Xuejun Guo
Shuhan Ma
Jingwen Wang1
Wenxue Ma- 1Department of Hematology, Puyang Oilfield General Hospital Affiliated to Henan Medical University, Puyang, Henan, China
- 2Puyang Cell Therapy Engineering Technology Research Center, Puyang, Henan, China
- 3Sanford Stem Cell Institute, Department of Medicine, Moores Cancer Center, China Medical University-the Queen’s University of Belfast Joint College, Shenyang, Liaoning, China
- 4Sanford Stem Cell Institute, Department of Medicine, Moores Cancer Center, University of California San Diego, La Jolla, CA, United States
Terminally exhausted CD8+ T cells (Ttex) are emerging as clinically relevant immune subsets across solid tumors, marked by sustained inhibitory receptor expression, loss of TCF1, and limited proliferative capacity. Once considered functionally inert, Ttex are now recognized for their residual cytotoxic potential and strong associations with tumor immunogenicity, including microsatellite instability (MSI), high tumor mutational burden (TMB), and neoantigen load. Importantly, the prognostic significance of Ttex is highly tumor-context-dependent, shaped by stromal architecture, mutational burden, and progenitor Tpex availability. This review examines the biology, spatial localization, and prognostic value of Ttex, highlighting the Ttex/CD8+ ratio as a promising biomarker in cancers such as colorectal, lung, and esophageal carcinoma. We summarize recent advances in multiplex imaging, digital pathology, and AI-driven quantification that support the clinical integration of Ttex assessment. In addition, we discuss emerging therapeutic strategies targeting Ttex through immune checkpoint combinations, thymocyte selection-associated high mobility group box protein (TOX) and circRNA-mediated reprogramming, and exhaustion-resistant T cell engineering. Finally, we outline translational priorities including assay harmonization, functional validation, and longitudinal profiling to advance Ttex-based precision oncology.
Highlights
● Ttex cells reflect chronic antigen stimulation and advanced exhaustion.
● Ttex/CD8+ ratio serves as a tumor-context–dependent prognostic biomarker.
● MSI and TMB are strongly associated with Ttex enrichment.
● Spatial and digital pathology enable standardized Ttex quantification.
● Ttex metrics inform emerging immunotherapies and combination strategies.
1 Introduction
Cytotoxic CD8+ T cells are central to the immune system’s ability to eliminate malignant cells and are a cornerstone of successful anti-tumor immunity (1, 2). However, in the setting of chronic antigen stimulation such as within the tumor microenvironment (TME) of solid tumors these cells progressively lose effector function, a process known as T cell exhaustion (3, 4). While T cell exhaustion was initially viewed as a uniform state of dysfunction, it is now recognized as a heterogeneous and dynamic spectrum ranging from progenitor exhausted (Tpex) to terminally exhausted (Ttex) phenotypes, each with distinct functional, transcriptional, and spatial profiles (5).
Ttex often defined by high expression of inhibitory receptors [e.g., programmed cell death protein 1 (PD-1), T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3)] and loss of proliferative capacity [e.g., transcription factor 1 (TCF1-)], represent a late-stage differentiation state (6, 7). Though functionally impaired, Ttex cells retain partial cytotoxic potential and are frequently enriched in immune-active TME (8). Recent studies across various solid tumors including colorectal, lung, esophageal, and head and neck cancers have demonstrated that the presence of Ttex, particularly when quantified relative to total CD8+ T cells (Ttex/CD8+ ratio), can serve as a prognostic indicator of patient outcomes (8, 9). These findings suggest that Ttex are not simply bystanders of immune dysfunction but may serve as valuable biomarkers of immune engagement and tumor control.
The emergence of multiplex imaging, spatial transcriptomics, and high-resolution single-cell profiling has enabled deeper interrogation of the localization, phenotype, and functional state of Ttex within the tumor milieu (10). Concurrently, digital pathology and machine learning tools offer promising avenues to quantify Ttex in a clinically applicable manner, bridging the gap between immunologic insights and diagnostic implementation (11, 12). These technological advances make this an opportune moment to consolidate current knowledge on Ttex, as their integration into biomarker-driven oncology and therapeutic design is now more feasible than ever.
In this review, we summarize the current understanding of terminally exhausted CD8+ T cells across solid tumors, with a focus on their prognostic relevance, immunogenomic correlates, and translational potential. We discuss how the Ttex/CD8+ ratio could be standardized into pathology workflows, explore strategies to target or modulate Ttex, and highlight future directions for integrating Ttex assessment into precision oncology.
2 Biology and definition of Ttex
Although exhaustion can occur in CD4+ T cells, γδ T cells, and NK-like T cells (11, 13), this review focuses specifically on CD8+ T cells because terminally exhausted CD8+ populations (Ttex) represent the most extensively characterized exhaustion state with the strongest biological, clinical, and translational relevance. T cell exhaustion represents a dynamic and heterogeneous state of CD8+ T cell dysfunction that arises in the context of chronic antigen exposure, such as during persistent viral infections or within the TME. Among the exhausted T cell subpopulations, Ttex are defined by distinct phenotypic, functional, and spatial characteristics that distinguish them from their progenitor exhausted (Tpex) counterparts (14). Increasing evidence demonstrates that exhaustion is not simply a terminal dysfunction, but a coordinated transcriptional, metabolic, and epigenetic program, shaped by persistent antigen, stromal cues, cytokine gradients, and tumor-derived metabolites (3, 15, 16). Ttex represent the fixed endpoint of this continuum, characterized by irreversible chromatin remodeling and profound metabolic impairment (17, 18).
2.1 Phenotypic markers
Ttex cells are typically characterized by sustained high expression of inhibitory receptors, including PD-1, TIM-3, and lymphocyte-activation gene 3 (LAG-3), coupled with the loss of the transcription factor TCF1 (encoded by Tcf7) (18–20). They also express exhaustion-associated transcriptional regulators such as thymocyte selection-associated high mobility group box protein (TOX), which are essential for establishing and stabilizing the exhausted state (21, 22). The PD1highTCF1– phenotype has been widely used as a practical immunohistochemical signature for identifying Ttex in human tumors (23, 24).
A practical molecular distinction is that Tpex retain TCF1 (TCF1+ PD-1int CXCR5+ SLAMF6+, TOXlow), whereas Ttex lose TCF1 and upregulate co-inhibitory receptors such as TIM-3, LAG-3, and CD39, together with high TOX levels (25). Compared to Tpex cells, which retain self-renewal and proliferative potential, Ttex cells represent a more differentiated and fixed state of exhaustion.
Recent studies demonstrate that Ttex exhibit de novo accessible chromatin regions enriched for TOX and NR4A motifs, establishing an epigenetically locked exhaustion module (15, 26). In parallel, upregulation of CD39, CD103, and CXCL13 identifies Ttex clusters that preferentially localize to CAF-rich or metabolically stressed niches (27, 28). High-dimensional profiling also reveals multiple Ttex subsets (e.g., TOXhigh, ZNF683high, and CD38hghi populations), highlighting that exhaustion encompasses multiple terminal states rather than a single uniform phenotype (29, 30).
2.2 Functional state
Despite their impaired cytokine production and proliferative capacity, Ttex cells are not entirely inert. They often retain residual cytolytic function, including expression of perforin and granzymes, and can contribute to tumor control under certain conditions (31). However, they are generally considered refractory to checkpoint blockade therapies, likely due to their epigenetically fixed exhaustion state (32, 33).
This functional limitation underscores the importance of maintaining a pool of Tpex cells for effective immune reinvigoration. Although Tpex share some features of exhaustion, they retain proliferative capacity, self-renewal, and lineage plasticity, acting as the progenitor population that continually replenishes the Ttex compartment, and responses to checkpoint blockade (34).
Recent mechanistic studies further show that Ttex undergo mitochondrial dysfunction driven by PGC1α suppression, impaired oxidative phosphorylation, and ROS accumulation (35, 36). Lipid peroxidation and cholesterol overload common features of the TME promote ferroptosis sensitivity in Ttex (37, 38). Additionally, chronic exposure to IL-10, TGF-β, and type I interferons reinforces the dysfunctional state by suppressing STAT5 and NF-κB signaling (39, 40). Together, these metabolic and cytokine-mediated constraints explain why Ttex rarely regain effector functions after PD-1 blockade.
2.3 Spatial context
Spatial localization profoundly shapes Ttex behavior. In multiple solid tumor types, Ttex cells accumulate within tumor nests or hypoxic stromal zones enriched with dysfunctional APCs and CAF-derived suppressive cues (3, 41, 42). In contrast, Tpex cells localize to peripheral regions, or tertiary lymphoid structures (TLS), where they remain proliferative and maintain therapeutic responsiveness (43, 44).
Spatial-omics technologies show that Ttex preferentially reside in CAF-dominated niches enriched in CXCL12, TGF-β, and dense collagen, which restrict T cell movement and exclude Tpex (45). High-resolution imaging demonstrates that Ttex form tightly packed clusters adjacent to hypoxic pockets and metabolically stressed APCs, suggesting a stromal–metabolic ecosystem that reinforces exhaustion fixation (17). Figure 1 provides a schematic overview of the spatial organization of progenitor exhausted (Tpex) and terminally exhausted (Ttex) CD8⁺ T-cell subsets within the tumor microenvironment, highlighting their distinct localization patterns and functional niches.
Figure 1. Spatial distribution of progenitor-like versus terminally exhausted CD8+ T cells in solid tumors. Schematic illustration depicting the organization of CD8+ T cell subsets across the TME. Left: Tpex cells reside predominantly in stromal or tertiary lymphoid structure (TLS) regions near the tumor boundary, where APC-rich niches support their proliferative capacity and maintain stem-like transcriptional programs (TCF1+PD-1int). Right: Ttex accumulate within the tumor nest/core region, often interspersed with non-tumor-reactive bystander T cells in CAF-rich and metabolically stressed niches. These Ttex cells (PD-1high, TCF1–, TOX+) exhibit limited proliferative capacity and residual cytotoxicity, reflecting advanced exhaustion and sustained immunosuppression. This spatial segregation highlights the distinct roles of Tpex and Ttex in immune surveillance, persistence, and therapeutic responsiveness.
2.4 Differentiation pathway
The developmental trajectory of exhausted CD8+ T cells typically follow a stepwise progression from naive cells to effector cells, then to Tpex cells, and ultimately to Ttex cells (5, 8, 46). Tpex cells function as a self-renewing progenitor pool capable of generating Ttex progeny under chronic antigenic stimulation (47, 48). This linear differentiation is regulated by transcriptional programs, metabolic regulation, and microenvironmental cues, whin include cytokine availability (e.g., IL-21 and IL-10), antigen burden, and spatial immune architecture (28, 49).
A deeper understanding of the transition from Tpex to Ttex is essential for developing strategies that preserve T cell functionality and prevent irreversible exhaustion. Recent mechanistic advances show that Tpex fate is stabilized by TCF1, BCL6, and memory-associated chromatin accessibility, which collectively maintain proliferative potential and lineage plasticity (15, 26). In contrast, commitment to the Ttex lineage is driven by sustained NFAT signaling, TOX-mediated chromatin remodeling, and the loss of CCR7/CXCR5-guided migratory programs (17, 27), marking the shift from a progenitor-like to a tissue-resident terminal state. Meanwhile, microenvironmental pressures including AMPK–mTOR imbalance, mitochondrial depolarization, and lactate-rich acidification further bias Tpex cells toward terminal differentiation (35, 39). Together, these findings establish exhaustion as a coordinated fate decision shaped jointly by chronic antigen burden and stromal-metabolic cues (28). Figure 2 illustrates the stepwise differentiation trajectory of CD8⁺ T cells under chronic antigen stimulation, from naïve and effector states through progenitor exhaustion (Tpex) to terminal exhaustion (Ttex).
Figure 2. Stepwise differentiation of CD8+ T cells toward exhaustion under chronic antigen exposure. Naïve CD8+ T cells differentiate into effector cells, which upon chronic antigen stimulation progress into Tpex and Ttex states. Tpex cells (PD-1int, TCF1+, TOXlow, CXCR5+, Slamf6+) maintain proliferative and self-renewal capacity and serve as a progenitor pool. With persistent antigenic stimulation and microenvironmental cues such as IL-21 and IL-10, Tpex cells give rise to Ttex cells (PD-1high, TCF1–, TOX+), which display limited proliferation and residual cytotoxicity. This trajectory highlights the balance between maintaining functional progenitor pools and terminal differentiation under chronic immune pressure.
3 Ttex across solid tumors: landscape and diversity
Ttex cells have been increasingly recognized as a distinct and functionally significant T cell population across a wide range of solid tumors. Advances in high-dimensional profiling technologies, including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multiplex immunohistochemistry (IHC), have enabled the detailed characterization of Ttex infiltration patterns, phenotypes, and prognostic implications in the TME (50, 51). While the presence of Ttex is consistently associated with chronic antigen stimulation, the extent, functional impact, and clinical significance of these cells vary across cancer types.
Across tumor types, Ttex commonly accumulate in chronically inflamed or antigen-rich regions, display conserved exhaustion signatures (TOX, PD-1high, TIM-3), and frequently localize within stromal or immune-excluded niches (18). However, their prognostic meaning diverges: MSI-H CRC and melanoma often show favorable associations linked to ongoing immune pressure, whereas NSCLC, HNSCC, ESCC, and HCC frequently demonstrate detrimental outcomes driven by stromal exclusion, CAF activity, or restricted Tpex reservoirs (8, 52). This review emphasizes tumor types with the most comprehensive single-cell or spatial data on Ttex (e.g., CRC, NSCLC, HNSCC, ESCC, melanoma, HCC). Breast and ovarian cancers are included briefly to illustrate the current knowledge gaps in less immune-inflamed tumors.
3.1 Colorectal cancer
In CRC, the biology and prognostic implications of Ttex differ markedly between microsatellite instability-high (MSI-H) and microsatellite stable (MSS) disease. In MSI-H CRC, which is characterized by high tumor mutational burden (TMB), and abundant neoantigens, Ttex infiltration is typically enriched and often colocalized with neoantigen-rich tumor regions. These Ttex cells often reside adjacent to tertiary lymphoid structures (TLS), and a higher Ttex/CD8+ ratio correlates with improved relapse-free survival, reflecting ongoing immune pressure despite features of exhaustion (9, 28). These findings demonstrates that the presence of exhausted cells does not necessarily signify immune failure but can instead mark active tumor–immune engagement.
In contrast, MSS CRC, which represents 85-90% of all CRC cases shows a different spatial and immunologic landscape (53, 54). MSS tumors generally have lower overall CD8+ T-cell infiltration and a reduced Ttex/CD8+ ratio compared with MSI-H CRC (55). Spatial profiling studies indicate that Ttex in MSS CRC are frequently confined to stromal or immune-excluded niches, with limited association with TLS or antigen-rich tumor nests (53, 56). These features reflect a weaker immune-reactive microenvironment and reduced availability of Tpex precursors capable of sustaining productive anti-tumor responses. As a result, Ttex abundance in MSS CRC often carries neutral or unfavorable prognostic implications, driven by stromal barriers, low neoantigen load, and restricted T cell access to tumor cores (8).
3.2 Non-small cell lung cancer
NSCLC is characterized by high mutational burden and frequent neoantigen presentation, providing fertile ground for T cell engagement and exhaustion (57, 58). Ttex cells in NSCLC exhibit PD-1high, TCF1– phenotypes and often coexpress T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) and other exhaustion markers (18, 59, 60). Their abundance has been associated with mixed outcomes: in some cohorts, high Ttex frequencies correlate with resistance to PD-1 blockade, while in others, their presence within TLS-rich regions suggests a role in maintaining long-term immune pressure (9, 23).
3.3 Head and neck squamous cell carcinoma
In HNSCC, Ttex cells have been identified as key components of immune-infiltrated tumors (3, 61). The presence of TCF1–PD-1high CD8+ T cells in intraepithelial regions correlates with a suppressive immune landscape and poorer overall survival (62, 63). These findings suggest that in certain contexts, Ttex may serve as markers of immune evasion or progression, particularly when uncoupled from a supportive Tpex population or TLS architecture.
3.4 Esophageal squamous cell carcinoma
Recent spatial atlases of ESCC have revealed that Ttex cells emerge during high-grade intraepithelial neoplasia, coinciding with early signs of immune suppression and stromal remodeling (9, 64). These cells accumulate in CAF-enriched niches and contribute to the formation of immune-excluded microenvironments (65, 66). While their prognostic role remains under investigation, their early presence suggests a potential role in initiating immune evasion mechanisms (8, 16, 67).
3.5 Melanoma
Melanoma has been a prototypical model for T cell exhaustion studies due to its immunogenicity and responsiveness to checkpoint inhibitors (68, 69). Ttex cells are abundant in chronic lesions and often display a fixed epigenetic program that limits reinvigoration by PD-1 blockade (70). Nonetheless, their presence especially when balanced with a Tpex reservoir may be indicative of ongoing immune responses and partial tumor control (14, 69).
3.6 Hepatocellular carcinoma
HCC is characterized by a tolerogenic immune environment that favors T cell dysfunction. Ttex cells in HCC express exhaustion markers including TOX, CD39, and PD-1, and are enriched in the peritumoral stroma (71, 72). High densities of these cells have been linked to poor prognosis, particularly in the absence of Tpex or effector cell support (73, 74). These findings underscore the importance of cellular context in interpreting Ttex significance.
3.7 Breast and ovarian cancers
In breast and ovarian cancers, where immune exclusion is common, Ttex cells are often confined to stromal zones and may represent a frustrated immune attempt to access tumor nests (3, 75). Limited studies suggest that increased Ttex presence correlates with better prognosis, particularly in triple-negative breast cancer and BRCA1-mutated tumors, where T cell infiltration tends to be higher (76–78). However, more data are needed to clarify their functional role in these tumor types.
3.8 Prognostic variability across tumor types
The prognostic impact of Ttex varies by tumor type and immune context. In immune-inflamed tumors with high TMB or TLS presence, Ttex may reflect an active, ongoing immune response, correlating with favorable outcomes (8, 27, 79). Conversely, in immune-excluded or stromal-rich tumors, a predominance of Ttex may indicate T cell dysfunction and failure to control tumor growth, thereby associating with worse prognosis (3, 80, 81). This duality underscores the importance of interpreting Ttex in the context of stromal composition, spatial exclusion, and Tpex reservoir availability.
Across cancers, terminal exhaustion arises through shared mechanisms including TOX-driven chromatin remodeling, PD-1/TIM-3/LAG-3 co-expression networks, and depletion of stem-like Tpex precursors under chronic antigen exposure (26, 34). These conserved features define a unifying exhaustion axis that could underpin pan-cancer biomarker development.
At the same time, prognostic heterogeneity reflects variation in TLS density, stromal permissiveness, and availability of Tpex capable of differentiation (82–84). In immune-excluded or CAF-dominated niches, Ttex enrichment more often marks immune failure rather than immune pressure (85, 86). These distinctions highlight the need to contextualize Ttex biology within both spatial and immunogenomic frameworks. Figure 3 summarizes the enrichment of Ttex cells and their tumor-context-dependent prognostic associations across major solid tumor types.
Figure 3. Ttex enrichment and prognostic outcomes across major solid tumors. Comparative schematic summarizing Ttex enrichment and prognostic outcomes across major solid tumor types. Favorable outcomes (green) are exemplified by MSI-H CRC, where TLS-associated Ttex support durable anti-tumor immunity. Poor prognosis (red) is linked to high accumulation of PD-1high Ttex in non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), and esophageal squamous cell carcinoma (ESCC), reflecting advanced exhaustion states. Mixed or context-dependent associations (yellow) are observed in melanoma, hepatocellular carcinoma (HCC), breast cancer, and ovarian cancer, where spatial distribution and immune contexture influence clinical outcomes.
4 Immunogenomic and spatial correlates
The presence, phenotype, and prognostic significance of Ttex in solid tumors are closely intertwined with the immunogenomic landscape and spatial architecture of the TME. Advances in multi-omics and spatial profiling technologies have revealed that Ttex abundance is not random but rather aligned with specific tumor-intrinsic features and localized immune structures (87–89). Understanding these associations provides a foundation for integrating Ttex-based metrics into biomarker strategies for precision oncology.
4.1 Association with MSI
MSI-H tumors, particularly in colorectal and endometrial cancers, are characterized by elevated mutation rates and robust immune infiltration (90, 91). These tumors often exhibit increased frequencies of Ttex, likely driven by chronic stimulation from a high neoantigen burden (92, 93). Notably, in MSI-H CRC, a higher Ttex/CD8+ ratio has been associated with improved relapse-free survival, suggesting that the presence of Ttex reflects a sustained immune response rather than immune failure (4, 6, 8). This observation contrasts with the traditional assumption that T cell exhaustion equates to dysfunction, highlighting the importance of genomic context in interpreting exhaustion phenotypes.
Recent MSI-focused spatial transcriptomics studies show that frameshift neoantigens induce chronic TCR signaling, driving TOX and NR4A expression and promoting Tpex to Ttex differentiation (94). MSI tumors also exhibit distinct cytokine gradients, particularly IL-21 and type I IFNs that sustain progenitor pools yet simultaneously promote terminal exhaustion in high-antigen niches (95, 96).
4.2 TMB and neoantigen load
High TMB has been linked to increased T cell infiltration, including the recruitment and differentiation of CD8+ T cells into an exhausted state (1, 97). Ttex are frequently enriched in TMB-high tumors, where continuous antigen exposure drives terminal differentiation (3, 23). Similarly, high neoantigen load has been positively correlated with Ttex abundance across several solid tumor types, including melanoma, NSCLC, and MSI-H CRC (3, 8, 98). These associations suggest that Ttex presence may serve as a surrogate marker for immunogenicity, particularly in settings where direct neoantigen profiling is not feasible.
High TMB tumors frequently harbor clonal neoantigens that sustain NFAT-dependent calcium signaling, a key driver of TOX upregulation (27, 57). Single-cell analyses further show that TMB-high tumors enrich for CXCL13+ Ttex clusters associated with exhausted yet tumor-reactive T cell clones (99, 100).
4.3 Tertiary lymphoid structures
Ttex often exist in juxtaposition to TLS organized lymphoid aggregates that serve as local hubs for antigen presentation and T cell priming (43). In tumors with mature TLS, Tpex cells are thought to originate or persist within these niches, while Ttex localize more distally within tumor nests, reflecting their terminal differentiation status (14, 83). The spatial relationship between TLS and Ttex may have prognostic implications (43, 44, 101). Tumors with coexisting TLS and Ttex infiltration often show improved clinical outcomes, suggesting a dynamic interplay between T cell priming and effector exhaustion within the tumor ecosystem (28, 43).
Recent deep-imaging studies reveal that TLS-associated dendritic cells deliver IL-21 and CD28 co-stimulation that preserve Tpex differentiation (102). Loss of TLS or spatial decoupling from antigen-presenting cells accelerates Tpex exhaustion and Ttex accumulation, altering clinical outcomes (14, 103).
4.4 High-resolution mapping technologies
The integration of high-dimensional spatial and single-cell tools has revolutionized the characterization of Ttex within the TME (86, 104). Multiplex immunofluorescence (mIF) enables simultaneous detection of multiple exhaustion and activation markers, such as PD-1, TCF1, TOX, and Granzyme B, within formalin-fixed tissues, thereby providing both quantitative and spatial information on Ttex in clinical samples (105). Spatial transcriptomics extends this capability by preserving tissue architecture while generating gene expression maps, allowing exhaustion gene signatures including TOX, LAG3, and CD39 to be localized to tumor margins, immune niches, or stromal barriers (106). scRNA-seq combined with protein barcoding technologies such as Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) or Cytometry by time-of-flight (CyTOF) offers even deeper resolution, capturing the transcriptional continuum from naïve to Tpex and Ttex states, as well as clonal relationships and functional potential (107).
Together, these approaches provide a nuanced understanding of how tumor-intrinsic features, such as MSI and TMB, interact with spatial immune architecture, including TLS, to shape the formation and persistence of Ttex. Collectively, they establish the foundation for biomarker development and patient stratification strategies based on T cell exhaustion states. A summary of these technologies, including their strengths, limitations, and clinical applicability, is provided in Table 1.
Table 1. Technologies for detecting and quantifying Ttex cells.
5 Prognostic value of Ttex and the Ttex/CD8+ ratio
The prognostic significance of Ttex cells has gained increasing attention in recent years, challenging traditional assumptions that exhaustion necessarily equates to immune failure (116). Instead, accumulating evidence suggests that the presence of Ttex especially when contextualized relative to total CD8+ T cell infiltration can serve as a meaningful biomarker of immune engagement and tumor control in multiple solid tumor types (28, 117).
5.1 Clinical outcomes associated with Ttex presence
Several retrospective and prospective studies across different tumor types have demonstrated that Ttex infiltration is associated with favorable clinical outcomes under certain conditions (118, 119). In MSI-H CRC, for example, the enrichment of PD-1high TCF1− CD8+ T cells within the tumor epithelium correlates with prolonged relapse-free survival (RFS) (120). Similarly, in NSCLC, the presence of Ttex has been linked to sustained immune pressure and improved survival in subsets of patients with immune-infiltrated tumors (6, 121). These findings underscore the importance of interpreting Ttex not in isolation but within the broader immune context.
5.2 Ttex/CD8+ ratio as a prognostic biomarker
Recent suggests that the Ttex/CD8+ ratio, rather than absolute number of Ttex cells, provides a more accurate reflection of the immune landscape within solid tumor (9, 69, 122). A high ratio indicates that a substantial proportion of infiltrating CD8+ T cells have progressed to terminal exhaustion, often reflecting chronic antigen exposure and prior immune activation (1, 9, 123). Importantly, the prognostic significance of this ratio is strongly cancer-type dependent. In MSI-H CRC, a higher Ttex/CD8+ ratio consistently predicts favorable outcomes and reduced recurrence risk, even when total CD8+ infiltration is modest (28, 124). In contrast, in NSCLC, HNSCC, and ESCC, elevated Ttex/CD8+ ratios are generally associated with poor prognosis, reflecting stromal exclusion, insufficient Tpex support, and dysfunctional immune pressure (61, 113, 125). Melanoma shows a more context-dependent pattern, where Ttex enrichment can be favorable when accompanied by robust Tpex reservoirs but unfavorable in immunosuppressed or immune-excluded tumors (34, 126). In hepatocellular carcinoma, high Ttex/CD8+ ratios correlate with reduced survival, consistent with the tolerogenic hepatic microenvironment (127, 128). Conversely, in triple-negative and BRCA1-mutant breast cancers, higher Ttex/CD8+ ratios may indicate active anti-tumor immunity in highly infiltrated tumors (129, 130). Collectively, these findings demonstrate that the Ttex/CD8+ ratio is a tumor-context-specific biomarker whose prognostic interpretation depends on mutational burden, stromal architecture, and the presence of supportive progenitor Tpex pools (131). Importantly, the same Ttex/CD8+ ratio may reflect fundamentally different biological states across tumor types, ranging from ongoing immune engagement to terminal immune failure, underscoring the necessity of tumor-context-specific interpretation.
5.3 Thresholds and risk stratification
While the Ttex/CD8+ ratio shows considerable promise as a prognostic biomarker, standardized thresholds for clinical use have not yet been defined (132, 133). Most existing studies rely on cohort-specific medians or quartiles to classify patients into high- versus low-ratio groups, which limits comparability across cohorts (134). To enable broader adoption, future efforts should establish universal cutoffs validated across diverse datasets and platforms. In addition, developing automated scoring systems using digital pathology or AI-assisted quantification, and integrating the ratio with contextual features such as proximity to tumor cells or the presence of tertiary lymphoid structures, will further enhance its clinical utility (135, 136). Together, these advances could support the incorporation of Ttex-based metrics into routine risk stratification models, particularly for patients undergoing adjuvant therapy or clinical surveillance. A summary of the prognostic associations of Ttex across solid tumors is provided in Table 2.
Table 2. Prognostic significance of Ttex cells across solid tumors.
5.4 Comparison with established immune biomarkers
Compared with widely used immune biomarkers such as immunoscore (based on CD3+ and CD8+ cell densities) and PD-L1 IHC, the Ttex/CD8+ ratio provides complementary and additional insight into the functional state of the CD8+ T cell compartment (152, 153). The immunoscore primarily quantifies T-cell abundance, while PD-L1 IHC reflects expression of a single inhibitory ligand on tumor or immune cells (154). In contrast, quantifying Ttex cell captures information about CD8+ T cell differentiation, exhaustion trajectory, and functional competence, providing a layer of biological interpretation not captured by quantity-based or single-marker assays (18, 69).
Importantly, the Ttex/CD8+ ratio is not intended to replace existing biomarkers. Instead, it may refine clinical patient stratification by adding resolution within intermediate-risk groups, including those patients with modest CD8+ infiltration or ambiguous PD-L1 status, thereby offering incremental prognostic and mechanistic value when used alongside established tools (117, 150).
6 Translational applications and integration into clinical practice
As the landscape of cancer immunotherapy continues to evolve, there is growing interest in translating immune biomarkers from research setting to real-world clinical practice (155, 156). Ttex cells and their relative abundance within the tumor immune context hold promise as prognostic and predictive tools, particularly when incorporated into modern pathology workflows (132, 157). Advances in digital pathology, artificial intelligence (AI), and multiplex imaging technologies now enable standardized quantification of Ttex in formalin-fixed paraffin-embedded (FFPE) tumor specimens (158, 159). However, successful translation also requires critical evaluation of technical, analytical, and regulatory barriers that may limit reproducibility and broader adoption.
6.1 Digital pathology and AI-enabled Ttex quantification
The identification of Ttex cells typically defined by PD-1high and TCF1– expression can be achieved using multiplex immunofluorescence (mIF) or multiplexed IHC (mIHC) panels (160, 161). When coupled with AI-based image analysis platforms, these techniques allow automated detection, phenotyping, and spatial localization of Ttex within tumor tissues (162). Recent studies have demonstrated the feasibility of digital quantification of exhausted T cell subsets across large clinical cohorts (163). Incorporating additional markers such as CD8, TOX, and TIM-3 further enhances assay robustness and enables reproducible measurement of the Ttex/CD8+ ratio (18, 164).
AI-assisted systems hold promise for standardized Ttex/CD8+ quantification, though validation across platforms remains an active area of investigation (165, 166). Technical limitations remain an important consideration in translating multiplex imaging and AI-based Ttex quantification into clinical use (167, 168). Significant inter-platform variability such as differences observed across Akoya, NanoString, and Leica systems can influence dynamic range, fluorophore stability, and antibody compatibility, ultimately affecting the reliable detection of exhaustion markers (167, 169). In addition, the absence of universally standardized antibody panels, particularly the inconsistent performance of commonly used clones for PD-1, TIM-3, and TOX, introduces site-specific bias that complicates cross-study comparison (60). AI-assisted pathology also faces notable challenges, including algorithmic model drift, limited generalizability across institutions with different staining or scanning protocols, and variability in training datasets that may compromise reproducibility (170). Furthermore, regulatory barriers remain substantial, as diagnostic-grade digital pathology and AI classifiers require formal CE-IVDR or FDA clearance before widespread deployment (171). Together, these limitations highlight the urgent need for assay harmonization, rigorous inter-laboratory calibration, and multicenter standardization frameworks before Ttex quantification can be reliably incorporated into routine clinical workflows.
To bridge these technical considerations with practical implementation, the following workflow summarizes how multiplex staining and AI-assisted pathology can be integrated to generate clinically actionable Ttex assessments. Figure 4 outlines a representative workflow integrating multiplex staining, AI-assisted pathology, and quantitative analysis to derive clinically actionable Ttex/CD8⁺ metrics from patient tumor samples.
Figure 4. Workflow for Ttex quantification in clinical practice. Schematic illustration of a proposed translational pipeline integrating Ttex evaluation into clinical pathology. Tumor samples are processed using multiplex IHC or immunofluorescence (IF), followed by AI-assisted digital pathology for automated cell recognition. Quantification of the Ttex/CD8+ ratio is performed to distinguish between favorable, poor, and mixed prognostic groups. Results are incorporated into the clinical pathology report and used for treatment stratification, guiding decisions such as allocation to therapeutic regimens (Tx A, Tx B, Tx C).
6.2 Feasibility in routine pathology workflows
The integration of Ttex scoring into diagnostic pipelines is increasingly feasible due to the widespread availability of whole-slide imaging, cloud-based computation, and digital biomarker platforms (86, 158). Unlike gene expression-based biomarkers, which often require RNA preservation or sequencing infrastructure, Ttex detection through protein-level multiplex staining is compatible with standard FFPE tissue and readily integrates into existing clinical workflows (172, 173).
However, several practical barriers must be acknowledged. Staining quality can vary across institutions due to differences in antigen-retrieval protocols and tissue-preservation methods, leading to inconsistent detection of exhaustion markers (11, 174). Batch effects introduced during multiplex staining or spectral unmixing further complicate cross-cohort comparability. Computational reproducibility also remains challenging, as analytical pipelines differ in segmentation models, thresholding strategies, and feature-extraction parameters (175). Furthermore, regulatory requirements add another layer of complexity, as AI-based diagnostic algorithms must undergo formal validation before clinical deployment (176, 177). Addressing these challenges through centralized AI models, ring-trial calibration, and consensus scoring guidelines will be essential for enabling large-scale and standardized clinical adoption.
6.3 Potential in adjuvant therapy decision-making
The prognostic value of Ttex, especially when evaluated using the Ttex/CD8+ ratio, may inform adjuvant therapy decisions in multiple tumor types (28, 178). In CRC, for example, high Ttex ratios have been associated with improved relapse-free survival, suggesting that these tumors may already be under active immunologic surveillance and may require less aggressive adjuvant treatment (179, 180). Conversely, tumors with low Ttex infiltration may benefit from escalated therapy or referral into clinical trials for immunomodulatory agents (181).
Similarly, in ESCC, spatial mapping studies demonstrate that early accumulation of Ttex coincide with immune exclusion (182, 183), identifying high-risk patients who may require closer monitoring or early intervention (43). Integration of Ttex profiling with existing clinicopathologic parameters may therefore enhance risk stratification beyond conventional staging systems (8, 27). At present, Ttex-based stratification strategies should be regarded as investigational, as prospective interventional trials validating their direct utility in clinical decision-making are still lacking.
6.4 Companion biomarker for immunotherapy and CAR-T cell trials
Beyond prognosis, Ttex metrics may serve as companion biomarkers for immune checkpoint inhibitors (ICIs) and chimeric antigen receptor T (CAR-T) therapies, especially in solid tumors where validated predictive biomarkers of response remain scarce (106, 184, 185). Ttex metrics are currently being explored as investigational biomarkers in ICI and CAR-T trials, with no regulatory approval to date. High Ttex abundance may indicate chronic antigen exposure and prior immune activation, identifying patients more likely to respond to PD-1/PD-L1 blockade or T cell reinvigoration strategies (186, 187). In contrast, a low Ttex/CD8+ ratio could signal insufficient T cell priming or immune exclusion, potentially guiding the selection of combination regimens such as anti-angiogenic agents, toll-like receptor (TLR) agonists, or stromal modulators (117, 188).
Additionally, in CAR-T cell trials, the pre-existing T cell exhaustion status in the TME may significantly influence CAR-T engraftment, expansion, and therapeutic durability (189, 190). Quantifying Ttex levels before treatment could enable more effective patient stratification and guide rational CAR design strategies aimed at resistant to exhaustion, such as modified co-stimulatory domains or exhaustion-resistant transcriptional programs (3, 191).
7 Therapeutic targeting and modulation of Ttex cells
Ttex cells are traditionally considered to be at the end of the exhaustion spectrum, with limited proliferative potential and responsiveness to immune checkpoint blockade (43, 192). However, recent research suggests that Ttex cells are not entirely inert and may still retain cytotoxic function or serve as indicators of past immune engagement (69, 74). This raises the important question: Can Ttex cells be reactivated or therapeutically modulated? (4, 193) While Tpex cells remain the primary responders to PD-1/PD-L1 inhibition, strategies aimed at enhancing or sustaining the functional capacity of Ttex cells are emerging and hold promise for improving immunotherapeutic outcomes (14, 194).
7.1 Can Ttex be reinvigorated?
Clinical and preclinical data suggest that Ttex are less responsive to checkpoint inhibitors than Tpex cells due to a fixed epigenetic state characterized by chromatin remodeling, TOX-driven transcriptional reprogramming, and metabolic exhaustion (195–197). Nonetheless, Ttex can still exert limited effector functions such as cytotoxicity and IFN-γ secretion, particularly in antigen-rich environments (198, 199). Recent studies show that PD-1 blockade primarily expands Tpex cells (200, 201), whereas Ttex, despite their limited proliferative capacity, can transiently support tumor control through short-lived cytotoxic activity (14, 187). Thus, Ttex cells may be therapeutically relevant in combination with approaches that prevent their accumulation, delay their terminal differentiation, or restore partial function (202). Thus, while Ttex retain limited residual effector capacity, current evidence does not support reliable functional reinvigoration of fully terminally exhausted T cells in patients, and most durable clinical responses appear to be driven by the Tpex compartment.
7.2 Emerging therapeutic strategies
Beyond conventional checkpoint blockade, several emerging strategies are being investigated to modulate or overcome terminal exhaustion. Combination checkpoint inhibitors targeting molecules frequently co-expressed with PD-1, such as TIM-3, LAG-3, and TIGIT, are showing promise in both preclinical and clinical studies, with the potential to address exhaustion at multiple stages (1, 203–205). Targeting transcriptional regulators, particularly TOX and the NR4A family, has demonstrated preclinical efficacy in delaying exhaustion and restoring partial effector function (206). Epigenetic reprogramming strategies, including siRNA or CRISPR-based modulation, also aim to reverse exhaustion-associated chromatin remodeling (207). In addition, non-coding RNAs such as circRNAs are emerging as novel regulators of T cell persistence and differentiation, representing potential tools for reprogramming exhaustion pathways (89). Engineering exhaustion-resistant T cells through CAR-T constructs with optimized costimulatory domains or CRISPR-mediated deletion of inhibitory genes offers another avenue to improve persistence and efficacy in solid tumors (208). Finally, metabolic modulation strategies targeting mitochondrial function or mTOR signaling may help sustain T cell fitness under chronic antigen stimulation (209).
To better conceptualize these approaches, Figure 5 provides a schematic overview of therapeutic strategies aimed at overcoming terminal exhaustion, including checkpoint blockade, transcriptional/epigenetic reprogramming, and engineered CAR-T cells. This figure highlights how interventions can either block inhibitory signaling, reprogram exhaustion pathways, or engineer resistance to exhaustion, thereby enhancing T cell persistence and antitumor activity.
Figure 5. Therapeutic strategies targeting Ttex. Schematic overview of approaches to modulate Ttex in cancer immunotherapy. (A) Checkpoint blockade therapies targeting PD-1, TIM-3, TIGIT, or LAG-3 to alleviate inhibitory signaling. (B) Transcriptional and epigenetic reprogramming strategies aimed at preserving Tpex pools and limiting terminal exhaustion (15). Specifically (1), VEGFA-VEGFR2 signaling promotes TOX induction and exhaustion differentiation (210, 211) (2); circRNA- or small-molecule-mediated TOX inhibition suppresses exhaustion-associated transcriptional programs (212, 213); and (3) maintenance of TCF1 expression preserves progenitor exhausted CD8+ T cell pool and sustains long-term immune responsiveness (214, 215). (C) Engineered CAR-T cells, using CRISPR/Cas9-based genome editing and monoclonal antibody-based modulation to generate exhaustion-resistant T cells with enhanced self-renewal capacity and antitumor activity (216). Together, these complementary strategies aim to overcome terminal T cell exhaustion and improve the durability and efficacy of cancer immunotherapy.
Table 3 provides a concise summary of emerging therapeutic strategies, outlining their molecular targets, supporting preclinical and clinical evidence, and translational potential in cancer immunotherapy. This overview complements the schematic by serving as a quick reference for the scope and current maturity of each approach.
Table 3. Emerging therapeutic strategies targeting Ttex cells.
7.3 Toward rational combination therapies
Given the multifactorial nature of T cell exhaustion, it is increasingly clear that combination strategies are more likely to yield durable benefits than single-agent interventions (226). Rational pairings under investigation include PD-1 blockade with TIM-3 or TIGIT inhibition to simultaneously address early and late checkpoints of exhaustion, as well as combinations of checkpoint inhibitors with metabolic modulators that enhance mitochondrial fitness or regulate mTOR signaling (16, 60). Another promising direction involves integrating immunotherapies with circRNA-targeting agents to reprogram exhaustion pathways at the transcriptional level (227, 228). Importantly, Ttex profiling before and during treatment could serve as a guide for patient selection, ensuring that these combination strategies are applied to individuals most likely to benefit (50). Such biomarker-driven approaches represent a step toward precision immunotherapy, in which exhaustion status informs both therapeutic design and clinical decision-making.
8 Challenges and future directions
Despite growing interest in Ttex cells as prognostic and translational biomarkers in solid tumors, several key challenges limit their widespread clinical application. To fully realize the potential of Ttex-based strategies in precision oncology, both technical and conceptual gaps must be addressed through coordinated research and clinical efforts.
8.1 Inconsistent marker definitions across platforms
One of the foremost challenges is the lack of standardized phenotypic definitions for Ttex across different experimental and clinical platforms (6). While PD-1high and TCF1− expression is widely used as a practical definition in multiplex immunofluorescence and flow cytometry studies, these markers do not fully capture the diversity of terminally exhausted states (23). Additional markers such as TOX, CD39, TIM-3, and TIGIT are variably included, and their expression patterns fluctuate depending on tissue site, tissue type, disease stage, and staining platform (229, 230). Variability in antibody clones, thresholds, and gating strategies further complicates cross-study comparisons and limits the development of universal diagnostic assays (231). To overcome these inconsistencies, future work must prioritize the development of harmonized, multi-center antibody panels and standardized staining and scoring pipelines, enabling more reproducible identification of Ttex across institutions and imaging platforms.
8.2 Need for functional validation
Most studies to date have inferred Ttex identity and function based on marker expression rather than direct assessment of cytotoxic capacity or cytokine production. While exhausted T cells are often assumed to be dysfunctional, emerging evidence indicates that Ttex retain residual activity under certain conditions (193, 232). Functional assays, including single-cell cytokine profiling, degranulation (e.g., CD107a), and cytotoxicity assays, are needed to validate the effector potential of Ttex across tumor types (233, 234). Addressing this gap will require integrating functional validation into phenotypic and spatial studies, establishing standardized functional benchmarks, and determining whether Ttex accumulation represents immune failure, adaptive engagement, or context-dependent restraint.
8.3 Lack of longitudinal and dynamic tracking
T cell exhaustion is a dynamic and context-dependent process. However, most studies provide only static snapshots of Ttex phenotypes at a single time point (6, 235). There is a critical need for longitudinal studies that track the evolution of Ttex populations during disease progression, treatment response, and relapse. Techniques such as serial tumor biopsies, circulating T cell analysis, and T cell receptor (TCR) tracking can help elucidate the clonal stability and functional trajectory of Ttex in patients over time (43, 236). Future studies should incorporate longitudinal sampling and TCR-based lineage tracing in clinical trials to define Ttex stability, differentiation trajectories, and treatment-associated shifts, allowing more accurate prognostic and predictive modeling.
In addition, substantial methodological variability across studies, including differences in marker panels, spatial platforms, and computational thresholds, currently limits direct cross-study comparability and may contribute to apparent discrepancies in reported Ttex prognostic behavior.
8.4 Future research directions
Large-scale, prospective clinical studies are urgently needed to validate the prognostic and predictive value of Ttex and the Ttex/CD8+ ratio, ideally using standardized marker panels, unified scoring criteria, and longitudinal outcome tracking to establish clinically meaningful thresholds and applications (237). A central priority will be defining a universal Ttex marker panel anchored by PD-1, TOX, CD39, and loss of TCF1 to enable consistent identification across platforms and institutions. At the same time, integrating Ttex assessment into multi-omics immunoprofiling frameworks including genomics, transcriptomics, epigenetics, and spatial proteomics will deepen our understanding of the molecular programs driving terminal exhaustion and may further clarify the molecular programs driving terminal exhaustion and may reveal novel therapeutic vulnerabilities (238).
To support clinical translation, future work must establish tumor-type-specific Ttex/CD8+ ratio thresholds, validate digital pathology and AI-assisted pipelines across platforms, and develop harmonized, open-access benchmark datasets that ensure algorithmic reproducibility and regulatory readiness (239). Multi-center ring trials, standardized multiplex-staining calibration, and consensus reporting guidelines will be essential steps toward incorporating Ttex metrics into pathology workflows, risk-stratification frameworks, and clinical decision-support systems (240).
In summary, these gaps highlight that current conclusions regarding Ttex as biomarkers and therapeutic targets must remain provisional until validated by standardized, prospective, and mechanistically integrated clinical studies.
9 Conclusion
Ttex cells are increasingly recognized not merely as hallmarks of chronic antigen stimulation but as clinically informative immune populations that reflect the history, intensity, and organization of anti-tumor responses within the TME (8). While traditionally considered dysfunctional, Ttex retain residual effector potential and when evaluated in relation to total CD8+ T cell infiltration can serve as prognostic indicators in multiple solid tumor types (6).
Recent advances in multiplex imaging, spatial transcriptomics, and AI-driven digital pathology have made it feasible to quantify and map Ttex in clinical specimens, enabling translational integration into risk stratification, treatment planning, and companion diagnostics (88). Moreover, the technologies summarized in 2provide the methodological foundation for reliable detection and quantification of Ttex, supporting their incorporation into clinical workflows and translational research (158). The Ttex/CD8+ ratio emerges as a promising biomarker that captures both the quality and differentiation state of the tumor-infiltrating T cell compartment.
Nevertheless, important challenges remain. Inconsistent marker definitions limited functional validation, and a lack of longitudinal tracking hinder broader clinical adoption (5). Future efforts should prioritize the harmonization of assays and scoring systems to ensure reproducible detection across platforms. Large-scale, prospective studies across multiple cancer types will also be required to validate the prognostic and predictive value of Ttex in diverse clinical settings. Equally important is the integration of Ttex assessment into ongoing immunotherapy trials, which would refine patient stratification and therapeutic monitoring in real time (241).
In addition, deeper biological insights are likely to emerge from multi-omics approaches that combine spatial, transcriptomic, epigenetic, and metabolic profiling to define the molecular determinants driving the transition from Tpex to Ttex states (193). On the therapeutic side, strategies aimed at preventing or reprogramming terminal exhaustion including circRNA-based modulation, CRISPR-mediated editing, and rational checkpoint blockade combinations represent promising avenues to enhance T cell persistence and anti-tumor activity (187, 192).
Together, these future directions underscore the dual role of Ttex as both biomarkers and therapeutic entry points in precision immuno-oncology, highlighting their substantial promise for advancing immune monitoring and therapeutic innovation. By addressing the challenges in standardization, functional validation, and prospective clinical testing, the field can move closer to translating biological insights into robust, clinically actionable tools that improve outcomes for patients with solid tumors.
Author contributions
XG: Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. SM: Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. JW: Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. YF: Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. WM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Acknowledgments
The figures in this manuscript were made with BioRender.
Conflict of interest
The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
WM holds the position of associate editor of Frontiers in Immunology at the time of submission. This had no impact on the peer review process or the final decision.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Abbreviations
AI, artificial intelligence; CAF, cancer-associated fibroblast; CAR, chimeric antigen receptor; CITE-seq, cellular Indexing of Transcriptomes and Epitopes by sequencing; CRC, colorectal cancer; CyTOF, cytometry by time-of-flight; FFRE, formalin-fixed paraffin-embedded; HCC, hepatocellular carcinoma; HNSCC, head and neck squamous cell carcinoma; ICI, immune checkpoint inhibitor; IHC, immunohistochemistry; LAG-3, lymphocyte-activation gene 3; mIF, multiplex immunofluorescence; MSI, microsatellite instability; NSCLC, non-small cell lung cancer; PD-1, programmed cell death protein 1; PD-L1, Programmed death-ligand 1; RFS, relapse-free survival; SCC, squamous cell carcinoma; scRNA-seq, single-cell RNA sequencing; TCF1, T cell factor 1 (encoded by TCF7); TCR, T cell receptor; TIL, Tumor-infiltrating lymphocyte; TIM-3, T-cell immunoglobulin and mucin domain-containing protein 3; TLS, Tertiary lymphoid structure; TMB, Tumor mutational burden; TME, Tumor microenvironment; TOX, thymocyte selection-associated high mobility group box protein; Tpex, progenitor exhausted T cell; Ttex, terminally exhausted T cell.
References
1. Yao Z, Zeng Y, Liu C, Jin H, Wang H, Zhang Y, et al. Focusing on CD8(+) T-cell phenotypes: improving solid tumor therapy. J Exp Clin Cancer Res. (2024) 43:266. doi: 10.1186/s13046-024-03195-5
2. Kersten K, Hu KH, Combes AJ, Samad B, Harwin T, Ray A, et al. Spatiotemporal co-dependency between macrophages and exhausted CD8(+) T cells in cancer. Cancer Cell. (2022) 40:624–38 e9. doi: 10.1016/j.ccell.2022.05.004
3. Nair R, Somasundaram V, Kuriakose A, Krishn SR, Raben D, Salazar R, et al. Deciphering T-cell exhaustion in the tumor microenvironment: paving the way for innovative solid tumor therapies. Front Immunol. (2025) 16:1548234. doi: 10.3389/fimmu.2025.1548234
4. Peralta RM, Xie B, Lontos K, Nieves-Rosado H, Spahr K, Joshi S, et al. Dysfunction of exhausted T cells is enforced by MCT11-mediated lactate metabolism. Nat Immunol. (2024) 25:2297–307. doi: 10.1038/s41590-024-01999-3
5. Zander R and Cui W. Exhausted CD8(+) T cells face a developmental fork in the road. Trends Immunol. (2023) 44:276–86. doi: 10.1016/j.it.2023.02.006
6. Wu H, Fan PW, Feng YN, Chang C, Gui T, Meng JB, et al. Role of T cell exhaustion and tissue-resident memory T cells in the expression and prognosis of colorectal cancer. Sci Rep. (2025) 15:28503. doi: 10.1038/s41598-025-14409-x
7. Rausch L and Kallies A. Molecular mechanisms governing CD8 T cell differentiation and checkpoint inhibitor response in cancer. Annu Rev Immunol. (2025) 43:515–43. doi: 10.1146/annurev-immunol-082223-044122
8. Lee JA, Park HE, Lee DW, Han SW, Kim TY, Jeong SY, et al. Immunogenomic characteristics and prognostic implications of terminally exhausted CD8(+) T cells in colorectal cancers. Front Immunol. (2025) 16:1601188. doi: 10.3389/fimmu.2025.1601188
9. Tanoue K, Ohmura H, Uehara K, Ito M, Yamaguchi K, Tsuchihashi K, et al. Spatial dynamics of CD39(+)CD8(+) exhausted T cell reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer. Nat Commun. (2024) 15:9033. doi: 10.1038/s41467-024-53262-w
10. Liu H, Dong A, Rasteh AM, Wang P, and Weng J. Identification of the novel exhausted T cell CD8 + markers in breast cancer. Sci Rep. (2024) 14:19142. doi: 10.1038/s41598-024-70184-1
11. Tietscher S, Wagner J, Anzeneder T, Langwieder C, Rees M, Sobottka B, et al. A comprehensive single-cell map of T cell exhaustion-associated immune environments in human breast cancer. Nat Commun. (2023) 14:98. doi: 10.1038/s41467-022-35238-w
12. Chen R, Zheng Y, Fei C, Ye J, and Fei H. Machine learning developed a CD8(+) exhausted T cells signature for predicting prognosis, immune infiltration and drug sensitivity in ovarian cancer. Sci Rep. (2024) 14:5794. doi: 10.1038/s41598-024-55919-4
13. Miggelbrink A, Jackson J, Lorrey SJ, Srinivasan E, Waibl-Polania J, Wilkinson D, et al. CD4 T-cell exhaustion: does it exist and what are its roles in cancer? Clin Cancer Res. (2021) 27:5742–52. doi: 10.1158/1078-0432.CCR-21-0206
14. Fang Z, Ding X, Huang H, Jiang H, Jiang J, and Zheng X. Revolutionizing tumor immunotherapy: unleashing the power of progenitor exhausted T cells. Cancer Biol Med. (2024) 21:499–512. doi: 10.20892/j.issn.2095-3941.2024.0105
15. Li C, Yuan Y, Jiang X, and Wang Q. Epigenetic regulation of CD8(+) T cell exhaustion: recent advances and update. Front Immunol. (2025) 16:1700039. doi: 10.3389/fimmu.2025.1700039
16. Tufail M, Jiang C, and Li N. Immune evasion in cancer: mechanisms and cutting-edge therapeutic approaches. Signal Transduct Target Ther. (2025) 10:227. doi: 10.1038/s41392-025-02280-1
17. Shangguan Y, Wang J, Ho P, and Xu Y. CD8(+) T cell stressors converge on shared metabolic-epigenetic networks. Trends Endocrinol Metab. (2025) S1043-2760(25)00190-0. doi: 10.1016/j.tem.2025.08.009
18. Minnie S, Waltner O, Zhang P, Takahashi S, Nemychenkov N, Ensbey K, et al. TIM-3(+) CD8 T cells with a terminally exhausted phenotype retain functional capacity in hematological Malignancies. Sci Immunol. (2024) 9:eadg1094. doi: 10.1126/sciimmunol.adg1094
19. Wang D, Fang J, Wen S, Li Q, Wang J, Yang L, et al. A comprehensive profile of TCF1(+) progenitor and TCF1(-) terminally exhausted PD-1(+)CD8(+) T cells in head and neck squamous cell carcinoma: implications for prognosis and immunotherapy. Int J Oral Sci. (2022) 14:8. doi: 10.1038/s41368-022-00160-w
20. Mok S, Liu H, Agac Cobanoglu D, Anang NAS, Mancuso JJ, Wherry EJ, et al. Anti-CTLA-4 generates greater memory response than anti-PD-1 via TCF-1. Proc Natl Acad Sci U S A. (2025) 122:e2418985122. doi: 10.1073/pnas.2418985122
21. Niu H and Wang H. TOX regulates T lymphocytes differentiation and its function in tumor. Front Immunol. (2023) 14:990419. doi: 10.3389/fimmu.2023.990419
22. Seo W, Jerin C, and Nishikawa H. Transcriptional regulatory network for the establishment of CD8(+) T cell exhaustion. Exp Mol Med. (2021) 53:202–9. doi: 10.1038/s12276-021-00568-0
23. Dutta N, Svensson J, Saad GA, Mello M, Eklund E, Altinonder I, et al. High baseline PD-1+ CD8 T Cells and TIGIT+ CD8 T Cells in circulation associated with response to PD-1 blockade in patients with non-small cell lung cancer. Cancer Immunol Immunother. (2025) 74:309. doi: 10.1007/s00262-025-04086-0
24. Im S, Obeng R, Nasti T, McManus D, Kamphorst A, Gunisetty S, et al. Characteristics and anatomic location of PD-1(+)TCF1(+) stem-like CD8 T cells in chronic viral infection and cancer. Proc Natl Acad Sci U S A. (2023) 120:e2221985120. doi: 10.1073/pnas.2221985120
25. Beltra J, Abdel-Hakeem M, Manne S, Zhang Z, Huang H, Kurachi M, et al. Stat5 opposes the transcription factor Tox and rewires exhausted CD8(+) T cells toward durable effector-like states during chronic antigen exposure. Immunity. (2023) 56:2699–718 e11. doi: 10.1016/j.immuni.2023.11.005
26. Huang Y, Ngiow S, Baxter A, Manne S, Park S, Wu J, et al. Continuous expression of TOX safeguards exhausted CD8 T cell epigenetic fate. Sci Immunol. (2025) 10:eado3032. doi: 10.1126/sciimmunol.ado3032
27. Wu B, Zhang B, Li B, Wu H, and Jiang M. Cold and hot tumors: from molecular mechanisms to targeted therapy. Signal Transduct Target Ther. (2024) 9:274. doi: 10.1038/s41392-024-01979-x
28. Sun Y, Yinwang E, Wang S, Wang Z, Wang F, Xue Y, et al. Phenotypic and spatial heterogeneity of CD8(+) tumour infiltrating lymphocytes. Mol Cancer. (2024) 23:193. doi: 10.1186/s12943-024-02104-w
29. Rigopoulos C, Georgakopoulos-Soares I, and Zaravinos A. A multi-omics analysis of an exhausted T cells’ Molecular signature in pan-cancer. J Pers Med. (2024) 14:765. doi: 10.3390/jpm14070765
30. Zhu Q, Balasubramanian A, Asirvatham J, Chatterjee M, Piyarathna B, Kaur J, et al. Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in Black American and White American patients with TNBC. Nat Commun. (2025) 16:6584. doi: 10.1038/s41467-025-61034-3
31. Jenkins E, Whitehead T, Fellermeyer M, Davis SJ, and Sharma S. The current state and future of T-cell exhaustion research. Oxf Open Immunol. (2023) 4:iqad006. doi: 10.1093/oxfimm/iqad006
32. Li Y, Han M, Wei H, Huang W, Chen Z, Zhang T, et al. Id2 epigenetically controls CD8(+) T-cell exhaustion by disrupting the assembly of the Tcf3-LSD1 complex. Cell Mol Immunol. (2024) 21:292–308. doi: 10.1038/s41423-023-01118-6
33. Tay T, Bommakanti G, Jaensch E, Gorthi A, Karapa Reddy I, Hu Y, et al. Degradation of IKZF1 prevents epigenetic progression of T cell exhaustion in an antigen-specific assay. Cell Rep Med. (2024) 5:101804. doi: 10.1016/j.xcrm.2024.101804
34. Ni L. Potential mechanisms of cancer stem-like progenitor T-cell bio-behaviours. Clin Transl Med. (2024) 14:e1817. doi: 10.1002/ctm2.1817
35. Chen X, Lin P, Lu Y, Zheng J, Lin Y, Zhou Z, et al. Mitochondrial regulation of CD8(+) T cells: mechanisms and therapeutic modulation. Adv Sci (Weinh). (2025) 12:e03095. doi: 10.1002/advs.202503095
36. Changaei M, Azimzadeh Tabrizi Z, Karimi M, Kashfi SA, Koochaki Chahardeh T, Hashemi S, et al. From powerhouse to modulator: regulating immune system responses through intracellular mitochondrial transfer. Cell Commun Signal. (2025) 23:232. doi: 10.1186/s12964-025-02237-5
37. Wang S, Xu L, Jiao X, Dong X, Zhong Z, Ren Q, et al. Iron overload-induced ferroptosis in CD8(+) T cells leads to functional abnormalities that promote endometriosis progression. BMC Med. (2025) 23:588. doi: 10.1186/s12916-025-04369-4
38. Ma C, Hu H, Liu H, Zhong C, Wu B, Lv C, et al. Lipotoxicity, lipid peroxidation and ferroptosis: a dilemma in cancer therapy. Cell Biol Toxicol. (2025) 41:75. doi: 10.1007/s10565-025-10025-7
39. Hu A, Sun L, Lin H, Liao Y, Yang H, and Mao Y. Harnessing innate immune pathways for therapeutic advancement in cancer. Signal Transduct Target Ther. (2024) 9:68. doi: 10.1038/s41392-024-01765-9
40. Gago da Graca C, Sheikh A, Newman D, Wen L, Li S, Shen J, et al. Stem-like memory and precursors of exhausted T cells share a common progenitor defined by ID3 expression. Sci Immunol. (2025) 10:eadn1945. doi: 10.1126/sciimmunol.adn1945
41. Gorchs L, Fernandez-Moro C, Asplund E, Oosthoek M, Solders M, Ghorbani P, et al. Exhausted tumor-infiltrating CD39+CD103+ CD8+ T cells unveil potential for increased survival in human pancreatic cancer. Cancer Res Commun. (2024) 4:460–74. doi: 10.1158/2767-9764.CRC-23-0405
42. Zhao L, Jin S, Wang S, Zhang Z, Wang X, Chen Z, et al. Tertiary lymphoid structures in diseases: immune mechanisms and therapeutic advances. Signal Transduct Target Ther. (2024) 9:225. doi: 10.1038/s41392-024-01947-5
43. Lin W, Li H, and Sun Z. T cell exhaustion initiates tertiary lymphoid structures and turbocharges cancer-immunity cycle. EBioMedicine. (2024) 104:105154. doi: 10.1016/j.ebiom.2024.105154
44. Hugaboom M, Wirth LV, Street K, Ruthen N, Jegede O, Schindler N, et al. Presence of tertiary lymphoid structures and exhausted tissue-resident T cells determines clinical response to PD-1 blockade in renal cell carcinoma. Cancer Discov. (2025) 15:948–68. doi: 10.1158/2159-8290.CD-24-0991
45. Zhang Y, Yang C, Chen X, Wu L, Yuan Z, Zhang F, et al. Cancer therapy resistance from a spatial-omics perspective. Clin Transl Med. (2025) 15:e70396. doi: 10.1002/ctm2.70396
46. Liu Z, Zhang Y, Ma N, Yang Y, Ma Y, Wang F, et al. Progenitor-like exhausted SPRY1(+)CD8(+) T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma. Cancer Cell. (2023) 41:1852–70 e9. doi: 10.1016/j.ccell.2023.09.011
47. Ojo O, Shen H, Ingram J, Bonner J, Welner R, Lacaud G, et al. Gfi1 controls the formation of effector-like CD8(+) T cells during chronic infection and cancer. Nat Commun. (2025) 16:4542. doi: 10.1038/s41467-025-59784-1
48. Sandu I and Oxenius A. T-cell heterogeneity, progenitor-progeny relationships, and function during latent and chronic viral infections. Immunol Rev. (2023) 316:136–59. doi: 10.1111/imr.13203
49. Li C, Guo H, Zhai P, Yan M, Liu C, Wang X, et al. Spatial and single-cell transcriptomics reveal a cancer-associated fibroblast subset in HNSCC that restricts infiltration and antitumor activity of CD8+ T cells. Cancer Res. (2024) 84:258–75. doi: 10.1158/0008-5472.CAN-23-1448
50. Unger S, Nunez N, Becher B, and Kreutmair S. Next-generation immune profiling - beyond blood cancer cells. Trends Mol Med. (2025) 31:1140–1153. doi: 10.1016/j.molmed.2025.06.004
51. Hu B, Sajid M, Lv R, Liu L, and Sun C. A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology. Front Immunol. (2022) 13:996721. doi: 10.3389/fimmu.2022.996721
52. Liu J, Zeng J, Zhou T, Wu M, and Weng X. Increased CD103(-)CD8(+) TILs with TPEX phenotype replenish anti-tumor T cell pool in mismatch repair-proficient CRC. Cancer Immunol Immunother. (2025) 74:358. doi: 10.1007/s00262-025-04214-w
53. Wang Q, Yu M, and Zhang S. The characteristics of the tumor immune microenvironment in colorectal cancer with different MSI status and current therapeutic strategies. Front Immunol. (2024) 15:1440830. doi: 10.3389/fimmu.2024.1440830
54. Su A, Lee H, Tran M, Dela Cruz R, Sathe A, Bai X, et al. The single-cell spatial landscape of stage III colorectal cancers. NPJ Precis Oncol. (2025) 9:101. doi: 10.1038/s41698-025-00853-5
55. Kong H, Yang Q, Wu C, Wu X, Yan X, Huang L, et al. Spatial context of immune checkpoints as predictors of overall survival in patients with resectable colorectal cancer independent of standard tumor-node-metastasis stages. Cancer Res Commun. (2024) 4:3025–35. doi: 10.1158/2767-9764.CRC-24-0270
56. Chambuso R and Meena S. Single-cell spatial immune profiling for precision immunotherapy in Lynch syndrome. J Natl Cancer Cent. (2025) 5:3–7. doi: 10.1016/j.jncc.2024.12.002
57. Sun S, Liu L, Zhang J, Sun L, Shu W, Yang Z, et al. The role of neoantigens and tumor mutational burden in cancer immunotherapy: advances, mechanisms, and perspectives. J Hematol Oncol. (2025) 18:84. doi: 10.1186/s13045-025-01732-z
58. Li X, Zhu YJ, Xue Y, Chen TT, Sun XK, and Shi HY. Neoantigen-based immunotherapy in lung cancer: advances, challenges and prospects. Cancers (Basel). (2025) 17:1953. doi: 10.3390/cancers17121953
59. Liu X, Xi X, Xu S, Chu H, Hu P, Li D, et al. Targeting T cell exhaustion: emerging strategies in non-small cell lung cancer. Front Immunol. (2024) 15:1507501. doi: 10.3389/fimmu.2024.1507501
60. Yan Z, Wang C, Wu J, Wang J, and Ma T. TIM-3 teams up with PD-1 in cancer immunotherapy: mechanisms and perspectives. Mol Biomed. (2025) 6:27. doi: 10.1186/s43556-025-00267-6
61. Zhang W, Qu M, Yin C, Jin Z, and Hu Y. Comprehensive analysis of T cell exhaustion related signature for predicting prognosis and immunotherapy response in HNSCC. Discov Oncol. (2024) 15:56. doi: 10.1007/s12672-024-00921-5
62. Tsai C, Hsu Y, Chu T, Hsu P, and Kuo C. Immune evasion in head and neck squamous cell carcinoma: roles of cancer-associated fibroblasts, immune checkpoints, and TP53 mutations in the tumor microenvironment. Cancers (Basel). (2025) 17:2590. doi: 10.3390/cancers17152590
63. Kirchner J, Plesca I, Rothe R, Resag A, Lock S, Benesova I, et al. Type I conventional dendritic cells and CD8(+) T cells predict favorable clinical outcome of head and neck squamous cell carcinoma patients. Front Immunol. (2024) 15:1414298. doi: 10.3389/fimmu.2024.1414298
64. Luo R, Liu J, Wang T, Zhao W, Wang Y, Wen J, et al. The landscape of Malignant transition: Unraveling cancer cell-of-origin and heterogeneous tissue microenvironment. Cancer Lett. (2025) 621:217591. doi: 10.1016/j.canlet.2025.217591
65. Liu L, Wan W, Chang Y, Ao L, Xu Y, and Xu X. Crosstalk between heterogeneous cancer-associated fibroblast subpopulations and the immune system in breast cancer: key players and promising therapeutic targets. J Exp Clin Cancer Res. (2025) 44:263. doi: 10.1186/s13046-025-03527-z
66. Kay E and Zanivan S. The tumor microenvironment is an ecosystem sustained by metabolic interactions. Cell Rep. (2025) 44:115432. doi: 10.1016/j.celrep.2025.115432
67. Feng L, Yuan Q, Yu H, Ye R, Xie Z, Xu J, et al. Identification of biomarkers associated with exhausted CD8 + T cells in the tumor microenvironment of intrahepatic cholangiocarcinoma based on Mendelian randomization and bioinformatics analysis. Discov Oncol. (2025) 16:1092. doi: 10.1007/s12672-025-02970-w
68. Ohta S, Misawa A, Kyi-Tha-Thu C, Matsumoto N, Hirose Y, and Kawakami Y. Melanoma antigens recognized by T cells and their use for immunotherapy. Exp Dermatol. (2023) 32:297–305. doi: 10.1111/exd.14741
69. Brunell A, Lahesmaa R, Autio A, and Thotakura A. Exhausted T cells hijacking the cancer-immunity cycle: Assets and liabilities. Front Immunol. (2023) 14:1151632. doi: 10.3389/fimmu.2023.1151632
70. Fu M, Sharma M, Yousefi P, Merrill S, Tan R, Samra S, et al. ASXL1 deficiency causes epigenetic dysfunction, combined immunodeficiency, and EBV-associated lymphoma. J Exp Med. (2025) 222:e20240945. doi: 10.1084/jem.20240945
71. Martinez-Gomez C, Michelas M, Scarlata C, Salvioni A, Gomez-Roca C, Sarradin V, et al. Circulating exhausted PD-1(+)CD39(+) helper CD4 T cells are tumor-antigen-specific and predict response to PD-1/PD-L1 axis blockade. Cancers (Basel). (2022) 14:3679. doi: 10.3390/cancers14153679
72. Zhou Q, Shao S, Minev T, and Ma W. Unleashing the potential of CD39-targeted cancer therapy: Breaking new ground and future prospects. BioMed Pharmacother. (2024) 178:117285. doi: 10.1016/j.biopha.2024.117285
73. Sun L, Su Y, Jiao A, Wang X, and Zhang B. T cells in health and disease. Signal Transduct Target Ther. (2023) 8:235. doi: 10.1038/s41392-023-01471-y
74. Waibl Polania J, Hoyt-Miggelbrink A, Tomaszewski W, Wachsmuth L, Lorrey S, Wilkinson D, et al. Antigen presentation by tumor-associated macrophages drives T cells from a progenitor exhaustion state to terminal exhaustion. Immunity. (2025) 58:232–46 e6. doi: 10.1016/j.immuni.2024.11.026
75. Yang W, Liu S, Mao M, Gong Y, Li X, Lei T, et al. T-cell infiltration and its regulatory mechanisms in cancers: insights at single-cell resolution. J Exp Clin Cancer Res. (2024) 43:38. doi: 10.1186/s13046-024-02960-w
76. Zhou Z and Zhou Q. Immunotherapy resistance in triple-negative breast cancer: Molecular mechanisms, tumor microenvironment, and therapeutic implications. Front Oncol. (2025) 15:1630464. doi: 10.3389/fonc.2025.1630464
77. Rosa M, Reinert T, Pauletto M, Sartori G, Graudenz M, and Barrios C. Implications of tumor-infiltrating lymphocytes in early-stage triple-negative breast cancer: clinical oncologist perspectives. Transl Breast Cancer Res. (2024) 5:4. doi: 10.21037/tbcr-23-43
78. Xie H, Xi X, Lei T, Liu H, and Xia Z. CD8(+) T cell exhaustion in the tumor microenvironment of breast cancer. Front Immunol. (2024) 15:1507283. doi: 10.3389/fimmu.2024.1507283
79. Lyu X, Han J, Lin C, Zhou Y, and Wang W. Beyond the tumor microenvironment: Orchestrating systemic T−cell response for next−generation cancer immunotherapy (Review). Int J Oncol. (2025) 67:56. doi: 10.3892/ijo.2025.5762
80. Zheng S, Wang W, Shen L, Yao Y, Xia W, and Ni C. Tumor battlefield within inflamed, excluded or desert immune phenotypes: the mechanisms and strategies. Exp Hematol Oncol. (2024) 13:80. doi: 10.1186/s40164-024-00543-1
81. Sabit H, Adel A, Abdelfattah M, Ramadan R, Nazih M, Abdel-Ghany S, et al. The role of tumor microenvironment and immune cell crosstalk in triple-negative breast cancer (TNBC): Emerging therapeutic opportunities. Cancer Lett. (2025) 628:217865. doi: 10.1016/j.canlet.2025.217865
82. Deng S, Chen Y, Song B, Wang H, Huang S, Wu K, et al. Tertiary lymphoid structures in cancer: spatiotemporal heterogeneity, immune orchestration, and translational opportunities. J Hematol Oncol. (2025) 18:97. doi: 10.1186/s13045-025-01754-7
83. Zhang Y, Xu M, Ren Y, Ba Y, Liu S, Zuo A, et al. Tertiary lymphoid structural heterogeneity determines tumour immunity and prospects for clinical application. Mol Cancer. (2024) 23:75. doi: 10.1186/s12943-024-01980-6
84. Zhang C, Bai R, Hu Y, Wang T, Ma B, Zhang J, et al. Heterogeneity and distribution characteristics of tertiary lymphoid structures predict prognostic outcome in esophageal squamous cell carcinoma. Front Immunol. (2025) 16:1606499. doi: 10.3389/fimmu.2025.1606499
85. Rudloff M, Zumbo P, Favret N, Roetman J, Detres Roman C, Erwin M, et al. Hallmarks of CD8(+) T cell dysfunction are established within hours of tumor antigen encounter before cell division. Nat Immunol. (2023) 24:1527–39. doi: 10.1038/s41590-023-01578-y
86. Le J, Dian Y, Zhao D, Guo Z, Luo Z, Chen X, et al. Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment. Mol Cancer. (2025) 24:221. doi: 10.1186/s12943-025-02426-3
87. Wang J, Alhaskawi A, Dong Y, Tian T, Abdalbary SA, and Lu H. Advances in spatial multi-omics in tumors. Tumori. (2024) 110:327–39. doi: 10.1177/03008916241271458
88. Liu X, Peng T, Xu M, Lin S, Hu B, Chu T, et al. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J Hematol Oncol. (2024) 17:72. doi: 10.1186/s13045-024-01596-9
89. Huang S, Xu J, Baran N, and Ma W. Advancing the next generation of cancer treatment with circular RNAs in CAR-T cell therapy. BioMed Pharmacother. (2024) 181:117753. doi: 10.1016/j.biopha.2024.117753
90. Greco L, Rubbino F, Dal Buono A, and Laghi L. Microsatellite instability and immune response: from microenvironment features to therapeutic actionability-lessons from colorectal cancer. Genes (Basel). (2023) 14:1169. doi: 10.3390/genes14061169
91. Shaikh R, Bhattacharya S, and Prajapati B. Microsatellite instability: A potential game-changer in colorectal cancer diagnosis and treatment. Results Chem. (2024) 7:101461. doi: 10.1016/j.rechem.2024.101461
92. Yaegashi H, Izumi K, Makino T, Naito R, Iwamoto H, Kawaguchi S, et al. Microsatellite instability-high and high tumor mutation burden frequencies in urologic Malignancies off-label for immune checkpoint inhibitors in Japan: A retrospective single-institutional cohort. Cureus. (2024) 16:e69366. doi: 10.7759/cureus.69366
93. Di Mauro A, Santorsola M, Savarese G, Sirica R, Ianniello M, Cossu AM, et al. High tumor mutational burden assessed through next-generation sequencing predicts favorable survival in microsatellite stable metastatic colon cancer patients. J Transl Med. (2024) 22:1107. doi: 10.1186/s12967-024-05927-9
94. Nakagawara K, Ando M, Srirat T, Mise-Omata S, Hayakawa T, Ito M, et al. NR4A ablation improves mitochondrial fitness for long persistence in human CAR-T cells against solid tumors. J Immunother Cancer. (2024) 12:e008665. doi: 10.1136/jitc-2023-008665
95. Vargas-Castellanos E and Rincon-Riveros A. Microsatellite instability in the tumor microenvironment: the role of inflammation and the microbiome. Cancer Med. (2025) 14:e70603. doi: 10.1002/cam4.70603
96. Li Q, Geng S, Luo H, Wang W, Mo Y, Luo Q, et al. Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy. Signal Transduct Target Ther. (2024) 9:266. doi: 10.1038/s41392-024-01953-7
97. Jung J, Heo YJ, and Park S. High tumor mutational burden predicts favorable response to anti-PD-(L)1 therapy in patients with solid tumor: a real-world pan-tumor analysis. J Immunother Cancer. (2023) 11:e006454. doi: 10.1136/jitc-2022-006454
98. Tang W, Battistone B, Bauer K, Weis A, Barba C, Fadlullah M, et al. A microRNA-regulated transcriptional state defines intratumoral CD8(+) T cells that respond to immunotherapy. Cell Rep. (2025) 44:115301. doi: 10.1016/j.celrep.2025.115301
99. Shorer O, Pinhasi A, and Yizhak K. Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy. Cell Genom. (2025) 5:100842. doi: 10.1016/j.xgen.2025.100842
100. Li X, Cheng Y, Ji M, Liu J, Zhao Z, and Zhao Q. Single-cell RNA sequencing analysis reveals a lack of CXCL13(+) T cell subsets associated with the recurrence of cervical squamous cell carcinoma following concurrent chemoradiotherapy. Cancer Immunol Immunother. (2025) 74:235. doi: 10.1007/s00262-025-04083-3
101. Sonoda H, Iwasaki T, Ishihara S, Mori T, Nakashima Y, and Oda Y. Impact of tertiary lymphoid structure on prognosis and tumor microenvironment in undifferentiated pleomorphic sarcoma. Cancer Sci. (2025) 116:1464–73. doi: 10.1111/cas.70018
102. Humblin E, Korpas I, Lu J, Filipescu D, van der Heide V, Goldstein S, et al. Sustained CD28 costimulation is required for self-renewal and differentiation of TCF-1(+) PD-1(+) CD8 T cells. Sci Immunol. (2023) 8:eadg0878. doi: 10.1126/sciimmunol.adg0878
103. Lan X, Mi T, Alli S, Guy C, Djekidel M, Liu X, et al. Antitumor progenitor exhausted CD8(+) T cells are sustained by TCR engagement. Nat Immunol. (2024) 25:1046–58. doi: 10.1038/s41590-024-01843-8
104. Barakat R, Chatterjee J, Mu R, Qi X, Gu X, Smirnov I, et al. Human single cell RNA-sequencing reveals a targetable CD8(+) exhausted T cell population that maintains mouse low-grade glioma growth. Nat Commun. (2024) 15:10312. doi: 10.1038/s41467-024-54569-4
105. Sheng W, Zhang C, Mohiuddin T, Al-Rawe M, Zeppernick F, Falcone F, et al. Multiplex immunofluorescence: A powerful tool in cancer immunotherapy. Int J Mol Sci. (2023) 24:3086. doi: 10.3390/ijms24043086
106. Xu S, Ma Y, Jiang X, Wang Q, and Ma W. CD39 transforming cancer therapy by modulating tumor microenvironment. Cancer Lett. (2024) 597:217072. doi: 10.1016/j.canlet.2024.217072
107. Wu X, Yang X, Dai Y, Zhao Z, Zhu J, Guo H, et al. Single-cell sequencing to multi-omics: technologies and applications. biomark Res. (2024) 12:110. doi: 10.1186/s40364-024-00643-4
108. Wang X, Danenberg E, Huang C, Egle D, Callari M, Bermejo B, et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature. (2023) 621:868–76. doi: 10.1038/s41586-023-06498-3
109. Li H, Zhang M, Zhang B, Lin W, Li SJ, Xiong D, et al. Mature tertiary lymphoid structures evoke intra-tumoral T and B cell responses via progenitor exhausted CD4(+) T cells in head and neck cancer. Nat Commun. (2025) 16:4228. doi: 10.1038/s41467-025-59341-w
110. Zhou M, Zhou Z, Hu L, Chen S, Meng F, Chen J, et al. Multiplex immunohistochemistry to explore the tumor immune microenvironment in HCC patients with different GPC3 expression. J Transl Med. (2025) 23:88. doi: 10.1186/s12967-025-06106-0
111. Tan W, Nerurkar S, Cai H, Ng H, Wu D, Wee Y, et al. Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Commun (Lond). (2020) 40:135–53. doi: 10.1002/cac2.12023
112. Guegan J, Peyraud F, Dadone-Montaudie B, Teyssonneau D, Palmieri L, Clot E, et al. Analysis of PD1, LAG3, TIGIT, and TIM3 expression in human lung adenocarcinoma reveals a 25-gene signature predicting immunotherapy response. Cell Rep Med. (2024) 5:101831. doi: 10.1016/j.xcrm.2024.101831
113. Parry E, Lemvigh C, Deng S, Dangle N, Ruthen N, Knisbacher B, et al. ZNF683 marks a CD8(+) T cell population associated with anti-tumor immunity following anti-PD-1 therapy for Richter syndrome. Cancer Cell. (2023) 41:1803–16 e8. doi: 10.1016/j.ccell.2023.08.013
114. Pohlmeyer-Esch G, Halsey C, Boisclair J, Ram S, Kirschner-Kitz S, Knight B, et al. Digital pathology and artificial intelligence applied to nonclinical toxicology pathology-the current state, challenges, and future directions. Toxicol Pathol. (2025) 53:516–35. doi: 10.1177/01926233251340622
115. McGenity C, Clarke E, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med. (2024) 7:114. doi: 10.1038/s41746-024-01106-8
116. Li S, Hao L, Zhang J, Deng J, and Hu X. Focus on T cell exhaustion: new advances in traditional Chinese medicine in infection and cancer. Chin Med. (2023) 18:76. doi: 10.1186/s13020-023-00785-x
117. Guan Q, Han M, Guo Q, Yan F, Wang M, Ning Q, et al. Strategies to reinvigorate exhausted CD8(+) T cells in tumor microenvironment. Front Immunol. (2023) 14:1204363. doi: 10.3389/fimmu.2023.1204363
118. Abrate C, Canale F, Bossio S, Tosello Boari J, Ramello M, Nunez N, et al. CD8(+) T cells in breast cancer tumors and draining lymph nodes: PD-1 levels, effector functions and prognostic relevance. Oncoimmunology. (2025) 14:2502354. doi: 10.1080/2162402X.2025.2502354
119. Zhang B, Liu J, Mo Y, Zhang K, Huang B, and Shang D. CD8(+) T cell exhaustion and its regulatory mechanisms in the tumor microenvironment: key to the success of immunotherapy. Front Immunol. (2024) 15:1476904. doi: 10.3389/fimmu.2024.1476904
120. Obino V, Giordano C, Carlomagno S, Setti C, Greppi M, Bozzo M, et al. Colorectal cancer-infiltrating NK cell landscape analysis unravels tissue-resident PD-1(+) NK cells in microsatellite instability tumors. Front Immunol. (2025) 16:1578444. doi: 10.3389/fimmu.2025.1578444
121. Zhang L, Guo X, Sun X, Liao J, Liu Q, Ye Y, et al. Analysis of tumor-infiltrating exhausted T cells highlights IL-6 and PD1 blockade as a combined immunotherapy strategy for non-small cell lung cancer. Front Immunol. (2025) 16:1486329. doi: 10.3389/fimmu.2025.1486329
122. Zhu W, Li Y, Han M, and Jiang J. Regulatory mechanisms and reversal of CD8(+)T cell exhaustion: A literature review. Biol (Basel). (2023) 12:541. doi: 10.3390/biology12040541
123. Sun Q, Zhao X, Li R, Liu D, Pan B, Xie B, et al. STAT3 regulates CD8+ T cell differentiation and functions in cancer and acute infection. J Exp Med. (2023) 220:e20220686. doi: 10.1084/jem.20220686
124. Tian S, Wang F, Zhang R, and Chen G. Global pattern of CD8(+) T-cell infiltration and exhaustion in colorectal cancer predicts cancer immunotherapy response. Front Pharmacol. (2021) 12:715721. doi: 10.3389/fphar.2021.715721
125. Feng J, Yan D, Jiang L, and Song F. CD8 T cell exhaustion-associated LncRNA signature reveals novel molecular subtypes and immune targets in hepatocellular carcinoma. Discov Oncol. (2025) 16:1828. doi: 10.1007/s12672-025-03675-w
126. Chun D, Park J, Lee S, Kim H, Park J, and Kang S. Flt3L enhances clonal diversification and selective expansion of intratumoral CD8(+) T cells while differentiating into effector-like cells. Cell Rep. (2024) 43:115023. doi: 10.1016/j.celrep.2024.115023
127. Noma T, Wada Y, Nishi M, Teraoku H, Yamada S, Saito Y, et al. The combination of CD8 and TIM3 expression predicts survival outcomes in hepatocellular carcinoma. J Med Invest. (2025) 72:354–60. doi: 10.2152/jmi.72.354
128. Lin Y, Ruze R, Zhang R, Tuergan T, Wang M, Tulahong A, et al. Immunometabolic targets in CD8(+) T cells within the tumor microenvironment of hepatocellular carcinoma. Liver Cancer. (2025) 14:474–96. doi: 10.1159/000542578
129. Wu B, Qi L, Chiang H, Pan H, Zhang X, Greenbaum A, et al. BRCA1 deficiency in mature CD8(+) T lymphocytes impairs antitumor immunity. J Immunother Cancer. (2023) 11:44. doi: 10.1136/jitc-2022-005852
130. Tenggara J, Rachman A, Prihartono J, Rachmadi L, Panigoro S, Heriyanto D, et al. The relationship between high ratios of CD4/FOXP3 and CD8/CD163 and the improved survivability of metastatic triple-negative breast cancer patients: a multicenter cohort study. BMC Res Notes. (2024) 17:44. doi: 10.1186/s13104-024-06704-z
131. Zhang Y and Liu Z. Insights into the mechanisms of immune-checkpoint inhibitors gained from spatiotemporal dynamics of the tumor microenvironment. Adv Sci (Weinh). (2025) 12:e08692. doi: 10.1002/advs.202508692
132. Casalegno Garduno R, Spitschak A, Pannek T, and Putzer BM. CD8+ T cell subsets as biomarkers for predicting checkpoint therapy outcomes in cancer immunotherapy. Biomedicines. (2025) 13:930. doi: 10.3390/biomedicines13040930
133. Mortezaee K and Majidpoor J. Mechanisms of CD8(+) T cell exclusion and dysfunction in cancer resistance to anti-PD-(L)1. BioMed Pharmacother. (2023) 163:114824. doi: 10.1016/j.biopha.2023.114824
134. Perez-Guerrero E, Guillen-Medina M, Marquez-Sandoval F, Vera-Cruz J, Gallegos-Arreola M, Rico-Mendez M, et al. Methodological and statistical considerations for cross-sectional, case-control, and cohort studies. J Clin Med. (2024) 13:4005. doi: 10.3390/jcm13144005
135. van Rijthoven M, Obahor S, Pagliarulo F, van den Broek M, Schraml P, Moch H, et al. Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors. Commun Med (Lond). (2024) 4:5. doi: 10.1038/s43856-023-00421-7
136. Makhlouf Y, Singh V, Craig S, McArdle A, French D, Loughrey M, et al. True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images. Comput Struct Biotechnol J. (2024) 23:174–85. doi: 10.1016/j.csbj.2023.11.048
137. Heregger R, Huemer F, Steiner M, Gonzalez-Martinez A, Greil R, and Weiss L. Unraveling resistance to immunotherapy in MSI-high colorectal cancer. Cancers (Basel). (2023) 15:5090. doi: 10.3390/cancers15205090
138. Li R, Lin Y, Shen X, Guo J, Zhang Y, Tang J, et al. Case Report: Failed response to anti-PD-1 immunotherapy in a colon cancer patient with high microsatellite instability. Front Oncol. (2025) 15:1636122. doi: 10.3389/fonc.2025.1636122
139. Liu Y, Qin D, and Fu J. T lymphocyte heterogeneity in NSCLC: implications for biomarker development and therapeutic innovation. Front Immunol. (2025) 16:1604310. doi: 10.3389/fimmu.2025.1604310
140. Elfving H, Yu H, Fessehatsion K, Brunnstrom H, Botling J, Gulyas M, et al. Spatial distribution of tertiary lymphoid structures in the molecular and clinical context of non-small cell lung cancer. Cell Oncol (Dordr). (2025) 48:801–13. doi: 10.1007/s13402-025-01052-x
141. Ferrara Y, Latino D, Costagliola di Polidoro A, Oliver A, Sarnella A, Caprio M, et al. A novel therapeutic approach targeting PD-L1 in HNSCC and bone marrow-derived mesenchymal stem cells hampers pro-metastatic features in vitro: perspectives for blocking tumor-stroma communication and signaling. Cell Commun Signal. (2025) 23:74. doi: 10.1186/s12964-025-02073-7
142. Noda Y, Atsumi N, Nakaya T, Iwai H, and Tsuta K. High-sensitivity PD-L1 staining using clone 73–10 antibody and spatial transcriptomics for precise expression analysis in non-tumorous, intraepithelial neoplasia, and squamous cell carcinoma of head and neck. Head Neck Pathol. (2025) 19:65. doi: 10.1007/s12105-025-01798-8
143. Baek S, Cha J, Hong M, Kim G, Koh Y, Kim D, et al. Comparative single-cell analysis of esophageal cancer subtypes reveals tumor microenvironment distinctions explaining varied immunotherapy responses. Cancer Commun (Lond). (2025) 45:1194–9. doi: 10.1002/cac2.70046
144. Huang Z, Cong Z, Luo J, Qiu B, Wang K, Gao C, et al. Association between cancer-associated fibroblasts and prognosis of neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma: a bioinformatics analysis based on single-cell RNA sequencing. Cancer Cell Int. (2025) 25:74. doi: 10.1186/s12935-025-03709-x
145. De Leon-Rodriguez S, Aguilar-Flores C, Gajon J, Juarez-Flores A, Mantilla A, Gerson-Cwilich R, et al. TCF1-positive and TCF1-negative TRM CD8 T cell subsets and cDC1s orchestrate melanoma protection and immunotherapy response. J Immunother Cancer. (2024) 12:e008739. doi: 10.1136/jitc-2023-008739
146. Kang X, Zhao S, Lin S, Li J, and Wang S. Synergistic upregulation of PD−L1 in tumor cells and CD39 in tumor−infiltrating CD8(+) T cells leads to poor prognosis in patients with hepatocellular carcinoma. Oncol Lett. (2024) 28:368. doi: 10.3892/ol.2024.14501
147. Dai S, Chen Y, Cai W, Dong S, Zhao J, Chen L, et al. Combination immunotherapy in hepatocellular carcinoma: synergies among immune checkpoints, TKIs, and chemotherapy. J Hematol Oncol. (2025) 18:85. doi: 10.1186/s13045-025-01739-6
148. Jin M, Fang J, Peng J, Wang X, Xing P, Jia K, et al. PD-1/PD-L1 immune checkpoint blockade in breast cancer: research insights and sensitization strategies. Mol Cancer. (2024) 23:266. doi: 10.1186/s12943-024-02176-8
149. Stanowska O, Kuczkiewicz-Siemion O, Debowska M, Olszewski W, Jagiello-Gruszfeld A, Tysarowski A, et al. PD-L1-positive high-grade triple-negative breast cancer patients respond better to standard neoadjuvant treatment-A retrospective study of PD-L1 expression in relation to different clinicopathological parameters. J Clin Med. (2022) 11:5524. doi: 10.3390/jcm11195524
150. Chakravarti M, Dhar S, Bera S, Sinha A, Roy K, Sarkar A, et al. Terminally exhausted CD8+ T cells resistant to PD-1 blockade promote generation and maintenance of aggressive cancer stem cells. Cancer Res. (2023) 83:1815–33. doi: 10.1158/0008-5472.CAN-22-3864
151. Wang K, Hou H, Zhang Y, Ao M, Luo H, and Li B. Ovarian cancer-associated immune exhaustion involves SPP1+ T cell and NKT cell, symbolizing more Malignant progression. Front Endocrinol (Lausanne). (2023) 14:1168245. doi: 10.3389/fendo.2023.1168245
152. Zeng L, Li SH, Xu SY, Chen K, Qin L, Liu X, et al. Clinical significance of a CD3/CD8-based immunoscore in neuroblastoma patients using digital pathology. Front Immunol. (2022) 13:878457. doi: 10.3389/fimmu.2022.878457
153. Qu J, Sun W, Li Y, Cai Z, Wu B, Shen Q, et al. Immune profiling identifies CD161(+)CD127(+)CD8(+) T cells as a predictive biomarker for Anti-PD-L1 therapy response in the SCLC-I subtype. J Transl Med. (2025) 23:711. doi: 10.1186/s12967-025-06724-8
154. Yang X, Luo B, Tian J, Wang Y, Lu X, Ni J, et al. Biomarkers and ImmuneScores in lung cancer: predictive insights for immunotherapy and combination treatment strategies. Biol Proced Online. (2025) 27:25. doi: 10.1186/s12575-025-00287-0
155. Ma W and Jamieson C. Chimeric antigen receptor-macrophage therapy enters the clinic: the first-in-human trial for HER2(+) solid tumors. MedComm (2020). (2025) 6:e70374. doi: 10.1002/mco2.70374
156. Ma W and Baran N. Expanding horizons in esophageal squamous cell carcinoma: The promise of induction chemoimmunotherapy with radiotherapy. World J Clin Oncol. (2025) 16:104959. doi: 10.5306/wjco.v16.i7.104959
157. Lai N, Farman A, and Byrne H. The impact of T-cell exhaustion dynamics on tumour-immune interactions and tumour growth. Bull Math Biol. (2025) 87:61. doi: 10.1007/s11538-025-01433-1
158. Rigamonti A, Viatore M, Polidori R, Rahal D, Erreni M, Fumagalli M, et al. Integrating AI-powered digital pathology and imaging mass cytometry identifies key classifiers of tumor cells, stroma, and immune cells in non-small cell lung cancer. Cancer Res. (2024) 84:1165–77. doi: 10.1158/0008-5472.CAN-23-1698
159. Guo X, Wang K, Liu Q, Baran N, and Ma W. The gut-immune axis in primary immune thrombocytopenia (ITP): a paradigm shifts in treatment approaches. Front Immunol. (2025) 16:1595977. doi: 10.3389/fimmu.2025.1595977
160. Roider T, Baertsch MA, Fitzgerald D, Vohringer H, Brinkmann BJ, Czernilofsky F, et al. Multimodal and spatially resolved profiling identifies distinct patterns of T cell infiltration in nodal B cell lymphoma entities. Nat Cell Biol. (2024) 26:478–89. doi: 10.1038/s41556-024-01358-2
161. Klapholz M, Drage MG, Srivastava A, and Anderson AC. Presence of Tim3(+) and PD-1(+) CD8(+) T cells identifies microsatellite stable colorectal carcinomas with immune exhaustion and distinct clinicopathological features. J Pathol. (2022) 257:186–97. doi: 10.1002/path.5877
162. Rojas F, Hernandez S, Lazcano R, Laberiano-Fernandez C, and Parra E. Multiplex immunofluorescence and the digital image analysis workflow for evaluation of the tumor immune environment in translational research. Front Oncol. (2022) 12:889886. doi: 10.3389/fonc.2022.889886
163. Manrique-Rincon A, Foster B, Horswell S, Goulding D, Adams DJ, and Speak AO. Simulating CD8 T cell exhaustion: A comprehensive approach. iScience. (2025) 28:112897. doi: 10.1016/j.isci.2025.112897
164. Singhaviranon S, Dempsey J, Hagymasi A, Mandoiu II, and Srivastava P. Low-avidity T cells drive endogenous tumor immunity in mice and humans. Nat Immunol. (2025) 26:240–51. doi: 10.1038/s41590-024-02044-z
165. Lu Z, Morita M, Yeager T, Lyu Y, Wang S, Wang Z, et al. Validation of artificial intelligence (AI)-assisted flow cytometry analysis for immunological disorders. Diagnostics (Basel). (2024) 14:420. doi: 10.3390/diagnostics14040420
166. Chitoran E, Rotaru V, Gelal A, Ionescu S, Gullo G, Stefan D, et al. Using artificial intelligence to develop clinical decision support systems-the evolving road of personalized oncologic therapy. Diagnostics (Basel). (2025) 15:2391. doi: 10.3390/diagnostics15182391
167. Bollhagen A and Bodenmiller B. Highly multiplexed tissue imaging in precision oncology and translational cancer research. Cancer Discov. (2024) 14:2071–88. doi: 10.1158/2159-8290.CD-23-1165
168. Saba L and d’Aloja E. Predictive techniques in medical imaging: opportunities, limitations, and ethical-economic challenges. NPJ Digit Med. (2025) 8:392. doi: 10.1038/s41746-025-01791-z
169. Locke D and Hoyt C. Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral imaging. Front Mol Biosci. (2023) 10:1051491. doi: 10.3389/fmolb.2023.1051491
170. El-Khoury R and Zaatari G. The rise of AI-assisted diagnosis: will pathologists be partners or bystanders? Diagnostics (Basel). (2025) 15:2308. doi: 10.3390/diagnostics15182308
171. Calderaro J, Morement H, Penault-Llorca F, Gilbert S, and Kather J. The case for homebrew AI in diagnostic pathology. J Pathol. (2025) 266:390–4. doi: 10.1002/path.6438
172. Kasikova L, Rakova J, Hensler M, Lanickova T, Tomankova J, Pasulka J, et al. Tertiary lymphoid structures and B cells determine clinically relevant T cell phenotypes in ovarian cancer. Nat Commun. (2024) 15:2528. doi: 10.1038/s41467-024-46873-w
173. Lokhande L, Nilsson D, de Matos Rodrigues J, Hassan M, Olsson LM, Pyl PT, et al. Quantification and profiling of early and late differentiation stage T cells in mantle cell lymphoma reveals immunotherapeutic targets in subsets of patients. Cancers (Basel). (2024) 16:2289. doi: 10.3390/cancers16132289
174. Chen Y, Wang D, Li Y, Qi L, Si W, Bo Y, et al. Spatiotemporal single-cell analysis decodes cellular dynamics underlying different responses to immunotherapy in colorectal cancer. Cancer Cell. (2024) 42:1268–85 e7. doi: 10.1016/j.ccell.2024.06.009
175. Yu Y, Mai Y, Zheng Y, and Shi L. Assessing and mitigating batch effects in large-scale omics studies. Genome Biol. (2024) 25:254. doi: 10.1186/s13059-024-03401-9
176. Christiansen F, Konuk E, Ganeshan A, Welch R, Pales Huix J, Czekierdowski A, et al. International multicenter validation of AI-driven ultrasound detection of ovarian cancer. Nat Med. (2025) 31:189–96. doi: 10.1038/s41591-024-03329-4
177. Mascarenhas M, Peixoto C, Freire R, Cavaco Gomes J, Cardoso P, Castro I, et al. Artificial intelligence and hysteroscopy: A multicentric study on automated classification of pleomorphic lesions. Cancers (Basel). (2025) 17:2559. doi: 10.3390/cancers17152559
178. Ye L, Ryu H, Granadier D, Nguyen L, Simoni Y, Dick I, et al. Stem-like exhausted CD8 T cells in pleural effusions predict improved survival in non-small cell lung cancer (NSCLC) and mesothelioma. Transl Lung Cancer Res. (2024) 13:2352–72. doi: 10.21037/tlcr-24-284
179. Rady M, Ashraf A, Abdelaziz H, and El-Azizi M. Adaptive immune changes in colorectal cancer: a focus on T and B cell activation genes. Discov Oncol. (2025) 16:1032. doi: 10.1007/s12672-025-02794-8
180. Li H, Zandberg D, Kulkarni A, Chiosea S, Santos P, Isett B, et al. Distinct CD8(+) T cell dynamics associate with response to neoadjuvant cancer immunotherapies. Cancer Cell. (2025) 43:757–75 e8. doi: 10.1016/j.ccell.2025.02.026
181. Shaik R, Royyala S, Inapanuri B, Durgam A, Khan H, and Unnisa A. Tumor infiltration therapy: from FDA approval to next-generation approaches. Clin Exp Med. (2025) 25:254. doi: 10.1007/s10238-025-01574-6
182. Wang Z, Zhang RY, Xu YF, Yue BT, Zhang JY, and Wang F. Unmasking immune checkpoint resistance in esophageal squamous cell carcinoma: Insights into the tumor microenvironment and biomarker landscape. World J Gastrointest Oncol. (2025) 17:109489. doi: 10.4251/wjgo.v17.i8.109489
183. Liu M, Zhao Y, Xiao Z, Zhou R, Chen X, Cui S, et al. CD39-expressing CD8(+) T cells as a new molecular marker for diagnosis and prognosis of esophageal squamous cell carcinoma. Cancers (Basel). (2023) 15:1184. doi: 10.3390/cancers15041184
184. Yu B and Ma W. Haptens-based cancer immunotherapy: From biomarkers to translational advances. BioMed Pharmacother. (2025) 189:118240. doi: 10.1016/j.biopha.2025.118240
185. Lv Y, Luo X, Xie Z, Qiu J, Yang J, Deng Y, et al. Prospects and challenges of CAR-T cell therapy combined with ICIs. Front Oncol. (2024) 14:1368732. doi: 10.3389/fonc.2024.1368732
186. Zehn D, Thimme R, Lugli E, de Almeida G, and Oxenius A. ‘Stem-like’ precursors are the fount to sustain persistent CD8(+) T cell responses. Nat Immunol. (2022) 23:836–47. doi: 10.1038/s41590-022-01219-w
187. Ahn T, Bae E, and Seo H. Decoding and overcoming T cell exhaustion: Epigenetic and transcriptional dynamics in CAR-T cells against solid tumors. Mol Ther. (2024) 32:1617–27. doi: 10.1016/j.ymthe.2024.04.004
188. Hashimoto M, Ramalingam S, and Ahmed R. Harnessing CD8 T cell responses using PD-1-IL-2 combination therapy. Trends Cancer. (2024) 10:332–46. doi: 10.1016/j.trecan.2023.11.008
189. Tao Z, Chyra Z, Kotulova J, Celichowski P, Mihalyova J, Charvatova S, et al. Impact of T cell characteristics on CAR-T cell therapy in hematological Malignancies. Blood Cancer J. (2024) 14:213. doi: 10.1038/s41408-024-01193-6
190. Carrillo M, Zhen A, Mu W, Rezek V, Martin H, Peterson C, et al. Stem cell-derived CAR T cells show greater persistence, trafficking, and viral control compared to ex vivo transduced CAR T cells. Mol Ther. (2024) 32:1000–15. doi: 10.1016/j.ymthe.2024.02.026
191. Zhang G, Bai M, Du H, Yuan Y, Wang Y, Fan W, et al. Current advances and challenges in CAR-T therapy for hematological and solid tumors. Immunotargets Ther. (2025) 14:655–80. doi: 10.2147/ITT.S519616
192. Feng B, Bai Z, Zhou X, Zhao Y, Xie Y, Huang X, et al. The type 2 cytokine Fc-IL-4 revitalizes exhausted CD8(+) T cells against cancer. Nature. (2024) 634:712–20. doi: 10.1038/s41586-024-07962-4
193. Chu T, Wu M, Hoellbacher B, de Almeida G, Wurmser C, Berner J, et al. Precursors of exhausted T cells are pre-emptively formed in acute infection. Nature. (2025) 640:782–92. doi: 10.1038/s41586-024-08451-4
194. Ma K, Xu Y, Cheng H, Tang K, Ma J, and Huang B. T cell-based cancer immunotherapy: opportunities and challenges. Sci Bull (Beijing). (2025) 70:1872–90. doi: 10.1016/j.scib.2025.03.054
195. Xiong D, Zhang L, and Sun ZJ. Targeting the epigenome to reinvigorate T cells for cancer immunotherapy. Mil Med Res. (2023) 10:59. doi: 10.1186/s40779-023-00496-2
196. Watowich M, Gilbert M, and Larion M. T cell exhaustion in Malignant gliomas. Trends Cancer. (2023) 9:270–92. doi: 10.1016/j.trecan.2022.12.008
197. Yang M, Zhang S, Sun L, Huang L, Yu J, Zhang J, et al. Targeting mitochondria: restoring the antitumor efficacy of exhausted T cells. Mol Cancer. (2024) 23:260. doi: 10.1186/s12943-024-02175-9
198. Zhong T, Sun S, Zhao M, Zhang B, and Xiong H. The mechanisms and clinical significance of CD8(+) T cell exhaustion in anti-tumor immunity. Cancer Biol Med. (2025) 22:460–80. doi: 10.20892/j.issn.2095-3941.2024.0628
199. Giles J, Globig A, Kaech S, and Wherry E. CD8(+) T cells in the cancer-immunity cycle. Immunity. (2023) 56:2231–53. doi: 10.1016/j.immuni.2023.09.005
200. Murer P, Petersen L, Egli N, Salazar U, Neubert P, Zurbach A, et al. ANV600 is a novel PD-1 targeted IL-2Rbetagamma agonist that selectively expands tumor antigen-specific T cells and potentiates PD-1 checkpoint inhibitor therapy. J Immunother Cancer. (2025) 13:e011905. doi: 10.1136/jitc-2025-011905
201. Gill A, Wang P, Lee J, Hudson W, Ando S, Araki K, et al. PD-1 blockade increases the self-renewal of stem-like CD8 T cells to compensate for their accelerated differentiation into effectors. Sci Immunol. (2023) 8:eadg0539. doi: 10.1126/sciimmunol.adg0539
202. Bulliard Y, Andersson B, Baysal M, Damiano J, and Tsimberidou A. Reprogramming T cell differentiation and exhaustion in CAR-T cell therapy. J Hematol Oncol. (2023) 16:108. doi: 10.1186/s13045-023-01504-7
203. Tojjari A, Saeed A, and Cavalcante L. Emerging IO checkpoints in gastrointestinal oncology. Front Immunol. (2025) 16:1575713. doi: 10.3389/fimmu.2025.1575713
204. Srikanth G, Beda D, Dwivedi A, Duddukuri N, Nanduri S, and Patel J. Promising new anti-TIGIT agents: stealthy allies in cancer immunotherapy. Clin Transl Sci. (2025) 18:e70212. doi: 10.1111/cts.70212
205. Ghasemi K. Tiragolumab and TIGIT: pioneering the next era of cancer immunotherapy. Front Pharmacol. (2025) 16:1568664. doi: 10.3389/fphar.2025.1568664
206. Srinivasan S, Armitage J, Nilsson J, and Waithman J. Transcriptional rewiring in CD8(+) T cells: implications for CAR-T cell therapy against solid tumours. Front Immunol. (2024) 15:1412731. doi: 10.3389/fimmu.2024.1412731
207. An Y, Wang Q, Gao K, Zhang C, Ouyang Y, Li R, et al. Epigenetic regulation of aging and its rejuvenation. MedComm (2020). (2025) 6:e70369.
208. Xiong D, Yu H, and Sun Z. Unlocking T cell exhaustion: Insights and implications for CAR-T cell therapy. Acta Pharm Sin B. (2024) 14:3416–31. doi: 10.1016/j.apsb.2024.04.022
209. Liu L, Hao Z, Yang X, Li Y, Wang S, and Li L. Metabolic reprogramming in T cell senescence: a novel strategy for cancer immunotherapy. Cell Death Discov. (2025) 11:161. doi: 10.1038/s41420-025-02468-y
210. Khan O, Giles J, McDonald S, Manne S, Ngiow S, Patel K, et al. TOX transcriptionally and epigenetically programs CD8(+) T cell exhaustion. Nature. (2019) 571:211–8. doi: 10.1038/s41586-019-1325-x
211. Seo H, Chen J, Gonzalez-Avalos E, Samaniego-Castruita D, Das A, Wang Y, et al. TOX and TOX2 transcription factors cooperate with NR4A transcription factors to impose CD8(+) T cell exhaustion. Proc Natl Acad Sci U S A. (2019) 116:12410–5. doi: 10.1073/pnas.1905675116
212. Wu B, Chang H, Singh P, Hostetler A, Xiang Y, Guo S, et al. Small molecule modulators of TOX protein re-invigorate T cell activity. bioRxiv. (2025) 2025.03.03.641115. doi: 10.1101/2025.03.03.641115
213. Ngiow S, Manne S, Huang Y, Azar T, Chen Z, Mathew D, et al. LAG-3 sustains TOX expression and regulates the CD94/NKG2-Qa-1b axis to govern exhausted CD8 T cell NK receptor expression and cytotoxicity. Cell. (2024) 187:4336–54 e19. doi: 10.1016/j.cell.2024.07.018
214. Quann K, Sacirbegovic F, Rosenberger S, McFerran E, Codispot K, Garcia-Dieguez L, et al. The role of TCF-1+CD8+ exhausted progenitors and TCF-1 in graft-versus-host responses. JCI Insight. (2025) 10:e181568. doi: 10.1172/jci.insight.181568
215. Wen S, Lu H, Wang D, Guo J, Dai W, and Wang Z. TCF-1 maintains CD8(+) T cell stemness in tumor microenvironment. J Leukoc Biol. (2021) 110:585–90. doi: 10.1002/JLB.5MR1120-778R
216. Hong M, Clubb J, and Chen Y. Engineering CAR-T cells for next-generation cancer therapy. Cancer Cell. (2020) 38:473–88. doi: 10.1016/j.ccell.2020.07.005
217. Lu C and Tan Y. Promising immunotherapy targets: TIM3, LAG3, and TIGIT joined the party. Mol Ther Oncol. (2024) 32:200773. doi: 10.1016/j.omton.2024.200773
218. Yin N, Li X, Zhang X, Xue S, Cao Y, Niedermann G, et al. Development of pharmacological immunoregulatory anti-cancer therapeutics: current mechanistic studies and clinical opportunities. Signal Transduct Target Ther. (2024) 9:126. doi: 10.1038/s41392-024-01826-z
219. Yin P, Yang J, Jiang Y, Han L, Xiong T, Liu H, et al. Enhanced FOS expression improves tumor clearance and resists exhaustion in NR4A3-deficient CAR T cells under chronic antigen exposure. Sci Adv. (2025) 11:eadw3571. doi: 10.1126/sciadv.adw3571
220. Wei Y, Sun J, and Zhu R. CRISPR-epigenetic crosstalk: From bidirectional regulation to therapeutic potential. Comput Struct Biotechnol J. (2025) 27:4496–504. doi: 10.1016/j.csbj.2025.10.031
221. Zheng R, Liu X, Zhang Y, Liu Y, Wang Y, Guo S, et al. Frontiers and future of immunotherapy for pancreatic cancer: from molecular mechanisms to clinical application. Front Immunol. (2024) 15:1383978. doi: 10.3389/fimmu.2024.1383978
222. Ashrafizadeh M, Dai J, Torabian P, Nabavi N, Aref A, Aljabali A, et al. Circular RNAs in EMT-driven metastasis regulation: modulation of cancer cell plasticity, tumorigenesis and therapy resistance. Cell Mol Life Sci. (2024) 81:214. doi: 10.1007/s00018-024-05236-w
223. Chen R, Chen L, Tang Y, Shen X, Wang Y, Tang P, et al. Enhancing the potency of CAR-T cells against solid tumors through transcription factor engineering. JCI Insight. (2025) 10:e193048. doi: 10.1172/jci.insight.193048
224. Du H, Xu T, Yu S, Wu S, and Zhang J. Mitochondrial metabolism and cancer therapeutic innovation. Signal Transduct Target Ther. (2025) 10:245. doi: 10.1038/s41392-025-02311-x
225. Lv Y, Li Z, Liu S, Zhou Z, Song J, Ba Y, et al. Metabolic checkpoints in immune cell reprogramming: rewiring immunometabolism for cancer therapy. Mol Cancer. (2025) 24:210. doi: 10.1186/s12943-025-02407-6
226. Yu L, Liu Y, and Lin X. Transitioning from native to synthetic receptors: broadening T-cell engineering and beyond. Cell Mol Immunol. (2025) 22:712–29. doi: 10.1038/s41423-025-01304-8
227. Wang Y, Cui Y, Li X, Jin SH, Wang H, Gaipl US, et al. CircRNAs: functions and emerging roles in cancer and immunotherapy. BMC Med. (2025) 23:477. doi: 10.1186/s12916-025-04306-5
228. Ma Y, Wang T, Zhang X, Wang P, and Long F. The role of circular RNAs in regulating resistance to cancer immunotherapy: mechanisms and implications. Cell Death Dis. (2024) 15:312. doi: 10.1038/s41419-024-06698-3
229. Weimer P, Wellbrock J, Sturmheit T, Oliveira-Ferrer L, Ding Y, Menzel S, et al. Tissue-specific expression of TIGIT, PD-1, TIM-3, and CD39 by gammadelta T cells in ovarian cancer. Cells. (2022) 11:964. doi: 10.3390/cells11060964
230. Witt M, Oliveira-Ferrer L, Koch-Nolte F, Menzel S, Hell L, Sturmheit T, et al. Expression of CD39 is associated with T cell exhaustion in ovarian cancer and its blockade reverts T cell dysfunction. Oncoimmunology. (2024) 13:2346359. doi: 10.1080/2162402X.2024.2346359
231. Czechowska K, Bonilla D, Cotty A, Dankar A, Mead P, and Nash V. Beyond the limits: how is spectral flow cytometry reshaping the clinical landscape and what is coming next? Cells. (2025) 14:997. doi: 10.3390/cells14130997
232. Shasha C, Glass D, Moelhman E, Islas L, Tian Y, Chour T, et al. Hallmarks of T-cell exhaustion and antigen experience are absent in multiple myeloma from diagnosis to maintenance therapy. Blood. (2025) 145:3113–23. doi: 10.1182/blood.2024025655
233. Koukoulias K, Papayanni P, Jones J, Kuvalekar M, Watanabe A, Velazquez Y, et al. Assessment of the cytolytic potential of a multivirus-targeted T cell therapy using a vital dye-based, flow cytometric assay. Front Immunol. (2023) 14:1299512. doi: 10.3389/fimmu.2023.1299512
234. Heim K, Sagar, Sogukpinar O, Llewellyn-Lacey S, Price DA, Emmerich F, et al. Attenuated effector T cells are linked to control of chronic HBV infection. Nat Immunol. (2024) 25:1650–62. doi: 10.1038/s41590-024-01928-4
235. Lan X, Zebley C, and Youngblood B. Cellular and molecular waypoints along the path of T cell exhaustion. Sci Immunol. (2023) 8:eadg3868. doi: 10.1126/sciimmunol.adg3868
236. Bao Y, Zhang D, Guo H, and Ma W. Beyond blood: Advancing the frontiers of liquid biopsy in oncology and personalized medicine. Cancer Sci. (2024) 115:1060–72. doi: 10.1111/cas.16097
237. Qu C, Yan X, Wei Y, Tang F, and Li Y. Establishment and validation of a novel CD8+ T cell-associated prognostic signature for predicting clinical outcomes and immunotherapy response in hepatocellular carcinoma via integrating single-cell RNA-seq and bulk RNA-seq. Discov Oncol. (2024) 15:235. doi: 10.1007/s12672-024-01092-z
238. Yu B, Shao S, and Ma W. Frontiers in pancreatic cancer on biomarkers, microenvironment, and immunotherapy. Cancer Lett. (2025) 610:217350. doi: 10.1016/j.canlet.2024.217350
239. Angeloni M, Rizzi D, Schoen S, Caputo A, Merolla F, Hartmann A, et al. Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system. Genome Med. (2025) 17:60. doi: 10.1186/s13073-025-01484-y
240. Tiwari A, Mishra S, and Kuo TR. Current AI technologies in cancer diagnostics and treatment. Mol Cancer. (2025) 24:159. doi: 10.1186/s12943-025-02369-9
Keywords: digital pathology, immunotherapy biomarkers, precision oncology, T cell exhaustion, terminally exhausted CD8⁺ T cells, tumor microenvironment
Citation: Guo X, Ma S, Wang J, Fu Y and Ma W (2026) Terminally exhausted CD8+ T cells in solid tumors: biology, biomarker potential and translational tools for precision oncology. Front. Immunol. 16:1709852. doi: 10.3389/fimmu.2025.1709852
Received: 21 September 2025; Accepted: 12 December 2025; Revised: 08 December 2025;
Published: 12 January 2026.
Edited by:
Xue-Yan He, Washington University, United StatesReviewed by:
Chien-Huan Weng, Cornell University, United StatesSaeed Khodayari, Tehran University of Medical Science, Iran
Xiaoli Wu, Harvard Medical School, United States
Longling Shui, Stony Brook University, United States
Copyright © 2026 Guo, Ma, Wang, Fu and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Wenxue Ma, d21hQGhlYWx0aC51Y3NkLmVkdQ==