LAG-3) is a next-generation IC molecule, expressed also by different immune system cells that binds with high affinity to MHC class II molecules, competing with CD4 binding [7]. These three ICs regulate immune response at different levels by distinct mechanisms of action, thus making the combinatorial targeting an appealing treatment approach.
However, despite striking efficacy in individual patients, tumor resistance to ICIs, as well as related toxicities, hampers their clinical utility [8]. This is not surprising, given the absence of biomarkers to guide the selection of patients, monitor efficacy, or develop more effective combinatorial strategies. Thus, an emerging critical unmet need is to elucidate the mechanisms of resistance to ICIs and leverage the understanding for developing (i) prognostic biomarkers to accurately stratify patients according to the level of pre-existing immunosuppression and (ii) predictive markers to inform the application of immunotherapy clinically [9,10,11].
To this end, identifying mechanisms that contribute to primary resistance to ICI has become a priority for investigators focused on improving the efficacy of immunotherapy. Among them, pre-existing immunity reflecting endogenous antitumor immune interactions before treatment initiation in an individualized manner may hold a predictive advantage. Thus, tumors with high levels of pre-existing immunity in the form of CD8+ PD1+ T cells are associated with favorable clinical outcomes, making these cells a preferable target of ICI-based immunotherapies [12,13]. In this review, we highlight the significance of pre-existing immune cells in tumor progression as well as in immunotherapy response. Moreover, we summarize recent preclinical and clinical evidence on the predictive value of different pre-existing T-cell populations in ICIs response.

2. Tumor—Immune Regulation in Cancer and Pre-Existing Immunity

Immunoediting theory consists of three phases and describes the complex interactions between endogenous pre-existing antitumor immunity and cancer cells. Immunosurveillance is a fundamental biological process aiming to identify and subsequently eliminate proliferating tumor cells before they become clinically detectable [14]. In parallel, immune pressure on resistant cancer cells results in dynamic genetic and epigenetic alterations, clonal selection, and expansion to counteract immune attack. These complex dynamics are also, at least in part, taking place during therapeutic innervations, where selective pressure will reveal resistant tumor variants [15]. Selected tumor clones escape immunosurveillance by developing additional resistance mechanisms, including expression of immune checkpoint ligands, release of suppressor factors, chemoattraction of regulatory T cells and myeloid—derived suppressor cells, and phenotype changes, including down-regulation of MHC molecules [3].
Immune-mediated tumor elimination presupposes infiltration of the elements of the adaptive and innate immune system in the tumor microenvironment (TME) (Figure 1). Sufficient clinical data demonstrate that inflamed tumors displaying pre-existing immunity are more likely to respond to immunotherapies [16]. It is also clear that ICI treatment can either reinvigorate pre-existing immune T cells or induce de novo responses. Thus, immune cells displaying antitumor reactivity in TME are a prerequisite for response to ICI treatment [17]. A prominent role in the intratumoral antitumor immunity possesses CD4+ and CD8+ T cells, which express ICIs and respond better to ICIs blockade therapies. These pre-existing T cells are the main targets of ICI therapy, and they can be reinvigorated, thereby inducing their cytotoxic functions or expanding and re-populating the tumor with effector-like T-cell clones. Consequently, studying the dynamics and molecular pathways that regulate the function of pre-existing T cells during immunotherapy may serve as a valuable tool for therapy response.

3. Predictive Pre-Existing T-Cell Subsets in the Context of Responses to ICI

Immunotherapeutic agents targeting different ICs affect multiple effector functions in immune cells. In this section, we provide evidence for the value of different pre-existing T-cell subsets in ICI efficacy by subdividing the findings into different immune checkpoint pathways.
Elegant studies in mice have proven the importance of CTLA-4 in maintaining self-tolerance [18]. Preclinically, blocking of CTLA-4 induces antitumor responses by enhancing T-cell activation [19]. Mechanistically, high expression of CTLA-4 in conventional T cells affects immunological synapse formation, thereby leading to reduced cytokine production and T-cell proliferation [20]. Moreover, CTLA-4 exerts tight control on Th2 cell differentiation by negatively regulating both the CD3/CD28 and the IL-4/STAT6 pathways [21]. Importantly, the role of constitutive CTLA-4 expression on Tregs is well established in increasing the suppressive properties of the system, leading to IDO production of dendritic cells and monocytes that reduce T-cell proliferation and promote T-cell anergy [22]. Thus, blocking CTAL-4 in the clinical setting has led to the occurrence of long-term survivors, indicating the induction of sustained antitumor immune responses [23]. In that direction, patients with high mRNA and protein levels of CTLA-4 in pre-treatment melanoma tumors, as well as promoter methylation, have been correlated with response to Ipilimumab (a-CTLA-4) [24]. Additionally, melanoma patients bearing high levels of pre-existing Tregs or memory CD8+ T cells in circulation respond better to Ipilimumab [25,26]. It is worth mentioning that ipilimumab monotherapy has shown only moderate responses in tumors other than melanoma [27,28]. Tremelimumab, an IgG2 isotype of a-CTLA-4, was recently approved (October 2022) in the US for unresectable hepatocellular carcinoma in combination with durvalumab [29]. It is hypothesized that these two a-CTLA-4 agents have different effectiveness due to differential binding kinetics and the capacity to mediate ADCC [30].
The PD-1/PD-L1 pathway also plays a fundamental role in restraining immune system hyperactivation [31]. Preclinical data have clearly revealed the role of the pathway in the negative regulation of T-cell activation locally within peripheral tissues [32]. PD-1 ligation controls immune response by acting mainly on T effectors and Treg cells. For Tregs, PD-L1 forces conversion of naïve CD4+ T cells to Tregs through manipulation of Akt, mTOR, ERK2, and PTEN, while also increasing Foxp3 expression and subsequently enhancing their immunosuppressive ability [33]. Regarding T effectors, pathway activation leads to dephosphorylation of the TCR activation signals by SHP-1 and SHP-2 phosphatases that further repress the PI3K/Akt pathway, promoting T-cell apoptosis [34]. Additionally, T-cell proliferation is repressed by blocking cell cycle progression, mainly by inhibition of the Ras/MEK/ERK pathway [35]. More importantly, PD-L1 expressed by cancer cells, as a tumor-immune suppressive mechanism, controls sustained proliferation and differentiation of T effectors by inducing a dysfunctional state, termed T-cell exhaustion [36].
Exhausted T cells are a post-thymic T-cell population with increased expression of inhibitory receptors (PD-1, TIM-3, LAG-3, TIGIT, and CTLA-4), reduced capacity to secrete cytokines, an altered transcriptional program, and a unique epigenetic landscape [37]. Progenitor exhausted T cells that are typically characterized as PD1hiTIM3lowTCF1+ have both stem cell and memory cell characteristics, and they are generated due to persistent tumor antigen stimulation in the tumor site. Additionally, terminally differentiated exhausted cells, typically PD1hiTIM3hiTCF1− arise from progenitors when continued antigen exposure persists [38]. Elegant studies revealed that progenitor exhausted T cells can respond to anti-PD-1 therapy, while terminally exhausted cells cannot [39]. Indeed, melanoma patients bearing high percentages of progenitor exhausted cells experience a longer duration of response when treated with ICI therapy [39]. Thus, an exhaustion signature can serve as a valuable prediction algorithm for anti-PD-1/PD-L1 response [40,41,42,43,44]. This can also be confirmed by the fact that in patients with a clinical response to a-PD-1 ICI, CD8+PD-1+ T-cells induce expression of activation and proliferation markers such as HLA-DR, CD69, and Ki-67, indicating reinvigoration [45,46,47]. Interestingly, the progressive decrease in T-exhausted cells during treatment with ICI has been found to act as a favorable predictive biomarker for clinical response [48] (Figure 2).
Subsequently, targeting the PD-1 axis and thereby boosting and/or restoring T-cell function in the clinical setting has led to lifelong immune-mediated survival benefit [49]. Moreover, it is clear that high pre-existing levels of PD-1 or PD-L1 CD8+ T cells in TME [41,45,47,50,51,52] as well as in circulation [26,53,54,55,56] are correlated positively with survival benefit. To this end, characterization of pre-existing CD8+ PD-L1+ or CD8+PD-1+ T cells indicates effector or memory T-cell phenotype (Table 1), both in TME and in circulation, possibly indicating immune suppression of tumor antigen-specific T cells.
LAG-3 also delivers inhibitory signals to T cells, thus regulating homeostasis. In murine cancer models, blocking LAG-3 enhances T-cell activation, while combination with PD-1 inhibitors increases antitumor responses [62,63,64]. LAG-3 is expressed mainly by CD4+ and CD8+ T cells as well as by Tregs [65,66]. It binds to MHC II and transmits inhibitory signals through its cytoplasmic domain, although the signal transduction pathway is still obscure [67]. For T cells, LAG-3 pathway activation reduces cytokine and granzyme production, proliferation, and promotes differentiation to Tregs [68]. On T-regs, LAG-3 expression enhances IL-10 and TGF-b production, thereby contributing to their suppressor activity [69]. Clinically, the levels of LAG-3 expression on immune cells in TME have been associated with its inhibitory function; thereby, LAG-3 levels are prognostically significant in different cancer types [70]. LAG-3 monotherapy has shown only moderate efficiency with limited effect in clinical trials so far [71,72]. LAG-3 expression in tissue biopsies, such as melanomas, is associated with resistance to a-PD-1 therapy [73]. Moreover, LAG-3 and PD-1 are usually co-expressed in different cancer types [63,64,74]. Consequently, it has been proposed that the LAG-3 immune suppressive activity may be complementary to PD-1 and may synergistically lead to T-cell exhaustion. Indeed, co-blockade of LAG-3 and PD-1 has shown longer PFS in unresectable/metastatic melanoma patients according to the RELATIVITY-047 trial that led to the first LAG-3 immunotherapy FDA approval. Although the results are encouraging, more data are needed to confirm LAG-3 efficiency in different tumor types, as well as more accurate predictive biomarkers than pre-existing LAG-3 and PD-1 co-expression on TILs.
Despite the fact that they are less studied, CD4+ T cells also play a significant role in antitumor immune response. They can profoundly manipulate TME through cytokine release as well as directly eliminate tumor cells. Except for the Tregs described earlier, a growing number of different subsets have been described to be prognostic as well as correlate with response to different immunotherapeutic agents. For example, high numbers of Th1 cells in NSCLC and colorectal cancer patients indicate a good prognostic factor, revealing a significant role in antitumor immune response [75,76]. On the other hand, Th17 cells exert a dual role in TME, having both tumor-promoting and tumor-suppressing properties depending on the context and the type of the tumor [77]. An ICOS+ CD4 effector T-cell subset was found to be expanded, while Th1 effector memory cells sufficient to mediate antitumor immune response were identified in melanoma patients treated with anti-CTLA-4 [78]. Pre-existing CD4+ T-cell immunity is also fundamental for a-PD-1/PD-L1 response, as high percentages of memory CD4+ T cells in lung cancer patients can serve as a biomarker for successful treatment [79]. Additionally, exhausted CD4+ T cells in TME can be reinvigorated upon PD-1 blocking, thereby leading to higher tumor-specific CD8 T-cell proliferation [80].

4. Cancer Vaccines and Pre-Existing Immunity

Therapeutic vaccines aim to activate pre-existing host antitumor immunity or to induce a new one directed against tumor neoantigens or tumor-associated non-mutated antigens, depending on the vaccine formulation [81]. In some cases, vaccine trials in humans have resulted in potent immunological responses, but still, clinical responses were modest, with some notable exceptions [82,83]. On a theoretical basis, an optimal vaccine formulation should be capable of inducing new robust antitumor immunity with tumor-specific memory while boosting the antitumor pre-existing immunological responses. Mechanistically, upon vaccination, vaccine tumor antigen-specific T cells in the periphery are established or activated, which will be finally localized in the TME. However, tumor intrinsic or extrinsic resistant mechanisms are employed that often compromise vaccine efficacy [84]. For example, genetic HLA-loss can be observed in patients due to intense CD8-mediated immunologic pressure [85]. Additionally, a TME bearing high numbers of immunosuppressive cell types, such as Tregs, MDSCs, and TAMs, will hamper vaccine-induced T-cell-mediated immune attack [86].
Combining cancer vaccines with ICI could be promising for the improvement in clinical benefits. As discussed in the previous section, not all patients will benefit from ICI therapy. ICI treatment is beneficial for patients with immunological hot tumors bearing pre-existing immunity in TME, such as NSCLC and melanomas, but the results are modest for cold tumors such as pancreatic and prostate [87,88,89]. On the other hand, cancer vaccines can newly prime tumor-specific T cells and also induce epitope spreading [90]. Tumor-specific T cells may phase deficits in tumoral costimulatory molecules, leading to T-cell anergy or exhaustion, thereby inducing immune checkpoint expression. Thus, administration of ICI following vaccination could lead to reversal of the exhausted pre-existing T-cell antitumor immunity via the blockade of checkpoint proteins. Preclinical studies have confirmed the induction of different checkpoint inhibitors (including PD-1, CTLA-4, LAG-3, and TIM-3) on tumor-specific CD8+ T cells upon vaccination [91,92,93,94,95]. Moreover, an improved clinical outcome was observed following blockade of PD-1 [96,97], CTLA-4 [98,99], or LAG-3 [100] after vaccination. Simultaneous blocking of immune checkpoints with complementary mechanisms of action has been superior in improving clinical outcomes after vaccination [100,101]. For example, simultaneous blockade of PD-1 and CTLA-4 after vaccination revealed a survival benefit in mice due to induction of T-effector to Treg cell ratios in the tumor site [98].
In the clinical setting, many clinical trials are ongoing using different types of vaccines in combination with ICs [84]. Of particular interest is the characterization of CD4+ and CD8+ vaccine-induced T cells, as they can serve as valuable biomarkers for response prediction as well as for ICI administration. For example, in a study for advanced-stage solid tumors treated with the lipoplex vaccine in combination with atezolizumab, the response rate was 7%. Characterization of vaccine neoantigen-specific CD8+ T cells indicated an effector-memory phenotype with elevated expression of PD-1 in only 5% of the patients, highlighting the importance of vaccine-induced T-cells prior to ICI administration [84]. Our team has extensively studied the safety, activity, and clinical response of the htert (vx-001) vaccine in different types of malignancies [102,103,104,105,106]. Our results for advanced or metastatic NSCLC indicated improved OS in patients with detectable Vx-001/TERT572 CD8+ cytotoxic T cells after vaccination [106]. Interestingly, pre-existing CD8+ cytotoxic T cells specific for the TERT572 as well as for other TAA antigens were a negative prognostic factor of vaccine response but a favorable biomarker for immunotherapy administration in NSCLC patients [107]. Evidence from mouse models also indicates that pre-existing CD8+ T cells are a bad prognostic factor for vaccine effectiveness [108]. Overall, pre-existing and vaccine-induced antitumor immunity should be considered carefully for effective treatment administration.

5. Conclusions

The immunoediting theory served as the cornerstone for appreciating the paramount role of the endogenous antitumor immunity in tumor immunosurveillance. Immune-mediated selective pressure shapes tumor arising variants while in parallel functions to keep the tumor in a dormant state. The presence of exhausted T cells in the tumor microenvironment strongly suggests that the endogenous antitumor immunological responses also occur during the escape phase, and this is of paramount importance for the successful outcome of ICI-based immunotherapies. Thus, an inflamed TME may favorably predict clinical responses that depend on the type, location, and density of the infiltrating immune cells [98].
Patients’ pre-existing immunity, which reflects the overall immune status before initiation of therapy, plays a very crucial role not only in cancer progression but also in response to treatment [99]. This is crucial for patients treated with ICIs, as this type of therapy directly targets the immune system cells. Although both CD4+ and CD8+ T-cells have a common core transcriptional signature, they still differ widely in terms of expression of other inhibitory receptors [100]. Subsequently, these subsets exhibit distinct reinvigoration levels and kinetics, which may significantly impact the outcome of the patients treated with immunotherapy. Thus, pre-existing antigen-specific CD8+ T cells may have a potential main role in the prediction of response to ICI. Studies on vaccines and ICI-treated patients have revealed an overall exhausted status of T cells, possibly due to repeated exposure of their TCRs to tumor antigens [100]. In terms of response, we should also consider that tumor-intrinsic mechanisms variously weaken the endogenous or the immunotherapy-induced intratumoral antitumor immunity, which results in the development of resistance to ICIs after an initial response [101]. Hence, combining different immunotherapeutic agents with complementary mechanisms of action can result in the expansion of unique T-cell repertoires and activate adaptive antitumor immunity. Additionally, epitope spreading has been reported to be important for expanding the T cell responses to vaccines. Thus, it is reasonable to propose that the extension of the T-cell repertoire via the generation of new tumor-antigen-specific T-cell clones and via reinvigoration of pre-existing ones is valuable to initiate a powerful regression process in the tumor, slowing down tumor growth and preventing metastases.
To this end, it is worth mentioning that pre-existing immune cells could be affected by other cancer therapies than vaccines as well. Chemotherapy and radiotherapy cause cancer cell death, thereby releasing tumor antigens and subsequently increasing the number of effector T cells [109,110]. Moreover, the use of nanoparticles in cancer immunotherapy is a growing field of research where targeted anticancer drugs can be used to enhance immunogenicity [111]. These pre-existing T-cell subsets can then form as key drivers of tumor immunity and a positive-target substrate for ICI therapy.
Therefore, different pre-existing immune cell subsets seem to have an indispensable role in immunotherapies. However, there are still many issues that need to be addressed in future studies in order to incorporate pre-existing immunity status as a predictive biomarker for ICI response. First, these pre-existing immunity subsets have mainly been studied in the baseline setting; therefore, fundamental information regarding the dynamic changes during ICI treatment is missing. Second, most of the studies rely on peripheral blood, indicating that it may not accurately reflect the complex immune landscape within the tumor microenvironment. Additionally, due to the nature of the existing preclinical studies, functional mechanistic information regarding the dynamic changes occurring in TME leading to immune escape and resistance is missing. To this end, it is important to understand the nature and dynamic changes of pre-existing immune cell subtypes across different tumor types and different patient treatment histories. Therefore, studies in selected subgroups of patients using specific datasets and protocols will lead to the incorporation of pre-existing immunity in the clinical setting.
Understanding the mechanisms underlying the regulation of the interactions between tumor cells and elements of the immune system within the tumor microenvironment will be essential to developing novel combination therapeutic modalities for improving clinical responses to immunotherapies.

Author Contributions

Conceptualization, A.X. and A.K.; methodology, A.X. and A.K.; software, A.X.; investigation, A.X. and A.K.; resources, A.X., F.K., K.V., K.T., I.S., E.C., A.L., V.G. and A.K.; data curation, all authors; writing—original draft preparation, A.X. and A.K.; writing—review and editing, all authors; visualization, A.X. and A.K.; supervision, A.K.; project administration, A.X.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research has been co-financed by the European Regional Development Fund of the European Union and Greek funds through the Operational Program Competitiveness Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T2EDK-02218). No external funding was received for the APC.

Acknowledgments

The authors would like to express their gratitude to the Society of Clinical and Laboratory Research in Oncology (SCLRO), as well as to the framework of the project SUB3. Applied Research for Precision Medicine through a Non-Profit Organization (NPO) under Private Law—‘Hellenic Precision Medicine Network’ (HPMN), which is cofinanced by Recovery and Resilience Fund and the NextGeneration EU through the General Secretariat for Research and Innovation of the Hellenic Ministry of Development (MIS 5184864). Finally, the authors warmly acknowledge the valuable assistance of the scientific secretary, Vasso Athanasaki, in the preparation of this manuscript.

Conflicts of Interest

The authors declare that they have no competing interests.

References

  1. Kruger, S.; Ilmer, M.; Kobold, S.; Cadilha, B.L.; Endres, S.; Ormanns, S.; Schuebbe, G.; Renz, B.W.; D’Haese, J.G.; Schloesser, H.; et al. Advances in cancer immunotherapy 2019—Latest trends. J. Exp. Clin. Cancer Res. 2019, 38, 268. [Google Scholar] [CrossRef]
  2. Wykes, M.N.; Lewin, S.R. Immune checkpoint blockade in infectious diseases. Nat. Rev. Immunol. 2018, 18, 91–104. [Google Scholar] [CrossRef]
  3. Tang, S.; Ning, Q.; Yang, L.; Mo, Z.; Tang, S. Mechanisms of immune escape in the cancer immune cycle. Int. Immunopharmacol. 2020, 86, 106700. [Google Scholar] [CrossRef]
  4. Wilson, R.A.M.; Evans, T.R.J.; Fraser, A.R.; Nibbs, R.J.B. Immune checkpoint inhibitors: New strategies to checkmate cancer. Clin. Exp. Immunol. 2018, 191, 133–148. [Google Scholar] [CrossRef]
  5. Krummel, M.F.; Allison, J.P. CD28 and CTLA-4 have opposing effects on the response of T cells to stimulation. J. Exp. Med. 1995, 182, 459–465. [Google Scholar] [CrossRef] [PubMed]
  6. Patsoukis, N.; Wang, Q.; Strauss, L.; Boussiotis, V.A. Revisiting the PD-1 pathway. Sci. Adv. 2020, 6, eabd2712. [Google Scholar] [CrossRef] [PubMed]
  7. Lui, Y.; Davis, S.J. LAG-3: A very singular immune checkpoint. Nat. Immunol. 2018, 19, 1278–1279. [Google Scholar] [CrossRef] [PubMed]
  8. Sharma, P.; Hu-Lieskovan, S.; Wargo, J.A.; Ribas, A. Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell 2017, 168, 707–723. [Google Scholar] [CrossRef]
  9. Bai, R.; Lv, Z.; Xu, D.; Cui, J. Predictive biomarkers for cancer immunotherapy with immune checkpoint inhibitors. Biomark. Res. 2020, 8, 34. [Google Scholar] [CrossRef]
  10. Li, H.; van der Merwe, P.A.; Sivakumar, S. Biomarkers of response to PD-1 pathway blockade. Br. J. Cancer 2022, 126, 1663–1675. [Google Scholar] [CrossRef]
  11. Roviello, G.; Bersanelli, M.; Catalano, M. The latest developments in biomarkers predicting response to immunotherapy. Immunotherapy 2022, 14, 1085–1088. [Google Scholar] [CrossRef]
  12. Bagchi, S.; Yuan, R.; Engleman, E.G. Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance. Annu. Rev. Pathol. 2021, 16, 223–249. [Google Scholar] [CrossRef]
  13. Marin-Acevedo, J.A.; Kimbrough, E.O.; Lou, Y. Next generation of immune checkpoint inhibitors and beyond. J. Hematol. Oncol. 2021, 14, 45. [Google Scholar] [CrossRef]
  14. 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. [Google Scholar] [CrossRef]
  15. Greaves, M.; Maley, C.C. Clonal evolution in cancer. Nature 2012, 481, 306–313. [Google Scholar] [CrossRef]
  16. Trujillo, J.A.; Sweis, R.F.; Bao, R.; Luke, J.J. T Cell-Inflamed versus Non-T Cell-Inflamed Tumors: A Conceptual Framework for Cancer Immunotherapy Drug Development and Combination Therapy Selection. Cancer Immunol. Res. 2018, 6, 990–1000. [Google Scholar] [CrossRef] [PubMed]
  17. Li, J.Y.; Chen, Y.P.; Li, Y.Q.; Liu, N.; Ma, J. Chemotherapeutic and targeted agents can modulate the tumor microenvironment and increase the efficacy of immune checkpoint blockades. Mol. Cancer 2021, 20, 27. [Google Scholar] [CrossRef] [PubMed]
  18. Tivol, E.A.; Borriello, F.; Schweitzer, A.N.; Lynch, W.P.; Bluestone, J.A.; Sharpe, A.H. Loss of CTLA-4 leads to massive lymphoproliferation and fatal multiorgan tissue destruction, revealing a critical negative regulatory role of CTLA-4. Immunity 1995, 3, 541–547. [Google Scholar] [CrossRef]
  19. Weber, J. Immune checkpoint proteins: A new therapeutic paradigm for cancer--preclinical background: CTLA-4 and PD-1 blockade. Semin. Oncol. 2010, 37, 430–439. [Google Scholar] [CrossRef] [PubMed]
  20. Schneider, H.; Downey, J.; Smith, A.; Zinselmeyer, B.H.; Rush, C.; Brewer, J.M.; Wei, B.; Hogg, N.; Garside, P.; Rudd, C.E. Reversal of the TCR stop signal by CTLA-4. Science 2006, 313, 1972–1975. [Google Scholar] [CrossRef]
  21. Nasta, F.; Ubaldi, V.; Pace, L.; Doria, G.; Pioli, C. Cytotoxic T-lymphocyte antigen-4 inhibits GATA-3 but not T-bet mRNA expression during T helper cell differentiation. Immunology 2006, 117, 358–367. [Google Scholar] [CrossRef]
  22. Wing, K.; Onishi, Y.; Prieto-Martin, P.; Yamaguchi, T.; Miyara, M.; Fehervari, Z.; Nomura, T.; Sakaguchi, S. CTLA-4 control over Foxp3+ regulatory T cell function. Science 2008, 322, 271–275. [Google Scholar] [CrossRef]
  23. McDermott, D.; Haanen, J.; Chen, T.T.; Lorigan, P.; O’Day, S. Efficacy and safety of ipilimumab in metastatic melanoma patients surviving more than 2 years following treatment in a phase III trial (MDX010-20). Ann. Oncol. 2013, 24, 2694–2698. [Google Scholar] [CrossRef]
  24. Fietz, S.; Zarbl, R.; Niebel, D.; Posch, C.; Brossart, P.; Gielen, G.H.; Strieth, S.; Pietsch, T.; Kristiansen, G.; Bootz, F.; et al. CTLA4 promoter methylation predicts response and progression-free survival in stage IV melanoma treated with anti-CTLA-4 immunotherapy (ipilimumab). Cancer Immunol. Immunother. 2021, 70, 1781–1788. [Google Scholar] [CrossRef]
  25. Martens, A.; Wistuba-Hamprecht, K.; Geukes Foppen, M.; Yuan, J.; Postow, M.A.; Wong, P.; Romano, E.; Khammari, A.; Dreno, B.; Capone, M.; et al. Baseline Peripheral Blood Biomarkers Associated with Clinical Outcome of Advanced Melanoma Patients Treated with Ipilimumab. Clin. Cancer Res. 2016, 22, 2908–2918. [Google Scholar] [CrossRef]
  26. Tietze, J.K.; Angelova, D.; Heppt, M.V.; Reinholz, M.; Murphy, W.J.; Spannagl, M.; Ruzicka, T.; Berking, C. The proportion of circulating CD45RO + CD8 + memory T cells is correlated with clinical response in melanoma patients treated with ipilimumab. Eur. J. Cancer 2017, 75, 268–279. [Google Scholar] [CrossRef]
  27. Lynch, T.J.; Bondarenko, I.; Luft, A.; Serwatowski, P.; Barlesi, F.; Chacko, R.; Sebastian, M.; Neal, J.; Lu, H.; Cuillerot, J.M.; et al. Ipilimumab in combination with paclitaxel and carboplatin as first-line treatment in stage IIIB/IV non-small-cell lung cancer: Results from a randomized, double-blind, multicenter phase II study. J. Clin. Oncol. 2012, 30, 2046–2054. [Google Scholar] [CrossRef]
  28. Kwon, E.D.; Drake, C.G.; Scher, H.I.; Fizazi, K.; Bossi, A.; van den Eertwegh, A.J.; Krainer, M.; Houede, N.; Santos, R.; Mahammedi, H.; et al. Ipilimumab versus placebo after radiotherapy in patients with metastatic castration-resistant prostate cancer that had progressed after docetaxel chemotherapy (CA184-043): A multicentre, randomised, double-blind, phase 3 trial. Lancet Oncol. 2014, 15, 700–712. [Google Scholar] [CrossRef]
  29. Abou-Alfa, G.K.; Lau, G.; Kudo, M.; Chan, S.L.; Kelley, R.K.; Furuse, J.; Sukeepaisarnjaroen, W.; Kang, Y.K.; Van Dao, T.; De Toni, E.N.; et al. Tremelimumab plus Durvalumab in Unresectable Hepatocellular Carcinoma. NEJM Evid. 2022, 1, EVIDoa2100070. [Google Scholar] [CrossRef]
  30. Furness, A.J.; Vargas, F.A.; Peggs, K.S.; Quezada, S.A. Impact of tumour microenvironment and Fc receptors on the activity of immunomodulatory antibodies. Trends Immunol. 2014, 35, 290–298. [Google Scholar] [CrossRef]
  31. Han, Y.; Liu, D.; Li, L. PD-1/PD-L1 pathway: Current researches in cancer. Am. J. Cancer Res. 2020, 10, 727–742. [Google Scholar]
  32. Fife, B.T.; Bluestone, J.A. Control of peripheral T-cell tolerance and autoimmunity via the CTLA-4 and PD-1 pathways. Immunol. Rev. 2008, 224, 166–182. [Google Scholar] [CrossRef]
  33. Dabrowska, A.; Grubba, M.; Balihodzic, A.; Szot, O.; Sobocki, B.K.; Perdyan, A. The Role of Regulatory T Cells in Cancer Treatment Resistance. Int. J. Mol. Sci. 2023, 24, 14114. [Google Scholar] [CrossRef]
  34. Hofmeyer, K.A.; Jeon, H.; Zang, X. The PD-1/PD-L1 (B7-H1) pathway in chronic infection-induced cytotoxic T lymphocyte exhaustion. BioMed Res. Int. 2011, 2011, 451694. [Google Scholar] [CrossRef]
  35. Patsoukis, N.; Brown, J.; Petkova, V.; Liu, F.; Li, L.; Boussiotis, V.A. Selective effects of PD-1 on Akt and Ras pathways regulate molecular components of the cell cycle and inhibit T cell proliferation. Sci. Signal. 2012, 5, ra46. [Google Scholar] [CrossRef]
  36. Barber, D.L.; Wherry, E.J.; Masopust, D.; Zhu, B.; Allison, J.P.; Sharpe, A.H.; Freeman, G.J.; Ahmed, R. Restoring function in exhausted CD8 T cells during chronic viral infection. Nature 2006, 439, 682–687. [Google Scholar] [CrossRef]
  37. McLane, L.M.; Abdel-Hakeem, M.S.; Wherry, E.J. CD8 T Cell Exhaustion During Chronic Viral Infection and Cancer. Annu. Rev. Immunol. 2019, 37, 457–495. [Google Scholar] [CrossRef]
  38. Blank, C.U.; Haining, W.N.; Held, W.; Hogan, P.G.; Kallies, A.; Lugli, E.; Lynn, R.C.; Philip, M.; Rao, A.; Restifo, N.P.; et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 2019, 19, 665–674. [Google Scholar] [CrossRef]
  39. Miller, B.C.; Sen, D.R.; Al Abosy, R.; Bi, K.; Virkud, Y.V.; LaFleur, M.W.; Yates, K.B.; Lako, A.; Felt, K.; Naik, G.S.; et al. Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 2019, 20, 326–336. [Google Scholar] [CrossRef]
  40. Anagnostou, V.; Niknafs, N.; Marrone, K.; Bruhm, D.C.; White, J.R.; Naidoo, J.; Hummelink, K.; Monkhorst, K.; Lalezari, F.; Lanis, M.; et al. Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer. Nat. Cancer 2020, 1, 99–111. [Google Scholar] [CrossRef]
  41. Djenidi, F.; Adam, J.; Goubar, A.; Durgeau, A.; Meurice, G.; de Montpreville, V.; Validire, P.; Besse, B.; Mami-Chouaib, F. CD8+CD103+ tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J. Immunol. 2015, 194, 3475–3486. [Google Scholar] [CrossRef]
  42. Duhen, T.; Duhen, R.; Montler, R.; Moses, J.; Moudgil, T.; de Miranda, N.F.; Goodall, C.P.; Blair, T.C.; Fox, B.A.; McDermott, J.E.; et al. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat. Commun. 2018, 9, 2724. [Google Scholar] [CrossRef]
  43. Hanna, G.J.; Lizotte, P.; Cavanaugh, M.; Kuo, F.C.; Shivdasani, P.; Frieden, A.; Chau, N.G.; Schoenfeld, J.D.; Lorch, J.H.; Uppaluri, R.; et al. Frameshift events predict anti-PD-1/L1 response in head and neck cancer. JCI Insight 2018, 3, e98811. [Google Scholar] [CrossRef]
  44. McDermott, D.F.; Huseni, M.A.; Atkins, M.B.; Motzer, R.J.; Rini, B.I.; Escudier, B.; Fong, L.; Joseph, R.W.; Pal, S.K.; Reeves, J.A.; et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat. Med. 2018, 24, 749–757. [Google Scholar] [CrossRef]
  45. Ji, R.R.; Chasalow, S.D.; Wang, L.; Hamid, O.; Schmidt, H.; Cogswell, J.; Alaparthy, S.; Berman, D.; Jure-Kunkel, M.; Siemers, N.O.; et al. An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol. Immunother. 2012, 61, 1019–1031. [Google Scholar] [CrossRef]
  46. Powles, T.; Eder, J.P.; Fine, G.D.; Braiteh, F.S.; Loriot, Y.; Cruz, C.; Bellmunt, J.; Burris, H.A.; Petrylak, D.P.; Teng, S.L.; et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 2014, 515, 558–562. [Google Scholar] [CrossRef]
  47. Tumeh, P.C.; Harview, C.L.; Yearley, J.H.; Shintaku, I.P.; Taylor, E.J.; Robert, L.; Chmielowski, B.; Spasic, M.; Henry, G.; Ciobanu, V.; et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014, 515, 568–571. [Google Scholar] [CrossRef]
  48. Ottonello, S.; Genova, C.; Cossu, I.; Fontana, V.; Rijavec, E.; Rossi, G.; Biello, F.; Dal Bello, M.G.; Tagliamento, M.; Alama, A.; et al. Association Between Response to Nivolumab Treatment and Peripheral Blood Lymphocyte Subsets in Patients with Non-small Cell Lung Cancer. Front. Immunol. 2020, 11, 125. [Google Scholar] [CrossRef]
  49. Ma, K.; Xu, Y.; Cheng, H.; Tang, K.; Ma, J.; Huang, B. T cell-based cancer immunotherapy: Opportunities and challenges. Sci. Bull. 2025, 70, 1872–1890. [Google Scholar] [CrossRef]
  50. Herbst, R.S.; Soria, J.C.; Kowanetz, M.; Fine, G.D.; Hamid, O.; Gordon, M.S.; Sosman, J.A.; McDermott, D.F.; Powderly, J.D.; Gettinger, S.N.; et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 2014, 515, 563–567. [Google Scholar] [CrossRef]
  51. Daud, A.I.; Loo, K.; Pauli, M.L.; Sanchez-Rodriguez, R.; Sandoval, P.M.; Taravati, K.; Tsai, K.; Nosrati, A.; Nardo, L.; Alvarado, M.D.; et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Investig. 2016, 126, 3447–3452. [Google Scholar] [CrossRef] [PubMed]
  52. George, S.; Miao, D.; Demetri, G.D.; Adeegbe, D.; Rodig, S.J.; Shukla, S.; Lipschitz, M.; Amin-Mansour, A.; Raut, C.P.; Carter, S.L.; et al. Loss of PTEN Is Associated with Resistance to Anti-PD-1 Checkpoint Blockade Therapy in Metastatic Uterine Leiomyosarcoma. Immunity 2017, 46, 197–204. [Google Scholar] [CrossRef]
  53. Wistuba-Hamprecht, K.; Martens, A.; Heubach, F.; Romano, E.; Geukes Foppen, M.; Yuan, J.; Postow, M.; Wong, P.; Mallardo, D.; Schilling, B.; et al. Peripheral CD8 effector-memory type 1 T-cells correlate with outcome in ipilimumab-treated stage IV melanoma patients. Eur. J. Cancer 2017, 73, 61–70. [Google Scholar] [CrossRef]
  54. 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. [Google Scholar] [CrossRef] [PubMed]
  55. Jacquelot, N.; Roberti, M.P.; Enot, D.P.; Rusakiewicz, S.; Ternes, N.; Jegou, S.; Woods, D.M.; Sodre, A.L.; Hansen, M.; Meirow, Y.; et al. Predictors of responses to immune checkpoint blockade in advanced melanoma. Nat. Commun. 2017, 8, 592. [Google Scholar] [CrossRef]
  56. Subrahmanyam, P.B.; Dong, Z.; Gusenleitner, D.; Giobbie-Hurder, A.; Severgnini, M.; Zhou, J.; Manos, M.; Eastman, L.M.; Maecker, H.T.; Hodi, F.S. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J. Immunother. Cancer 2018, 6, 18. [Google Scholar] [CrossRef]
  57. Galsky, M.D.; Saci, A.; Szabo, P.M.; Han, G.C.; Grossfeld, G.; Collette, S.; Siefker-Radtke, A.; Necchi, A.; Sharma, P. Nivolumab in Patients with Advanced Platinum-resistant Urothelial Carcinoma: Efficacy, Safety, and Biomarker Analyses with Extended Follow-up from CheckMate 275. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2020, 26, 5120–5128. [Google Scholar] [CrossRef] [PubMed]
  58. Hsu, C.L.; Ou, D.L.; Bai, L.Y.; Chen, C.W.; Lin, L.; Huang, S.F.; Cheng, A.L.; Jeng, Y.M.; Hsu, C. Exploring Markers of Exhausted CD8 T Cells to Predict Response to Immune Checkpoint Inhibitor Therapy for Hepatocellular Carcinoma. Liver Cancer 2021, 10, 346–359. [Google Scholar] [CrossRef]
  59. 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. [Google Scholar] [CrossRef]
  60. Kim, C.G.; Kim, G.; Kim, K.H.; Park, S.; Shin, S.; Yeo, D.; Shim, H.S.; Yoon, H.I.; Park, S.Y.; Ha, S.J.; et al. Distinct exhaustion features of T lymphocytes shape the tumor-immune microenvironment with therapeutic implication in patients with non-small-cell lung cancer. J. Immunother. Cancer 2021, 9, e002780. [Google Scholar] [CrossRef]
  61. 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. [Google Scholar] [CrossRef]
  62. Goldberg, M.V.; Drake, C.G. LAG-3 in Cancer Immunotherapy. In Current Topics in Microbiology and Immunology; Springer: Berlin/Heidelberg, Germany, 2011; Volume 344, pp. 269–278. [Google Scholar] [CrossRef]
  63. Woo, S.R.; Turnis, M.E.; Goldberg, M.V.; Bankoti, J.; Selby, M.; Nirschl, C.J.; Bettini, M.L.; Gravano, D.M.; Vogel, P.; Liu, C.L.; et al. Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 2012, 72, 917–927. [Google Scholar] [CrossRef]
  64. Matsuzaki, J.; Gnjatic, S.; Mhawech-Fauceglia, P.; Beck, A.; Miller, A.; Tsuji, T.; Eppolito, C.; Qian, F.; Lele, S.; Shrikant, P.; et al. Tumor-infiltrating NY-ESO-1-specific CD8+ T cells are negatively regulated by LAG-3 and PD-1 in human ovarian cancer. Proc. Natl. Acad. Sci. USA 2010, 107, 7875–7880. [Google Scholar] [CrossRef]
  65. Huang, C.T.; Workman, C.J.; Flies, D.; Pan, X.; Marson, A.L.; Zhou, G.; Hipkiss, E.L.; Ravi, S.; Kowalski, J.; Levitsky, H.I.; et al. Role of LAG-3 in regulatory T cells. Immunity 2004, 21, 503–513. [Google Scholar] [CrossRef] [PubMed]
  66. Blackburn, S.D.; Shin, H.; Haining, W.N.; Zou, T.; Workman, C.J.; Polley, A.; Betts, M.R.; Freeman, G.J.; Vignali, D.A.; Wherry, E.J. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat. Immunol. 2009, 10, 29–37. [Google Scholar] [CrossRef] [PubMed]
  67. Graydon, C.G.; Mohideen, S.; Fowke, K.R. LAG3’s Enigmatic Mechanism of Action. Front. Immunol. 2020, 11, 615317. [Google Scholar] [CrossRef]
  68. Durham, N.M.; Nirschl, C.J.; Jackson, C.M.; Elias, J.; Kochel, C.M.; Anders, R.A.; Drake, C.G. Lymphocyte Activation Gene 3 (LAG-3) modulates the ability of CD4 T-cells to be suppressed in vivo. PLoS ONE 2014, 9, e109080. [Google Scholar] [CrossRef] [PubMed]
  69. Chien, C.H.; Chiang, B.L. Regulatory T cells induced by B cells: A novel subpopulation of regulatory T cells. J. Biomed. Sci. 2017, 24, 86. [Google Scholar] [CrossRef]
  70. Shi, A.P.; Tang, X.Y.; Xiong, Y.L.; Zheng, K.F.; Liu, Y.J.; Shi, X.G.; Lv, Y.; Jiang, T.; Ma, N.; Zhao, J.B. Immune Checkpoint LAG3 and Its Ligand FGL1 in Cancer. Front. Immunol. 2021, 12, 785091. [Google Scholar] [CrossRef]
  71. Brignone, C.; Gutierrez, M.; Mefti, F.; Brain, E.; Jarcau, R.; Cvitkovic, F.; Bousetta, N.; Medioni, J.; Gligorov, J.; Grygar, C.; et al. First-line chemoimmunotherapy in metastatic breast carcinoma: Combination of paclitaxel and IMP321 (LAG-3Ig) enhances immune responses and antitumor activity. J. Transl. Med. 2010, 8, 71. [Google Scholar] [CrossRef]
  72. Wang-Gillam, A.; Plambeck-Suess, S.; Goedegebuure, P.; Simon, P.O.; Mitchem, J.B.; Hornick, J.R.; Sorscher, S.; Picus, J.; Suresh, R.; Lockhart, A.C.; et al. A phase I study of IMP321 and gemcitabine as the front-line therapy in patients with advanced pancreatic adenocarcinoma. Investig. New Drugs 2013, 31, 707–713. [Google Scholar] [CrossRef]
  73. Chen, P.L.; Roh, W.; Reuben, A.; Cooper, Z.A.; Spencer, C.N.; Prieto, P.A.; Miller, J.P.; Bassett, R.L.; Gopalakrishnan, V.; Wani, K.; et al. Analysis of Immune Signatures in Longitudinal Tumor Samples Yields Insight into Biomarkers of Response and Mechanisms of Resistance to Immune Checkpoint Blockade. Cancer Discov. 2016, 6, 827–837. [Google Scholar] [CrossRef] [PubMed]
  74. Chen, J.; Chen, Z. The effect of immune microenvironment on the progression and prognosis of colorectal cancer. Med. Oncol. 2014, 31, 82. [Google Scholar] [CrossRef]
  75. Laheurte, C.; Dosset, M.; Vernerey, D.; Boullerot, L.; Gaugler, B.; Gravelin, E.; Kaulek, V.; Jacquin, M.; Cuche, L.; Eberst, G.; et al. Distinct prognostic value of circulating anti-telomerase CD4(+) Th1 immunity and exhausted PD-1(+)/TIM-3(+) T cells in lung cancer. Br. J. Cancer 2019, 121, 405–416. [Google Scholar] [CrossRef]
  76. Ling, A.; Lundberg, I.V.; Eklof, V.; Wikberg, M.L.; Oberg, A.; Edin, S.; Palmqvist, R. The infiltration, and prognostic importance, of Th1 lymphocytes vary in molecular subgroups of colorectal cancer. J. Pathol. Clin. Res. 2016, 2, 21–31. [Google Scholar] [CrossRef] [PubMed]
  77. Asadzadeh, Z.; Mohammadi, H.; Safarzadeh, E.; Hemmatzadeh, M.; Mahdian-Shakib, A.; Jadidi-Niaragh, F.; Azizi, G.; Baradaran, B. The paradox of Th17 cell functions in tumor immunity. Cell. Immunol. 2017, 322, 15–25. [Google Scholar] [CrossRef]
  78. Spitzer, M.H.; Carmi, Y.; Reticker-Flynn, N.E.; Kwek, S.S.; Madhireddy, D.; Martins, M.M.; Gherardini, P.F.; Prestwood, T.R.; Chabon, J.; Bendall, S.C.; et al. Systemic Immunity Is Required for Effective Cancer Immunotherapy. Cell 2017, 168, 487–502 e15. [Google Scholar] [CrossRef] [PubMed]
  79. 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. [Google Scholar] [CrossRef]
  80. Balanca, C.C.; Salvioni, A.; Scarlata, C.M.; Michelas, M.; Martinez-Gomez, C.; Gomez-Roca, C.; Sarradin, V.; Tosolini, M.; Valle, C.; Pont, F.; et al. PD-1 blockade restores helper activity of tumor-infiltrating, exhausted PD-1hiCD39+ CD4 T cells. JCI Insight 2021, 6, e142513. [Google Scholar] [CrossRef]
  81. Baxevanis, C.N.; Fortis, S.P.; Ardavanis, A.; Perez, S.A. Exploring Essential Issues for Improving Therapeutic Cancer Vaccine Trial Design. Cancers 2020, 12, 2908. [Google Scholar] [CrossRef]
  82. Sahin, U.; Oehm, P.; Derhovanessian, E.; Jabulowsky, R.A.; Vormehr, M.; Gold, M.; Maurus, D.; Schwarck-Kokarakis, D.; Kuhn, A.N.; Omokoko, T.; et al. An RNA vaccine drives immunity in checkpoint-inhibitor-treated melanoma. Nature 2020, 585, 107–112. [Google Scholar] [CrossRef]
  83. Schlom, J.; Hodge, J.W.; Palena, C.; Tsang, K.Y.; Jochems, C.; Greiner, J.W.; Farsaci, B.; Madan, R.A.; Heery, C.R.; Gulley, J.L. Therapeutic cancer vaccines. Adv. Cancer Res. 2014, 121, 67–124. [Google Scholar] [CrossRef]
  84. Kim, C.G.; Sang, Y.B.; Lee, J.H.; Chon, H.J. Combining Cancer Vaccines with Immunotherapy: Establishing a New Immunological Approach. Int. J. Mol. Sci. 2021, 22, 8035. [Google Scholar] [CrossRef]
  85. Paulson, K.G.; Voillet, V.; McAfee, M.S.; Hunter, D.S.; Wagener, F.D.; Perdicchio, M.; Valente, W.J.; Koelle, S.J.; Church, C.D.; Vandeven, N.; et al. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat. Commun. 2018, 9, 3868. [Google Scholar] [CrossRef] [PubMed]
  86. Ganesan, A.P.; Clarke, J.; Wood, O.; Garrido-Martin, E.M.; Chee, S.J.; Mellows, T.; Samaniego-Castruita, D.; Singh, D.; Seumois, G.; Alzetani, A.; et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat. Immunol. 2017, 18, 940–950. [Google Scholar] [CrossRef] [PubMed]
  87. Chae, Y.K.; Arya, A.; Iams, W.; Cruz, M.R.; Chandra, S.; Choi, J.; Giles, F. Current landscape and future of dual anti-CTLA4 and PD-1/PD-L1 blockade immunotherapy in cancer; lessons learned from clinical trials with melanoma and non-small cell lung cancer (NSCLC). J. Immunother. Cancer 2018, 6, 39. [Google Scholar] [CrossRef] [PubMed]
  88. Beer, T.M.; Kwon, E.D.; Drake, C.G.; Fizazi, K.; Logothetis, C.; Gravis, G.; Ganju, V.; Polikoff, J.; Saad, F.; Humanski, P.; et al. Randomized, Double-Blind, Phase III Trial of Ipilimumab Versus Placebo in Asymptomatic or Minimally Symptomatic Patients with Metastatic Chemotherapy-Naive Castration-Resistant Prostate Cancer. J. Clin. Oncol. 2017, 35, 40–47. [Google Scholar] [CrossRef]
  89. Schizas, D.; Charalampakis, N.; Kole, C.; Economopoulou, P.; Koustas, E.; Gkotsis, E.; Ziogas, D.; Psyrri, A.; Karamouzis, M.V. Immunotherapy for pancreatic cancer: A 2020 update. Cancer Treat. Rev. 2020, 86, 102016. [Google Scholar] [CrossRef]
  90. Brossart, P. The Role of Antigen Spreading in the Efficacy of Immunotherapies. Clin. Cancer Res. 2020, 26, 4442–4447. [Google Scholar] [CrossRef]
  91. Lutz, E.R.; Wu, A.A.; Bigelow, E.; Sharma, R.; Mo, G.; Soares, K.; Solt, S.; Dorman, A.; Wamwea, A.; Yager, A.; et al. Immunotherapy converts nonimmunogenic pancreatic tumors into immunogenic foci of immune regulation. Cancer Immunol. Res. 2014, 2, 616–631. [Google Scholar] [CrossRef]
  92. Soares, K.C.; Rucki, A.A.; Wu, A.A.; Olino, K.; Xiao, Q.; Chai, Y.; Wamwea, A.; Bigelow, E.; Lutz, E.; Liu, L.; et al. PD-1/PD-L1 blockade together with vaccine therapy facilitates effector T-cell infiltration into pancreatic tumors. J. Immunother. 2015, 38, 1–11. [Google Scholar] [CrossRef]
  93. Rekoske, B.T.; Smith, H.A.; Olson, B.M.; Maricque, B.B.; McNeel, D.G. PD-1 or PD-L1 Blockade Restores Antitumor Efficacy Following SSX2 Epitope-Modified DNA Vaccine Immunization. Cancer Immunol. Res. 2015, 3, 946–955. [Google Scholar] [CrossRef]
  94. Zumwalde, N.A.; Domae, E.; Mescher, M.F.; Shimizu, Y. ICAM-1-dependent homotypic aggregates regulate CD8 T cell effector function and differentiation during T cell activation. J. Immunol. 2013, 191, 3681–3693. [Google Scholar] [CrossRef]
  95. Colluru, V.T.; Zahm, C.D.; McNeel, D.G. Mini-intronic plasmid vaccination elicits tolerant LAG3(+) CD8(+) T cells and inferior antitumor responses. Oncoimmunology 2016, 5, e1223002. [Google Scholar] [CrossRef] [PubMed]
  96. Rice, A.E.; Latchman, Y.E.; Balint, J.P.; Lee, J.H.; Gabitzsch, E.S.; Jones, F.R. An HPV-E6/E7 immunotherapy plus PD-1 checkpoint inhibition results in tumor regression and reduction in PD-L1 expression. Cancer Gene Ther. 2015, 22, 454–462. [Google Scholar] [CrossRef]
  97. Antonios, J.P.; Soto, H.; Everson, R.G.; Orpilla, J.; Moughon, D.; Shin, N.; Sedighim, S.; Yong, W.H.; Li, G.; Cloughesy, T.F.; et al. PD-1 blockade enhances the vaccination-induced immune response in glioma. JCI Insight 2016, 1, e87059. [Google Scholar] [CrossRef]
  98. Quezada, S.A.; Peggs, K.S.; Curran, M.A.; Allison, J.P. CTLA4 blockade and GM-CSF combination immunotherapy alters the intratumor balance of effector and regulatory T cells. J. Clin. Investig. 2006, 116, 1935–1945. [Google Scholar] [CrossRef]
  99. Wada, S.; Jackson, C.M.; Yoshimura, K.; Yen, H.R.; Getnet, D.; Harris, T.J.; Goldberg, M.V.; Bruno, T.C.; Grosso, J.F.; Durham, N.; et al. Sequencing CTLA-4 blockade with cell-based immunotherapy for prostate cancer. J. Transl. Med. 2013, 11, 89. [Google Scholar] [CrossRef]
  100. Zahm, C.D.; Moseman, J.E.; Delmastro, L.E.; Mcneel, D.G. PD-1 and LAG-3 blockade improve anti-tumor vaccine efficacy. Oncoimmunology 2021, 10, 1912892. [Google Scholar] [CrossRef] [PubMed]
  101. Wei, S.C.; Anang, N.A.S.; Sharma, R.; Andrews, M.C.; Reuben, A.; Levine, J.H.; Cogdill, A.P.; Mancuso, J.J.; Wargo, J.A.; Pe’er, D.; et al. Combination anti-CTLA-4 plus anti-PD-1 checkpoint blockade utilizes cellular mechanisms partially distinct from monotherapies. Proc. Natl. Acad. Sci. USA 2019, 116, 22699–22709. [Google Scholar] [CrossRef] [PubMed]
  102. Gridelli, C.; Ciuleanu, T.; Domine, M.; Szczesna, A.; Bover, I.; Cobo, M.; Kentepozidis, N.; Zarogoulidis, K.; Kalofonos, C.; Kazarnowisz, A.; et al. Clinical activity of a htert (vx-001) cancer vaccine as post-chemotherapy maintenance immunotherapy in patients with stage IV non-small cell lung cancer: Final results of a randomised phase 2 clinical trial. Br. J. Cancer 2020, 122, 1461–1466. [Google Scholar] [CrossRef]
  103. Bolonaki, I.; Kotsakis, A.; Papadimitraki, E.; Aggouraki, D.; Konsolakis, G.; Vagia, A.; Christophylakis, C.; Nikoloudi, I.; Magganas, E.; Galanis, A.; et al. Vaccination of patients with advanced non-small-cell lung cancer with an optimized cryptic human telomerase reverse transcriptase peptide. J. Clin. Oncol. 2007, 25, 2727–2734. [Google Scholar] [CrossRef] [PubMed]
  104. Kotsakis, A.; Vetsika, E.K.; Christou, S.; Hatzidaki, D.; Vardakis, N.; Aggouraki, D.; Konsolakis, G.; Georgoulias, V.; Christophyllakis, C.; Cordopatis, P.; et al. Clinical outcome of patients with various advanced cancer types vaccinated with an optimized cryptic human telomerase reverse transcriptase (TERT) peptide: Results of an expanded phase II study. Ann. Oncol. 2012, 23, 442–449. [Google Scholar] [CrossRef] [PubMed]
  105. Kotsakis, A.; Papadimitraki, E.; Vetsika, E.K.; Aggouraki, D.; Dermitzaki, E.K.; Hatzidaki, D.; Kentepozidis, N.; Mavroudis, D.; Georgoulias, V. A phase II trial evaluating the clinical and immunologic response of HLA-A2(+) non-small cell lung cancer patients vaccinated with an hTERT cryptic peptide. Lung Cancer 2014, 86, 59–66. [Google Scholar] [CrossRef] [PubMed]
  106. Vetsika, E.K.; Konsolakis, G.; Aggouraki, D.; Kotsakis, A.; Papadimitraki, E.; Christou, S.; Menez-Jamet, J.; Kosmatopoulos, K.; Georgoulias, V.; Mavroudis, D. Immunological responses in cancer patients after vaccination with the therapeutic telomerase-specific vaccine Vx-001. Cancer Immunol. Immunother. 2012, 61, 157–168. [Google Scholar] [CrossRef]
  107. Xagara, A.; Vasilieva, K.; Kokkalis, A.; Lazarou, A.; Christodoulopoulos, G.; Papadopoulos, V.; Chantzara, E.; Koinis, F.; Saloustros, E.; Kallergi, G.; et al. Profiling of circulating T-cells with prognostic and predictive value in early and advanced stage PDAC. Cancer Immunol. Immunother. 2025, 74, 333. [Google Scholar] [CrossRef]
  108. Beyranvand Nejad, E.; Ratts, R.B.; Panagioti, E.; Meyer, C.; Oduro, J.D.; Cicin-Sain, L.; Fruh, K.; van der Burg, S.H.; Arens, R. Demarcated thresholds of tumor-specific CD8 T cells elicited by MCMV-based vaccine vectors provide robust correlates of protection. J. Immunother. Cancer 2019, 7, 25. [Google Scholar] [CrossRef]
  109. Zhang, L.; Feng, D.; Yu, L.X.; Tsung, K.; Norton, J.A. Preexisting antitumor immunity augments the antitumor effects of chemotherapy. Cancer Immunol. Immunother. 2013, 62, 1061–1071. [Google Scholar] [CrossRef]
  110. Koksal, H.; Herbst, M.; Perreira, P.; Nater, M.; Regli, N.; Boudjeniba, C.; Erdem Borgoni, N.; Cecconi, V.; van den Broek, M. Pre-existing intratumoral stem-like CD8(+) T cells drive radiotherapy-induced tumor immunity. Cell Rep. 2025, 44, 115566. [Google Scholar] [CrossRef]
  111. Fallatah, M.M.; Alradwan, I.; Alfayez, N.; Aodah, A.H.; Alkhrayef, M.; Majrashi, M.; Jamous, Y.F. Nanoparticles for Cancer Immunotherapy: Innovations and Challenges. Pharmaceuticals 2025, 18, 1086. [Google Scholar] [CrossRef]
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Figure 1. ICI efficacy depends on the composition of immune cells in TME. (a) Immune excluded tumors without pre-existing immune T cells do not respond to ICI immunotherapy. They are characterized by impaired T-cell infiltration and low PD-L1 expression. (b) Immune suppressive tumors contain a high density of immunosuppressive cells, such as Tregs and MDSCs. (c) Immune infiltrated tumors respond better to immunotherapy due to the presence of pre-existing immune T cells, high levels of PD-L1, and exhausted T cells.
Figure 1. ICI efficacy depends on the composition of immune cells in TME. (a) Immune excluded tumors without pre-existing immune T cells do not respond to ICI immunotherapy. They are characterized by impaired T-cell infiltration and low PD-L1 expression. (b) Immune suppressive tumors contain a high density of immunosuppressive cells, such as Tregs and MDSCs. (c) Immune infiltrated tumors respond better to immunotherapy due to the presence of pre-existing immune T cells, high levels of PD-L1, and exhausted T cells.
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Figure 2. ICI treatment reinvigorates exhausted T cells in cancer. Neoantigens and tumor-associated antigens (TAA) released by apoptotic tumor cells are presented by Antigen Presenting Cells (APCs) and activate naïve T cells. Activated neoantigen/TAA-specific T cells, due to repeated antigen stimulation on the tumor, become exhausted. Exhausted T cells express high levels of inhibitory receptors, have low effector functions, and are categorized as exhausted (Tex) and terminally exhausted (termTex). (In red) ICI therapy blocks the inhibitory receptors, leading to induction of activation and proliferation signals on Tex cells, thereby inducing their cytotoxic activity. (In green) Secreting factors that influence T-cell function positively. APC, Antigen Presenting Cell; Tem, T-effector memory; Teff, T-effector; Tex, T-exhausted; term Tex, terminally exhausted; CTL, cytotoxic lymphocyte; Trm, T-resting memory; ICI, Immune checkpoint inhibition.
Figure 2. ICI treatment reinvigorates exhausted T cells in cancer. Neoantigens and tumor-associated antigens (TAA) released by apoptotic tumor cells are presented by Antigen Presenting Cells (APCs) and activate naïve T cells. Activated neoantigen/TAA-specific T cells, due to repeated antigen stimulation on the tumor, become exhausted. Exhausted T cells express high levels of inhibitory receptors, have low effector functions, and are categorized as exhausted (Tex) and terminally exhausted (termTex). (In red) ICI therapy blocks the inhibitory receptors, leading to induction of activation and proliferation signals on Tex cells, thereby inducing their cytotoxic activity. (In green) Secreting factors that influence T-cell function positively. APC, Antigen Presenting Cell; Tem, T-effector memory; Teff, T-effector; Tex, T-exhausted; term Tex, terminally exhausted; CTL, cytotoxic lymphocyte; Trm, T-resting memory; ICI, Immune checkpoint inhibition.
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Table 1. Pre-existing CD8+ T cell subtypes that are positively correlated with response to ICI.
Table 1. Pre-existing CD8+ T cell subtypes that are positively correlated with response to ICI.
Cancer TypeTreatmentT-Cell SubtypeDetection
Method		References
TissueNSCLCAnti–PD-1/PD-L1 CD8+CD103+IHC[41]
SCCHNAnti–PD-1/PD-L1 CD8+CD45RO+CCR7−FACS[57]
RCCAtezolizumabCD8+CD45RO+CCR7−IHC[44]
HCCAnti–PD-1/PD-L1 LAG3, CD244, CCL5, CXCL9, CXCL13, MSR1, CSF3R, CYBB, and KLRK1IHC[58]
NSCLCNivolumabCD8, TIM-3, Lag-3, CTLA-4, CD200, CD109, CD39 mRNA[59]
NSCLCAnti–PD-1/PD-L1 CD8+PD1+CD39+CD103+FACS[60]
BloodMelanomaIipilimumabCD8+CD45RO+CCR7+FACS[53]
MelanomaIipilimumabCD8+CD45RO+FACS[26]
MelanomaPembolizumabCD45RAloCD27hi CTLA-4+ PD-1+ EOMES+FACS[54]
MelanomaIipilimumab
+ Nivolumab		CD8+CD137+FACS[55]
MelanomaAnti-CTLA-4/anti-PD-1CD8+CD45RA−CCR7−CyTOF[56]
NSCLCNivolumabCD8+PD1+Eomes+FACS[48]
NSCLCAtezolizumabCD8+ with high KLRG-1, 2B4, CD57, CD161, TIGIT, and CD25FACS[61]
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MDPI and ACS Style

Xagara, A.; Koinis, F.; Tsapakidis, K.; Samaras, I.; Chantzara, E.; Vasilieva, K.; Lazarou, A.; Georgoulias, V.; Kotsakis, A. Pre-Existing Immunity Shapes Cancer Immunotherapy Efficacy. Onco 2026, 6, 4. https://doi.org/10.3390/onco6010004

AMA Style

Xagara A, Koinis F, Tsapakidis K, Samaras I, Chantzara E, Vasilieva K, Lazarou A, Georgoulias V, Kotsakis A. Pre-Existing Immunity Shapes Cancer Immunotherapy Efficacy. Onco. 2026; 6(1):4. https://doi.org/10.3390/onco6010004

Chicago/Turabian Style

Xagara, Anastasia, Filippos Koinis, Konstantinos Tsapakidis, Ioannis Samaras, Evangelia Chantzara, Konstantina Vasilieva, Alexandros Lazarou, Vassilis Georgoulias, and Athanasios Kotsakis. 2026. "Pre-Existing Immunity Shapes Cancer Immunotherapy Efficacy" Onco 6, no. 1: 4. https://doi.org/10.3390/onco6010004

APA Style

Xagara, A., Koinis, F., Tsapakidis, K., Samaras, I., Chantzara, E., Vasilieva, K., Lazarou, A., Georgoulias, V., & Kotsakis, A. (2026). Pre-Existing Immunity Shapes Cancer Immunotherapy Efficacy. Onco, 6(1), 4. https://doi.org/10.3390/onco6010004

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