The role of tumor metabolic reprogramming in acquired anti-PD-1/PD-L1 resistance

Introduction

Programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) blockade therapy-targeted immunotherapy has dramatically transformed the landscape of cancer treatment (1). By inhibiting the interaction between PD-1 on activated T cells and PD-L1 on tumor or antigen-presenting cells, these therapies reverse tumor-induced immunosuppression and reinvigorate cytotoxic T cells function (2,3). This reactivation of anti-tumor immunity has yielded durable clinical responses across multiple malignancies, including melanoma, non-small cell lung cancer (NSCLC), and renal cell carcinoma (4). As a result, PD-1/PD-L1 blockade has become a cornerstone of modern oncology, rendering previously refractory cancers more manageable and providing a foundation for novel combination strategies to overcome resistance and expand patient benefit.

Although PD-1/PD-L1 blockade has achieved durable responses in some cancer patients, most ultimately relapse due to secondary resistance, which greatly limits long-term efficacy (5,6). Tumors may transition from an immunologically ‘hot’ to a ‘cold’ state, often accompanied by metabolic reprogramming and the accumulation of inhibitory metabolites like lactate and tryptophan, which suppress T cells function (7,8). Genetic alterations disrupting antigen presentation, including major histocompatibility complex class I (MHC-I) loss or β2-microglobulin mutation, further enable immune evasion (9). In addition, the infiltration of regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs), together with the compensatory upregulation of alternative immune checkpoints such as TIM-3 and LAG-3, further strengthens tumor-induced immunosuppression (10-13). Progressive T cells exhaustion and epigenetic remodeling further diminish effector function, collectively driving secondary resistance to PD-1/PD-L1 immunotherapy (14).

Recent evidence indicates that tumor metabolic reprogramming is a critical driver of immune evasion (15). Under continuous immune pressure, tumor cells dynamically remodel their metabolic networks to establish multi-layered mechanisms of immune escape (16). By enhancing glycolysis and amino acid metabolism, tumor cells intensify nutrient competition within the tumor microenvironment (TME), inducing a metabolic ‘fuel crisis’ for effector T cells. Simultaneously, immunosuppressive metabolites produced by tumor cells—such as lactate and kynurenine—directly hinder effector T cells function while promoting the expansion of immunosuppressive cell populations (12,17-19). Notably, metabolic heterogeneity enables the selection of tumor subclones with enhanced mitochondrial activity during therapy, allowing these cells to thrive in nutrient-deprived conditions while sustaining immune suppression through metabolic reorganization (20). Thus, clarifying how metabolic reprogramming contributes to acquired resistance against PD-1/PD-L1 blockade is fundamental for both gaining mechanistic insight and guiding metabolism-focused combination therapies aimed at enhancing immunotherapy success.

This review summarizes current advances linking tumor metabolic reprogramming to immune escape and acquired resistance to PD-1/PD-L1 inhibitors, with particular emphasis on the mechanisms through which metabolic remodeling contributes to secondary resistance. In addition, it highlights emerging metabolic-targeted strategies aimed at overcoming resistance and improving the efficacy of anti-PD-1/PD-L1 immunotherapy.

Notably, many of the metabolic mechanisms discussed have been extensively validated in lung cancer, particularly in NSCLC, where they play critical roles in tumor progression and therapeutic resistance.

While previous reviews have primarily focused on combining metabolic inhibitors with immune checkpoint blockade, this work provides a distinct, mechanism-oriented perspective by systematically dissecting the metabolic basis of resistance to PD-1/PD-L1–based immunotherapy. Specifically, we propose an integrated conceptual framework that links tumor-intrinsic metabolic reprogramming with immune cell dysfunction and emphasizes the dynamic evolution of these processes—referred to here as “metabolic drift”—during the development of acquired resistance.

Metabolic adaptations of the tumor driving acquired anti-PD-1/PD-L1 resistance

The metabolic landscape of the tumor is highly complex, dynamic, and heterogeneous (21). A central feature of this ecosystem is the fierce metabolic competition between tumor cells and immune cells for scarce nutrients, often coupled with the accumulation of immunosuppressive metabolites (Figure 1) (16,19). In this metabolically adapted milieu, effector immune cells are functionally suppressed, while immunosuppressive cell populations expand, establishing a foundation for tumor-driven immune escape and therapeutic resistance (17).

Figure 1 Tumor metabolic suppression of t cells immunity. Tumor metabolic rewiring shapes a suppressive landscape that facilitates immune escape. Excess lactate, tryptophan depletion and kynurenine generation via IDO1/TDO2, and immunomodulatory lipid species collectively restrict T cells metabolism and function. This integrated metabolic barrier establishes resistance to immunotherapy. α-KG, α-ketoglutarate; AhR, aryl hydrocarbon receptor; ATP, adenosine triphosphate; CoA, coenzyme A; HIF-1α, hypoxia-inducible factor 1α; HK2, hexokinase 2; IDO1, indoleamine 2,3-dioxygenase 1; IFN-γ, interferon gamma; LDHA, lactate dehydrogenase A; KMO, kynurenine 3-monooxygenase; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; QA, quinolinic acid; TCA, tricarboxylic acid; TDO2, tryptophan 2,3-dioxygenase; Trp, tryptophan.

Glucose competition and lactic acidosis

Glucose deprivation stress

Tumor cells induce persistent glucose deprivation stress within the TME through sustained activation of the Warburg effect (22). Evidence from longitudinal studies and preclinical models increasingly suggests that these metabolic traits are not merely static baseline features but are dynamically enriched during the evolutionary trajectory of tumor recurrence. For instance, longitudinal genomic and transcriptomic profiling of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) has demonstrated a selective enrichment of subclones harboring LKB1 (STK11) and KEAP1 mutations in post-treatment relapsed lesions compared to treatment-naïve biopsies (23,24). These mutations converge to rewire the tumor into a 'cold' and highly glycolytic state, effectively outcompeting T cells for essential nutrients under the selective pressure of PD-1 blockade.

Furthermore, preclinical lineage tracing studies in mouse models of melanoma and lung cancer have confirmed that T cells-mediated immune pressure acts as a selective filter; tumor cells capable of sustaining high glycolytic flux or activating alternative metabolic pathways are more likely to survive the initial phase of therapy. These “metabolic survivors” are thought to contribute to the formation of a recurrent TME that tends to be more nutrient-depleted (25).

Lactic acid accumulation

Accumulating evidence from longitudinal preclinical models suggests that lactate-driven metabolic landscapes are not merely static but appear to undergo dynamic changes (18). For instance, longitudinal metabolomic profiling in syngeneic murine models of melanoma has revealed that tumors escaping from PD-1 blockade exhibit significantly higher intra-tumoral lactate concentrations and upregulated lactate dehydrogenase A (LDHA) expression compared to their treatment-naive counterparts (26). This enrichment suggests that the T cells-mediated elimination of glycolytically-weak clones acts as a selective filter, potentially favoring the survival of hyper-glycolytic subclones that utilize lactate as a key mediator contributing to the immunosuppressive niche. Notably, similar lactate-driven immunosuppressive mechanisms have been reported in NSCLC, where increased glycolytic activity and lactate accumulation are associated with impaired T cells function and resistance to immune checkpoint blockade (27).

Furthermore, sequential biopsy analyses from limited clinical cohorts of NSCLC and melanoma patients who progressed on ICIs have shown a trending increase in the expression of monocarboxylate transporters (MCT1/4) and acidic pH signatures in relapsed lesions (28). These ‘metabolic survivors’ not only sustain their own energetic needs but actively drive the self-reinforcing immunosuppressive network described in Table 1.

However, most clinical data are derived from static, single-timepoint biopsies or genomic proxies (such as LDHA amplification) (34), which may not fully capture the dynamic and adaptive nature of tumor metabolism. The technical challenges involved in obtaining longitudinal, high-quality metabolic measurements in patients remain substantial, including limited tissue availability, intratumoral heterogeneity, and the invasive nature of repeated biopsies (34,35).

To more directly investigate metabolic drift, longitudinal metabolomics profiling of patient samples represents the most critical and informative approach, enabling the dynamic assessment of metabolic states across disease progression and treatment (36). Such strategies, although technically challenging, are essential for validating the temporal evolution of tumor metabolism in clinical settings.

Complementary approaches, including multi-omics integration (e.g., transcriptomics and proteomics), advanced imaging techniques [such as PET, hyperpolarized magnetic resonance imaging (MRI), and mass spectrometry imaging], and computational modeling, can provide additional spatial, functional, and mechanistic context (37-39). In particular, single-cell sequencing and preclinical longitudinal models further support the feasibility of tracking metabolic adaptations at higher resolution (40).

Therefore, the clinical validation of “metabolic drift” will require a metabolomics-centered framework, supported by multi-modal technologies and longitudinal sampling strategies, to robustly capture the dynamic evolution of tumor metabolism during disease progression and therapeutic resistance.

Metabolic adaptation by hypoxia

The aberrant vasculature and uncontrolled proliferation of tumor tissues create a chronic imbalance between oxygen supply and demand, resulting in persistent intratumoral hypoxia. Hypoxia-inducible factor 1α (HIF-1α), the principal regulator of hypoxia-driven signaling, impairs immunotherapy efficacy through dual metabolic and immune mechanisms (17). On one hand, HIF-1α drives the expression of glycolytic genes—including GLUT1, HK2, and LDHA—thereby amplifying the Warburg effect and increasing competition for glucose between tumor and immune cells (41). On the other hand, HIF-1α directly interacts with the PD-L1 promoter, enhancing its transcription and creating a molecular barrier that protects tumor cells from immune attack (16). This metabolic-immune axis is progressively reinforced during treatment, allowing tumors to sustain immunosuppression via PD-L1 upregulation while gaining metabolic advantages through enhanced glycolysis. Additionally, HIF-1α can upregulate additional immune checkpoint molecules, including CD47, and establish a positive feedback loop with VEGF and angiogenic signaling pathways, collectively driving acquired resistance to PD-1/PD-L1 blockade (42).

However, it must be pointed out that there are still limited direct clinical research data on quantifying the degree of hypoxia within patients’ tumors during immunotherapy. Most clinical evidence is limited to static, single-time-point immunohistochemical staining for HIF-1α or CAIX, or genomic indicators such as “hypoxia-related genes”. Continuous measurement of oxygen concentration throughout the dosing interval remains a significant technical challenge for obtaining the evolution of hypoxia time during the acquisition of drug resistance.

Amino acid metabolism

Tryptophan metabolism

Within the TME, both tumor cells and MDSCs exploit aberrant tryptophan metabolism to suppress immune responses. Elevated expression of IDO1 and TDO2 depletes tryptophan and produces kynurenine, contributing to a two-pronged mechanism that restrains T cells activity (43). Depletion of tryptophan activates the GCN2 kinase pathway in T cells, inducing cell cycle arrest and apoptosis. Meanwhile, kynurenine, acting as a natural ligand for the aryl hydrocarbon receptor (AhR), has been reported to suppress the effector functions of T cells and NK cells, while also promoting the differentiation of Tregs (44,45). Under sustained immune checkpoint blockade, this abnormal tryptophan-kynurenine metabolism may be further amplified, creating a self-reinforcing immunosuppressive niche that persists despite PD-1/PD-L1 inhibition, thereby potentially contributing to secondary resistance (46). This mechanism underlies the synergistic effects observed upon combining IDO1 inhibitors with PD-1/PD-L1 blockade in preclinical models. This pathway has also been implicated in NSCLC, where dysregulated tryptophan metabolism contributes to immune suppression and therapeutic resistance.

Arginine metabolism

In normal physiological settings, arginine is a key metabolic substrate supporting the synthesis of polyamines and nitric oxide. Within the TME, however, MDSCs and certain tumor cells overexpress ARG1, depleting extracellular arginine and generating an arginine-deficient immunosuppressive milieu (47,48). This metabolic deprivation profoundly impairs T cells function: arginine deficiency downregulates the CD3ζ chain within the T cells receptor complex, disrupting activation signaling, and suppresses mTORC1 activity, resulting in G0–1 cell cycle arrest and compromised clonal expansion (49,50). Under sustained immunotherapy pressure, this metabolic imbalance evolves dynamically, with increased MDSC infiltration and elevated ARG1 activity forming a self-reinforcing suppressive circuit (51,52). Similar arginine-depletion–driven immunosuppressive mechanisms have been observed in lung cancer, further supporting their relevance in shaping resistance to immunotherapy. Consequently, even with effective PD-1/PD-L1 blockade, arginine depletion acts as a persistent metabolic checkpoint that restricts T cells expansion and effector activity, ultimately contributing to acquired resistance (Table 1).

Accumulation of lipid metabolism

Accumulating evidence suggests that prolonged treatment with PD-1/PD-L1 inhibitors drives tumor cells to gradually increase fatty acid uptake and oxidation, enabling them to meet energy demands and sustain membrane lipid synthesis necessary for continued proliferation (53). This lipid metabolic adaptation not only supports tumor survival but also appears to reshape the immune milieu, as tumor-derived oxidized lipids—such as oxidized LDL and cholesterol metabolites—accumulate and reinforce immunosuppressive conditions (16,54). These lipid mediators blunt T cells expansion and effector responses, interfere with immune synapse formation, and promote Treg recruitment and M2 macrophage polarization, collectively contributing to an immunosuppressive immune landscape (Table 1). Emerging evidence suggests that lipid metabolic reprogramming also contributes to immune evasion in NSCLC, particularly through its effects on macrophage polarization and T cell dysfunction (55). Consequently, this lipid metabolism-driven immunosuppressive barrier persists even under PD-1/PD-L1 blockade, preventing effective T cells reactivation and contributing to the development of acquired resistance (56).

Metabolic heterogeneity and plasticity

Metabolic subclones

Within tumors, distinct metabolic subpopulations coexist, exhibiting heterogeneous adaptations to environmental stress. Among them, certain metabolic subclones, although initially less competitive, possess an intrinsic tolerance to metabolic stressors such as hypoxia and limited glucose availability (57). Under the persistent selective pressure of immunotherapy, these stress-tolerant subclones may become progressively enriched and, in some cases, evolve into dominant populations. They sustain survival in nutrient-limited conditions through diverse adaptive strategies, including enhanced glycolytic efficiency, upregulated autophagic activity, and reprogrammed mitochondrial metabolism (58,59). Furthermore, these metabolically distinct subclones can establish metabolic symbiosis, exchanging metabolites and energy intermediates to collectively strengthen tumor resilience and therapy resistance (34,60). As a result, therapy-driven metabolic evolution may ultimately weaken the efficacy of PD-1/PD-L1 blockade, allowing resistant disease to emerge.

Metabolic adaptability

Tumor metabolic plasticity has emerged as a central driver of resistance to PD-1/PD-L1 blockade. Upon inhibition of specific metabolic pathways, tumor cells rapidly activate compensatory metabolic circuits to sustain survival and proliferation (61). For instance, the suppression of glycolysis can be offset by enhanced mitochondrial oxidative phosphorylation, while glucose deprivation drives a shift toward glutaminolysis, to replenish the tricarboxylic acid cycle through anaplerotic flux (62,63). This remarkable metabolic flexibility not only enables tumor cells to adapt to fluctuating nutrient availability but also facilitates escape from therapies targeting single metabolic pathways. Furthermore, this reprogramming exerts a broad impact on TME, modulating the profile of immunosuppressive metabolites and further compromising T cells effector functions (64,65). Consequently, therapeutic inhibition of single metabolic pathways is frequently bypassed through compensatory metabolic rewiring, which not only preserves tumor cell survival but also sustains an immunosuppressive metabolic milieu, thereby driving acquired resistance to PD-1/PD-L1 blockade.

Metabolic reprogramming of T cells in acquired PD-1/PD-L1 resistance

The primary mechanism of PD-1/PD-L1 blockade is to relieve inhibitory signaling and restore T cells activity. However, clinical evidence indicates that releasing immune suppression alone is often insufficient to sustain durable anti-tumor immunity. Fundamentally, even when inhibitory checkpoints are lifted, T cells deprived of the metabolic “fuel” required for activation and effector function—may remain insufficient to effectively eliminate tumor cells (66,67).

T cells metabolic disorders

Mechanisms of metabolic resistance to PD-1/PD-L1 blockade

Although the classic PD-1/SHP2/PI3K axis is the core pathway for T cells metabolic inhibition, in the context of acquired resistance, the simple signaling activation model is no longer sufficient to explain the sustained metabolic inhibition after receptor blockade (68). Studies have shown that this logical contradiction mainly stems from the synergistic action of three types of mechanisms: Firstly, the early persistent PD-1 signal can induce T cells to produce epigenetic “metabolic scars”, a deep reprogramming that makes mitochondrial dysfunction remain fixed after the receptor is physically blocked and difficult to reverse (69-71); secondly, blockade of the PD-1 pathway may induce compensatory upregulation of alternative immune checkpoints, including TIM-3, LAG-3, and TIGIT, which can sustain suppression of the Akt/mTORC1 pathway via redundant inhibitory signaling (72). Moreover, the restricted tumor stromal infiltration or rapid receptor renewal lead to incomplete occupation of the receptor, resulting in residual metabolic inhibition thresholds in the local microenvironment (73). Notably, T cell metabolic exhaustion has been extensively observed in NSCLC, including in metastatic settings where T cells exhibit reduced functionality and impaired responsiveness to immunotherapy (74).

Therefore, acquired resistance should not be simply regarded as the reactivation of the PD-1 signal; rather, it is a “refractory metabolic state” driven by persistent epigenetic imprints and bypass inhibitory pathways. This state is associated with reduced responsiveness of the PI3K-Akt-mTOR axis to checkpoint blockade, forming a metabolic barrier that transcends a single immune checkpoint. This understanding requires that future intervention strategies must shift from simple signal blockade to the repair of epigenetic states and the collaborative reconfiguration of multi-target metabolic pathways.

Reversible metabolic quiescence and irreversible metabolic dysfunction in T cells

T cells activation and effector functions are closely linked to aerobic glycolysis, which allows rapid ATP production and supplies critical biosynthetic precursors for proliferation and cytokine secretion (75). This glycolytic dependency underlies the robust proliferative and cytotoxic potential of effector T cells. However, activation of PD-1 signaling suppresses mTORC1 activity while enhancing the AMPK pathway, driving a metabolic shift from aerobic glycolysis toward mitochondrial oxidative phosphorylation (OXPHOS) and fatty acid oxidation (FAO) (76-78). This reconfiguration slows biosynthetic flux and limits the production of key macromolecules such as nucleotides, amino acids, and lipids, promoting a metabolically restrained state.

Importantly, this metabolic phenotype is not uniformly associated with therapeutic resistance. A subset of T cells exhibiting a quiescent, memory-like metabolic profile—often characterized by TCF1 expression and reliance on OXPHOS—has been identified as a progenitor exhausted population capable of proliferative expansion and reinvigoration following PD-1/PD-L1 blockade. In contrast, resistance is more closely linked to terminally exhausted T cells, which display irreversible transcriptional and epigenetic repression of effector programs together with severe metabolic dysfunction. In these cells, impaired metabolic flexibility limits restoration of glycolytic activity and effector functions such as clonal expansion, cytokine secretion [e.g., interferon gamma (IFN-γ), tumor necrosis factor-α (TNF-α)], and cytolytic activity, thereby contributing to incomplete immune recovery during therapy (79-81). This mechanism can be interpreted within the proposed tumor-immune-metabolism framework, highlighting the coordinated impact of metabolic constraints on immunotherapy resistance.

Dysfunction of T cells mitochondria

Mitochondrial structural and functional abnormalities

Mitochondrial dysfunction is a defining feature of exhausted T cells and is characterized by both structural and functional abnormalities. Exhausted T cells exhibit compensatory mitochondrial enlargement, loss or rarefaction of cristae, and reduced mitochondrial membrane potential (82,83). These alterations impair oxidative phosphorylation, decrease electron transport chain efficiency, and limit ATP production, ultimately compromising key effector functions such as cytokine secretion and proliferation (84,85). Mitochondrial impairment also disrupts metabolic homeostasis and alters the activity of transcription factors involved in T cells differentiation, promoting a stable dysfunctional state (86). Consequently, T cells fail to fully restore effector capacity even after PD-1/PD-L1 blockade, highlighting mitochondrial damage-driven metabolic exhaustion as an important mechanism underlying acquired resistance to immunotherapy (87,88).

Mitochondrial epigenetic remodeling

Mitochondrial dysfunction profoundly influences T cells function at the epigenetic level by disrupting key metabolic intermediates (89). A decline in acetyl-CoA, the essential substrate for histone acetylation, reduces genome-wide histone acetylation, particularly at the promoters of effector genes such as IFN-γ and TNF-α, thereby suppressing their transcription (90). Concurrently, reduced levels of α-ketoglutarate (α-KG)—a critical cofactor for TET-family DNA and histone demethylases—impair demethylase activity, maintaining a hypermethylated state in effector gene promoters (91). This metabolic-epigenetic coupling establishes a stable negative feedback loop: mitochondrial dysfunction alters metabolite availability, driving epigenetic reprogramming that reinforces the exhausted T cells phenotype and locks cells into a state of persistent functional inhibition (92). These observations support a broader conceptual model in which metabolic reprogramming functions as a central driver of immune dysfunction, rather than merely a secondary consequence.

Immunosuppressive cells

Within the metabolic landscape of the TME, immunosuppressive cell populations, including Tregs and MDSCs, exhibit remarkable metabolic adaptability that confers survival and functional advantages (Table 2). This adaptability enables them to outcompete effector T cells for limited metabolic resources and actively reinforce the immunosuppressive milieu (Figure 2) (12,103).

Figure 2 Metabolic reprogramming of immunosuppressive cells within the tumor microenvironment during acquired PD-1/PD-L1 resistance. Immunosuppressive TME components—including Tregs, MDSCs, M2 TAMs, and CAFs—adapt to nutrient deprivation via distinct metabolic programs (FAO, amino acid/cholesterol metabolism, OXPHOS, and glycolysis). Their coordinated metabolic activity depletes key nutrients, generates suppressive byproducts, and enhances PD-L1 expression, ultimately fostering an environment conducive to PD-1/PD-L1 blockade resistance. ARG1, arginase 1; ATP, adenosine triphosphate; CAF, cancer-associated fibroblast; CD3ζ, CD3 zeta chain; Foxp3, forkhead box P3; MCT1, monocarboxylate transporter 1; MDSC, myeloid-derived suppressor cell; METTL3, methyltransferase-like 3; mTORC1, mechanistic target of rapamycin complex 1; NADPH, nicotinamide adenine dinucleotide phosphate; NK, natural killer; NO, nitric oxide; NOX2, NADPH oxidase 2; OXPHOS, oxidative phosphorylation; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; ROS, reactive oxygen species; SCFA, short-chain fatty acid; T-AOC, total antioxidant capacity; TAM, tumor-associated macrophage; TCA, tricarboxylic acid; TCR, T cell receptor; TGF-β, transforming growth factor-beta; Treg, regulatory T cell.

Tregs

The metabolic plasticity of Tregs within the TME is essential for sustaining their immunosuppressive function (104). Unlike effector T cells that depend primarily on glycolysis, Tregs rely on FAO as their main energy source, providing a distinct survival advantage in the glucose-deprived TME, where fatty acids are relatively abundant and stable (105,106). Moreover, Tregs can metabolically adapt to high-lactate conditions by converting lactate into a usable energy substrate and exhibit heightened resistance to oxidative stress due to their elevated expression of antioxidant enzymes (107-111). FAO-derived metabolites—particularly those influenced by gut microbiota—enhance Foxp3 expression and stability, further reinforcing the suppressive phenotype of Tregs (112). In NSCLC, Tregs exhibit enhanced metabolic adaptability, enabling them to maintain immunosuppressive activity under nutrient-deprived and lactate-rich conditions (113). Collectively, these metabolic advantages allow Tregs to maintain activity even when effector immune cells are metabolically impaired, thereby preserving the immunosuppressive TME and diminishing the therapeutic efficacy of PD-1/PD-L1 blockade (114).

MDSCs

These heterogeneous immature myeloid cells, known as MDSCs, proliferate in chronic inflammatory environments associated with cancer and exert potent inhibitory effects on anti-tumor immunity (115,116). Within the TME, MDSCs are known to exert inhibitory effects on T cells and NK cells through profound metabolic reprogramming. These cells primarily depend on cholesterol and amino acid metabolism to sustain their survival and immunosuppressive activity (117,118). Notably, MDSCs deplete extracellular arginine via high ARG1 expression, leading to downregulation of the CD3ζ chain within the T cells receptor complex, cell cycle arrest, and impaired T cells activation signaling (47). In parallel, MDSCs generate excessive reactive oxygen species (ROS) through the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase system, directly disrupting T cells signaling and inducing apoptosis (119). Moreover, nitric oxide (NO) produced by inducible nitric oxide synthase (iNOS) reacts with superoxide to form peroxynitrite, a powerful oxidant that nitrates and inactivates components of the T cells receptor, thereby further dampening T cells immune responses (120,121). Through these concerted metabolic and oxidative mechanisms, MDSCs contribute to the establishment of an immunosuppressive TME, thereby reinforcing resistance to PD-1/PD-L1 targeted immunotherapy.

M2 tumor-associated macrophages (TAMs)

M2 TAMs predominantly rely on FAO and OXPHOS as their major energy metabolism pathways. Such a metabolic program provides high adaptability, allowing tumor cells to thrive despite the limited nutrient availability in the TME (122). In contrast to pro-inflammatory M1 macrophages, which metabolize arginine to generate cytotoxic NO via iNOS, M2 macrophages express high levels of ARG1 that catalyzes arginine into ornithine and polyamines (100). These metabolites not only mediate tissue repair but also promote Tregs activation and function while suppressing effector T cells proliferation and cytotoxicity (123).

Furthermore, M2 TAMs have been shown to upregulate METTL3 expression through the TGF-β-Smad2/3 signaling pathway in the TME, thereby enhancing m6A methylation of PD-L1 mRNA, which facilitates PD-L1 expression, malignant progression, and immune escape in bladder cancer (101). In addition, M2 macrophages inhibit glycolysis—reducing ATP generation—and the pentose phosphate pathway—limiting NADPH production—thereby suppressing ferroptosis in tumor cells. This metabolic suppression establishes a microenvironment conducive to tumor growth and immune evasion, further dampening anti-tumor immunity (16,124).

Studies have shown that the interaction between lipid metabolism in TME and stromal cells [such as cancer-associated fibroblasts (CAFs)] plays a crucial role in shaping macrophage polarization (125). Although CAFs can transfer lipids to neighboring cells through extracellular vesicles (EVs) or membrane exchange, the current direct mechanistic evidence mostly stems from the fatty acids (such as palmitic acid and oleic acid) secreted by tumors (126).

Although it is known that the availability of extracellular lipids is functionally linked to the polarization of TAMs, there is currently a lack of direct evidence to prove the complete mechanism axis of “CAF-derived lipid transport—FAO activation—TAM polarization”. Therefore, the causal relationship of this axis is still at the speculative stage and requires further experimental verification.

CAFs

CAFs are the most abundant stromal cells in the TME and play a central role in creating and sustaining immunosuppressive conditions through distinct metabolic reprogramming. Notably, CAFs exhibit a pronounced “reverse Warburg effect”, in which they engage in intensive glycolysis while competing with both tumor and immune cells for the limited glucose resources available in the TME (30,102). Lactic acid can function as a signaling molecule via its receptor GPR81, promoting macrophage polarization toward the M2 phenotype and enhancing the immunosuppressive activity of Tregs (127,128). This CAF-driven metabolic reprogramming establishes a self-reinforcing metabolic loop: tumor cells utilize CAFs-derived lactic acid as a carbon source to fuel their anabolic metabolism, while tumor-secreted cytokines further sustain CAFs activation. Such metabolic symbiosis not only promotes tumor growth and survival but, more critically, creates an acidic and nutrient-depleted microenvironment that suppresses anti-tumor immunity and indirectly contributes to acquired resistance to PD-1/PD-L1 ICIs (129).

Collectively, these findings further support the role of metabolic reprogramming as a key regulator of immune cell function within the TME.

Dynamic monitoring during treatment

Longitudinal assessment of metabolic evolution within the TME is essential for the early detection of resistance and timely therapeutic adjustment. In clinical settings, this can be achieved through an integrated, multi-modal monitoring framework that combines imaging, circulating biomarkers, and tissue-based profiling to capture both spatial and temporal metabolic dynamics.

Non-invasive metabolic imaging

Non-invasive imaging represents the most clinically accessible approach for longitudinal monitoring. Conventional fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) is already widely used in oncology to assess tumor glucose uptake and metabolic activity over time (38). However, its ability to resolve specific metabolic pathways is limited.

Emerging technologies such as ¹³C-hyperpolarized MRI provide a more direct and dynamic assessment of metabolic flux (130). By tracking the real-time conversion of injected substrates (e.g., pyruvate-to-lactate), this technique enables visualization of pathway-specific metabolic activity in vivo (38). In contrast to PET, which primarily reflects tracer uptake, hyperpolarized MRI can distinguish downstream metabolic products and thus offers improved resolution of metabolic pathway activity (131).

Furthermore, the development of immuno-PET technology targeting metabolic transport proteins (such as MCT1/4 or GLUT1) may further enhance the specificity of metabolic imaging. In summary, these methods may be able to conduct repeated systemic evaluations of metabolic changes during the treatment process in the future, making them suitable for monitoring the emergence of drug-resistant lesions in solid tumors.

Liquid biopsy and circulating biomarkers

Liquid biopsy provides a minimally invasive and repeatable strategy for tracking metabolic evolution over time. In clinical studies, serial blood sampling enables longitudinal analysis of circulating metabolites (e.g., lactate, kynurenine) and metabolomic signatures that reflect systemic metabolic alterations (132).

In addition, tumor-derived EVs carry metabolic enzymes, mRNA, and regulatory molecules that mirror the metabolic state of tumor cells (133). Characterization of EV-associated metabolic signatures may therefore provide indirect yet dynamic insights into tumor metabolism without the need for repeated invasive biopsies (134).

Importantly, liquid biopsy approaches provide a minimally invasive and longitudinal monitoring strategy that enables the capture of dynamic metabolic fluctuations through serial sampling. In lung cancer, such approaches are increasingly being explored for the early detection of resistance to PD-1/PD-L1 blockade, potentially preceding radiographic progression and enabling more timely therapeutic intervention (135).

Longitudinal multi-omics profiling

Tissue-based approaches remain essential for high-resolution characterization of metabolic evolution. Serial tumor biopsies, when clinically feasible, can be integrated with multi-omics technologies—including transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of metabolic reprogramming (136).

Recent advances in spatial transcriptomics and mass spectrometry imaging further enable the mapping of metabolic-immune interactions within the TME at high spatial resolution. These approaches allow identification of metabolically distinct subclones (e.g., hyper-glycolytic or FAO-dependent populations) and their interaction with immune cells during therapy (137,138).

Taken together, the integration of imaging-based, blood-based, and tissue-based monitoring strategies offers a feasible and clinically relevant framework for tracking metabolic evolution in patients. Such multi-modal approaches may enable earlier detection of resistance, improve patient stratification, and guide adaptive therapeutic interventions in the era of precision immunometabolism.

Targeting tumor metabolic reprogramming to restore the efficacy of PD-1/PD-L1 blockade

Rather than focusing solely on combinatorial therapeutic strategies, we highlight how a deeper understanding of metabolic resistance mechanisms can inform the rational design of next-generation interventions. Recent studies highlight the central role of tumor metabolic reprogramming in this process, which remodels the microenvironment and suppresses T cells activity (Table 3) (153). Consequently, increasing attention has been directed toward metabolic-targeted strategies, which aim to alleviate metabolic constraints within TME and thereby enhance the therapeutic efficacy of immunotherapy (Figure 3).

Figure 3 Therapeutic strategies targeting tumor and T cells metabolic reprogramming to restore sensitivity to PD-1/PD-L1 blockade. Pharmacologic blockade of LDHA, MCT1/4, and IDO1 reduces tumor-derived lactate and other suppressive metabolites, whereas T cells–centric metabolic augmentation—via AKT/PPAR-γ signaling or engineered metabolic rewiring—promotes oxidative fitness and effector function. The convergence of these strategies reprograms the tumor metabolic landscape, ultimately restoring T cells-mediated immune surveillance. AhR, aryl hydrocarbon receptor; AKT, serine/threonine-protein kinase AKT; ATP, adenosine triphosphate; GLUT1, glucose transporter 1; IDO1, indoleamine 2,3-dioxygenase 1; LDHA, lactate dehydrogenase A; MCT, monocarboxylate transporter; mTOR, mechanistic target of rapamycin; PI3K, phosphoinositide 3-kinase; PPAR-γ, peroxisome proliferator-activated receptor-gamma; ROS, reactive oxygen species; TCA, tricarboxylic acid; TCR, T cell receptor; TD02, tryptophan 2,3-dioxygenase; THF, tetrahydrofolate.

Reversing nutrient competition and detoxifying the TME

Lactic acid metabolism accumulation

To counteract lactic acid-mediated immunosuppression, current research explores therapeutic strategies at three major levels. First, inhibition of LDHA suppresses lactic acid production at its source, alleviating microenvironmental acidification and disrupting tumor metabolic symbiosis (139). Second, targeting monocarboxylate transporters (MCT1/4) effectively blocks the intercellular shuttle of lactic acid—MCT4 inhibitors prevent its efflux from tumor cells and cancer-associated fibroblasts, whereas MCT1 inhibitors interfere with lactate uptake and utilization by oxidative tumor cells (154,155). Third, lactic acid clearance strategies such as oral sodium bicarbonate to buffer extracellular acidity or enzymatic degradation using lactate oxidase can further normalize the tumor pH (140). In preclinical studies, these strategies have shown potential synergy with PD-1/PD-L1 inhibition in preclinical studies, potentially reactivating effector T cells and alleviating metabolic constraints imposed by TME (156). Nevertheless, challenges remain, including compensatory activation of alternative metabolic pathways, potential off-target toxicity, and limited drug delivery efficiency. Future work should prioritize developing highly selective inhibitors, optimizing combination timing, and establishing biomarker-guided precision strategies to facilitate the clinical translation of therapies targeting lactate metabolism.

IDO1 inhibitors

A significant hurdle in clinical translation is the functional redundancy and compensatory upregulation of alternative metabolic pathways. A prime example is the failure of the ECHO-301/KEYNOTE-252 trial, which combined epacadostat (an IDO1 inhibitor) with pembrolizumab. Post-trial analyses suggested that the mere inhibition of IDO1 may be insufficient due to the compensatory activation of TDO2 or other kynurenine-producing pathways, which continue to activate the AhR in T cells and maintain an immunosuppressive TME. This highlights the necessity of dual-targeting strategies or more comprehensive metabolic profiling to circumvent such adaptive resistance mechanisms (157).

Currently, the application prospects of IDO1 inhibitors have turned to more precise combination regimens, including the development of dual inhibitors of IDO1/TDO2 to block metabolic compensation bypasses in drug-resistant cells, as well as through multi-target strategies such as combined CD73 blockade to collaboratively address the metabolic heterogeneity of TME (43,158). Moreover, combining immunogenic cell death induced by radiotherapy and chemotherapy to optimize sequential administration, and stratifying patients based on metabolic active characteristics, are likely to represent important strategies for improving clinical value of PD-1/PD-L1 combination therapy and overcoming adaptive resistance in the future.

Glutamine antagonists

Glutamine antagonists, such as CB-839, represent a key strategy for targeting tumor metabolic vulnerabilities. By inhibiting glutaminase, these agents disrupt the metabolic plasticity of tumor cells, depriving them of essential carbon and nitrogen sources required for the tricarboxylic acid cycle, nucleotide biosynthesis, and redox homeostasis, thereby suppressing tumor proliferation (159,160). However, glutamine metabolism is also critical for effector T cells activation and clonal expansion, highlighting the dual-edged nature of this intervention. Recent studies suggest that optimizing treatment schedules—such as adopting intermittent dosing to exploit the differential sensitivity and recovery kinetics between tumor and immune cells—or combining glutamine inhibitors with PD-1/PD-L1 blockade may preserve immune function while maintaining antitumor efficacy (147,149,161). Although early clinical trials of CB-839 in combination with checkpoint inhibitors have shown encouraging results in select tumor types, overall responses remain modest. Future efforts should focus on biomarker-guided patient selection, particularly identifying tumors with high glutamine dependence, to maximize therapeutic benefit (162).

Reprogramming T cells metabolism

AKT signaling and peroxisome proliferator-activated receptor-gamma (PPAR-γ)-mediated lipid metabolism

Metabolic dysfunction in T cells within the TME often undermines their effector responses. Pharmacological activation of the PI3K-AKT-mTOR signaling pathway by AKT agonists promotes glucose transporter translocation and enhances glycolytic enzyme activity, thereby improving glucose utilization and supporting T cells proliferation and effector functions under nutrient-limited conditions (149,163).

In parallel, PPAR-γ agonists primarily regulate lipid uptake, lipid storage, and adipogenesis, and promote anti-inflammatory transcriptional programs. Through these mechanisms, PPAR-γ signaling can modulate lipid metabolism and contribute to the maintenance of metabolic homeostasis in T cells within the TME (150). However, the effects of PPAR-γ activation on T cell metabolism are complex and may vary depending on the cellular context and immune environment, and are not limited to the enhancement of FAO.

Importantly, excessive AKT activation can induce premature differentiation and exhaustion, while global PPAR-γ activation may exert broad effects on multiple immune cell populations (164,165). Emerging strategies thus focus on metabolic preconditioning of T cells ex vivo for adoptive transfer or on TME–targeted delivery systems to achieve precise metabolic modulation, ultimately strengthening antitumor responses and improving the efficacy of PD-1/PD-L1 blockade.

T cells modification

Metabolic engineering of T cells during ex vivo expansion has emerged as a promising strategy to enhance the persistence, fitness, and antitumor activity of adoptive cell therapies such as adoptive cell therapy (ACT) and chimeric antigen receptor T (CAR-T) cells (166). These approaches can be broadly implemented through metabolic preconditioning, genetic modification of key metabolic regulators, and pharmacological modulation (167).

For instance, optimization of culture conditions to promote oxidative phosphorylation and mitochondrial fitness can favor memory-like T cell differentiation, while genetic or pharmacological interventions targeting pathways such as mTOR, AMPK, or redox balance may further improve metabolic adaptability (168-171).

Importantly, such strategies may enhance T cell fitness under the metabolically hostile TME, characterized by nutrient deprivation and immunosuppressive metabolites. While this area is rapidly evolving, a detailed discussion is beyond the scope of this review.

Clinical translation of metabolic biomarkers

Although various metabolic biomarkers have been introduced in this review, including lactate accumulation, the ratio of Kyn/Trp, and metabolic imaging features, their feasibility for clinical application varies greatly. Clearly distinguishing between clinically feasible biomarkers and those still in the exploration stage is crucial for guiding future clinical applications.

Clinically feasible biomarkers

Among the existing candidate indicators, circulating or tumor-related lactate levels and the Kyn/Trp ratio are the most easily translatable biomarkers. Lactate can be indirectly assessed through serum testing, lactate dehydrogenase levels, or imaging indicators, and is associated with tumor burden, hypoxic state, and adverse events to immune checkpoint blockade (172). Similarly, the leucine/tryptophan ratio, which reflects the activity of the IDO1/TDO2 pathway, can be measured in plasma using standardized metabolomics detection methods and has been included in early clinical studies for patient stratification (173). Moreover, metabolic imaging methods such as ¹⁸F-FDG PET can provide clinically accessible readings of tumor glycolytic activity and show potential in predicting responses to PD-1/PD-L1 blockade or resistance (174).

Exploratory biomarkers in emerging development

In contrast, some metabolic indicators have yet to bridge the gap from research to clinical practice. These indicators include spatially resolved intratumoral metabolic gradient (such as lactate distribution, oxygen tension), single-cell metabolic status of immune subsets, and mitochondrial adaptive markers in tumor-infiltrating lymphocytes (40,84,175). Although these parameters have high mechanistic resolution, their clinical application is currently limited by technical complexity, lack of standardization, and the need for invasive sampling or advanced multi-omics platforms (62).

Key translational challenges

The clinical translation of metabolic biomarkers remains hindered by several challenges that parallel those encountered with established protein-based biomarkers. The dynamic and heterogeneous nature of tumor metabolism limits the reliability of single time-point measurements, resembling the spatiotemporal variability observed in PD-L1 immunohistochemistry (IHC), where expression can differ between primary and metastatic lesions and evolve under therapeutic pressure. In addition, the lack of standardization across metabolite detection platforms leads to substantial inter-laboratory variability, analogous to discrepancies among PD-L1 assays using different antibody clones and platforms (e.g., Dako 22C3/28-8 vs. Ventana SP263/SP142) (176). Furthermore, the absence of universally validated cutoff values limits robust patient stratification, similar to the context-dependent thresholds applied in PD-L1 scoring systems [e.g., tumor proportion score ≥1% vs. ≥50%] (177).

To address these challenges, future studies should prioritize longitudinal monitoring strategies to capture metabolic dynamics over time, rather than relying on static measurements (178). Integrating metabolic biomarkers with immune and genomic features may further enhance predictive accuracy, drawing on the evolving paradigm established by PD-L1-based combination strategies (179). In parallel, the development of standardized detection workflows and minimally invasive approaches—such as liquid biopsy-based metabolomics—will be critical for enabling reproducible and real-time assessment (180). Collectively, these efforts are essential for establishing clinically actionable metabolic biomarkers to guide precision immunometabolic therapies.

Conclusions

Unlike previous reviews that primarily emphasize combinatorial therapeutic strategies, this work provides a mechanism-oriented synthesis that integrates tumor metabolism, immune cell dysfunction, and microenvironmental constraints into a unified framework of immunotherapy resistance. This perspective offers a conceptual basis for the development of more precise and dynamic intervention strategies in cancer immunotherapy.

In this context, the paradigm of cancer immunotherapy is shifting from a focus on signal transduction-based inhibition to a broader framework that incorporates metabolic regulation. Accumulating evidence suggests that metabolic reprogramming is not merely a bystander in tumor progression, but rather functions as a critical “metabolic checkpoint” that contributes to resistance to PD-1/PD-L1 blockade.

Specifically, the emergence of resistant tumor subclones—often driven by genetic alterations such as STK11/LKB1 or KEAP1 mutations—can reshape the TME into an immunosuppressive state characterized by lactate accumulation, acidosis, and nutrient deprivation. These metabolic constraints impair effector T cell function and responsiveness, thereby limiting the efficacy of ICIs.

Preclinical model evidence indicates that therapeutic synergy can be achieved by precisely targeting key metabolic nodes (such as LDHA/MCT1/4 connections to reduce lactate accumulation, or CD39/CD73/adenosine pathways to alleviate immunosuppressive signals). Additionally, the metabolic “adaptability” of adaptive T cells enhanced by AKT or PPAR-γ agonists (to promote oxidative phosphorylation and a memory-like state) provides an effective strategy to overcome the metabolic exhaustion observed in “cold” tumors.

In summary, tumor metabolic reprogramming represents a formidable barrier to the sustained efficacy of PD-1/PD-L1 blockade. The acquisition of resistance is driven by a complex interplay of nutritional competition, the accumulation of immunosuppressive metabolites like lactate and kynurenine, and the metabolic exhaustion of effector T cells. While preclinical data for metabolic modulators remain promising, clinical translation has faced significant challenges, ranging from metabolic plasticity to systemic toxicity. Future efforts should prioritize the identification of robust metabolic biomarkers for patient stratification and the development of rational combination regimens that simultaneously address tumor-intrinsic metabolism and T-cell bioenergetic fitness. Ultimately, a deep understanding of the ‘metabolic-immune’ dialogue will be essential to transform our metabolic insights into durable clinical benefits for patients with advanced malignancies.

Therefore, future translational research should prioritize several key directions: (I) the identification of reliable biomarkers, such as baseline leucine/tryptophan ratios and glucose metabolism-based imaging features, to enable more precise patient stratification; (II) implementing integrated monitoring platforms—combining non-invasive metabolic imaging (e.g., hyperpolarized ¹³C-MRI), liquid biopsy-based metabolite tracking, and spatial multi-omics—to capture the dynamic “metabolic drift” and spatial heterogeneity of acquired resistance in real-time; and (III) the development of metabolic-targeted therapeutic strategies aimed at overcoming immunotherapy resistance while minimizing treatment failure.

Importantly, lung cancer represents a clinically relevant context in which metabolic reprogramming plays a central role in shaping therapeutic responses. A deeper understanding of tumor-immune metabolic interactions may not only help to disentangle the dynamic “metabolic trace” changes associated with immune exhaustion, but also facilitate the development of more effective combination strategies to overcome resistance to PD-1/PD-L1 blockade, ultimately improving durable clinical outcomes and advancing precision oncology.

Acknowledgments

During the preparation of this review, artificial intelligence–based tools were used solely to assist with language editing and to improve clarity and readability. These tools were not used for generating scientific content, performing data analysis, or drawing conclusions.

All intellectual content, including literature selection, synthesis, and interpretation, was independently developed and approved by the authors. The authors take full responsibility for the accuracy and integrity of the work.

Footnote

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0171/prf

Funding: This work was supported by the Key Special Projects of the 14th Five-Year Plan National Key R&D Program of China (Nos. 2023YFC2508601, 2023YFC2508604 and 2023YFC2508605), the National Natural Science Foundation of China (Nos. 82072568 and 82373320), the Shanghai Shenkang Hospital Development Center (No. SHDC12020110), the Shanghai Shenkang Development Research Physician Project (No. SHDC2022CRD048), and the Tongji University Medicine-X Interdisciplinary Research Initiative (No. 2025-0554-ZD-08).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0171/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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