Lipid metabolism in γδ T-cell activation: Implications for immunotherapy in ovarian cancer

Abstract

Ovarian cancer (OC) remains a leading cause of cancer-related mortality among women worldwide, largely due to asymptomatic progression, late-stage diagnosis, therapeutic resistance, and profound immunosuppression within the tumor microenvironment (TME). This is particularly relevant in advanced disease, where metastatic spread to the omentum creates a lipid-rich niche that promotes tumor growth and weakens anti-tumor immunity. This review examines lipid metabolic reprogramming and γδ T-cell function as a targetable immunometabolic axis in OC, with selected discussion of iNKT and CD8+ T-cells as comparative models. Direct evidence from OC patient samples demonstrates that the omental TME drives CD36-mediated lipid uptake and fatty acid (FA) oxidation, while chronic lipid exposure impairs γδ T-cell mitochondrial oxidative phosphorylation and promotes exhaustion, including up-regulation of PD-1 and TIGIT. The major γδ T-cell subsets exhibit distinct metabolic vulnerabilities: Vδ1 T-cells can recognize CD1-presented lipid antigens and rely on FA oxidation, favoring pro-tumoral IL-17 production, whereas Vδ2 T-cells detect phosphoantigens via BTN3A and depend more strongly on glycolysis, rendering them susceptible to glucose deprivation in the OC TME. Concurrent adenosine signaling via A2A receptors, potentially amplified by lipid-induced CD39/CD73 up-regulation, further suppresses γδ T-cell function. We also evaluate nanoparticle-based platforms for co-delivery of metabolic modulators, γδ T-cell agonists, and immune checkpoint blockers, while highlighting key translational barriers, including variable enhanced permeability and retention effects, limited γδ T-cell-specific targeting, and potential systemic toxicity of FASN or CD36 inhibition. Finally, this review proposes future strategies including single-cell metabolomics to map subset-specific vulnerabilities, CRISPR-based validation of exhaustion mechanisms, metabolic engineering of CAR-γδ T-cells through CD36 knockout or CPT1A overexpression, intraperitoneal nanoparticle delivery in patient-derived xenograft models, and TME-restricted delivery systems. Together, these approaches may help overcome lipid-driven immune dysfunction and support the development of next-generation immunotherapies for OC.

Introduction

Ovarian cancer (OC) remains one of the most lethal gynecological malignancies worldwide and is commonly diagnosed at an advanced stage, when treatment is less effective [1]. Despite improvements in surgery, platinum-based chemotherapy, and maintenance strategies, long-term survival remains poor for many patients, particularly those with recurrent or therapy-resistant disease [2,3]. This high mortality is largely attributed to asymptomatic tumor growth, delayed symptom onset, the absence of effective population-level screening, and the frequent emergence of treatment resistance [4].

The most common risk factors for OC include advanced age, postmenopausal status, endometriosis, and inherited genetic predisposition, including BRCA mutations and Lynch syndrome [3]. Lifestyle factors such as obesity, diet, and smoking may also contribute to OC development [5], while pregnancy, lactation, and oral contraceptive use have protective associations [3]. Hormone replacement therapy has been associated with increased risk in certain histological subtypes [3].

Current maintenance therapies, including poly ADP–ribose polymerase (PARP) inhibitors, bevacizumab, and agents targeting homologous recombination deficiency, have improved outcomes in selected patient groups [6]. However, recurrent disease remains difficult to treat. Immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, or CTLA-4 have transformed treatment in several cancers, but their clinical efficacy in OC has been limited [[7], [8], [9], [10]]. This limited response is partly driven by the highly immunosuppressive OC tumor microenvironment (TME), where chronic inflammation, metabolic stress, ascites, stromal remodeling, and lipid accumulation restrict effective anti-tumor immunity [11,12].

A defining feature of advanced OC is its preferential spread to the omentum, a lipid-rich adipose tissue niche that supports tumor growth and immune evasion. Within this environment, tumor and stromal cells promote lipid uptake, FA oxidation, and metabolic competition, creating conditions that can impair immune-cell function. γδ T-cells are of particular interest in this context because they combine innate-like rapid effector function with non-MHC-restricted tumor recognition, yet their activity can be profoundly shaped by local metabolic signals [13,14].

This review examines how lipid metabolic reprogramming within the OC TME, particularly in the omental niche, regulates γδ T-cell function, exhaustion, and therapeutic potential. Where relevant, evidence from iNKT-cells, CD8+ T-cells, or broader cancer metabolism is discussed as a comparative model to highlight distinct or unresolved aspects of γδ T-cell biology. Evidence extrapolated from other immune-cell populations is identified as hypothesis-generating, with emphasis placed on direct OC and γδ T-cell data where available.

Lipid metabolism in cancer

Lipid metabolism involves synthesis, degradation, and storage of fats. Enzymes including acetyl-CoA carboxylase, ATP citrate lyase, stearoyl-CoA desaturase, and FA synthase (FASN) drive de novo FA synthesis [15]. FAs are required for energy storage, membrane synthesis, and signalling molecule production [16].

Metabolic reprogramming in cancer

Altered metabolism is a recognized cancer hallmark, with tumor cells undergoing metabolic reprogramming to meet energy demands [17]. The Warburg effect describes preferential usage of glycolysis regardless of oxygen availability [18]. Direct evidence from OC demonstrates that FASN overexpression is critical for maintaining high proliferation rates [[19], [20], [21]]. This is directly demonstrated in OC and breast cancer models [[19], [20], [21], [22]], representing a potential therapeutic target. Beyond FA, cholesterol biosynthesis is dysregulated in cancer. The mevalonate pathway correlates with tumorigenic transformation and poor prognosis [23], and inhibiting key enzymes induces anti-tumor effects in OC [[23], [24], [25]].

Lipid dysregulation in ovarian cancer: direct evidence

Direct evidence from OC patient studies demonstrates significant lipid alterations. Niemi et al. [28] established that triglycerides with longer FA chains were increased while those with short chains were decreased in OC serum, attributed to decreased ABCD1 gene expression [26]. This study analyzed 180 OC patients across serous, mucinous, and endometrioid subtypes, providing robust patient-level evidence. Lipid classes including phosphatidylcholines (PCs), lysophosphatidylcholines, and cholesteryl esters were consistently decreased [26]. These changes hold potential as early-stage diagnostic biomarkers and correlate with poor prognosis [26]. Additional studies demonstrated decreased serum levels of PCs, phosphatidylethanolamines, and sphingomyelins, while ceramides were elevated and continued to increase throughout disease progression [27,28]. Critically, increased plasma acylcarnitines were detected, indicating enhanced FA oxidation in OC patients compared to benign controls [29,30]. This metabolic shift from glucose to lipid metabolism has direct implications for immune cell function in the TME.

Lipid metabolism within the omental tumor microenvironment

OC preferentially metastasizes to the omentum, a visceral fat pad serving as a major lipid reservoir [31]. This omental niche is unique to OC and fundamentally shapes the metabolic landscape. Cancer cells induce lipolysis in adjacent adipocytes, releasing free FAs and cholesterol that are absorbed via FA transporter proteins, enhancing proliferation and migration [32,33]. CD36, a FA translocase transporter, facilitates lipid uptake [34]. Omental adipocytes alter tumor metabolism to accommodate exogenous FA and cholesterol supplies, with CD36 activation playing a central role [35]. Direct evidence from OC patient samples shows that CD36 expression correlates with tumor progression and poor prognosis, and CD36 inhibition reduces migration significantly in OC cell lines [36]. The ascites fluid in OC patients also contains elevated levels of free FAs, cholesterol, and immunosuppressive cytokines, creating a lipid-rich environment that directly bathes peritoneal tumor deposits and infiltrating immune cells (Fig. 1) including γδ T-cells [37,38].

Fig. 1.

The Omental Lipid-Rich Tumour Microenvironment in Ovarian Cancer. This schematic depicts: (a) OC metastasis to the omental fat pad; (b) adipocyte lipolysis releasing free FAs and cholesterol; (c) CD36-mediated lipid uptake by tumour cells and γδ T cells; (d) resulting metabolic reprogramming with increased FAO and oxidative stress; (e) subsequent γδ T cell exhaustion characterised by PD-1, TIGIT upregulation and IL-17 skewing. Image was generated using AI-image-generator tools “https://manus.im”.

Key oncogenic pathways regulating lipid metabolism: links to γδ T-Cells

The PI3K/Akt/mTOR pathway promotes proliferation and regulates lipid metabolism through Akt-driven FA accumulation and mTORC1-mediated de novo lipogenesis. It suppresses γδ T-cells directly in OC. Tumor-cell PI3K/Akt activation up-regulates FASN and CD36, increasing free FA secretion into the TME. These extracellular FAs are internalized by γδ T-cells via CD36, driving metabolic reprogramming toward exhaustion. This mechanism is relevant to up to 30% of clear cell and endometrioid OC [[39], [40], [41], [42], [43]]. The Wnt/β-catenin pathway regulates lipogenesis in adipocytes. Wnt-5A is elevated in OC tumors and correlates with poorer survival, but direct evidence that Wnt signaling influences γδ T-cell function is lacking. Indirect effects via TME remodeling have been hypothesized [[44], [45], [46]]. In contrast, NF-κB activation in OC regulates lipid desaturases supporting cancer stemness. It also promotes secretion of IL-6 and TNF-α, which act directly on γδ T-cells to up-regulate PD-1 and impair cytotoxicity [47,48]. Nevertheless, direct evidence demonstrating how tumor-cell-specific activation of PI3K, Wnt, or NF-κB signaling specifically modulates γδ T-cell metabolism in OC remains limited. Future studies employing co-culture systems with pathway-specific inhibitors are urgently needed [49,50].

γδ T-cells in ovarian cancer

γδ T-cells are unconventional T lymphocytes that recognize a broad range of stress-induced, microbial, metabolic, and tumor-associated antigens, often independent of classical MHC restriction [51,52]. This allows them to respond rapidly to transformed cells and positions them as attractive candidates for cancer immunotherapy. In ovarian cancer (OC), however, γδ T-cell activity is shaped by the complex metabolic and immunosuppressive tumor microenvironment (TME), particularly within lipid-rich omental and peritoneal niches.

γδ T-cell subsets: a critical comparative analysis

Human γδ T-cells comprise several subsets with distinct tissue distributions, antigen-recognition pathways, metabolic preferences, and functional roles (Fig. 2). The two best-characterized subsets in cancer are Vδ1 and Vδ2 T-cells, which appear to have divergent roles in OC (Table 1). Understanding these differences is essential for rational therapeutic targeting, particularly in the context of lipid-rich tumor environments.

Fig. 2.

γδ T Cell Subset-Specific Metabolism and Functional Outcomes. This figure provides a side-by-side comparison of Vδ1 and Vδ2 T cells across: (a) tissue distribution (tissue-resident vs. circulating); (b) antigen recognition (CD1-lipid vs. BTN3A-pAg); (c) metabolic fuel preference (FAO vs. glycolysis); (d) functional polarity (IL-17+ pro-tumoral vs. IFN-γ+ anti-tumoral); (e) susceptibility to lipid-rich TME (high vs. low); (f) therapeutic implications (depletion/reprogramming vs. activation). Image was generated using AI-image-generator tools “https://manus.im”.

Table 1.

Comprehensive comparison of Vδ1 and Vδ2 T cell subsets in ovarian cancer.

Feature	Vδ1 T Cells	Vδ2 T Cells (mainly Vγ9Vδ2)
Predominant Location	Tissues (epithelia, mucosa, solid tumours, omentum)	Peripheral blood
Key Antigen Recognised	Lipid antigens presented by CD1c & CD1d; stress-induced self-lipids [62,66]	Phosphoantigens (PAgs) via BTN3A/BTN2A [55]
Link to Lipid Metabolism	Direct: Recognises lipid antigens. Fuelled by FAO and lipid uptake [44]	Indirect: Detects dysregulated cholesterol synthesis (PAg production) [55]
Primary Metabolic Fuel	Lipids / FA Oxidation (FAO) [44]	Glycolysis (glucose-dependent) [68]
Predominant Cytokine	IL-17 (pro-tumoral in OC) & IFN-γ [65]	IFN-γ & Granzymes (anti-tumoral) [57]
Role in OC TME	Pro-tumoral / Exhausted. Skewed to IL-17+, promotes inflammation. High PD-1, TIGIT, TIM-3 [58,63]	Anti-tumoral but suppressed. Impaired by glucose starvation, galectin-3 [59]
Prognostic Association	High infiltration correlates with advanced stage & poor prognosis [58]	Presence associated with better outcomes [58]
Therapeutic Implications	Target for reprogramming or depletion (checkpoint inhibition)	Target for activation (BTN3A antibody ICT01, adoptive transfer) [83,85]
Susceptibility to Lipid-Rich TME	High (directly fuelled by lipids, leading to IL-17 skewing) [43]	Low (indirect; primarily affected by glucose deprivation) [74]

Vγ9Vδ2 T-cells are the dominant γδ T-cell subset in peripheral blood and are generally associated with anti-tumor effector functions. Unlike some tissue-resident γδ T-cells, Vδ2 T-cells do not recognize CD1-presented lipids [53]. Instead, they detect phosphoantigens, including isopentenyl pyrophosphate, through BTN3A- and BTN2A-dependent mechanisms [54,55]. These phosphoantigens accumulate in tumor cells with dysregulated mevalonate metabolism, resulting in Vδ2 T-cell activation. Functionally, Vδ2 T-cells can produce IFN-γ and granzymes and contribute to tumor-cell killing [56]. In OC, higher Vδ2 infiltration has been associated with improved outcomes [57], although their activity may be suppressed by tumor-derived factors such as galectin-3 [58].

In contrast, Vδ1 T-cells are enriched in tissues and are often the predominant γδ T-cell subset within solid tumors, including OC [59,60]. Their biology is more heterogeneous and context-dependent. Some Vδ1 T-cells recognize lipid antigens presented by CD1c and CD1d, making them particularly relevant in lipid-rich tumor settings [61]. In OC, Vδ1 T-cells have been associated with exhaustion-associated phenotypes, including increased TIGIT, PD-1, and TIM-3 expression, as well as IL-17 production [[62], [63], [64]]. Increased Vδ1 infiltration has also been linked with advanced stage and poorer prognosis in OC [57], suggesting that this subset may acquire pro-tumor or dysfunctional properties within the OC TME.

The capacity of some γδ T-cells to respond to lipid antigens provides a direct link between altered tumor lipid metabolism and γδ T-cell biology. Human γδ T-cells can recognize several CD1-presented lipid species, including sulfatides, mycobacterial lipopeptides, and α-galactosylceramide analogues [61,65]. In OC, where lipidomic studies have identified altered ceramides, lysophosphatidylcholines, and other lipid species, the CD1-presented antigen repertoire may be reshaped in ways that influence γδ T-cell activation or suppression [26,27,66]. Whether these altered lipid species function as agonists, antagonists, or indirect modulators of γδ T-cell responses remains unresolved and requires direct investigation.

Vδ3 T-cells are less well characterized in cancer. They are found predominantly in the liver and intestine, although they are also present in peripheral blood. Some Vδ3 T-cells can recognize CD1d-presented lipid antigens, but the relevant lipid ligands and their functional role in OC remain poorly defined [67]. Given the lipid-rich nature of the OC TME, further investigation of less abundant γδ T-cell subsets may reveal additional layers of immunometabolic regulation.

Lipid metabolic regulation of γδ T-cell function and exhaustion

The lipid-rich OC TME can influence γδ T-cell metabolism and function profoundly. Elevated free FA and cholesterol may be taken up through lipid transporters such as CD36 and SR-B1, leading to intracellular lipid accumulation, lipotoxic stress, and altered effector function [42,68]. Studies in murine and human γδ T-cells have shown that lipid-rich conditions can impair mitochondrial integrity, increase reactive oxygen species production, and reduce oxidative phosphorylation, thereby contributing to functional exhaustion [42,69]. Lipid exposure may also alter γδ T-cell differentiation through nuclear receptor signaling. FA can activate PPARγ, promoting IL-17-producing γδT17 phenotypes while suppressing IFN-γ-associated anti-tumor responses [42,70]. In parallel, oxidized cholesterol derivatives may activate liver X receptors (LXRs), a mechanism previously linked to exhaustion and PD-1 up-regulation in CD8+ T-cells [71,72]. Whether LXR-dependent exhaustion occurs directly in γδ T-cells within OC remains to be established, but this represents an important hypothesis for future study.

The metabolic requirements of γδ T-cell subsets may determine how they respond to the OC TME [73]. Vδ1 T-cells, particularly IL-17-producing populations, rely more heavily on oxidative metabolism and lipid uptake, which may render them vulnerable to lipid overload and skewing toward dysfunctional or pro-tumor phenotypes [42,74]. Vδ2 T-cells, in contrast, are more glycolytic and may be impaired by glucose deprivation within metabolically competitive tumor sites [73]. Thus, the same lipid-rich, glucose-poor environment may affect Vδ1 and Vδ2 T-cells in different ways: promoting lipid-associated dysfunction in Vδ1 cells while metabolically constraining glycolysis-dependent Vδ2 effector responses (Fig. 3). These subset-specific differences have direct therapeutic implications, as interventions that enhance metabolic fitness in one γδ T-cell subset may not benefit, and could potentially impair another.

Fig. 3.

Mechanisms of Lipid-Induced γδ T Cell Exhaustion. This mechanistic schematic illustrates: (a) CD36/SR-B1-mediated lipid uptake; (b) mitochondrial ROS production and oxidative phosphorylation impairment; (c) PPARγ activation driving IL-17 production; (d) LXR activation (extrapolated from CD8+ T cells) upregulating PD-1; (e) adenosine pathway crosstalk with CD39/CD73 upregulation and A2AR signalling; (f) convergence on exhausted phenotype with decreased cytotoxicity and proliferation. Image was generated using AI-image-generator tools “https://manus.im”.

The adenosine pathway as a metabolic checkpoint

In addition to lipid dysregulation, adenosine signaling is a major immunosuppressive pathway in the OC TME. Extracellular ATP is converted to adenosine by the ectoenzymes CD39 and CD73, and adenosine accumulation is favored under hypoxic and inflammatory conditions [[75], [76], [77], [78]]. Through A2A receptor signaling, adenosine can suppress anti-tumor immunity and alter T-cell activation, proliferation, and cytokine production. γδ T-cells are closely connected to this pathway. Resting γδ T-cells can express CD73 and contribute to adenosine generation, while activated γδ T-cells may up-regulate A2A receptors and become responsive to extracellular adenosine [79,80]. Recent evidence suggests that adenosine signaling through A2A receptors can influence γδ T-cell proliferation and promote Th17-like skewing [70]. In OC, where IL-17-associated inflammation may support tumor progression, this pathway could contribute to the emergence of pro-tumor γδ T-cell phenotypes [81] The expression of CD39, CD73, and A2AR may differ across γδ T-cell subsets, and targeting adenosine signaling carries the risk of inadvertently promoting IL-17-associated peritumoral inflammation. These subset-specific effects must be evaluated carefully in future studies.

Crosstalk between lipid metabolism and adenosine signaling

An emerging area of interest is the potential crosstalk between lipid accumulation and adenosine signaling, which may synergistically drive γδ T-cell dysfunction within the OC TME. Rather than operating as independent immunosuppressive axes, these two pathways may engage in a feedforward loop that reinforces metabolic stress and exhaustion. Mechanistically, lipid overload can activate pro-inflammatory cascades, including NF-κB and TLR signaling, which in turn up-regulate the ectoenzymes CD39 and CD73 on tumor, stromal, and myeloid cells, thereby increasing local adenosine production [[82], [83], [84]]. Conversely, adenosine signaling via the A2A receptor can promote lipid uptake and fatty acid oxidation by inducing CD36 expression [85,86]. This creates a vicious cycle: Lipid accumulation fuels adenosine generation, which in turn facilitates further lipid internalization.

A direct mechanistic precedent for this lipid-driven T-cell failure comes from studies of CD8+ tumor-infiltrating lymphocytes. Xu and colleagues demonstrated that CD36-mediated uptake of oxidized low-density lipoproteins induces lipid peroxidation and p38 kinase activation, directly driving functional exhaustion [87]. Critically, disrupting this pathway via CD36 blockade or GPX4 overexpression restored T-cell effector function. While this specific mechanism has not yet been validated in γδ T- cells, the expression of CD36 on γδ subsets combined with their exposure to oxidized lipids in the omental OC TME suggests a striking vulnerability. Supporting this broader concept, lipid-rich environments have also been shown to up-regulate CD39 and CD73 on regulatory T-cells [88], raising the possibility that the omental niche simultaneously enhances adenosine generation while promoting lipid uptake, thereby creating convergent immunosuppressive pressure.

Consequently, the lipid–adenosine axis may be particularly deleterious for γδ T-cells, given their sensitivity to both metabolic stress and cytokine-mediated functional skewing. Preclinical cancer models outside OC have shown that combined targeting of CD73/A2A receptor signaling and lipid metabolic pathways restores T-cell function more effectively than either approach alone [89,90]. Whether similar combinatorial strategies can restore γδ T-cell anti-tumor activity in OC remains a crucial translational question that warrants dedicated investigation [91].

Therapeutic strategies

γδ T-cell-based immunotherapy

The ability of γδ T-cells to recognize malignant cells independent of classical MHC presentation has generated considerable interest in their therapeutic application. Vγ9Vδ2 T-cells can be activated by phosphoantigens or aminobisphosphonates, leading to tumor-cell killing and production of anti-tumor cytokines such as IFN-γ. Several clinical trials have explored γδ T-cell activation or adoptive transfer, although no γδ T-cell-based therapy has yet been licensed. One notable approach is the BTN3A-targeting antibody ICT01, investigated in the EVICTION trial, which activates Vγ9Vδ2 T-cells through the BTN3A axis [54]. ICT01 has shown encouraging activity in hematological malignancies, including when combined with azacitidine and venetoclax in acute myeloid leukemia (AML). The FDA has granted Orphan Drug Designation (July 2025) and Breakthrough Therapy Designation (January 2026) to ICT01 for frontline AML in patients unfit for intensive chemotherapy, based on data from the Phase I/II EVICTION trial that demonstrated high remission rates and a manageable safety profile [[92], [93], [94]]. However, these findings cannot be directly extrapolated to OC, where the suppressive, lipid-rich solid tumor microenvironment presents additional barriers to γδ T-cell infiltration, persistence, and effector function [54,95].

CAR-modified γδ T-cells are also being developed to combine innate-like tumor recognition with engineered antigen specificity [95]. These approaches may offer advantages over conventional CAR-αβ T-cells, including reduced risk of graft-versus-host disease and the potential for broader tumor recognition. However, as with other cellular therapies, efficacy in solid tumors remains limited by metabolic suppression, poor trafficking, stromal barriers, antigen heterogeneity, and local immune checkpoints [96,97]. In OC, strategies that enhance γδ T- cell metabolic fitness may be necessary to overcome the lipid-rich and adenosine-rich TME. This could include engineering approaches such as CD36 modulation, enhancement of mitochondrial function, or increased resistance to exhaustion-associated signaling pathways.

Nanoparticle-mediated strategies: addressing translational barriers

Nanoparticle-based platforms offer a potential means of delivering metabolic modulators, γδ T-cell agonists, and immune checkpoint inhibitors directly to the tumor microenvironment. These systems may improve local drug concentration, reduce systemic exposure, and enable co-delivery of agents with complementary mechanisms [98,99]. In OC, nanoparticle strategies are particularly attractive because the disease frequently spreads within the peritoneal cavity, creating opportunities for intraperitoneal delivery.

A major challenge for nanoparticle-based therapy is the variability of the enhanced permeability and retention (EPR) effect in human solid tumors [100]. While EPR-mediated accumulation is often effective in preclinical models, it is inconsistent in patients and may limit tumor delivery following intravenous administration. For OC, intraperitoneal administration may help bypass this limitation by delivering nanoparticles directly to the anatomical compartment where tumor deposits, ascites, and omental metastases are located [101]. Preclinical studies comparing intravenous and intraperitoneal delivery in patient-derived xenograft and orthotopic OC models will be important for defining the most clinically relevant route of administration [102].

Another unresolved challenge is the lack of γδ T-cell-specific nanoparticle targeting. Many current immunomodulatory nanoparticle systems are designed to target conventional T-cells or broadly activate immune responses rather than selectively modulating γδ T-cell subsets [103]. Future platforms could incorporate γδ T-cell-relevant targeting elements, such as phosphoantigens, BTN3A-directed agents, or γδ TCR-specific ligands, to enhance subset-selective delivery [54,104]. Such strategies may be particularly valuable if Vδ1 and Vδ2 T-cells require different forms of metabolic or functional modulation.

Payload selection also remains a key translational issue. Co-delivery of metabolic modulators, γδ T-cell agonists, and checkpoint inhibitors requires careful optimization of drug ratios, release kinetics, and tissue distribution [105]. For example, combining a lipid metabolic inhibitor with a γδ T-cell activator may be beneficial only if the timing and localization of each payload are appropriately controlled. Manufacturing complexity, batch-to-batch variability, and scalability must also be considered before such systems can be advanced clinically [106].

Systemic metabolic modulation carries additional safety concerns. Inhibitors of FASN, CD36, or related lipid pathways may affect normal tissues that depend on FA metabolism, including heart, liver, muscle, and adipose tissue [107,108]. TME-restricted delivery systems could help reduce on-target, off-tumor toxicity by confining metabolic modulation to tumor sites [109]. Table 2 summarizes candidate nanoparticle strategies, their evidence levels, and the major translational barriers that must be addressed before clinical implementation (Fig. 4).

Table 2.

Nanoparticle strategies for targeting the lipid-γδ T cell axis: evidence levels and barriers.

Strategy	Payload	Mechanism	Evidence Level (with explicit notation)	γδ T Cell-Specific?	Translational Barrier (Critical Discussion)
Metabolic Reprogramming	PPARα agonist (fenofibrate) [104]	Enhances FAO in T cells	Preclinical (CD8+ T cells; not validated in γδ T cells)	No	Not validated in γδ T cells; requires subset-specific testing
Lipid Depletion	FASN inhibitor [36]	Reduces tumour lipid synthesis	Preclinical (tumour cells; OC cell lines)	No	Systemic toxicity due to FASN role in normal tissues [108]
Lipid Uptake Blockade	CD36 inhibitor [36]	Prevents FA internalisation	Preclinical (OC cell lines; not tested in γδ T cells)	No	Off-tumour effects on lipid metabolism in heart, muscle [109]
γδ T Cell Activation	BTN3A agonist (ICT01) [55]	Activates Vγ9Vδ2 T cells	Phase I/II (AML; not tested in OC)	Yes	Not tested in OC; requires IP or EPR-based delivery [95]
Checkpoint Blockade	Anti-PD-1, anti-TIGIT [64]	Reverses exhaustion	Clinical (other cancers; limited OC efficacy) [10]	Indirect	Limited single-agent efficacy in OC; requires combination [9]
Adenosine Inhibition	A2AR antagonist (ciforadenant) [115]	Prevents adenosine-mediated suppression	Phase I/II (OC ongoing; NCT04904197)	Indirect	Focused on αβ T cells; needs γδ T cell validation
Multi-Payload NP	Metabolic + γδ agonist + ICI	Simultaneous TME remodelling	Conceptual (no published OC-specific data)	Proposed	EPR variability, manufacturing complexity, payload compatibility[101,107]
IP-Delivered NP	Any of above	Bypasses EPR variability	Preclinical (PDX models needed)[102]	Proposed	Requires PDX validation; catheter-related complications[103]

Fig. 4.

Nanoparticle-Based Therapeutic Strategies for Targeting the Lipid-γδ T Cell Axis. This figure illustrates: (a) intravenous vs. intraperitoneal administration routes; (b) EPR effect variability and IP bypass strategy; (c) multi-payload NP co-delivering metabolic modulator (FASN inhibitor), γδ T cell agonist (BTN3A antibody), and checkpoint inhibitor (anti-PD-1); (d) TME-restricted release; (e) proposed effects on Vδ1 (reprogramming) and Vδ2 (activation) subsets. Image was generated using AI-image-generator tools “https://manus.im”.

Rational combination strategies for OC

Given the complexity of the OC TME, single-agent immunometabolic therapies are unlikely to be sufficient. Rational combination strategies may be required to restore γδ T-cell function, reduce lipid-driven suppression, and overcome immune checkpoints simultaneously. One possible approach is to pair metabolic reprogramming with γδ T-cell activation, for example by combining a PPARα agonist such as fenofibrate with a BTN3A agonist such as ICT01 [103,110]. This could potentially support γδ T-cell metabolic fitness while promoting Vγ9Vδ2 activation, although this remains to be validated in OC-specific models.

Another strategy is to reduce tumor-derived lipid accumulation while reversing exhaustion-associated signaling. FASN inhibition combined with PD-1 blockade may help limit lipid availability within the TME while restoring T-cell effector function [111]. Similarly, combined targeting of adenosine signaling and lipid uptake, for example through A2A receptor antagonists and CD36 inhibition, may disrupt reinforcing pathways of metabolic and immune suppression [112,113]. However, A2A antagonists may inadvertently promote IL-17 skewing; subset-specific effects must be evaluated. Ongoing OC trials involving adenosine-pathway inhibitors, including oleclumab and ciforadenant, provide a foundation for this approach, although most current studies focus primarily on conventional αβ T-cells rather than γδ T-cell biology [114].

Future therapeutic development should therefore incorporate γδ T-cell-specific endpoints, including subset frequency, activation state, exhaustion marker expression, lipid uptake, mitochondrial fitness, and cytokine profile. This will be essential for determining whether immunometabolic interventions genuinely restore γδ T-cell-mediated anti-tumor immunity in OC.

Conclusions and future directions

Lipid metabolic reprogramming is a defining feature of the OC tumor microenvironment and has important implications for anti-tumor immunity. The omental and peritoneal niches expose tumor-infiltrating immune cells to high levels of FA, cholesterol, inflammatory mediators and adenosine, creating a metabolically hostile environment that can impair effector function and promote immune exhaustion. γδ T-cells are particularly relevant in this context because their antigen-recognition pathways, tissue distribution, and metabolic requirements position them at the interface between tumor metabolism and immune surveillance.

A central challenge is the functional divergence between γδ T-cell subsets. Vδ1 T-cells, which are enriched in tissues and solid tumors, may be particularly sensitive to lipid-rich environments and can acquire IL-17-producing or exhaustion-associated phenotypes. Vδ2 T-cells, which are more glycolytic and generally associated with anti-tumor effector function, may instead be constrained by glucose deprivation and other forms of metabolic competition. Defining the metabolic vulnerabilities of these subsets in patient-derived OC samples should therefore be a priority. Single-cell transcriptomic, proteomic, and metabolomic profiling of γδ T-cells from ascites, tumors, and omental tissue would help identify subset-specific dependencies and therapeutic targets [115].

Several mechanistic questions remain unresolved. It is not yet clear whether LXR activation directly drives γδ T-cell exhaustion in OC, as has been suggested for CD8+ T-cells in lipid-rich tumor environments [71,116,117]. CRISPR-based perturbation of LXR signaling in γδ T-cells, followed by exposure to OC-derived lipid-rich conditions, could help establish causality. Similarly, the relationship between adenosine signaling and lipid uptake requires further investigation. A2A receptor agonist and antagonist studies, combined with measurements of CD36 expression, FA uptake, and mitochondrial function, would clarify whether adenosine directly reinforces lipid-associated γδ T-cell dysfunction[85,118].

Therapeutically, metabolic engineering of γδ T-cells may provide a route to improving cellular therapy in OC. CAR-γδ T-cells engineered to resist lipid-induced dysfunction, for example through CD36 modulation, CPT1A overexpression, or enhanced mitochondrial fitness, could be evaluated in lipid-rich OC models [119,120]. However, such approaches will need to account for subset-specific biology, as metabolic interventions that benefit Vδ2 effector cells may have different consequences in Vδ1 populations.

Delivery strategy will also be critical. Intraperitoneal nanoparticle delivery may be particularly suited to OC because it can target tumor deposits within the peritoneal cavity and potentially bypass the variability of systemic EPR-dependent delivery [101,121]. Patient-derived xenograft and orthotopic OC models should be used to compare intravenous and intraperitoneal delivery of nanoparticles carrying metabolic modulators, γδ T-cell agonists, and checkpoint inhibitors. At the same time, TME-restricted delivery systems will be needed to minimize systemic toxicity associated with targeting essential lipid metabolic pathways such as FASN or CD36 [109,122].

In conclusion, the lipid-rich OC TME represents both a barrier to effective anti-tumor immunity and an opportunity for therapeutic intervention. A deeper understanding of γδ T-cell subset-specific metabolism, lipid-induced exhaustion, adenosine–lipid crosstalk, and tumor-restricted delivery strategies will be essential for translating immunometabolic concepts into effective OC therapies. By integrating γδ T-cell biology with targeted metabolic modulation, future approaches may help overcome immune suppression and improve the efficacy of next-generation immunotherapies in ovarian cancer [[123], [124], [125]].

CRediT authorship contribution statement

Karolina Stirblyte: Writing – review & editing, Writing – original draft, Visualization, Conceptualization. Yazid Ghanem: Writing – review & editing, Writing – original draft, Visualization, Conceptualization. Mark Bates: Writing – review & editing, Writing – original draft, Visualization. Steven G. Gray: Writing – review & editing, Writing – original draft, Visualization, Conceptualization. Feras Abu Saadeh: Writing – review & editing, Writing – original draft, Visualization. Cara Martin: Writing – review & editing, Writing – original draft, Conceptualization. Sharon O'Toole: Writing – review & editing, Writing – original draft, Visualization. John J. O'Leary: Writing – review & editing, Writing – original draft, Visualization, Conceptualization. Derek G. Doherty: Writing – review & editing, Writing – original draft, Visualization, Conceptualization. Bashir M. Mohamed: Writing – review & editing, Writing – original draft, Visualization, Validation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.