Skip to main content

Main menu

  • Home
  • About
    • About CBM
    • Editorial Board
    • Announcement
  • Articles
    • Ahead of print
    • Current Issue
    • Archive
    • Collections
    • Cover Story
  • For Authors
    • Instructions for Authors
    • Resources
    • Submit a Manuscript
  • For Reviewers
    • Become a Reviewer
    • Instructions for Reviewers
    • Resources
    • Outstanding Reviewer
  • Subscription
  • Alerts
    • Email Alerts
    • RSS Feeds
    • Table of Contents
  • Contact us
  • Other Publications
    • cbm

User menu

  • My alerts

Search

  • Advanced search
Cancer Biology & Medicine
  • Other Publications
    • cbm
  • My alerts
Cancer Biology & Medicine

Advanced Search

 

  • Home
  • About
    • About CBM
    • Editorial Board
    • Announcement
  • Articles
    • Ahead of print
    • Current Issue
    • Archive
    • Collections
    • Cover Story
  • For Authors
    • Instructions for Authors
    • Resources
    • Submit a Manuscript
  • For Reviewers
    • Become a Reviewer
    • Instructions for Reviewers
    • Resources
    • Outstanding Reviewer
  • Subscription
  • Alerts
    • Email Alerts
    • RSS Feeds
    • Table of Contents
  • Contact us
  • Follow cbm on Twitter
  • Visit cbm on Facebook
Review ArticleReview
Open Access

CD4+ T cells in cancer: dual roles, exhaustion, and therapeutic breakthroughs

Yangyang Zhang, Jingli Xu, Siwei Pan, Yuqi Wang, Qianyu Zhao, Ziyang Huang, Can Hu and Xiangdong Cheng
Cancer Biology & Medicine January 2026, 23 (1) 42-59; DOI: https://doi.org/10.20892/j.issn.2095-3941.2025.0414
Yangyang Zhang
1College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jingli Xu
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Siwei Pan
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yuqi Wang
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Qianyu Zhao
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ziyang Huang
1College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Can Hu
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiangdong Cheng
2Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou 310014, China
3Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310014, China
4Zhejiang Key Laboratory of Prevention, Diagnosis and Therapy for Gastrointestinal Cancer, Hangzhou 310014, China
5Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou 310014, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Xiangdong Cheng
  • For correspondence: chengxd{at}zjcc.org.cn
  • Article
  • Figures & Data
  • Info & Metrics
  • References
  • PDF
Loading

Abstract

In recent years the crucial role of CD4+ T cells in tumor immunomodulation has garnered increasing recognition. While conventional cancer immunotherapy research has predominantly focused on the cytotoxic function of CD8+ T cells, emerging evidence has now shown that CD4+ T cells enhance antitumor immunity by delivering co-stimulatory signals, secreting cytokines, and promoting cytotoxic T lymphocyte (CTL) activation and display unique immunoregulatory capabilities through direct tumor cell killing or remodeling of the tumor microenvironment. The high heterogeneity and functional plasticity of CD4+ T cell subsets significantly influence clinical responses to immunotherapy with underlying mechanisms involving multi-level regulatory networks, including epigenetic modulation and metabolic reprogramming. Deciphering the functional heterogeneity of CD4+ T cells and the interactions with the tumor microenvironment will provide essential mechanistic insights for next-generation immunotherapies, such as immune checkpoint inhibitors and chimeric antigen receptor T (CAR-T) therapies, thereby advancing personalized treatment paradigms.

keywords

  • Mechanism of differentiation
  • dual role
  • T cell exhaustion
  • immunotherapeutic strategies
  • translational clinical applications

Introduction

With the emergence of immunotherapy as a promising cancer treatment strategy, CD4+ T cells have attracted significant attention due to a critical role in mediating antitumor immune responses. As a key immunologic component that expresses the T cell receptor (TCR), CD4+ T cells are known to perform cytokine-mediated helper functions and directly lyse target cells. CD4+ T cells possess potent immunomodulatory capabilities with considerable therapeutic potential. CD4+ T cells exhibit a broad range of antitumor effects, including a critical role in acquired immunity, promoting antibody production by B cells, enhancing CD8+ T cell-mediated cytotoxic T lymphocyte (CTL) responses, and maintaining immune homeostasis1,2. Moreover, emerging research has further elucidated the functional mechanisms underlying cytotoxic CD4+ T cell subsets in the context of cancer immunotherapy3. This manuscript begins with a discussion involving the biological fundamentals of CD4+ T cells, including the activation mechanisms, subset heterogeneity, and differentiation regulatory networks within the tumor microenvironment (TME), to examine the dual roles in the context of cancer, encompassing the capacity to enhance anti-tumor immunity and the detrimental effects in tumor immune escape. Specifically, this review provides a comprehensive analysis of the exhaustion characteristics and mechanisms underlying CD4+ T cells in the TME utilizing single-cell RNA sequencing (scRNA-seq) technology and highlights the interplay between immunologic tolerance and tumor-associated immune resistance. This review also underscores recent clinical advances in immunotherapy, including immunomodulatory strategies targeting CD4+ T cells, with particular emphasis on the therapeutic potential of CD4+ CTL subsets. Through these novel analyses, the review presents a comprehensive conceptual framework for the multifaceted roles of CD4+ T cells in cancer immunotherapy, offering new perspectives for the development of future clinical applications and therapeutic strategies.

The biological characteristics of CD4+ T cells

Activation mechanisms underlying CD4+ T cell-mediated antitumor immunity

Tumor immunotherapy induces durable antitumor responses by activating T cells, which begins with the release of tumor-associated antigens (TAAs) from dying tumor cells. These TAAs are presented to naïve T cells in tumor-draining lymph nodes (TDLNs), which initiate the activation of tumor-specific CD4+ T cells necessary for systemic antitumor immunity4,5. Naïve T cells are derived from hematopoietic stem cells in the bone marrow and mature in the thymus, where cytokines and stromal cells support T cell development6. T cell maturation is a multi-step process, during which the expression of a functional TCR is a pivotal event7. This TCR specifically recognizes peptide-major histocompatibility complex (MHC) complexes presented by antigen-presenting cells (APCs), such as conventional type 1 dendritic cells (cDC1s) and cDC2s. These APCs capture tumor antigens from the TME and migrate to TDLNs for antigen presentation, representing the first signal required for CD4+ T cell activation8,9. The second signal for activation is co-stimulation, which primarily occurs via CD28 receptor binding to CD80/CD86 molecules on APCs. This interaction activates signaling pathways, including PI3K/AKT/mTOR and NF-κB10. Moreover, the inducible co-stimulator (ICOS), a member of the CD28 family, and the ICOS ligand (ICOSL) enhance cytokine production, thereby contributing to T cell activation11,12. Co-stimulatory molecules in the tumor necrosis factor receptor (TNFR) superfamily, such as CD27, CD40, and OX40, also provide additional co-stimulation by binding to respective ligands, thereby exerting effects similar to the CD28 family13–15. This binding contributes to the activation of CD4+ T cells and CD8+ CTLs. A third signal, mediated by cytokines in the TME, is involved in influencing the differentiation of CD4+ T cells16. This process involves conventional signal transduction and nutrients, which act as signaling molecules that help regulate T cell function. Some studies suggest that nutrients may serve as a fourth signal licensing T cell immunity, although this concept has not gained widespread acceptance (Box 1).

Box 1

Nutrients: signal 4 in T cell immunity

Signaling pathways are crucial during CD4+ T cell activation and differentiation but after activation immune cells undergo metabolic reprogramming from oxidative phosphorylation (OXPHOS) to aerobic glycolysis. Recent studies emphasize the importance of nutrients in regulating T cell immune responses, influencing activation, differentiation, and antitumor immunity. In addition to signals 1–3, T cells utilize nutrient availability and metabolism to establish a distinct ‘signal 4’ through coordinated transport, sensing, and signaling17–19. Upon encountering tumor antigens, naïve T cells are activated via the TCR and CD28, initiating the PI3K/AKT/mTOR pathway. This pathway drives metabolic reprogramming, enhancing glycolytic factors, like MYC and hypoxia-inducible factor-1 alpha (HIF-1α), and increases glucose uptake through glucose transporters (GLUTs)20. GLUT1 deficiency impairs glucose uptake and glycolysis, affecting effector T cell survival and differentiation. However, regulatory T cells (Tregs) are less affected. Excessive GLUT1 expression disrupts Forkhead box protein P3 (FOXP3), destabilizing the Treg lineage21. Metabolic reprogramming also involves amino acids and fatty acids. Leucine and glutamine promote Th1 and Th17 differentiation by regulating DNA and histone methylation. Th17 cells are less dependent on glutamine but rely on leucine to suppress Th1 differentiation22,23. Tregs mainly depend on fatty acid oxidation (FAO) and exhibit increased AMP-activated protein kinase (AMPK) activity, supporting survival and stability24. Tumor cells consume nutrients and produce metabolic byproducts, like lactate, which accumulates in the TME. Lactate enhances Treg function and promotes Treg differentiation from naïve CD4+ T cells, while inhibiting Th1 and Th17 differentiation, contributing to an immunosuppressive environment that favors tumor progression25. Mechanistically, lactate decreases Th1 cell levels by inducing deacetylation and degradation of T-bet via sirtuin 1(SIRT1)26. Lactate also activates lactate dehydrogenase A (LDHA) in naïve CD4+ T cells, converting alpha-ketoglutaric acid (α-KG) to 2-hydroxyglutaric acid (2-HG), which inhibits ATP5B, suppressing mTOR, HIF-1α, and FOXP3 degradation. This cascade favors Treg differentiation and inhibits Th17 differentiation27,28. Recent studies have also explored the role of lactate in promoting Treg differentiation through a transforming growth factor-beta (TGF-β)-dependent mechanism29. These findings highlight the complex relationship between T cell metabolism and immune regulation, providing new opportunities for optimizing antitumor therapies by targeting nutrient pathways.

Heterogeneous characteristics of CD4+ T cell subsets

CD4+ helper T (Th) cells represent a heterogeneous population that can differentiate into various subsets in response to cues from the TME. This plasticity enables CD4+ T cells to serve as central coordinators of antitumor immune responses. The classification of CD4+ T cell subsets originated from seminal studies involving Th1/Th2 cells30. Th1 and Th2 cells are defined by signature cytokine secretion profiles, as follows: Th1 cells primarily produce interferon-γ (IFN-γ), interleukin-2 (IL-2), and tumor necrosis factor-alpha (TNF-α); and Th2 cells are characterized by the production of IL-4, IL-5, and IL-13. The introduction of the Th1/Th2 paradigm marked a new phase in the study of CD4+ T cell subsets. To date, several conventional subsets have been identified within the TME, including Th1, Th2, Th17, Th9, Th22, follicular helper T (Tfh) cells, and Treg cells (Figure 1)31. These subsets form a broad and intricate network, underscoring the diverse and complex roles of CD4+ T cells in the tumor context.

Figure 1
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1

Activation and differentiation mechanisms underlying major CD4+ T cell effector subsets. APCs capture the tumor antigens presented by tumor cells and present the tumor antigens to the TCR on the surface of naïve T cells via the MHC-II, serving as the first signal for the activation of CD4+ T cells. The co-stimulatory signals between the ligands and receptors expressed on APCs and T cells serve as the second signal for CD4+ T cell activation, such as the interaction between CD40L\ICOSL expressed on CD4+ T cells and the homologous receptor CD40\ICOS on APCs. Cytokines in the TME act as the third signal to activate distinct signaling pathways in CD4+ T cells. The nutrients are transported, sensed, and signal pathways act as the fourth signal to initiate metabolic reprogramming. Th1 cell differentiation: polarization factors (IFN-γ and IL-12) promote the expression of chemokine receptors (CXCR3\CCR1\CCR2\CCR5) of the Th1 subgroup and promote the expression of the master transcription factor (T-bet) through STAT1/STAT4 and eventually secrete IFN-γ, TNF-α, and IL-2. Th2 cell differentiation: IL-2 and IL-4 promote the expression of chemokine receptors (CCR1\CCR2\CCR3) of the Th2 and promote the expression of the GATA3 through STAT6 and eventually secrete IL-4, IL-5, and IL-13. Th9 cell differentiation: IL-4 and TGF-β promote the expression of chemokine receptors (CXCR3) of the Th9 and promote the expression of the PU.1 through STAT6 and eventually secrete IL-9. Th17 cell differentiation: IL-6, IL-1β, and TGF-β promote the expression of chemokine receptors (CCR4\CCR9) of the Th9 and promote the expression of the RORγt through STAT3 and eventually secrete IL-17 and IL-22. Th22 cell differentiation: IL-6 and TNF-α promote the expression of chemokine receptors (CCR4\CCR6\CCR10) of the Th22 and promote the expression of the AHR through STAT3 and eventually secrete IL-22. Tfh cell differentiation: IL-6 and IL-21 promote the expression of chemokine receptors (CXCR5) of the Th22 and promote the expression of the BCL6 through STAT3 and eventually secrete IL-21. Treg cell differentiation: TGF-β promotes the expression of chemokine receptors (CCR4) of the Treg and promote the expression of the FOXP3 through STAT5 and eventually secrete TGF-β. AHR, aryl hydrocarbon receptor; APC, antigen-presenting cell; BCL6, B-cell lymphoma 6 protein; CCR1, C-C motif chemokine receptor 1; CD40L, CD40 ligand; CXCR3, C-X-C motif chemokine receptor 3; FOXP3, forkhead box P3; GATA-3, GATA binding protein 3; ICOS, inducible T-cell co-stimulator; IFN-γ, interferon gamma; IL, interleukin; MHC, major histocompatibility complex; PU.1, purine-rich box 1; RORγt, RAR-related orphan receptor gamma, isoform t; STAT, signal transducer and activator of transcription; T-bet, T-box transcription factor; TCR, T cell receptor; Tfh, follicular helper T cells; Th, T helper cell; TNF-α, tumor necrosis factor alpha; Treg, regulatory T cells. The figure was created with BioRender.com.

The advent of scRNA-seq and spatial transcriptomics has greatly advanced our understanding of CD4+ T cell heterogeneity. scRNA-seq enables high-resolution profiling of individual T cells, uncovering distinct transcriptional states and functional potentials. Among commonly used platforms, 10× Genomics (Pleasanton, CA, US) offers high-throughput surface marker analysis, while Smart-seq2 (Illumina, San Diego, CA, US) provides greater sensitivity for low-abundance and full-length transcripts. These technologies have facilitated the discovery of novel CD4+ T cell subsets and states in human cancers32. scRNA-seq revealed two distinct CD4+ T cell subsets in bladder cancer with differing clinical significance, as follows: PD-1hiCD200hi cells exhibit pro-angiogenic properties and mediate therapeutic resistance via the UTP–P2RY6 axis; and PD-1hiCD200low cells are associated with improved clinical outcomes33. Integrated single-cell RNA and TCR sequencing also delineated CD4+ CTLs, including granzyme B (GZMB)+ and granzyme K (GZMK)+ subsets, which are capable of killing MHC-II-restricted tumor cells34,35. scRNA-seq further facilitated the identification of rare immunosuppressive Treg populations in non-small cell lung cancer (NSCLC), such as tumor necrosis factor receptor superfamily member 9 (TNFRSF9)+ Tregs, which overexpress inhibitory molecules, including layilin (LAYN) and correlate with poor prognosis36. Spatial transcriptomics enables the identification of distinct cell populations, while preserving spatial localization within tissue and providing critical data on the relationships between cellular function, phenotype, and anatomic context within the TME37. Spatial transcriptomics in pancreatic ductal adenocarcinoma revealed that chemokine (C-X-C motif) ligand 13 (CXCL13)+ effector CD4+ T cells were enriched at the tumor periphery. These CXCL13+ CD4+ T cells exhibited a more favorable response to immunotherapy compared to T cells within the tumor core38. Importantly, several of these transcriptionally distinct populations have shown potential as clinical biomarkers. For example, circulating CXCR3+ CCR6+ CD4+ T cells have emerged as a non-invasive predictive indicator of response to anti-PD-1 therapy39. In summary, these findings highlight the profound heterogeneity and functional plasticity of CD4+ T cells within the TME.

Differentiation regulatory networks of CD4+ T cells in the TME

The differentiation of CD4+ T cells is governed by specific stimulation conditions that modulate the expression of key transcription factors, thereby determining the developmental trajectory and subsequent production of cytokines. The diversity of the cytokine signal underlies the functional plasticity of CD4+ T cells, driving the proliferation and polarization into distinct effector subsets. Naïve CD4+ T cells can differentiate into various subsets depending on the signals encountered. This differentiation is not a rigid, linear progression but rather a dynamic and tightly regulated struggle that is orchestrated by master transcription factors and shaped by complex signaling networks and molecular interactions40. The differentiation of Th1 cells provides a classic paradigm of cytokine-directed Th cell specialization. This process requires two essential cytokines (IL-12 and IFN-γ)41. Upon cytokine stimulation, specific members of the signal transducer and activator of transcription (STAT) family are activated to drive lineage commitment42. The Th1 differentiation program involves the following: neutralization of IFN-γ (the characteristic cytokine of Th1 cells) in vitro significantly impairs Th1 differentiation in the IFN-γ/STAT1 pathway; the IL-12/STAT4 pathway cooperates with IFN-γ signaling; and synergistic induction of the major transcription factor, T-box expressed in T cells (T-bet) suggests core transcriptional regulation.

Th1 cell differentiation is negatively regulated by Th2-polarizing cytokines, particularly IL-4 and the master transcription factor, GATA3. Recent studies have identified additional cytokine-mediated mechanisms that extend beyond classical differentiation pathways. For example, Wen et al. demonstrated that IL-16 activates GSK signaling suppresses glutaminase activity in Th1 cells, thereby preventing glutamine hydrolysis into glutamate and elevating intracellular glutamine levels. This metabolic reprogramming promotes antitumor Th1 differentiation and enhances the efficacy of PD-L1 blockade. Clinically, high IL-16 expression correlates with significantly improved survival in immune checkpoint blockade (ICB)-treated patients, positioning IL-16 as a predictive biomarker and a potential co-target for immunotherapy43. In addition to cytokines and lineage-specifying transcription factors, other regulatory elements modulate CD4+ T cell fate. Interferon regulatory factors (IRFs), especially IRF-1 and IRF-2, are induced by IFN-γ and act as transcriptional repressors to inhibit Th2 differentiation44. An alternative IRF1 isoform, IRF1Δ7, impairs full-length IRF1 activity at the Il12rb1 promoter, resulting in diminished IFN-γ secretion by Th1 cells. IRF1Δ7 is enriched in the TME, the inhibition of which has been shown to potentiate Th1-mediated antitumor responses45. Runt-related transcription factor (RUNX) has key roles in T cell development by repressing CD4+ T cell gene expression in naïve T cells, thereby favoring CD8+ T cell lineage commitment. RUNX3 modulates CTL differentiation within the TME by limiting T-bet overexpression. This restraint on terminal differentiation helps preserve the memory potential of nascent CTLs46,47. The differentiation of Th1 cells is orchestrated by a T-bet-centered transcriptional network that integrates signals from STAT family proteins, RUNX factors, and other modulators. This regulatory framework ensures lineage commitment and functional competence of Th1 effector cells.

Multi-dimensional roles of CD4+ T cells in tumor immunology

Molecular basis of direct tumor cell cytotoxicity by CD4+ T cells

CD4+ CTLs

CD4+ CTLs are distinguished from conventional CD4+ T cells by an ability to directly lyse target cells, complementing the cytotoxicity of CD8+ T cells. Although CD4+ CTLs exhibit lower effector potency compared to CD8+ CTLs, CD4+ CTLs still mediate significant tumoricidal activity, particularly when present in large numbers within the TME. This functional compensation becomes especially important in cases of tumor MHC-I loss, in which CD8+ T cell responses are impaired48. A study by Chun et al. found no significant difference in CD8+ T cell infiltration between tumor and normal tissues. However, CD4+ T cells were more abundant in tumors and exhibited distinct compartmentalization. CD4+ CTL subsets were enriched in tumors with the following two specialized populations identified: GZMB+ (9% of FOXP3− CCR7− CD4+ T cells); and GZMK+ (16% of FOXP3− CCR7− CD4+ T cells). Bladder cancer cells maintained MHC-II expression, allowing recognition by CD4+ CTLs. Flow cytometry of 11 other bladder tumor specimens confirmed that CD4+ CTLs produced effector cytokines and underwent clonal expansion49. These tumor-infiltrating CD4+ T cells demonstrated cytotoxic activity, inducing tumor cell apoptosis. Moreover, the infiltration of CD4+ CTLs correlated with clinical responses to anti-PD-1 therapy, a finding replicated in NSCLC studies, underscoring the critical role of CD4+ CTLs in enhancing checkpoint immunotherapy efficacy50. CD4+ CTLs represent a unique subset of CD4+ T cells endowed with antigen-specific cytotoxic capacity. However, this population currently lacks definitive surface markers, making the identification of discriminative markers to distinguish CD4+ CTLs from other Th subsets an active area of investigation. CD4+ CTLs share key transcriptional regulators with Th1 cells, including T-bet and RUNX3 expression. Notably, although Th1-polarizing cytokines, like IL-12, can potentiate CD4+ CTL cytotoxicity, these Th1-associated features are dispensable in CD4+ CTL development and function. Analysis of CD4+ CTL origins revealed that the differentiation of CD4+ CTLs depends on IL-2. Preclinical murine melanoma models demonstrated that IL-2-induced BLIMP-1 expression in CD4+ T cells promotes CTL differentiation via STAT5, although BLIMP-1 deficiency only partially reduces the effector capacity of CD4+ CTLs51. IL-2 also upregulates CRTAM expression, enabling CD4+ T cells to acquire cytotoxicity through an Eomesodermin (EOMES)-dependent pathway. Targeting CRTAM enhances CD4+ CTL function in preclinical settings because CRTAM+ CD4+ CTLs exhibit stronger cytotoxic activity in murine TC-1 lung cancer models with the CRTAM+ CD4+ CTL infiltration closely correlated to reduced tumor size. However, the transient nature of CRTAM expression limits utility as a reliable marker. Overall, identifying precise and definitive markers remains a key focus in CD4+ CTL research52,53. CD4+ CTLs are widely present in humans and mice. CD4+ CTL differentiation is programmed according to environmental requirements and consequently produces different direct cell killing effects. CD4+ CTL can produce cytotoxicity through direct expression of GZM, perforin (PRF), and other granule-associated proteins (Figure 2)54,55. Studies in melanoma have shown that CD4+ CTLs mediate tumor cell apoptosis through the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) pathway56.

Figure 2
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2

The dual role of CD4+ T cells in tumor immunity. CD4+ T cells have complex roles in tumor immunity, including direct killing of tumor cells, indirect antitumor functions, and immunosuppressive effects. (A) Direct tumor cell killing. CD4+ T cells directly interact with and kill cancer cells in an MHC-dependent manner or via the TRAIL pathway or by secreting GZM/PRF to induce tumor cell cytotoxicity. Furthermore, CD4+ T cells can secrete cytokines, such as IFN-γ or TNF-α, to inhibit tumor angiogenesis and the proliferation of tumor cells. (B) Indirect antitumor functions. CD4+ T cells secrete IL-2 to enhance the effector function of CD8+ T cells or promote CD8+ T cell activation through interactions with dendritic cells. Additionally, CD8+ T cells stimulate antibody production by activating B cells. (C) Immunosuppression FOXP3-expressing Treg cells inhibit or kill CD8+ T cells by competitively binding to co-stimulatory receptors (CD80/CD86) on dendritic cells through the expression of inhibitory receptors, such as CTLA-4, or by secreting cytokines, such as TGF-β, IL-10, IL-35, GZM, and PRF. Additionally, Treg cells can competitively acquire IL-2, thereby preventing IL-2 signaling in effector T cells. CTLA-4, cytotoxic T-lymphocyte antigen 4; DC, dendritic cell; GZM, granzyme; PRF, perforin; TGF-β, transforming growth factor beta; TRAIL, tumor necrosis factor-related apoptosis-inducing ligand.

Cytokines

IFN-γ has direct antiproliferative and pro-apoptotic effects by reprogramming genes involved in cell cycle regulation, proliferation, and metabolism. IFN-γ inhibits cell cycle progression, suppresses tumor proliferation, induces cell quiescence, promotes senescence, and triggers apoptosis via classical signaling pathways57,58. Immunohistochemical analysis of brain tumor samples has shown that IFN-γ reduces endothelial cell density and induces vascular disruption, leading to tumor necrosis. Angiogenesis is regulated by a balance of pro- and anti-angiogenic factors, with VEGF serving as a key pro-angiogenic molecule in tumors. Tumor-associated macrophages (TAMs) are major VEGF producers. Studies have shown that IFN-γ downregulates VEGF in brain tumors, suppressing angiogenesis and promoting vascular disruption and necrosis. Additionally, IFN-γ enhances monocyte/macrophage infiltration into tumors while inhibiting TAM differentiation, thereby reducing TAM polarization and VEGF expression, ultimately attenuating tumor angiogenesis59–62.

Tumor necrosis factor-α (TNF-α) is produced by CD4+ Th1 cells and exerts biological effects by binding to tumor necrosis factor receptor 1 (TNFR1) and TNFR2. TNF-α activates signaling pathways by binding to TNFR1, including MAPK and NF-κB, which transmit pro-apoptotic signals in tumor and endothelial cells, thereby disrupting the structure and function of tumor vasculature63. In addition, TNF-α has been shown to play a crucial role in regulating the cell cycle, influencing cell proliferation and growth64. Although research on cytokines has mainly focused on individual factors, the combined effects of TNF-α and IFN-γ in anti-tumor immunity and therapy have not been established. Studies have shown that the TNF-α and IFN-γ combination enhances tumor cell killing65. TNF-α and IFN-γ exhibit anti-tumor effects in vitro and in vivo with recombinant TNF-α and IFN-γ showing antiproliferative effects on pancreatic cancer cells66. TNF-α and IFN-γ promote NF-κB-mediated apoptosis in colorectal cancer by enhancing Fas expression67. Nitric oxide and PI3K signaling are involved in the synergistic pro-apoptotic effects68. In addition, the TNF-α/IFN-γ combination induces tumor cell senescence by altering cell cycle regulation and inhibiting tumor growth through blood vessel normalization69. Studies on the mechanism of combined application of TNF-α and IFN-γ have revealed that IFN-γ enhances the sensitivity of endothelial cells to TNF, inducing selective killing of tumor endothelial cells by TNF and inhibiting tumor growth70.

Synergistic enhancement mechanisms of CD4+ T cells on CD8+ T cell anti-tumor function

CD4+ T cells are essential in facilitating the proliferation and differentiation of CD8+ T cells. This helper function is exemplified by enhanced recruitment efficiency, increased proliferative capacity, and improved effector functions of CD8+ T cells within the TME.

Th cells activate CD8+ T cells

The maintenance of CD8+ T cell quantity and functional competence depends on IL-2-producing Th cells. Adoptive transfer experiments have demonstrated that Th cells isolated from IL-2−/− mice fail to induce tumor regression in tumor-bearing recipients. In contrast, Th cells derived from IL-2+/+ mice effectively suppress tumor growth71. Bos et al. demonstrated that IL-2 produced by tumor-specific CD4+ T cells promotes CD8+ T cell proliferation and GZMB expression, while IFN-γ-induced chemokines from these CD4+ T cells accelerate CD8+ T cell recruitment. These findings collectively indicated that tumor-specific CD4+ T cells significantly enhance intratumoral CD8+ T cell recruitment, expansion, and effector function. Conversely, CD8+ T cells exhibit impaired survival and clonal expansion in the absence of CD4+ T cell help72. CX3CR1-expressing CD8+ T cell subset exhibits potent cytolytic activity. The development of this cytotoxic population is critically dependent on CD4+ T cell-derived IL-21. This developmental pathway can be therapeutically exploited to enhance the tumoricidal capacity of tumor-infiltrating CD8+ T cells73. Subsequent studies revealed that the basic leucine zipper ATF-like transcription factor (BATF), a downstream transcription factor of IL-21 signaling, has a critical role in the mechanism underlying IL-21-mediated CD4+ T cell help for CD8+ T cells. CD4+ T cells operating through the IL-21-BATF axis are essential for sustaining the effector function of tumor-infiltrating CD8+ T cells74. Espinosa-Carrasco et al. also noted that the effective activation and killing function of tumor-specific CD8+ T cells depend on the formation of a triad with CD4+ T cells and antigen-presenting cells because this “iron triangle” combination leads to reprogramming of intratumoral CD8+ T cells and even reversal of the dysfunction, thereby exerting cytotoxic effects and eliminating tumors75. In addition, it has been reported that CD8+ T cells directly receive help from CD4+ T cells via CD40-CD40L as the basis for CD8+ T cell memory generation76.

CD4+ T cells amplify the antigen presentation process

CD4+ T cells assist in initiating the gene expression program of CD8+ T cells and enhancing CTL function through multiple molecular mechanisms. The antigen-specific contact between CD4+ T cells and DCs enables the DCs to optimize antigen presentation and deliver specific cytokines and co-stimulatory signals to CD8+ T cells. This process indirectly promotes the clonal expansion of CD8+ T cells and the differentiation into effector or memory T cells. As early as the 1980s, researchers discovered that DCs also require signals from CD4+ T cells, such as CD40-CD40L, to acquire an enhanced ability to activate naïve CD8+ T cells, a process referred to as “T cell help”77. Recent studies have suggested that activated CD4+ T cells enable human ex vivo cDC1s to induce CTL responses against tumor-associated antigens. These findings highlight the critical role of CD4+ T cells in supporting cDC1 function within the TME and suggest establishing cDC1 transcriptomic signatures as diagnostic markers for cancer78. Studies have demonstrated that this enhancement mechanism mediated by CD4+ T cells must collaborate with innate immune stimulation. Pretreatment with innate immune signaling endows DCs with the ability to more rapidly and efficiently express multiple co-stimulatory molecules or bioactive mediators by enhancing the activity of transcription factors (p65 and IRF1) after receiving CD40 stimulation from CD4+ T cells, which subsequently optimizes CD8+ T cell immune responses. Moreover, DCs that are primed by T cells enable CD8+ T cells to exhibit superior cytotoxic capabilities79.

Borst et al.80 systematically summarized three models of the roles of innate signals and CD4+ T cell helper signals in CTL priming: innate signals and CD4+ T cell helper signals are comparable with similar effects on CTL priming (redundancy model); CD4+ T cells amplify innate signals delivered to DCs in the second phase of CTL priming through helper function (amplification model); and CD4+ T cell helper signals and innate stimulation deliver distinct signals to DCs (complementary model). Each signal influences effector and memory differentiation pathways in unique ways, collectively determining the strength of effector and memory CTL responses.

These findings suggest that innate immune signals cannot replace CD4+ T cell help for optimal CTL quality, further highlighting the unique role of CD4+ T cells.

Regulatory pathways of CD4+ T cells in driving B cell humoral immune responses

The interaction between CD4+ T cells and B cells is primarily mediated by Tfh cells, a subset of CD4+ T cells that is characterized by high CXCR5 expression. Tfh cells support the formation of germinal centers (GCs), as well as the maturation, differentiation, and antibody production of B cells, thus having a critical role in maintaining immune homeostasis81. Cancer research has established a strong link between Tfh cell responses and anti-tumor immunity. Multiple studies have shown a positive correlation between the presence of Tfh and B cells and prolonged survival and improved prognosis in various tumors, particularly in breast cancer82,83. There are three potential mechanisms through which Tfh cells exert protective functions in infection and cancer.

  • Promoting tertiary lymphoid structure (TLS) formation: Tfh cell responses significantly drive the formation of TLSs composed of B cells, T cells, natural killer (NK) cells, and APCs in chronically inflamed non-lymphoid organs. Mature intratumoral TLSs represent anti-tumor structures containing pro-inflammatory cytokines, activated complement cascades, and potent cytotoxic lymphocytes84,85. Tumor-infiltrating Tfh cells expressing high levels of CXCL13 and IL-21 have a critical role in the formation of intratumoral TLSs in close association with CD8+ T and B cell infiltration, as well as prolonged survival in cancer patients86. Craft and colleagues identified germinal center B (GC B) and Tfh cells in lung adenocarcinoma (LUAD) tissue through single-cell sequencing. A stronger tumor-specific Tfh and GC B cell response was induced compared to a model without the antigen in a subcutaneous LUAD xenograft model with B cell antigen recognition, highlighting the importance of antigen recognition. Further studies showed that the effector function of CD8+ T cells was compromised in the absence of Tfh or GC B cells. The researchers suggested that IL-21 secreted by Tfh cells have a crucial role. Knocking out the IL-21 receptor reduced CD8+ T cell infiltration and accelerated tumor growth. The study concluded that tumor-specific B cells present tumor antigens and enhance CD8+ T cell function by promoting Tfh cell differentiation and increasing IL-21 production, thereby inhibiting tumor growth87.

  • Enhancing B cell responses and antibody functions: Tfh cells promote B cell GC responses and the production of functional antibodies88. Effective anti-tumor immunity depends on antibody-mediated effector functions, such as antibody-dependent cellular cytotoxicity (ADCC), complement activation, and antibody-mediated tumor cell phagocytosis. Tumors with high levels of Tfh cells and mature TLSs typically exhibit a dense population of diverse B cells, plasma cells, and anti-tumor antibodies, which contribute to a robust anti-tumor immune response89.

  • Sustaining memory B cell generation: Tfh cells support the production of memory B cells, which are critical for rapid responses to reinfection and long-term immune protection90.

Immunosuppressive transformation and pro-tumorigenic effects of tumor-associated CD4+ T cells

The pro-tumorigenic effects of CD4+ T cells have been observed across multiple subsets but are most pronounced in the Treg cell population, which represents the primary immunosuppressive subset of CD4+ T cells65. Treg cells precisely regulate immune responses to maintain immune tolerance and prevent chronic inflammation under normal physiologic conditions. However, in the context of cancer the role of Treg cells undergoes a shift. Specifically, in most tumors the presence of Treg cells is associated with poor clinical outcomes because Treg cells inhibit anti-tumor immunity, promote immune evasion, and enhance tumor growth and metastasis91,92. Interestingly, the opposite effect has been observed in some cancers, such as colorectal and ovarian cancer, primarily due to the high functional heterogeneity of Treg cells. Treg cells with anti-tumor functions typically express low levels of FOXP3 and secrete inflammatory cytokines93–95. Intratumoral FOXP3+ Treg cells are predominantly activated and highly proliferative compared to Treg cells in non-tumor tissues96. Several functionally distinct FOXP3+ Treg subsets have been identified, including quiescent Treg, effector Treg (eTreg), pro-inflammatory Treg, and T follicular regulatory (TFR) cells. Among these FOXP3+ Treg subsets, the highly suppressive eTreg subset, which expresses TNFRSF family members, such as OX40, GITR, and 4-1BB, is strongly correlated with resistance to immunotherapy. Four pro-tumorigenic mechanisms of Treg cells have been established.

  • Inhibitory receptor-associated mechanisms: Treg cells exert a negative impact on the tumor immune process by expressing a range of co-inhibitory molecules at high levels, including CTLA-4, PD-1, LAG-3, TIM-3, and TIGIT97. CTLA-4 competes with the co-stimulatory receptor, CD28, on effector T cells for binding to CD80/CD86 on APCs, thereby inhibiting the co-stimulatory signals required for effector T cell activation98. In addition, CTLA-4 induces the internalization and degradation of CD80/CD86 through endocytosis and phagocytosis, preventing these ligands from activating T cell responses and ultimately resulting in T cell anergy99,100.

  • IL-2 sequestration: Treg cells constitutively express the high-affinity IL-2 heterotrimeric receptor containing the IL-2Rα chain, CD25. Compared to resting effector T cells that express low-affinity IL-2 receptors, Treg cells acquire more IL-2 through a process known as “IL-2 sequestration.” This process prevents IL-2 signaling in effector T cells, leading to immune suppression101,102.

  • Immunosuppressive cytokine expression: Treg cells induce immunosuppression by producing immunosuppressive cytokines, such as IL-10, IL-33, IL-35 and TGF-β. TGF-β exerts its function on Th cells by inhibiting the transcription of pro-inflammatory cytokines, such as IFN-γ and GZMB, as well as Th cell subset master transcription factors, such as T-bet and GATA-3. TGF-β also suppresses the cytotoxic activity of CD8+ T cells and prevent the ability to traffic to tumours103.

  • Direct cytolysis: Treg cells can form membrane gap junctions through which Treg cells transfer cytolytic molecules (PRF, GZMA, and GZMB), leading to suppression through the killing of effector T cells104,105.

Functional exhaustion of CD4+ T cells in the TME

The term “exhaustion” was first used to describe non-responsive antigen-specific CD8+ T cells with long-term persistence in mouse models of chronic viral infection. Despite expressing markers associated with T cell activation, these exhausted CD8+ T (CD8+ Tex) cells are unable to kill virally infected cells and fail to produce IFN-γ upon stimulation106. Currently, the understanding of CD8+ Tex has been expanded to encompass impaired effector function, reduced proliferative capacity, elevated expression of negative inhibitory receptors (NIRs), and altered cellular programming107.

T cell functional impairment significantly impacts physiologic balance and disease progression. However, the current definition of CD4+ T cell exhaustion remains incomplete and is mainly based on CD8+ T cell paradigms (Figure 3)108. A deeper understanding of shared and distinct features of CD4+ and CD8+ T cell exhaustion is crucial for clinical research (Figure 3). Evidence supports CD4+ T cell exhaustion in solid and hematologic malignancies that is characterized by upregulated NIRs (PD-1, CTLA-4, LAG-3, TIM-3, and TIGIT)109–111. Reduced marker expression correlates with treatment success in cancer therapy, while increased expression is associated with recurrence112,113. Chronic tumor exposure upregulates IRs and suppressive signals (e.g., PD-L1/indoleamine 2,3-dioxygenase 1 [IDO1]) in colorectal cancer, reducing effector molecules and increasing CXCL chemokines, which recruit more suppressive cells. Co-stimulatory signals, like CD86/ICOSL, induce exhaustion in CD4+ T cells and blocking these signals reduces CD4+ Tex. CD4+ T cell exhaustion is driven by direct cell contact and secreted factors in the tumor TME114,115. ICB, such as anti-PD-1, restores CD4+ T cell function, promotes clonal expansion, and reduces exhaustion markers, supporting PD-1/PD-L1 blockade in clinical applications116.

Figure 3
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3

Comparison of characteristics between CD4+ T cell exhaustion and CD8+ T cells. Exhausted CD4+ and CD8+ T cells share the following characteristics. Effector function: Reduced cytotoxicity and helper function. Inhibitory receptors: Upregulation of inhibitory receptor expression, including PD-1, LAG-3, CTLA-4, and TIM-3. Proliferation: CD4+ T cell exhaustion and CD8+ T cells show reduced proliferative abilities. Cytokines: Decreased cytokine production. Expression of the common transcription factors: EOMES, T-bet, BATF, BLIMP1, and SLAMF6. Differences exist between exhausted CD4+ T cells and exhausted CD8+ T cells. Exclusive transcription factors: Exhausted CD8+ T cells express TCF-1. Metabolic features: Exhausted CD8+ T cells exhibit features of metabolic reprogramming of mitochondrial function, primarily characterized by upregulation of ROS and downregulation of PGC1α. Exhausted CD4+ T cells show downregulation of FAO, glycolysis, and OXPHOS. BATF, basic leucine zipper ATF-like transcription factor; BLIMP-1, B lymphocyte-induced maturation protein 1; EOMES, eomesodermin; FAO, fatty acid oxidation; LAG-3, lymphocyte-activation gene 3; OXPHOS, oxidative phosphorylation; PD-1, programmed death-1; PGC1α, peroxisome proliferator-activated receptor gamma coactivator 1-alpha; ROS, reactive oxygen species; SLAMF6, signaling lymphocytic activation molecule family member 6; TIM-3, T cell immunoglobulin and mucin domain-containing protein 3.

However, upregulation of NIRs alone is insufficient to fully define CD4+ T cell exhaustion. Therefore, it is essential to investigate changes in epigenetics, transcriptomics, and metabolic patterns to gain a comprehensive understanding of CD4+ T cell exhaustion.

CD8+ Tex cells undergo extensive transcriptional and epigenetic reprogramming during chronic infection, disrupting genes related to TCR signaling, migration, and metabolism117. This Tex cell phenotype differs significantly from the effector and memory T cells generated during acute infection. Antigen-specific CD4+ T cells exhibit a unique transcriptional profile in chronic viral infections, suggesting an independent differentiation pathway for CD4+ T cell exhaustion. Although CD4+ Tex cells differ from CD8+ Tex cells, CD4+ Tex cells share key exhaustion-associated transcription factors (e.g., Batf, Tbx21, Eomes, and Prdm1), indicating a potential common regulatory mechanism118. Tumor-infiltrating CD4+ T cells lose TCF-1 and Slamf6 expression in melanoma, indicating transient or eventual exhaustion, which is partially reversible at the epigenetic level. PD-L1 blockade restores TCF-1 expression and downregulates TIM-3 and LAG-3119.

Metabolic reprogramming is a key feature of CD8+ Tex cells. Tex cells exhibit impaired glucose uptake and reduced mitochondrial quality within the TME, both of which are crucial for T cell function17. These metabolic defects correlate with the upregulation of NIRs, highlighting a link between T cell exhaustion and metabolic dysfunction. Persistent hypoxia in the TME stimulates peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α)-dependent mitochondrial reprogramming, which further impairs mitochondrial function. The accumulation of reactive oxygen species (ROS) causes cellular damage and accelerates the loss of Tex cell function120. Methionine depletion in the TME is identified as a key factor in CD4+ T cell exhaustion. Tumors preferentially uptake methionine through high expression of the SLC43A2 transporter, leading to methionine deficiency in CD4+ T cells. This uptake of methionine causes downregulation of H3K79me2 methylation and upregulation of PD-1 expression. PD-1 upregulation is associated with proteins involved in amino acid and fatty acid metabolism, such as AMPK. In addition, fatty acids and glucose accumulate, while lactate levels decrease, indicating inhibition of glycolysis and fatty acid oxidation121. Similar metabolic changes are observed in CD39high PD-1+ CD4+ Tex cells122. Furthermore, erythropoietin-induced CD4+ T cell exhaustion is accompanied by reduced oxidative phosphorylation and the mTOR pathway123. While existing evidence highlights metabolic changes in CD4+ Tex cells, a comprehensive understanding of exhaustion-induced metabolic reprogramming is still needed (Table 1).

View this table:
  • View inline
  • View popup
Table 1

Surface molecules, key transcription factors, and metabolic features of exhausted CD4+ T cells

Advances in CD4+ T cell-directed tumor immunotherapy

Enhancing the function of CD4+ T cells can improve CTL responses in cancer immunotherapy. This CD4+ T cell enhancement can be achieved through several approaches. CD4+ T cells have shown significant therapeutic potential in treating various diseases, including infections, autoimmune disorders, and cancer. Researchers are developing methods to modulate CD4+ T cell activity to improve treatment outcomes for these conditions. Current therapeutic strategies targeting CD4+ T cell responses can be broadly classified into passive immunotherapy (e.g., adoptive cell therapy [ACT]), ICB, and CD4+ T cell-targeted vaccines (Figure 4). In addition, combination immunotherapies are increasingly used to overcome the limitations of individual treatments by enhancing efficacy or minimizing side effects.

Figure 4
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 4

Clinical applications of CD4+ T cells. (A) Immune checkpoint blockade. Expression of inhibitory receptors on the surface of CD4+ T cells is primarily suppressed through antibodies targeting PD-1/CTLA-4, thereby enabling immune attack against cancer cells. (B) Cancer vaccine. Cancer vaccines activate CD4+ T cells through APC, promoting tumor cell infiltration and the supportive role of CD4+ T cells in lymph nodes. (C) CAR-T cell. CAR-T cells activate CD4+ Th cells through the secretion of IFN or induce apoptosis in cancer cells by secreting GZM and IFN-γ. CAR-T, chimeric antigen receptor T cell.

Neoantigen-based CD4+ T cell vaccine development strategies

Cancer vaccines, as a promising area in tumor immunotherapy, aim to activate the immune system, particularly T cells, to target tumor cells expressing tumor-specific antigens (TSAs). TSAs enhance immune recognition and attack tumors through four mechanisms: antigen release; antigen processing and presentation; T cell activation; and the cytotoxic activity of immune effector cells125.

In vaccine design algorithms predict epitopes for binding to MHC-I molecules, activating CD8+ T cells for direct tumor killing. However, vaccines often activate CD4+ T cells instead, which bind to MHC-II molecules126. These CD4+ T cells provide helper signals that enhance CD8+ T cell cytotoxicity. Incorporating MHC-II epitopes in vaccines can boost CD4+ T cell activation and amplify the immune response. It has been shown that CD4+ T cell responses often persist longer than CD8+ T cell responses, offering durable immune protection that helps prevent tumor recurrence127. Synthetic long peptides (SLPs), containing MHC class I and II epitopes, induce CD8+ and CD4+ T cell responses. While clinical trials of SLPs show a predominance of CD4+ T cell induction, achieving better therapeutic efficacy may require strategies to enhance CD8+ T cell responses for more effective tumor lysis128,129.

Emerging evidence also highlights the importance of B cells in cancer vaccines. Activated B cells have a key role in priming and expanding T cells, especially CD4+ T cells. A novel B-cell-targeting nanoparticle vaccine has shown promise in boosting B and T cell responses, leading to enhanced tumor suppression130. In combination immunotherapy neoantigen vaccines that stimulate both CD4+ and CD8+ T cells can overcome ICB resistance, remodel the TME, and expand T cell populations for sustained tumor suppression131. Future cancer vaccines can be designed to enhance CD4+ T cell responses and overall immune activation by advancing an understanding of MHC-II epitopes and improving prediction tools, thereby improving tumor immunotherapy outcomes.

Immune checkpoint blockade in reprogramming CD4+ T cell anti-tumor functions

ICB therapy targets NIRs on immune or cancer cells to restore effector functions. ICB enhances the ability of the immune system ability to target and eliminate tumor cells by disrupting inhibitory signaling pathways and preventing tumor immune evasion132. Growing evidence indicates that the pre-treatment status of specific CD4+ T cell subsets has a critical role in determining the efficacy of ICB therapy133. CD4+ T cell immune function has a critical role in PD-L1/PD-1 blockade therapy, especially in NSCLC patients. The functional state of CD4+ T cells directly influences the treatment response. Patients with functional CD4+ T cell immunity typically show a higher objective response rate (approximately 50%), whereas no significant response has been demonstrated in patients with impaired CD4+ T cell function134. Notably, different ICB agents have varying effects on CD4+ T cell dynamics. Anti-CTLA-4 antibodies promote the expansion and differentiation of ICOS+ CD4+ Th1-like subsets (FOXP3-negative) within the TME in mouse models of melanoma (B16) and colorectal adenocarcinoma (MC38), whereas anti-PD-1 therapy primarily expands exhausted CD8+ TILs with minimal impact on CD4+ TILs135. Several models have demonstrated that PD-1 blockade directly enhances tumor cell killing by promoting CD4+ CTL differentiation136.

A major limitation of ICIs is the primary resistance to monotherapy. To address this limitation, combination therapies targeting distinct immune checkpoints have been studied. The advantages of combination therapy lie in an ability to significantly enhance the immune system’s response to tumors. Single-cell trajectory analysis demonstrated that combination therapy significantly promoted the differentiation of CD4+ T cells from a naïve/central memory phenotype to a Th1 effector phenotype in a cohort of 20 patients with recurrent or metastatic head and neck squamous cell carcinoma. Anti-CTLA4 treatment activated CD4+ T cells in the TDLNs, enhancing the migration to the tumor and functional differentiation. In contrast, monotherapy only expanded the pre-existing CD8+ T cells within the tumor. Spatial transcriptomics revealed that T cell activation zones highly expressed IFN-γ, GZMB, and CXCL9/10 (T cell recruiting factors), co-occurring with antigen presentation signals from DCs. CD4+ T cells act as “organizers” in the immune microenvironment, coordinating the interactions between CD8+ T cells, DCs, and plasma cells, thereby promoting the formation of a functional immune response137. In addition to these established targets, anti-LAG-3 antibodies have emerged as a promising new therapy138,139. Relatlimab, the first Food and Drug Administration (FDA)-approved LAG-3 inhibitor, established LAG-3 as the third clinically validated immune checkpoint, after PD-1/PD-L1 and CTLA-4. A phase 2/3 randomized trial demonstrated that the combination of relatlimab and nivolumab (anti-PD-1) led to a 47.7% 12-month progression-free survival (PFS) rate in melanoma patients compared to 36% with nivolumab monotherapy. Based on these results, the FDA approved the fixed-dose combination, opdualag (relatlimab/nivolumab), in 2022 for unresectable or metastatic melanoma139. Rolig et al. explored the mechanism underlying anti-LAG-3 and anti-PD-1 combination therapy and reported that the combination of PD-1 and LAG-3 inhibitors could overcome resistance to PD-1 blockade in various mouse tumor models. Further investigation revealed that the therapeutic efficacy of this combination treatment was closely related to the instability of the Treg phenotype. Specifically, this instability was characterized by the loss of FOXP3 expression and the conversion of Tregs into effector T cells. Moreover, an increased proportion of unstable Tregs was significantly associated with a higher response rate and improved survival in patients receiving combination therapy in a cohort of 117 patients with metastatic melanoma. This finding suggests that the instability of the Treg phenotype may serve as a potential biomarker for predicting the response to immunotherapy140.

Technologic breakthroughs and clinical translation in adoptive CD4+ T cell therapy

ACT is an immunotherapy approach that involves collecting the patient’s immune cells, genetically modifying or expanding the cells in vitro to enhance tumor-targeting capabilities, then reinfusing these activated cells back into the patient to eliminate tumor cells. ACT can be broadly classified into three types based on the delivered cell product: TILs; chimeric antigen receptor T (CAR-T) cells; and T cell receptor-engineered T (TCR-T) cells.

CAR-T cell immunotherapy has achieved remarkable success in treating some hematologic malignancies with growing evidence supporting the critical role of CD4+ CAR-T cells in sustaining therapeutic effects. Studies on CD19-CAR-T cell therapy in patients with chronic lymphocytic leukemia (CLL) have shown that CD4+ CAR-T cells express a variety of cytotoxicity-associated genes and upregulate PRF and GZMA upon in vitro stimulation, exhibiting cytotoxic characteristics. These cells are predominant in the peripheral blood of patients who achieve long-term complete remission141,142. However, CD4+ CAR-T cells are more effective in activating host immunity compared to CD8+ CAR-T cells. CD4+ CAR-T cells can promote the production of Th cells by generating cytokines, such as IFN-γ and IL-12, but have less direct tumor-killing ability143. In addition, Bousso and colleagues reported that the antitumor activity of CD4+ CAR-T cells does not rely on cytotoxicity. Instead, CD4+ CAR-T cells exert indirect, large-scale, and long-distance effects on tumor cells through the production of IFN-γ and selectively killing tumors that are sensitive to IFN-γ-induced apoptosis144. Although CAR-T therapy has shown remarkable efficacy in hematologic malignancies, CAR-T therapy application in solid tumors is hindered by the challenging TME, which is characterized by glucose deprivation, hypoxia, and lactic acid accumulation. These metabolic obstacles limit CAR-T cell energy supply, cause metabolic exhaustion, and reduce antitumor efficacy. Notably, recent studies have indicated that FOXP3 interacts with the mitochondrial fission protein (dynamin-related protein 1 [Drp1]) to remodel CAR-T cell metabolism, suppressing glycolysis and OXPHOS while promoting lipid metabolism. This adaptation improves CAR-T cell survival and sustained antitumor function in solid tumors, offering new theoretical and translational avenues for CAR-T therapy in these settings145. CD4+ anti-TGF-β CART cells have also been shown to directly kill solid tumors by producing GZMB and IFN-γ and the ability to reduce T cell exhaustion. Relevant clinical trials are ongoing146.

In addition, Kruse et al. showed that adoptive CD4+ T cell therapy, when combined with innate immune stimulation, reprograms monocyte populations in the TME, enabling indirect tumor killing147. This CD4+ T cell-mediated indirect antitumor mechanism provides a rationale for combining adoptive T cell therapy with innate immune stimulation to promote the regression of established tumors. The in vitro and in vivo experimental results of engineered CD4 TCR T cells targeting the conserved high-affinity TCR for NY-ESO-1 demonstrated significant tumor regression with no apparent off-target toxicity. This finding greatly expands the potential patient population who could benefit considering the widespread expression of the targeted antigen across various cancers148.

Conclusions

With the continuous advances in cancer immunotherapy the regulation of CD4+ T cells has become a critical factor in improving therapeutic efficacy and overcoming immune resistance. In-depth research on the activation mechanisms, metabolic pathways, and exhaustion characteristics of CD4+ T cells is expected to uncover new therapeutic targets, thereby driving the clinical application of immunotherapy. This review discusses the critical role of CD4+ T cells in tumor immunity with a focus on the heterogeneity of CD4+ T cell subsets and the broad functional implications, as revealed based on scRNA-seq. By analyzing the dual role of CD4+ T cells in tumor immunity this article highlights the positive contributions to anti-tumor immunity but also points out the potential immunosuppressive effects in tumor progression.

However, research on CD4+ T cells is still underdeveloped in many areas compared to CD8+ T cells. For example, the transcriptional and epigenetic mechanisms underlying CD4+ Tex cells are not fully understood and the metabolic characteristics remain inadequately described. These gaps in research partially limit the efficacy of CD4+ CAR-T cells in treating solid tumors.

Metabolic reprogramming offers vast potential for further exploration in tumor immunity, especially in regulating immune responses. Therapeutic approaches targeting T cell metabolism may provide new breakthroughs for optimizing existing anti-tumor strategies. The combined application of metabolic inhibitors and immunotherapy may offer more effective ways to modulate CD4+ T cell anti-cancer mechanisms. Moreover, the advent of multiplexed error-robust fluorescence in situ hybridization (MERFISH) technology, which allows high-resolution detection of spatial heterogeneity in gene expression and provides robust support for future studies on the dynamic changes of CD4+ T cells and the role in the TME.

In conclusion, with the ongoing research into the functions and metabolic characteristics of CD4+ T cells, coupled with the application of emerging technologies, cancer immunotherapy is expected to achieve more precise and effective breakthroughs in the future.

Conflict of interest statement

No potential conflicts of interest are disclosed.

Author contributions

Conceived and designed the analysis: Yangyang Zhang, Xiangdong Cheng, Can Hu.

Collected the data: Yangyang Zhang, Jingli Xu, Yuqi Wang.

Contributed data or analysis tools: Qianyu Zhao, Ziyang Huang.

Performed the analysis: Siwei Pan.

Wrote the paper: Yangyang Zhang, Jingli Xu.

Acknowledgments

We appreciate the great technical support from the Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer and Zhejiang Key Lab of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer.

  • Received July 23, 2025.
  • Accepted October 31, 2025.
  • Copyright: © 2026, The Authors

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. 1.↵
    1. Topchyan P,
    2. Lin S,
    3. Cui W.
    The role of CD4 T cell help in CD8 T cell differentiation and function during chronic infection and cancer Immune Netw. 2023; 23: e41.
  2. 2.↵
    1. Bonilha CS.
    The synapse revisited: molecular networks of CD4 T cell–DC interactions. Int Immunopharmacol. 2025; 167: 115715.
    OpenUrlPubMed
  3. 3.↵
    1. Malyshkina A,
    2. Brüggemann A,
    3. Paschen A,
    4. Dittmer U.
    Cytotoxic CD4+ T cells in chronic viral infections and cancer. Front Immunol. 2023; 14: 1271236.
  4. 4.↵
    1. Chopp L,
    2. Redmond C,
    3. O’Shea JJ,
    4. Schwartz DM.
    From thymus to tissues and tumors: a review of T-cell biology. J Allergy Clin Immunol. 2023; 151: 81–97.
    OpenUrl
  5. 5.↵
    1. Koukourakis MI,
    2. Giatromanolaki A.
    Tumor draining lymph nodes, immune response, and radiotherapy: towards a revisal of therapeutic principles. Biochim Biophys Acta Rev Cancer. 2022; 1877: 188704.
  6. 6.↵
    1. Cardenas MA,
    2. Kissick HT.
    Stem-like cells at the center of CD4 T cell differentiation. Trends Cell Biol. 2025. https://doi.org/10.1016/j.tcb.2025.06.004.
  7. 7.↵
    1. Dutta A,
    2. Zhao B,
    3. Love PE.
    New insights into TCR β-selection. Trends Immunol. 2021; 42: 735–50.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Wculek SK,
    2. Cueto FJ,
    3. Mujal AM,
    4. Melero I,
    5. Krummel MF,
    6. Sancho D.
    Dendritic cells in cancer immunology and immunotherapy. Nat Rev Immunol. 2020; 20: 7–24.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Yang K,
    2. Halima A,
    3. Chan TA.
    Antigen presentation in cancer – mechanisms and clinical implications for immunotherapy. Nat Rev Clin Oncol. 2023; 20: 604–23.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Lotze MT,
    2. Olejniczak SH,
    3. Skokos D.
    CD28 co-stimulation: novel insights and applications in cancer immunotherapy. Nat Rev Immunol. 2024; 24: 878–95.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Amatore F,
    2. Gorvel L,
    3. Olive D.
    Role of Inducible Co-Stimulator (ICOS) in cancer immunotherapy. Expert Opin Biol Ther. 2020; 20: 141–50.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Upadhyay S,
    2. Kaur B,
    3. Gabr MT.
    CD28 and ICOS in immune regulation: structural insights and therapeutic targeting. Bioorg Med Chem Lett. 2025; 127: 130310.
  13. 13.↵
    1. Thapa B,
    2. Kato S,
    3. Nishizaki D,
    4. Miyashita H,
    5. Lee S,
    6. Nesline MK, et al.
    OX40/OX40 ligand and its role in precision immune oncology. Cancer Metastasis Rev. 2024; 43: 1001–13.
    OpenUrlCrossRefPubMed
  14. 14.
    1. Pazoki A,
    2. Dadfar S,
    3. Shadab A,
    4. Haghmorad D,
    5. Oksenych V.
    Soluble cd40 ligand as a promising biomarker in cancer diagnosis. Cells. 2024; 13: 1267.
    OpenUrl
  15. 15.↵
    1. Franzese O.
    Tumor microenvironment drives the cross-talk between co-stimulatory and inhibitory molecules in tumor-infiltrating lymphocytes: implications for optimizing immunotherapy outcomes. Int J Mol Sci. 2024; 25: 12848.
  16. 16.↵
    1. Sun L,
    2. Su Y,
    3. Jiao A,
    4. Wang X,
    5. Zhang B.
    T cells in health and disease. Signal Transduct Target Ther. 2023; 8: 235.
    OpenUrlPubMed
  17. 17.↵
    1. Xuekai L,
    2. Yan S,
    3. Jian C,
    4. Yifei S,
    5. Xinyue W,
    6. Wenyuan Z, et al.
    Advances in reprogramming of energy metabolism in tumor T cells. Front Immunol. 2024; 15: 1347181.
  18. 18.
    1. Raynor JL,
    2. Chi H.
    Nutrients: signal 4 in T cell immunity. J Exp Med. 2024; 221: e20221839.
  19. 19.↵
    1. Trefny MP,
    2. Kroemer G,
    3. Zitvogel L,
    4. Kobold S.
    Metabolites as agents and targets for cancer immunotherapy. Nat Rev Drug Discov. 2025; 24: 764–84.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Liu Y,
    2. Zhou Y,
    3. Zhang J,
    4. Li J,
    5. Zou L.
    Regulation of CD4+ T cell differentiation and function by glucose metabolism. Genes Immun. 2025; 26: 287–96.
    OpenUrlPubMed
  21. 21.↵
    1. Gerriets VA,
    2. Kishton RJ,
    3. Johnson MO,
    4. Cohen S,
    5. Siska PJ,
    6. Nichols AG, et al.
    Foxp3 and Toll-like receptor signaling balance Treg cell anabolic metabolism for suppression. Nat Immunol. 2016; 17: 1459–66.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Chisolm DA,
    2. Savic D,
    3. Moore AJ,
    4. Ballesteros-Tato A,
    5. León B,
    6. Crossman DK, et al.
    CCCTC-binding factor translates interleukin 2-and α-ketoglutarate-sensitive metabolic changes in T cells into context-dependent gene programs. Immunity. 2017; 47: 251–67.e7.
    OpenUrlPubMed
  23. 23.↵
    1. Ma S,
    2. Ming Y,
    3. Wu J,
    4. Cui G.
    Cellular metabolism regulates the differentiation and function of T-cell subsets. Cell Mol Immunoly. 2024; 21: 419–35.
    OpenUrl
  24. 24.↵
    1. Gualdoni GA,
    2. Mayer KA,
    3. Göschl L,
    4. Boucheron N,
    5. Ellmeier W,
    6. Zlabinger GJ.
    The AMP analog AICAR modulates the Treg/Th17 axis through enhancement of fatty acid oxidation. FASEB J. 2016; 30: 3800–9.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Llibre A,
    2. Kucuk S,
    3. Gope A,
    4. Certo M,
    5. Mauro C.
    Lactate: a key regulator of the immune response. Immunity. 2025; 58: 535–54.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Comito G,
    2. Iscaro A,
    3. Bacci M,
    4. Morandi A,
    5. Ippolito L,
    6. Parri M, et al.
    Lactate modulates CD4+ T-cell polarization and induces an immunosuppressive environment, which sustains prostate carcinoma progression via TLR8/miR21 axis. Oncogene. 2019; 38: 3681–95.
    OpenUrlCrossRefPubMed
  27. 27.↵
    1. Zhang Y-T,
    2. Xing M-L,
    3. Fang H-H,
    4. Li W-D,
    5. Wu L,
    6. Chen Z-P.
    Effects of lactate on metabolism and differentiation of CD4+ T cells. Mol Immunol. 2023; 154: 96–107.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. Lopez Krol A,
    2. Nehring HP,
    3. Krause FF,
    4. Wempe A,
    5. Raifer H,
    6. Nist A, et al.
    Lactate induces metabolic and epigenetic reprogramming of pro-inflammatory Th17 cells. EMBO Rep. 2022; 23: e54685.
  29. 29.↵
    1. Rao D,
    2. Stunnenberg JA,
    3. Lacroix R,
    4. Dimitriadis P,
    5. Kaplon J,
    6. Verburg F, et al.
    Acidity-mediated induction of FoxP3+ regulatory T cells. Eur J Immunol. 2023; 53: 2250258.
  30. 30.↵
    1. Mosmann TR,
    2. Cherwinski H,
    3. Bond MW,
    4. Giedlin MA,
    5. Coffman RL.
    Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins. J Immunol. 1986; 136: 2348–57.
    OpenUrlAbstract
  31. 31.↵
    1. Chraa D,
    2. Naim A,
    3. Olive D,
    4. Badou A.
    T lymphocyte subsets in cancer immunity: friends or foes. J Leukoc Biol. 2019; 105: 243–55.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. An Q,
    2. Duan L,
    3. Wang Y,
    4. Wang F,
    5. Liu X,
    6. Liu C, et al.
    Role of CD4+ T cells in cancer immunity: a single-cell sequencing exploration of tumor microenvironment. J Transl Med. 2025; 23: 179.
    OpenUrlPubMed
  33. 33.↵
    1. Wu C,
    2. Duan L,
    3. Li H,
    4. Liu X,
    5. Cai T,
    6. Yang Y, et al.
    PD1hiCD200hi CD4+ exhausted T cell increase immunotherapy resistance and tumour progression by promoting epithelial–mesenchymal transition in bladder cancer. Clin Transl Med. 2023; 13: e1303.
  34. 34.↵
    1. Sacher AG,
    2. St Paul M,
    3. Paige CJ,
    4. Ohashi PS.
    Cytotoxic CD4+ T cells in bladder cancer—a new license to kill. Cancer Cell. 2020; 38: 28–30.
    OpenUrlPubMed
  35. 35.↵
    1. Lau D,
    2. Khare S,
    3. Stein MM,
    4. Jain P,
    5. Gao Y,
    6. BenTaieb A, et al.
    Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer. Nat Commun. 2022; 13: 4053.
    OpenUrlPubMed
  36. 36.↵
    1. Hui Z,
    2. Zhang J,
    3. Zheng Y,
    4. Yang L,
    5. Yu W,
    6. An Y, et al.
    Single-cell sequencing reveals the transcriptome and TCR characteristics of pTregs and in vitro expanded iTregs. Front Immunol. 2021; 12: 619932.
  37. 37.↵
    1. Khaliq AM,
    2. Rajamohan M,
    3. Saeed O,
    4. Mansouri K,
    5. Adil A,
    6. Zhang C, et al.
    Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity. Nat Genet. 2024; 56: 2455–65.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Moncada R,
    2. Barkley D,
    3. Wagner F,
    4. Chiodin M,
    5. Devlin JC,
    6. Baron M, et al.
    Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol. 2020; 38: 333–42.
    OpenUrlCrossRefPubMed
  39. 39.↵
    1. Kagamu H,
    2. Yamasaki S,
    3. Kitano S,
    4. Yamaguchi O,
    5. Mouri A,
    6. Shiono A, et al.
    Single-cell analysis reveals a CD4+ T-cell cluster that correlates with PD-1 blockade efficacy. Cancer Res. 2022; 82: 4641–53.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. Zhu J.
    T helper cell differentiation, heterogeneity, and plasticity. Cold Spring Harb Perspect Biol. 2018; 10: a030338.
  41. 41.↵
    1. Meitei HT,
    2. Lal G.
    T cell receptor signaling in the differentiation and plasticity of CD4+ T cells. Cytokine Growth Factor Rev. 2023; 69: 14–27.
    OpenUrlPubMed
  42. 42.↵
    1. Saravia J,
    2. Chapman NM,
    3. Chi H.
    Helper T cell differentiation. Cell Mol Immunol. 2019; 16: 634–43.
    OpenUrlCrossRefPubMed
  43. 43.↵
    1. Wen Z,
    2. Liu T,
    3. Xu X,
    4. Acharya N,
    5. Shen Z,
    6. Lu Y, et al.
    Interleukin-16 enhances anti-tumor immune responses by establishing a Th1 cell-macrophage crosstalk through reprogramming glutamine metabolism in mice. Nat Commun. 2025; 16: 2362.
    OpenUrlPubMed
  44. 44.↵
    1. Wang L,
    2. Zhu Y,
    3. Zhang N,
    4. Xian Y,
    5. Tang Y,
    6. Ye J, et al.
    The multiple roles of interferon regulatory factor family in health and disease. Signal Transduct Target Ther. 2024; 9: 282.
    OpenUrlPubMed
  45. 45.↵
    1. Bernard A,
    2. Hibos C,
    3. Richard C,
    4. Viltard E,
    5. Chevrier S,
    6. Lemoine S, et al.
    The tumor microenvironment impairs Th1 IFNγ secretion through alternative splicing modifications of Irf1 Pre-mRNA. Cancer Immunol Res. 2021; 9: 324–36.
    OpenUrlAbstract/FREE Full Text
  46. 46.↵
    1. Korinfskaya S,
    2. Parameswaran S,
    3. Weirauch MT,
    4. Barski A.
    Runx transcription factors in T cells—what is beyond thymic development? Front Immunol. 2021; 12: 701924.
  47. 47.↵
    1. Wang D,
    2. Diao H,
    3. Getzler AJ,
    4. Rogal W,
    5. Frederick MA,
    6. Milner J, et al.
    The transcription factor Runx3 establishes chromatin accessibility of cis-regulatory landscapes that drive memory cytotoxic T lymphocyte formation. Immunity. 2018; 48: 659–74.e6.
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. Montauti E,
    2. Oh DY,
    3. Fong L.
    CD4+ T cells in antitumor immunity. Trends Cancer. 2024; 10: 969–85.
    OpenUrlPubMed
  49. 49.↵
    1. Oh DY,
    2. Kwek SS,
    3. Raju SS,
    4. Li T,
    5. McCarthy E,
    6. Chow E, et al.
    Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell. 2020; 181: 1612–25.e13.
    OpenUrlCrossRefPubMed
  50. 50.↵
    1. Iwahori K,
    2. Uenami T,
    3. Yano Y,
    4. Ueda T,
    5. Tone M,
    6. Naito Y, et al.
    Peripheral T cell cytotoxicity predicts the efficacy of anti-PD-1 therapy for advanced non-small cell lung cancer patients. Sci Rep. 2022; 12: 17461.
    OpenUrlCrossRefPubMed
  51. 51.↵
    1. Śledzińska A,
    2. Vila de Mucha M,
    3. Bergerhoff K,
    4. Hotblack A,
    5. Demane DF,
    6. Ghorani E, et al.
    Regulatory T cells restrain interleukin-2- and Blimp-1-dependent acquisition of cytotoxic function by CD4+ T cells. Immunity. 2020; 52: 151–66.e6.
    OpenUrlCrossRefPubMed
  52. 52.↵
    1. Takeuchi A,
    2. Badr MESG,
    3. Miyauchi K,
    4. Ishihara C,
    5. Onishi R,
    6. Guo Z, et al.
    CRTAM determines the CD4+ cytotoxic T lymphocyte lineage. J Exp Med. 2016; 213: 123–38.
    OpenUrlAbstract/FREE Full Text
  53. 53.↵
    1. Ducellier S,
    2. Demeules M,
    3. Letribot B,
    4. Gaetani M,
    5. Michaudel C,
    6. Sokol H, et al.
    Dual molecule targeting HDAC6 leads to intratumoral CD4+ cytotoxic lymphocytes recruitment through MHC-II upregulation on lung cancer cells. J Immunother Cancer. 2024; 12: e007588.
  54. 54.↵
    1. Oh DY,
    2. Fong L.
    Cytotoxic CD4+ T cells in cancer: expanding the immune effector toolbox. Immunity. 2021; 54: 2701–11.
    OpenUrlCrossRefPubMed
  55. 55.↵
    1. Porakishvili N,
    2. Kardava L,
    3. Jewell AP,
    4. Yong K,
    5. Glennie MJ,
    6. Akbar A, et al.
    Cytotoxic CD4+ T cells in patients with B cell chronic lymphocytic leukemia kill via a perforin-mediated pathway. Haematologica. 2004; 89: 435–43.
    OpenUrlAbstract/FREE Full Text
  56. 56.↵
    1. Zhang G,
    2. Xu M,
    3. Zhang X,
    4. Ma L,
    5. Zhang H.
    TRAIL produced by SAM-1-activated CD4+ and CD8+ subgroup T cells induces apoptosis in human tumor cells through upregulation of death receptors. Toxicol Appl Pharmacol. 2021; 427: 115656.
  57. 57.↵
    1. Song M,
    2. Ping Y,
    3. Zhang K,
    4. Yang L,
    5. Li F,
    6. Zhang C, et al.
    Low-dose IFNγ induces tumor cell stemness in tumor microenvironment of non-small cell lung cancer. Cancer Res. 2019; 79: 3737–48.
    OpenUrlAbstract/FREE Full Text
  58. 58.↵
    1. Martínez-Sabadell A,
    2. Arenas EJ,
    3. Arribas J.
    IFNγ signaling in natural and therapy-induced antitumor responses. Clin Cancer Res. 2022; 28: 1243–9.
    OpenUrlCrossRefPubMed
  59. 59.↵
    1. Xu J,
    2. Ding L,
    3. Mei J,
    4. Hu Y,
    5. Kong X,
    6. Dai S, et al.
    Dual roles and therapeutic targeting of tumor-associated macrophages in tumor microenvironments. Signal Transduct Target Ther. 2025; 10: 268.
    OpenUrlPubMed
  60. 60.
    1. Sun L,
    2. Kees T,
    3. Almeida AS,
    4. Liu B,
    5. He X-Y,
    6. Ng D, et al.
    Activating a collaborative innate-adaptive immune response to control metastasis. Cancer Cell. 2021; 39: 1361–74.e9.
    OpenUrlCrossRefPubMed
  61. 61.
    1. Briesemeister D,
    2. Sommermeyer D,
    3. Loddenkemper C,
    4. Loew R,
    5. Uckert W,
    6. Blankenstein T, et al.
    Tumor rejection by local interferon gamma induction in established tumors is associated with blood vessel destruction and necrosis. Int J Cancer. 2011; 128: 371–8.
    OpenUrlCrossRefPubMed
  62. 62.↵
    1. Sun T,
    2. Yang Y,
    3. Luo X,
    4. Cheng Y,
    5. Zhang M,
    6. Wang K, et al.
    Inhibition of tumor angiogenesis by interferon-γ by suppression of tumor-associated macrophage differentiation. Oncol Res. 2014; 21: 227–35.
    OpenUrlCrossRefPubMed
  63. 63.↵
    1. Skartsis N,
    2. Ferreira LMR,
    3. Tang Q.
    The dichotomous outcomes of TNFα signaling in CD4+ T cells. Front Immunol. 2022; 13: 1042622.
  64. 64.↵
    1. Seung E,
    2. Xing Z,
    3. Wu L,
    4. Rao E,
    5. Cortez-Retamozo V,
    6. Ospina B, et al.
    A trispecific antibody targeting HER2 and T cells inhibits breast cancer growth via CD4 cells. Nature. 2022; 603: 328–34.
    OpenUrlPubMed
  65. 65.↵
    1. Speiser DE,
    2. Chijioke O,
    3. Schaeuble K,
    4. Münz C.
    CD4+ T cells in cancer. Nat Cancer. 2023; 4: 317–29.
    OpenUrlPubMed
  66. 66.↵
    1. Schmiegel WH,
    2. Caesar J,
    3. Kalthoff H,
    4. Greten H,
    5. Schreiber HW,
    6. Thiele HG.
    Antiproliferative effects exerted by recombinant human tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) on human pancreatic tumor cell lines. Pancreas. 1988; 3: 180–8.
    OpenUrlCrossRefPubMed
  67. 67.↵
    1. Kimura M,
    2. Haisa M,
    3. Uetsuka H,
    4. Takaoka M,
    5. Ohkawa T,
    6. Kawashima R, et al.
    TNF combined with IFN-α accelerates NF-κB-mediated apoptosis through enhancement of Fas expression in colon cancer cells. Cell Death Differ. 2003; 10: 718–28.
    OpenUrlCrossRefPubMed
  68. 68.↵
    1. Wright K,
    2. Kolios G,
    3. Westwick J,
    4. Ward SG.
    Cytokine-induced apoptosis in epithelial HT-29 cells is independent of nitric oxide formation. Evidence for an interleukin-13-driven phosphatidylinositol 3-kinase-dependent survival mechanism. J Biol Chem. 1999; 274: 17193–201.
    OpenUrlAbstract/FREE Full Text
  69. 69.↵
    1. Braumüller H,
    2. Wieder T,
    3. Brenner E,
    4. Aßmann S,
    5. Hahn M,
    6. Alkhaled M, et al.
    T-helper-1-cell cytokines drive cancer into senescence. Nature. 2013; 494: 361–5.
    OpenUrlCrossRefPubMed
  70. 70.↵
    1. Huyghe L,
    2. Van Parys A,
    3. Cauwels A,
    4. Van Lint S,
    5. De Munter S,
    6. Bultinck J, et al.
    Safe eradication of large established tumors using neovasculature-targeted tumor necrosis factor-based therapies. EMBO Mol Med. 2020; 12: e11223.
  71. 71.↵
    1. Antony PA,
    2. Piccirillo CA,
    3. Akpinarli A,
    4. Finkelstein SE,
    5. Speiss PJ,
    6. Surman DR, et al.
    CD8+ T cell immunity against a tumor/self-antigen is augmented by CD4+ T helper cells and hindered by naturally occurring T regulatory cells. J Immunol. 2005; 174: 2591–601.
    OpenUrlAbstract/FREE Full Text
  72. 72.↵
    1. Bos R,
    2. Sherman LA.
    CD4+ T-cell help in the tumor milieu is required for recruitment and cytolytic function of CD8+ T lymphocytes. Cancer Res. 2010; 70: 8368–77.
    OpenUrlAbstract/FREE Full Text
  73. 73.↵
    1. Zander R,
    2. Schauder D,
    3. Xin G,
    4. Nguyen C,
    5. Wu X,
    6. Zajac A, et al.
    CD4+ T cell help is required for the formation of a cytolytic CD8+ T cell subset that protects against chronic infection and cancer. Immunity. 2019; 51: 1028–42.e4.
    OpenUrlCrossRefPubMed
  74. 74.↵
    1. Topchyan P,
    2. Xin G,
    3. Chen Y,
    4. Zheng S,
    5. Burns R,
    6. Shen J, et al.
    Harnessing the IL-21-BATF pathway in the CD8+ T cell anti-tumor response. Cancers. 2021; 13: 1263.
    OpenUrlPubMed
  75. 75.↵
    1. Espinosa-Carrasco G,
    2. Chiu E,
    3. Scrivo A,
    4. Zumbo P,
    5. Dave A,
    6. Betel D, et al.
    Intratumoral immune triads are required for immunotherapy-mediated elimination of solid tumors. Cancer Cell. 2024; 42: 1202–16.e8.
    OpenUrlCrossRefPubMed
  76. 76.↵
    1. Bourgeois C,
    2. Rocha B,
    3. Tanchot C.
    A role for CD40 expression on CD8+ T cells in the generation of CD8+ T cell memory. Science. 2002; 297: 2060–3.
    OpenUrlAbstract/FREE Full Text
  77. 77.↵
    1. Schoenberger SP,
    2. Toes RE,
    3. Van der Voort EI,
    4. Offringa R,
    5. Melief CJ.
    T-cell help for cytotoxic T lymphocytes is mediated by CD40–CD40L interactions. Nature. 1998; 393: 480–3.
    OpenUrlCrossRefPubMed
  78. 78.↵
    1. Lei X,
    2. Khatri I,
    3. de Wit T,
    4. de Rink I,
    5. Nieuwland M,
    6. Kerkhoven R, et al.
    CD4+ helper T cells endow cDC1 with cancer-impeding functions in the human tumor micro-environment. Nat Commun. 2023; 14: 217.
    OpenUrlCrossRefPubMed
  79. 79.↵
    1. Gressier E,
    2. Schulte-Schrepping J,
    3. Petrov L,
    4. Brumhard S,
    5. Stubbemann P,
    6. Hiller A, et al.
    CD4+ T cell calibration of antigen-presenting cells optimizes antiviral CD8+ T cell immunity. Nat Immunol. 2023; 24: 979–90.
    OpenUrlCrossRefPubMed
  80. 80.↵
    1. Borst J,
    2. Ahrends T,
    3. Bąbała N,
    4. Melief CJM,
    5. Kastenmüller W.
    CD4+ T cell help in cancer immunology and immunotherapy. Nat Rev Immunol. 2018; 18: 635–47.
    OpenUrlCrossRefPubMed
  81. 81.↵
    1. Gutiérrez-Melo N,
    2. Baumjohann D.
    T follicular helper cells in cancer. Trends Cancer. 2023; 9: 309–25.
    OpenUrlPubMed
  82. 82.↵
    1. Gu-Trantien C,
    2. Loi S,
    3. Garaud S,
    4. Equeter C,
    5. Libin M,
    6. De Wind A, et al.
    CD4+ follicular helper T cell infiltration predicts breast cancer survival. J Clin Invest. 2013; 123: 2873–92.
    OpenUrlCrossRefPubMed
  83. 83.↵
    1. Fujioka M,
    2. Fujioka S,
    3. Yoshitomi H,
    4. Hamanishi J,
    5. Suzuki H,
    6. Ukita M, et al.
    Oligoclonal expansion of IgG+ B cells along with Tfh cell response is associated with a better outcome in endometrial cancer. Int Immunol. 2025:dxaf049.
  84. 84.↵
    1. Fridman WH,
    2. Meylan M,
    3. Petitprez F,
    4. Sun C-M,
    5. Italiano A,
    6. Sautès-Fridman C.
    B cells and tertiary lymphoid structures as determinants of tumour immune contexture and clinical outcome. Nat Rev Clin Oncol. 2022; 19: 441–57.
    OpenUrlCrossRefPubMed
  85. 85.↵
    1. Radandish M,
    2. Mashhadi N,
    3. Aghayan AH,
    4. Taghizadeh M,
    5. Salehianfard S,
    6. Yahyazadeh S, et al.
    In-depth insight into tumor-infiltrating stromal cells linked to tertiary lymphoid structures and their prospective function in cancer immunotherapy. Exp Hematol Oncol. 2025; 14: 105.
    OpenUrlPubMed
  86. 86.↵
    1. Lin X,
    2. Ye L,
    3. Wang X,
    4. Liao Z,
    5. Dong J,
    6. Yang Y, et al.
    Follicular helper T cells remodel the immune microenvironment of pancreatic cancer via secreting CXCL13 and IL-21. Cancers. 2021; 13: 3678.
    OpenUrlPubMed
  87. 87.↵
    1. Cui C,
    2. Wang J,
    3. Fagerberg E,
    4. Chen P-M,
    5. Connolly KA,
    6. Damo M, et al.
    Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses. Cell. 2021; 184: 6101–18.e13.
    OpenUrlCrossRefPubMed
  88. 88.↵
    1. Hou Y,
    2. Cao Y,
    3. He Y,
    4. Dong L,
    5. Zhao L,
    6. Dong Y, et al.
    SIRT3 negatively regulates TFH-cell differentiation in cancer. Cancer Immunol Res. 2024; 12: 891–904.
    OpenUrlPubMed
  89. 89.↵
    1. Garaud S,
    2. Zayakin P,
    3. Buisseret L,
    4. Rulle U,
    5. Silina K,
    6. De Wind A, et al.
    Antigen specificity and clinical significance of IgG and IgA autoantibodies produced in situ by tumor-infiltrating B cells in breast cancer. Front Immunol. 2018; 9: 2660.
    OpenUrlCrossRefPubMed
  90. 90.↵
    1. Baumjohann D,
    2. Brossart P.
    T follicular helper cells: linking cancer immunotherapy and immune-related adverse events. J Immunother Cancer. 2021; 9: e002588.
  91. 91.↵
    1. Saleh R,
    2. Elkord E.
    FoxP3+ T regulatory cells in cancer: prognostic biomarkers and therapeutic targets. Cancer Lett. 2020; 490: 174–85.
    OpenUrlCrossRefPubMed
  92. 92.↵
    1. Stenström J,
    2. Hedenfalk I,
    3. Hagerling C.
    Regulatory T lymphocyte infiltration in metastatic breast cancer—an independent prognostic factor that changes with tumor progression. Breast Cancer Res. 2021; 23: 27.
    OpenUrlPubMed
  93. 93.↵
    1. Shan F,
    2. Somasundaram A,
    3. Bruno TC,
    4. Workman CJ,
    5. Vignali DAA.
    Therapeutic targeting of regulatory T cells in cancer. Trends Cancer. 2022; 8: 944–61.
    OpenUrlPubMed
  94. 94.
    1. Dikiy S,
    2. Rudensky AY.
    Principles of regulatory T cell function. Immunity. 2023; 56: 240–55.
    OpenUrlCrossRefPubMed
  95. 95.↵
    1. Li Y,
    2. Zhang C,
    3. Jiang A,
    4. Lin A,
    5. Liu Z,
    6. Cheng X, et al.
    Potential anti-tumor effects of regulatory T cells in the tumor microenvironment: a review. J Transl Med. 2024; 22: 293.
    OpenUrlPubMed
  96. 96.↵
    1. Gao R,
    2. Shi G-P,
    3. Wang J.
    Functional diversities of regulatory T cells in the context of cancer immunotherapy. Front Immunol. 2022; 13: 833667.
  97. 97.↵
    1. Raffin C,
    2. Vo LT,
    3. Bluestone JA.
    Treg cell-based therapies: challenges and perspectives. Nate Rev Immunol. 2020; 20: 158–72.
    OpenUrl
  98. 98.↵
    1. Wing K,
    2. Onishi Y,
    3. Prieto-Martin P,
    4. Yamaguchi T,
    5. Miyara M,
    6. Fehervari Z, et al.
    CTLA-4 control over Foxp3+ regulatory T cell function. Science. 2008; 322: 271–5.
    OpenUrlAbstract/FREE Full Text
  99. 99.↵
    1. Qureshi OS,
    2. Zheng Y,
    3. Nakamura K,
    4. Attridge K,
    5. Manzotti C,
    6. Schmidt EM, et al.
    Trans-endocytosis of CD80 and CD86: a molecular basis for the cell-extrinsic function of CTLA-4. Science. 2011; 332: 600–3.
    OpenUrlAbstract/FREE Full Text
  100. 100.↵
    1. Tekguc M,
    2. Wing JB,
    3. Osaki M,
    4. Long J,
    5. Sakaguchi S.
    Treg-expressed CTLA-4 depletes CD80/CD86 by trogocytosis, releasing free PD-L1 on antigen-presenting cells. Proc Natl Acad Sci USA. 2021; 118: e2023739118.
  101. 101.↵
    1. Pipkin ME,
    2. Sacks JA,
    3. Cruz-Guilloty F,
    4. Lichtenheld MG,
    5. Bevan MJ,
    6. Rao A.
    Interleukin-2 and inflammation induce distinct transcriptional programs that promote the differentiation of effector cytolytic T cells. Immunity. 2010; 32: 79–90.
    OpenUrlCrossRefPubMed
  102. 102.↵
    1. Boyman O,
    2. Sprent J.
    The role of interleukin-2 during homeostasis and activation of the immune system. Nat Rev Immunol. 2012; 12: 180–90.
    OpenUrlCrossRefPubMed
  103. 103.↵
    1. Nixon BG,
    2. Gao S,
    3. Wang X,
    4. Li MO.
    TGFβ control of immune responses in cancer: a holistic immuno-oncology perspective. Nat Re Immunol. 2023; 23: 346–62.
    OpenUrl
  104. 104.↵
    1. Cao X,
    2. Cai SF,
    3. Fehniger TA,
    4. Song J,
    5. Collins LI,
    6. Piwnica-Worms DR, et al.
    Granzyme B and perforin are important for regulatory T cell-mediated suppression of tumor clearance. Immunity. 2007; 27: 635–46.
    OpenUrlCrossRefPubMed
  105. 105.↵
    1. Gondek D,
    2. Lu LF,
    3. Quezada SA,
    4. Sakaguchi S,
    5. Noelle RJ.
    Cutting edge: contact-mediated suppression by CD4+CD25+ regulatory cells involves a granzyme B-dependent, perforin-independent mechanism. J Immunol. 2005; 174: 1783–6.
    OpenUrlAbstract/FREE Full Text
  106. 106.↵
    1. Gallimore A,
    2. Glithero A,
    3. Godkin A,
    4. Tissot AC,
    5. Plückthun A,
    6. Elliott T, et al.
    Induction and exhaustion of lymphocytic choriomeningitis virus-specific cytotoxic T lymphocytes visualized using soluble tetrameric major histocompatibility complex class I-peptide complexes. J Exp Med. 1998; 187: 1383–93.
    OpenUrlAbstract/FREE Full Text
  107. 107.↵
    1. Baessler A,
    2. Vignali DAA.
    T cell exhaustion. Annu Rev Immunol. 2024; 42: 179–206.
    OpenUrlCrossRefPubMed
  108. 108.↵
    1. Miggelbrink AM,
    2. Jackson JD,
    3. Lorrey SJ,
    4. Srinivasan ES,
    5. Waibl-Polania J,
    6. Wilkinson DS, et al.
    CD4 T-cell exhaustion: does it exist and what are its roles in cancer? Clin Cancer Res. 2021; 27: 5742–52.
    OpenUrlAbstract/FREE Full Text
  109. 109.↵
    1. Dixon KO,
    2. Lahore GF,
    3. Kuchroo VK.
    Beyond T cell exhaustion: TIM-3 regulation of myeloid cells. Sci Immunol. 2024; 9: eadf2223.
  110. 110.
    1. Zhang P,
    2. Liu X,
    3. Gu Z,
    4. Jiang Z,
    5. Zhao S,
    6. Song Y, et al.
    Targeting TIGIT for cancer immunotherapy: recent advances and future directions. Biomark Res. 2024; 12: 7.
    OpenUrlPubMed
  111. 111.↵
    1. Aggarwal V,
    2. Workman CJ,
    3. Vignali DA.
    LAG-3 as the third checkpoint inhibitor. Nat Immunol. 2023; 24: 1415–22.
    OpenUrlCrossRefPubMed
  112. 112.↵
    1. Nakano M,
    2. Ito M,
    3. Tanaka R,
    4. Yamaguchi K,
    5. Ariyama H,
    6. Mitsugi K, et al.
    PD-1+ TIM-3+ T cells in malignant ascites predict prognosis of gastrointestinal cancer. Cancer Sci. 2018; 109: 2986–92.
    OpenUrlPubMed
  113. 113.↵
    1. Yuan L,
    2. Xu B,
    3. Yuan P,
    4. Zhou J,
    5. Qin P,
    6. Han L, et al.
    Tumor-infiltrating CD4+ T cells in patients with gastric cancer. Cancer Cell Int. 2017; 17: 114.
    OpenUrlPubMed
  114. 114.↵
    1. Ozkazanc D,
    2. Yoyen-Ermis D,
    3. Tavukcuoglu E,
    4. Buyukasik Y,
    5. Esendagli G.
    Functional exhaustion of CD4+ T cells induced by co-stimulatory signals from myeloid leukaemia cells. Immunology. 2016; 149: 460–71.
    OpenUrl
  115. 115.↵
    1. Stachowiak M,
    2. Becker WJ,
    3. Olkhanud PB,
    4. Moreno PA,
    5. Markowicz S,
    6. Berzofsky JA, et al.
    Cancer cells accelerate exhaustion of persistently activated mouse CD4+ T cells. OncoImmunology. 2025; 14: 2521392.
  116. 116.↵
    1. Tracy SI,
    2. Venkatesh H,
    3. Hekim C,
    4. Heltemes-Harris LM,
    5. Knutson TP,
    6. Bachanova V, et al.
    Combining nilotinib and PD-L1 blockade reverses CD4+ T-cell dysfunction and prevents relapse in acute B-cell leukemia. Blood. 2022; 140: 335–48.
    OpenUrlCrossRefPubMed
  117. 117.↵
    1. Doering TA,
    2. Crawford A,
    3. Angelosanto JM,
    4. Paley MA,
    5. Ziegler CG,
    6. Wherry EJ.
    Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity. 2012; 37: 1130–44.
    OpenUrlCrossRefPubMed
  118. 118.↵
    1. Crawford A,
    2. Angelosanto JM,
    3. Kao C,
    4. Doering TA,
    5. Odorizzi PM,
    6. Barnett BE, et al.
    Molecular and transcriptional basis of CD4+ T cell dysfunction during chronic infection. Immunity. 2014; 40: 289–302.
    OpenUrlCrossRefPubMed
  119. 119.↵
    1. Fu J,
    2. Yu A,
    3. Xiao X,
    4. Tang J,
    5. Zu X,
    6. Chen W, et al.
    CD4+ T cell exhaustion leads to adoptive transfer therapy failure which can be prevented by immune checkpoint blockade. Am J Cancer Res. 2020; 10: 4234–50.
    OpenUrlPubMed
  120. 120.↵
    1. Scharping NE,
    2. Rivadeneira DB,
    3. Menk AV,
    4. Vignali PDA,
    5. Ford BR,
    6. Rittenhouse NL, et al.
    Mitochondrial stress induced by continuous stimulation under hypoxia rapidly drives T cell exhaustion. Nat Immunol. 2021; 22: 205–15.
    OpenUrlCrossRefPubMed
  121. 121.↵
    1. Pandit M,
    2. Kil Y-S,
    3. Ahn J-H,
    4. Pokhrel RH,
    5. Gu Y,
    6. Mishra S, et al.
    Methionine consumption by cancer cells drives a progressive upregulation of PD-1 expression in CD4 T cells. Nat Commun. 2023; 14: 2593.
    OpenUrlPubMed
  122. 122.↵
    1. Balança C-C,
    2. Salvioni A,
    3. Scarlata C-M,
    4. Michelas M,
    5. Martinez-Gomez C,
    6. Gomez-Roca C, et al.
    PD-1 blockade restores helper activity of tumor-infiltrating, exhausted PD-1hiCD39+ CD4 T cells. JCI Insight. 2021; 6: e142513.
  123. 123.↵
    1. Bessoles S,
    2. Chiron A,
    3. Sarrabayrouse G,
    4. De La Grange P,
    5. Abina AM,
    6. Hacein-Bey-Abina S.
    Erythropoietin induces tumour progression and CD39 expression on immune cells in a preclinical model of triple-negative breast cancer. Immunology. 2024; 173: 360–80.
    OpenUrl
  124. 124.
    1. Duan ZQ,
    2. Li YX,
    3. Qiu Y,
    4. Shen Y,
    5. Wang Y,
    6. Zhang YY, et al.
    CD39 expression defines exhausted CD4+ T cells associated with poor survival and immune evasion in human gastric cancer. Clin Transl Immunology. 2024; 13: e1499.
  125. 125.↵
    1. Zaidi N,
    2. Jaffee EM,
    3. Yarchoan M.
    Recent advances in therapeutic cancer vaccines. Nat Rev Cancer. 2025; 25: 517–33.
    OpenUrlPubMed
  126. 126.↵
    1. Alspach E,
    2. Lussier DM,
    3. Miceli AP,
    4. Kizhvatov I,
    5. DuPage M, et al.
    MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature. 2019; 574: 696–701.
    OpenUrlCrossRefPubMed
  127. 127.↵
    1. Huff AL,
    2. Longway G,
    3. Mitchell JT,
    4. Andaloori L,
    5. Davis-Marcisak E,
    6. Chen F, et al.
    CD4 T cell-activating neoantigens enhance personalized cancer vaccine efficacy. JCI Insight. 2023; 8: e174027.
  128. 128.↵
    1. Melief CJ,
    2. Van Der Burg SH.
    Immunotherapy of established (pre)malignant disease by synthetic long peptide vaccines. Nat Rev Cancer. 2008; 8: 351–60.
    OpenUrlCrossRefPubMed
  129. 129.↵
    1. Braun DA,
    2. Moranzoni G,
    3. Chea V,
    4. McGregor BA,
    5. Blass E,
    6. Tu CR, et al.
    A neoantigen vaccine generates antitumour immunity in renal cell carcinoma. Nature. 2025; 639: 474–82.
    OpenUrlCrossRefPubMed
  130. 130.↵
    1. Li C,
    2. Clauson R,
    3. Bugada LF,
    4. Ke F,
    5. He B,
    6. Yu Z, et al.
    Antigen-clustered nanovaccine achieves long-term tumor remission by promoting B/CD 4 T cell crosstalk. ACS Nano. 2024; 18: 9584–604.
    OpenUrlCrossRefPubMed
  131. 131.↵
    1. Dolina JS,
    2. Lee J,
    3. Brightman SE,
    4. McArdle S,
    5. Hall SM,
    6. Thota RR, et al.
    Linked CD4+/CD8+ T cell neoantigen vaccination overcomes immune checkpoint blockade resistance and enables tumor regression. J Clin Invest. 2023; 133: e164258.
  132. 132.↵
    1. Topalian SL,
    2. Forde PM,
    3. Emens LA,
    4. Yarchoan M,
    5. Smith KN,
    6. Pardoll DM.
    Neoadjuvant immune checkpoint blockade: a window of opportunity to advance cancer immunotherapy. Cancer Cell. 2023; 41: 1551–66.
    OpenUrlCrossRefPubMed
  133. 133.↵
    1. Carlino MS,
    2. Larkin J,
    3. Long GV.
    Immune checkpoint inhibitors in melanoma. Lancet. 2021; 398: 1002–14.
    OpenUrlCrossRefPubMed
  134. 134.↵
    1. Zuazo M,
    2. Arasanz H,
    3. Fernández-Hinojal G,
    4. García-Granda MJ,
    5. Gato M,
    6. Bocanegra A, et al.
    Functional systemic CD4 immunity is required for clinical responses to PD-L1/PD-1 blockade therapy. EMBO Mol Med. 2019; 11: e10293.
  135. 135.↵
    1. Wei SC,
    2. Levine JH,
    3. Cogdill AP,
    4. Zhao Y,
    5. Anang N-AAS,
    6. Andrews MC, et al.
    Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell. 2017; 170: 1120–33.e17.
    OpenUrlCrossRefPubMed
  136. 136.↵
    1. Nagasaki J,
    2. Togashi Y,
    3. Sugawara T,
    4. Itami M,
    5. Yamauchi N,
    6. Yuda J, et al.
    The critical role of CD4+ T cells in PD-1 blockade against MHC-II-expressing tumors such as classic Hodgkin lymphoma. Blood Adv. 2020; 4: 4069–82.
    OpenUrlCrossRefPubMed
  137. 137.↵
    1. Franken A,
    2. Bila M,
    3. Mechels A,
    4. Kint S,
    5. Van Dessel J,
    6. Pomella V, et al.
    CD4+ T cell activation distinguishes response to anti-PD-L1+anti-CTLA4 therapy from anti-PD-L1 monotherapy. Immunity. 2024; 57: 541–58.e7.
    OpenUrlCrossRefPubMed
  138. 138.↵
    1. Hodi FS,
    2. Chiarion-Sileni V,
    3. Gonzalez R,
    4. Grob J-J,
    5. Rutkowski P,
    6. Cowey CL, et al.
    Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 2018; 19: 1480–92.
    OpenUrlCrossRefPubMed
  139. 139.↵
    1. Long GV,
    2. Stephen Hodi F,
    3. Lipson EJ,
    4. Schadendorf D,
    5. Ascierto PA,
    6. Matamala L, et al.
    Overall survival and response with nivolumab and relatlimab in advanced melanoma. NEJM Evid. 2023; 2: EVIDoa2200239.
  140. 140.↵
    1. Rolig AS,
    2. Peng X,
    3. Sturgill ER,
    4. Holay N,
    5. Kasiewicz M,
    6. Mick C, et al.
    The response to anti-PD-1 and anti-LAG-3 checkpoint blockade is associated with regulatory T cell reprogramming. Sci Transl Med. 2025; 17: eadk3702.
  141. 141.↵
    1. Melenhorst JJ,
    2. Chen GM,
    3. Wang M,
    4. Porter DL,
    5. Chen C,
    6. Collins MA, et al.
    Decade-long leukaemia remissions with persistence of CD4+ CAR T cells. Nature. 2022; 602: 503–9.
    OpenUrlCrossRefPubMed
  142. 142.↵
    1. Cimons JM,
    2. DeGolier KR,
    3. Burciaga SD,
    4. Yarnell MC,
    5. Novak AJ,
    6. Rivera-Reyes AM, et al.
    T-bet overexpression enhances CAR T cell effector functions and antigen sensitivity. J Immunother Cancer. 2025; 13: e010962.
  143. 143.↵
    1. Boulch M,
    2. Cazaux M,
    3. Loe-Mie Y,
    4. Thibaut R,
    5. Corre B,
    6. Lemaître F, et al.
    A cross-talk between CAR T cell subsets and the tumor microenvironment is essential for sustained cytotoxic activity. Sci Immunol. 2021; 6: eabd4344.
  144. 144.↵
    1. Boulch M,
    2. Cazaux M,
    3. Cuffel A,
    4. Guerin MV,
    5. Garcia Z,
    6. Alonso R, et al.
    Tumor-intrinsic sensitivity to the pro-apoptotic effects of IFN-γ is a major determinant of CD4+ CAR T-cell antitumor activity. Nat Cancer. 2023; 4: 968–83.
    OpenUrlPubMed
  145. 145.↵
    1. Niu C,
    2. Wei H,
    3. Pan X,
    4. Wang Y,
    5. Song H,
    6. Li C, et al.
    Foxp3 confers long-term efficacy of chimeric antigen receptor-T cells via metabolic reprogramming. Cell Metab. 2025; 37: 1426–41.e7.
    OpenUrlPubMed
  146. 146.↵
    1. Zheng D,
    2. Qin L,
    3. Lv J,
    4. Che M,
    5. He B,
    6. Zheng Y, et al.
    CD4+ anti-TGF-β CAR T cells and CD8+ conventional CAR T cells exhibit synergistic antitumor effects. Cell Rep Med. 2025; 6: 102020.
  147. 147.↵
    1. Kruse B,
    2. Buzzai AC,
    3. Shridhar N,
    4. Braun AD,
    5. Gellert S,
    6. Knauth K, et al.
    CD4+ T cell-induced inflammatory cell death controls immune-evasive tumours. Nature. 2023; 618: 1033–40.
    OpenUrlCrossRefPubMed
  148. 148.↵
    1. Saillard M,
    2. Cenerenti M,
    3. Reichenbach P,
    4. Guillaume P,
    5. Su Z,
    6. Hafezi M, et al.
    Engineered CD4 TCR T cells with conserved high-affinity TCRs targeting NY-ESO-1 for advanced cellular therapies in cancer. Sci Adv. 2025; 11: eadu5754.
PreviousNext
Back to top

In this issue

Cancer Biology & Medicine: 23 (1)
Cancer Biology & Medicine
Vol. 23, Issue 1
15 Jan 2026
  • Table of Contents
  • Index by author
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on Cancer Biology & Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
CD4+ T cells in cancer: dual roles, exhaustion, and therapeutic breakthroughs
(Your Name) has sent you a message from Cancer Biology & Medicine
(Your Name) thought you would like to see the Cancer Biology & Medicine web site.
Citation Tools
CD4+ T cells in cancer: dual roles, exhaustion, and therapeutic breakthroughs
Yangyang Zhang, Jingli Xu, Siwei Pan, Yuqi Wang, Qianyu Zhao, Ziyang Huang, Can Hu, Xiangdong Cheng
Cancer Biology & Medicine Jan 2026, 23 (1) 42-59; DOI: 10.20892/j.issn.2095-3941.2025.0414

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
CD4+ T cells in cancer: dual roles, exhaustion, and therapeutic breakthroughs
Yangyang Zhang, Jingli Xu, Siwei Pan, Yuqi Wang, Qianyu Zhao, Ziyang Huang, Can Hu, Xiangdong Cheng
Cancer Biology & Medicine Jan 2026, 23 (1) 42-59; DOI: 10.20892/j.issn.2095-3941.2025.0414
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • The biological characteristics of CD4+ T cells
    • Multi-dimensional roles of CD4+ T cells in tumor immunology
    • Functional exhaustion of CD4+ T cells in the TME
    • Advances in CD4+ T cell-directed tumor immunotherapy
    • Conclusions
    • Conflict of interest statement
    • Author contributions
    • Acknowledgments
    • References
  • Figures & Data
  • Info & Metrics
  • References
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Click chemistry-driven tumor theranostics: recent advances, challenges, and future perspectives
  • Targeting tumor-infiltrating regulatory T cells based on immunometabolism
  • Applying microfluidic technology to interpret the tumor immune microenvironment and cancer immunotherapy
Show more Review

Similar Articles

Keywords

  • Mechanism of differentiation
  • dual role
  • T cell exhaustion
  • immunotherapeutic strategies
  • translational clinical applications

Navigate

  • Home
  • Current Issue

More Information

  • About CBM
  • About CACA
  • About TMUCIH
  • Editorial Board
  • Subscription

For Authors

  • Instructions for authors
  • Journal Policies
  • Submit a Manuscript

Journal Services

  • Email Alerts
  • Facebook
  • RSS Feeds
  • Twitter

 

© 2026 Cancer Biology & Medicine

Powered by HighWire