Abstract
Postoperative recurrence remains a major obstacle to durable remission in patients with solid tumors, even after complete macroscopic resection. Growing evidence suggests that surgery creates a transient yet highly permissive biological window characterized by inflammatory signaling, coagulation activation, endothelial disruption, and systemic immune suppression. Together, these processes foster a protective niche that enables microscopic residual disease to evade immune surveillance and initiate metastatic outgrowth. Although modern adjuvant therapies have improved outcomes, their effectiveness is often limited by inadequate tumor-site specificity, systemic toxicity, poor immune cell trafficking, and tumor heterogeneity. Consequently, a critical unmet clinical need persists for biologically precise strategies capable of eliminating residual tumor cells at their point of vulnerability. Platelets, traditionally viewed as mediators of hemostasis, are now recognized as active regulators of tumor progression. By facilitating fibrin deposition, shielding circulating tumor cells from immune attack, and shaping inflammatory networks, platelets inadvertently support the survival of postoperative tumors. Paradoxically, these same wound-targeting properties create a compelling therapeutic opportunity: leveraging platelet-driven homing mechanisms to direct immunotherapy precisely to fibrin-rich surgical beds where recurrence often originates. In this review, we propose a platelet-guided CAR-T platform that leverages endogenous wound biology to create a precision immunotherapeutic delivery system. This strategy integrates platelet membrane cloaking or platelet–CAR-T conjugation with thrombin-responsive biomaterial depots to enhance local effector retention, amplify effector-to-target ratios, and prolong functional persistence. Programmable safety features, including affinity tuning, logic-gated activation, and inducible suicide switches, are used to reduce thrombo-inflammatory risk while preserving therapeutic efficacy. These mechanisms restrict activity to appropriate contexts and allow controlled shutdown in case of adverse events, improving overall safety. When coupled with minimal residual disease–guided patient selection using circulating biomarkers, this approach establishes a clinically actionable framework for perioperative intervention. Emerging preclinical evidence suggests that localized platelet-assisted delivery can reduce circulating tumor cell burden, enhance antigen presentation when combined with immune adjuvants, and suppress recurrence more effectively than systemic therapies. With rigorous safety validation, scalable manufacturing, and biomarker-enriched clinical trials, platelet-guided CAR-T therapy has the potential to transform the postoperative microenvironment from a sanctuary of tumor survival into a targeted domain for durable immune-mediated eradication.
Clinical trial number
Not applicable.
Keywords: CAR-T cell therapy, Drug delivery systems, Immunotherapy, Minimal residual disease, Platelet-guided drug delivery, Tumor recurrence, Tumor microenvironment
Highlights
Identifies the postoperative fibrin-rich niche as a transient, targetable reservoir for microscopic residual disease.
Introduces platelet-guided CAR-T delivery via membrane cloaking or platelet–T cell conjugation with thrombin-responsive biomaterial depots.
Enables spatially confined immune activation within the perioperative fibrin scaffold.
Integrates programmable safety circuits (affinity tuning, logic gating, suicide switches) to limit systemic toxicity and thromboinflammation.
Proposes MRD-guided perioperative stratification using ctDNA and circulating biomarkers.
Demonstrates preclinical enhancement of local effector function, reduced tumor dissemination, and superior recurrence control.
Defines a translational roadmap spanning GLP safety profiling, GMP platelet engineering, and biomarker-enriched adaptive trials.
Graphical Abstract

Introduction
Recurrence after curative resection of solid tumors remains a frequent and clinically devastating event, affecting approximately 15–30% of patients within five years, depending on tumor type and stage [1]. Although the timing of recurrence varies across malignancies, a substantial proportion of recurrences occur during the early postoperative period, particularly in colorectal and certain lung cancers, when intensive surveillance is most critical and clinical outcomes are generally less favorable [2, 3]. Recurrence encompasses diverse patterns, from resectable local relapse to oligometastatic disease and widespread systemic progression, each associated with distinct prognoses and treatment strategies [4, 5]. Importantly, recurrence significantly reduces long-term and cancer-specific survival compared with recurrence-free patients [5], while also worsening quality of life, increasing symptom burden, and contributing to psychological distress [6, 7]. This increased utilization of healthcare resources imposes a substantial economic burden at both the individual and societal levels. Comparative analyses have demonstrated that direct medical costs per patient rise severalfold following disease recurrence, while evaluations across national healthcare systems indicate that the management of recurrent disease accounts for a considerable proportion of overall cancer-related expenditures [7, 8]. Clinical heterogeneity, inconsistent definitions of recurrence, and differences in surveillance and reporting across studies complicate cross-study comparisons and make it difficult to generate universal risk estimates, underscoring the need for standardized recurrence definitions and harmonized reporting in surgical oncology research [9]. Earlier detection may improve the chance of curative salvage in selected patients, yet intensive follow-up carries trade-offs, including higher costs, false positives, and patient anxiety [10, 11].
Despite the transformative successes of immune-based therapies in several cancers, translating these advances to the broad spectrum of solid tumors has been hampered by biologic, anatomic, and safety constraints that are largely absent in hematologic malignancies [12, 13]. At the biological level, antigen heterogeneity and the paucity of truly tumor-specific surface antigens in most solid cancers limit target selection and enable antigen-escape, a major mechanism of treatment failure [14]. Concurrently, the immunosuppressive tumor microenvironment (TME), characterized by inhibitory checkpoint signaling, suppressive immune cell populations, immunoregulatory cytokines, and metabolic dysfunction, impairs effector immune responses and promotes T-cell exhaustion [12]. Physical and vascular barriers within solid tumors, including a dense extracellular matrix, elevated interstitial fluid pressure, and abnormal vasculature, further restrict immune cell infiltration and limit the uniform intratumoral distribution of therapeutic agents [15].
Chimeric antigen receptor T cells (CAR-T) therapy exemplifies this gap between promise and performance: although it achieves durable remissions in B-cell cancers by targeting lineage-restricted antigens such as CD19, solid tumors lack comparable targets and display marked antigen variability, complicating selective CAR design [13, 14, 16]. Consequently, therapeutic activity is often limited by poor tumor infiltration, insufficient persistence, and rapid dysfunction within the suppressive TME, despite emerging strategies such as armored constructs, regional delivery, and logic-gated receptors showing incremental progress. [17, 18]. Safety and pharmacologic challenges further constrain translation. On-target/off-tumor toxicity, along with inflammatory complications such as cytokine release syndrome (CRS) and immune effector-associated neurotoxicity syndrome (ICANS), remain significant concerns. Beyond acute toxicities, CAR-T cells may exhibit poor pharmacokinetics in patients with solid tumors: cells can be sequestered in non-target organs (lungs, liver, spleen), face rapid attrition due to lack of supportive survival signals, or be functionally suppressed by soluble factors and immunosuppressive cells, limiting durable circulation and tumor engagement [17, 19]. Antigen sinks (high antigen expression on normal tissues or shed antigen in circulation) and pre-existing anti-vector or anti-CAR immune responses can further reduce effective CAR-T exposure at tumor sites and complicate repeat dosing. To address these limitations, multiple engineering strategies, including safety switches, affinity-tuned receptors, logic-gated CARs, transient CAR expression, and localized delivery approaches, have been developed; however, each introduces tradeoffs related to efficacy, manufacturing complexity, scalability, and regulatory feasibility that continue to constrain widespread clinical implementation [20, 21].
Building on these limitations, platelets have emerged as biologically plausible vehicles for targeted anticancer delivery due to their multifaceted roles in tumor biology beyond hemostasis [22]. Tumor cells rapidly recruit and become cloaked by platelets during intravasation, protecting circulating tumor cells from shear stress and immune clearance while creating a natural tropism mediated by receptor–ligand interactions such as P-selectin–PSGL-1/CD24 and integrin αIIbβ3/GPIb. Within TME, platelets release growth and angiogenic factors, promote vascular remodeling, and facilitate extravasation, linking their accumulation directly to tumor progression and making them attractive candidates for targeted therapeutic delivery [23]. Translational strategies, therefore, leverage intact or engineered platelets, platelet membranes, or platelet-mimetic nanoparticles to inherit long circulation, immune evasion (e.g., CD47), and adhesive capabilities, with preclinical studies demonstrating improved tumor retention and therapeutic index compared with conventional nanoparticles.
Platelet-derived microparticles and extracellular vesicles further expand this platform by enabling efficient delivery of drugs, RNA, and photosensitizers while limiting off-target exposure, and can be engineered to co-deliver immune modulators to reshape the TME and enhance therapeutic synergy [24, 25]. Nevertheless, key challenges remain, including the risk of thrombosis or tumor promotion from unintended activation, manufacturing variability due to membrane heterogeneity, and potential immune recognition or antigen sinks that may compromise specificity and safety [26, 27].
Given the clinical imperative to prevent and more effectively treat post-surgical recurrence and the limitations of current immuno- and cell-based therapies in solid tumors, this review synthesizes the mechanistic literature and translational evidence supporting platelet-guided targeting. We will (i) summarize platelet–tumor interactions and receptor-mediated recruitment relevant to targeting, (ii) review engineered platelet and platelet-mimetic delivery platforms (including membrane-coated nanoparticles and platelet-derived vesicles) and their preclinical performance, (iii) assess safety, manufacturing, and regulatory challenges (thrombosis, immunogenicity, donor variability, scalability), and (iv) identify key knowledge gaps and propose rational experimental and translational priorities to accelerate safe clinical translation. By integrating mechanistic insights with practical considerations, we aim to clarify where platelet-guided approaches may complement or overcome existing limitations of solid-tumor immunotherapies.
Solid tumors and their microenvironment
Tumor architecture and heterogeneity
Solid tumors are increasingly recognized as highly heterogeneous and spatially organized ecosystems rather than uniform masses of malignant cells, in which genetically diverse tumor populations coexist with stromal, vascular, and immune components that collectively shape tumor progression and therapeutic response [28, 29]. Intratumoral heterogeneity is driven by genomic instability, epigenetic remodeling, and microenvironmental selective pressures, resulting in distinct cellular subclones with variable proliferative, metastatic, and drug-resistant phenotypes within the same lesion [30]. This diversity is further amplified by spatial gradients of oxygen, nutrients, pH, and interstitial pressure that generate specialized microdomains, including hypoxic cores, invasive fronts, and perivascular niches, each characterized by unique transcriptional programs and treatment sensitivities [30–32].
Non-malignant stromal elements, particularly cancer-associated fibroblasts (CAFs), endothelial cells, and extracellular matrix (ECM) components, actively regulate tumor architecture and biomechanical properties through continuous matrix remodeling [33, 34]. Regional alterations in ECM stiffness, collagen crosslinking, and hyaluronan deposition promote mechanotransduction pathways associated with epithelial-to-mesenchymal transition (EMT), stemness, invasion, and therapeutic resistance [35, 36]. Simultaneously, abnormal tumor vasculature characterized by disorganized, leaky, and poorly perfused vessels sustains hypoxia, restricts drug penetration, and supports protective perivascular stem-cell niches [37, 38].
The immune landscape of solid tumors is similarly heterogeneous. While certain regions exhibit dense infiltration of cytotoxic T lymphocytes and antigen-presenting dendritic cells, other areas are dominated by immunosuppressive populations such as regulatory T cells, myeloid-derived suppressor cells (MDSCs), and M2-like macrophages that facilitate immune evasion and disease progression [28, 39]. Functionally specialized niches, including hypoxic and invasive regions, serve as reservoirs of metastatic and therapy-resistant cells by promoting adaptive phenotypes and limiting therapeutic exposure [30, 31]. Moreover, tumor composition evolves dynamically over time under therapeutic and immunological pressures, contributing to relapse and treatment failure through the emergence of resistant subclones [40, 41].
Recent advances in single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multiplex imaging technologies have substantially improved the understanding of tumor ecosystem complexity by enabling high-resolution mapping of cellular states, spatial interactions, and microenvironmental signaling networks in situ. Collectively, these findings highlight that structural heterogeneity, stromal remodeling, vascular dysfunction, and immunosuppressive niche formation are central determinants of therapeutic resistance in solid tumors. Consequently, emerging translational strategies increasingly focus on integrating spatially resolved diagnostics with combinatorial therapies targeting both malignant cells and their supporting microenvironment, including ECM-modifying agents, vascular normalization approaches, and advanced cell- or nanoparticle-based delivery systems [42, 43]. Fig. 1 summarizes the spatial heterogeneity of solid tumors and how distinct microenvironmental niches drive tumor progression, immune evasion, and therapeutic resistance, while highlighting spatial mapping approaches for targeted therapy design.
Fig. 1.

Spatiotemporal dynamics, microenvironmental mapping, and therapeutic implications of solid tumor heterogeneity. (A) The macroscopic architecture of solid tumors is dictated by profound spatial gradients (oxygen, nutrients, pH, and interstitial pressure) that drive the segregation of specialized microdomains, including the hypoxic core, invasive front, and perivascular niche. These distinct zones foster continuous clonal evolution through genomic instability, epigenetic remodeling, and localized selective pressures, giving rise to diverse malignant subclones. (B) Mechanistic interrogation of these microdomains reveals niche-specific drivers of tumor progression and therapeutic resistance. In the stromal compartment, cancer-associated fibroblast (CAF) subtypes orchestrate extracellular matrix (ECM) remodeling, increasing regional stiffness and activating mechanotransduction pathways (e.g., Integrin/FAK/YAP/TAZ) that promote stemness and invasion. Concurrently, aberrant tumor vasculature restricts drug penetrance and sustains protective perivascular niches for cancer stem cells (CSCs), while the spatial compartmentalization of the immune landscape creates stark contrasts between immune-active invasive fronts and deeply immunosuppressive cores dominated by Tregs, MDSCs, and M2-like macrophages. Under therapeutic pressure, this dynamic cellular ecosystem undergoes bottleneck selection, facilitating the survival of persister cells and subsequent relapse via drug-resistant subclones. (C) The integration of advanced high-resolution mapping technologies—such as single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multiplexed imaging—elucidates these complex in situ interactions, providing a critical framework for emerging combinatorial therapies. These next-generation strategies aim to simultaneously target malignant populations and their supportive microenvironments through ECM modification, vascular normalization, immune modulation, and niche-targeted advanced delivery systems
Cellular composition of the tumor microenvironment
Solid tumors are composed of highly interactive malignant, stromal, vascular, and immune cell populations that collectively regulate tumor growth, metastatic dissemination, and therapeutic response [44, 45]. Tumor cells exhibit marked genetic and phenotypic heterogeneity driven by clonal evolution, epigenetic remodeling, and microenvironmental pressures, generating subpopulations with distinct proliferative, invasive, and therapy-resistant phenotypes [46, 47]. Importantly, cancer cells retain substantial transcriptional plasticity and can transition between epithelial, mesenchymal, and stem-like states in response to hypoxia, remodeling, and paracrine signaling, thereby facilitating metastatic progression and adaptive resistance [47, 48].
Among stromal components, CAFs are central regulators of tumor architecture and microenvironmental remodeling. CAF subsets actively deposit and reorganize ECM components, secrete cytokines and growth factors, and establish biochemical and physical barriers that promote tumor progression while impairing therapeutic penetration [49, 50]. Altered ECM composition, including collagen crosslinking, increased stiffness, and hyaluronan accumulation, further modulates mechanotransduction signaling, cell migration, and drug diffusion within the tumor niche [51, 52]. In parallel, endothelial cells contribute to the formation of abnormal tumor vasculature characterized by disorganized, leaky, and poorly perfused vessels that sustain hypoxia, restrict immune-cell trafficking, and support protective perivascular stem-like niches resistant to therapy [34, 53].
The immune compartment of TME is similarly heterogeneous and spatially dynamic. While immunologically hot regions contain abundant cytotoxic T lymphocytes and antigen-presenting cells associated with favorable responses to immunotherapy, cold or immune-excluded regions are enriched with immunosuppressive populations such as regulatory T cells (Tregs), MDSCs, and tumor-associated macrophages (TAMs) [54, 55]. Fig. 2 depicts strategies for converting immunologically cold tumors into hot tumors by overcoming key barriers of immune exclusion and enhancing T-cell infiltration and anti-tumor immune activation through combined therapeutic approaches. TAMs display diverse activation states and can promote angiogenesis, metabolic adaptation, immune suppression, and tumor invasion depending on local cytokine and metabolic conditions [56, 57]. Additional immune populations, including natural killer (NK) cells and B cells organized within tertiary lymphoid structures, further contribute to the functional complexity of the TME [44, 45]. Table 1 summarizes the major cellular components of TME and their respective contributions to ECM remodeling, stromal reprogramming, and tumor progression.
Fig. 2.

Strategies to convert immunologically cold tumours into hot tumours. This schematic summarizes (A) The immunosuppressive cellular and stromal features that define cold TMEs—dense ECM and activated CAFs, hypoxia/adenosinergic and metabolic hostility (low CXCL9/CXCL10, high Treg/MDSC infiltration, active TGF-β signaling and poor antigen presentation)—which together exclude effector T cells and blunt type-I IFN signatures; (B) The therapeutic levers depicted in the figure that have been pursued to reverse these barriers (i.e., increase tumour antigenicity and cross-presentation, induce innate sensing/type-I interferons with STING/TLR/RIG-I agonists or oncolytic viruses, normalize vasculature to improve perfusion and immune access, deplete or reprogram suppressive myeloid/Treg compartments via CSF1R/CCR2/CXCR2 or low-dose cyclophosphamide, inhibit TGF-β/exclusion pathways, exploit epigenetic and metabolic modulators, and combine cytokine/immune-stimulatory therapies with checkpoint blockade or adoptive cell transfers); and (C) The desired hot-TME phenotype—high CD8+ TIL density, robust IFN signatures, tertiary lymphoid structures (TLS), permissive vasculature and checkpoint-expressing, clonally expanded effector lymphocytes—that predicts improved response to immune-checkpoint inhibitors. Together, these panels emphasize a multipronged, mechanism-guided strategy (vascular and stromal remodeling, innate immune activation, suppression of TGF-β/MDSC/Treg axes, metabolic/epigenetic reprogramming, and rational combinations with ICIs or cellular therapies) to convert exclusionary cold niches into immune-inflamed, therapy-responsive tumours
Table 1.
Major cellular components of the tumor microenvironment involved in ECM remodeling and tumor progression
| Cellular component | ECM-related function | Tumor biological impact | Core mediators/pathways | Direction of effect | Key studies |
|---|---|---|---|---|---|
| Cancer-associated fibroblasts (CAFs) | Major producers and organizers of tumor ECM; deposit collagen, fibronectin, periostin, and hyaluronan | Matrix stiffening, invasion, immune exclusion, therapy resistance | TGF-β, α-SMA, FAK, YAP/TAZ, LOX, integrin α5β1 | Predominantly pro-tumor | [58, 59] |
| Tumor-associated macrophages (TAMs) | Remodel collagen-rich niches and coordinate inflammatory ECM remodeling | Immunosuppression, angiogenesis, metastatic dissemination | MMP9, TGF-β, CSF1, integrins, collagen turnover | Mostly pro-tumor; context-dependent | [60, 61] |
| Tumor-associated neutrophils (TANs)/NETs | Release NETs and proteases that reshape laminin-rich ECM | Awakening of dormant cells, invasion, metastatic seeding | NETosis, neutrophil elastase, MPO, MMP9 | Mainly pro-tumor | [62] |
| CD8+ T cells | Navigate through collagen-dense matrices to reach tumor nests | Anti-tumor cytotoxicity restricted by dense ECM barriers | Collagen density, T-cell motility programs, matrix stiffness | Anti-tumor but ECM-restricted | [63, 64] |
| Natural killer (NK) cells | ECM architecture regulates NK migration and cytotoxic contact | Reduced killing in collagen-dense tumors | Collagen I/III, elastin, integrin-linked signaling | Anti-tumor but matrix-inhibited | [65] |
| Dendritic cells (DCs) | Interact with hyaluronan- and collagen-rich matrices during antigen presentation | Altered maturation and impaired T-cell priming in fibrotic tumors | HA–CD44, TLR4, integrin signaling | Context-dependent | [66, 67] |
| Endothelial cells/TECs | Build vascular ECM scaffolds and remodel the endothelial basement membrane | Neovascularization and metastatic niche formation | VEGF, collagen IV, fibronectin, biglycan | Mostly pro-tumor | [68, 69] |
| Pericytes | Contribute to perivascular ECM remodeling and vessel stabilization | Vessel maturation or invasive vascular remodeling, depending on the state | PDGFRβ, collagen I, fibronectin | Context-dependent | [70, 71] |
| Mesenchymal stromal cells (MSCs) | Produce proinvasive ECM and support matrix remodeling | Promote EMT, fibrosis, and metastatic plasticity | IL-6, TGF-β, CXCL12, MMPs | Predominantly pro-tumor | [72, 73] |
| Adipocytes/cancer-associated adipocytes | Produce collagen VI and inflammatory adipose ECM | Support invasion, fibrosis, and metabolic adaptation | Collagen VI, endotrophin, leptin | Pro-tumor | [74, 75] |
| Mast cells | Release ECM-remodeling proteases and angiogenic mediators | Promote angiogenesis and stromal activation | Tryptase, chymase, VEGF, MMP9 | Mostly pro-tumor | [76] |
| Platelets | Deliver profibrotic mediators into the tumor stroma | Enhance CAF activation and metastatic niche formation | TGF-β, PDGF, thrombospondin-1 | Pro-tumor | [77] |
Importantly, these cellular components continuously communicate through cytokines, extracellular vesicles, adhesion molecules, and ECM-mediated signaling pathways, generating specialized niches such as hypoxic cores, invasive fronts, and perivascular sanctuaries that concentrate metastatic and therapy-resistant cell populations. Consequently, the cellular organization of the TME represents a major determinant of treatment failure, as stromal barriers, vascular abnormalities, and immunosuppressive signaling collectively limit drug delivery and antitumor immunity. These insights have driven the development of combinatorial therapeutic strategies aimed at simultaneously targeting malignant cells and reprogramming stromal, vascular, and immune compartments to achieve more durable clinical responses [78, 79].
Extracellular matrix remodeling and tumor vascular niches
ECM and tumor vasculature constitute interconnected structural and signaling networks that critically regulate tumor progression, immune exclusion, and therapeutic accessibility in solid tumors [80, 81]. Continuous ECM remodeling by malignant cells and CAFs promotes excessive collagen deposition, hyaluronan accumulation, and lysyl oxidase (LOX)-mediated crosslinking, generating a dense and mechanically stiff microenvironment that enhances tumor invasion, mechanotransduction signaling, and resistance to therapy [82, 83]. These matrix alterations also elevate interstitial fluid pressure and restrict intratumoral diffusion, thereby limiting the penetration and spatial distribution of chemotherapeutics, antibodies, and nanoparticle-based agents [33, 84].
Concurrently, tumor-associated vasculature is highly abnormal, consisting of disorganized, tortuous, and hyperpermeable vessels that produce heterogeneous perfusion and persistent hypoxic niches [81, 85]. Hypoxia not only promotes aggressive and metastatic phenotypes but also impairs immune-cell infiltration and therapeutic delivery, creating immune-excluded and treatment-resistant microdomains within the tumor microenvironment [80, 84]. The spatial interaction between ECM stiffening and vascular dysfunction, therefore, establishes protective niches that sustain residual malignant cells and contribute to disease recurrence following therapy.
Several ECM-associated pathways have emerged as therapeutically actionable targets. Strategies aimed at reducing collagen density, inhibiting LOX-mediated crosslinking, or degrading hyaluronan matrices have demonstrated the ability to decrease tissue stiffness, lower interstitial pressure, and improve drug penetration in preclinical and early translational studies [83, 86]. Similarly, vascular normalization approaches using anti-angiogenic agents or endothelial reprogramming strategies can transiently restore perfusion, enhance oxygenation, and improve immune-cell and drug delivery when administered within optimized therapeutic windows [85, 87]. Targeted ECM-degrading systems, including tumor-specific hyaluronidase platforms and enzyme-conjugated delivery systems, further represent promising approaches for selectively opening dense stromal barriers while minimizing systemic toxicity [88, 89]. An overview of the major ECM components and remodeling-associated factors implicated in tumor progression, including their structural alterations, biological functions, and key mechanistic mediators, is provided in Table 2.
Table 2.
Major extracellular matrix components and remodeling programs in solid tumors: structural alterations, functional consequences, and mechanistic mediators
| Matrix component | ECM-related function | Tumor biological impact | Core mediators/pathways | Direction of effect | Key studies |
|---|---|---|---|---|---|
| Collagen I | Fibrillar collagen | Excess deposition, alignment, and LOX-dependent crosslinking → increased stiffness, invasion tracks, immune exclusion | Integrins, DDR1/2, LOX, MMPs | Strongly pro-tumor | [91, 92] |
| Collagen III | Fibrillar collagen | Stromal organization and dormancy-associated niche maintenance; in breast cancer, higher type III collagen can mark less invasive/more restrictive regions | DDR1, integrins, collagen-remodeling enzymes | Context-dependent, often tumor-restrictive in breast cancer | [93, 94] |
| Collagen IV | Basement membrane collagen | Basement-membrane disruption and proteolysis at invasive fronts → invasion and dissemination | MMP-2/9, laminin interactions | Pro-invasive, context-dependent | [95] |
| Collagen VI | Microfibrillar collagen | Upregulated in adipose-rich/fibrotic stroma; endotrophin release promotes fibrosis, metastasis, and therapy resistance | Endotrophin-driven signaling, integrin-linked pathways | Pro-tumor | [75, 96]; |
| Fibronectin | Adhesive glycoprotein | CAF-driven fibrillogenesis and alignment → directional migration, invasion, EMT-like plasticity | Integrin α5β1, FAK/Src | Strongly pro-tumor | [58, 97] |
| Laminins | Basement membrane glycoproteins | Laminin-332/laminin isoform remodeling at invasive fronts → migration, invasion, anoikis resistance, metastatic colonization | Integrins (α3β1, α6β4), dystroglycan | Context-dependent, often pro-invasive | [98, 99] |
| Hyaluronan (HA) | Glycosaminoglycan | Accumulation and abnormal turnover → high interstitial stress, vessel compression, impaired perfusion, immune suppression; context can be size-dependent | CD44, RHAMM, HAS2 | Mostly pro-tumor, context-dependent | [100, 101] |
| Tenascin-C | Matricellular protein | Enrichment in metastatic niches → survival of micrometastases and stemness support | Integrins, niche-signaling pathways | Pro-tumor | [102] |
| Periostin | Matricellular protein | Pre-metastatic niche formation and stromal enrichment → metastatic colonization and therapy resistance | Integrins (αvβ3/αvβ5), FAK/AKT | Pro-tumor | [103, 104] |
| Osteopontin | Secreted phosphoprotein | Increased stromal/immune-cell expression → EMT, migration, macrophage recruitment, metastatic progression | CD44, αvβ3/αvβ5 integrins | Pro-tumor | [105, 106] |
| Versican | Chondroitin sulfate proteoglycan | Stromal/myeloid accumulation in pre-metastatic and metastatic niches → macrophage recruitment, MET, metastatic outgrowth | TLR2/4, CD44 | Pro-tumor | [107, 108] |
| Decorin | Small leucine-rich proteoglycan | Often reduced in aggressive tumors; loss removes growth-factor buffering and anti-fibrotic restraint | EGFR, TGF-β sequestration | Anti-tumor, context-dependent | [109, 110] |
| Biglycan | Small leucine-rich proteoglycan | Upregulated in tumor vasculature/stroma → inflammation, angiogenesis, pro-metastatic signaling | TLR2/4, VEGF-linked signaling | Mostly pro-tumor | [69, 111], |
| Perlecan (HSPG2) | Basement membrane heparan sulfate proteoglycan | Altered basement-membrane distribution and shedding → growth-factor retention/angiogenesis; endorepellin can counteract angiogenesis | VEGF/FGF binding, endorepellin | Context-dependent | [112, 113] |
| Nidogen-1 | Basement membrane linker protein | Dysregulated BM stability at invasive interfaces; effects vary by tumor type and stage | Laminin–collagen IV bridging, integrin-linked signaling | Context-dependent | [114, 115] |
| Elastin/elastin-derived peptides | Elastic fiber component | Fragmentation during remodeling → pro-migratory and pro-angiogenic signaling | Elastin receptor complex, proteases | Mostly pro-tumor | [116] |
| SPARC | Matricellular regulator | Dysregulated stromal expression alters collagen assembly and vascular BM organization; effect depends on cell source and cancer type | Collagen-binding domains, integrins | Context-dependent | [117, 118] |
| LOX-mediated collagen crosslinking | ECM-remodeling program | Collagen crosslinking and matrix rigidity → mechanotransduction and pre-metastatic niche formation | LOX/LOXL2, FAK/Src | Strongly pro-tumor | [92, 119]. |
| Matrix metalloproteinases (MMPs) | ECM-degrading enzymes | Accelerated ECM proteolysis and basement-membrane turnover → invasion, angiogenesis, metastatic escape | MMP-2, MMP-9, MT1-MMP | Predominantly pro-tumor | [95, 120]. |
| Heparanase | ECM-degrading enzyme | Cleavage of heparan sulfate-rich matrices → release of growth factors, invasion, angiogenesis | Heparan sulfate proteoglycans, VEGF/FGF axis | Pro-tumor | [121, 122] |
Importantly, emerging evidence suggests that combinatorial approaches integrating ECM modulation, vascular normalization, and immune reprogramming may provide the greatest therapeutic benefit by converting poorly perfused and immune-excluded tumors into treatment-responsive microenvironments [83, 84]. In parallel, advances in bioengineering, including ECM-responsive nanoparticles, biomimetic carriers, and platelet- or membrane-coated delivery systems, have provided innovative strategies to overcome stromal and vascular barriers while enhancing tumor-selective drug delivery [86, 90]. Collectively, these findings establish ECM remodeling and vascular niche dysfunction as central determinants of therapeutic resistance and key translational targets in solid tumors.
Metabolic reprogramming in the tumor microenvironment and emerging therapeutic strategies
Metabolic reprogramming is a hallmark of TME in which malignant and stromal cells coordinately reshape glucose, amino-acid, and lipid metabolism to sustain proliferation, survival, immune evasion, and therapeutic resistance [123, 124]. Enhanced aerobic glycolysis, commonly referred to as the Warburg effect, drives excessive glucose consumption and lactate accumulation even under oxygenated conditions, generating acidic and metabolically hostile niches within solid tumors [125, 126]. Lactate-rich microenvironments suppress dendritic-cell maturation, impair cytotoxic T-cell activity, and promote immunosuppressive phenotypes in regulatory T cells and myeloid populations through mechanisms including histone and protein lactylation [127, 128]. Consequently, lactate transporters such as monocarboxylate transporters (MCT1/MCT4) have emerged as promising therapeutic targets, with early clinical studies of MCT1 inhibitors, including AZD3965, demonstrating initial pharmacodynamic activity and translational potential [129, 130].
Amino-acid metabolism represents another critical metabolic dependency in the TME. Glutamine supports tricarboxylic acid (TCA) cycle anaplerosis, nucleotide biosynthesis, and redox homeostasis, whereas serine/glycine metabolism contributes to one-carbon metabolism and epigenetic regulation required for rapid tumor growth [131, 132]. Pharmacological inhibition of glutaminase, including agents such as telaglenastat (CB-839), has shown the capacity to disrupt tumor bioenergetics and partially restore antitumor immunity in preclinical models; however, metabolic plasticity and compensatory pathways frequently limit the efficacy of monotherapy approaches [133, 134]. These findings have accelerated the development of combinatorial strategies integrating metabolic inhibitors with immune checkpoint blockade or targeted therapies.
Lipid metabolic remodeling further contributes to tumor progression and immune suppression through increased fatty-acid uptake, de novo lipogenesis, and fatty-acid oxidation [135, 136]. These metabolic alterations support membrane synthesis and oncogenic signaling while simultaneously promoting immunosuppressive phenotypes in tumor-associated macrophages and regulatory T cells. Accordingly, inhibitors targeting fatty-acid synthase (FASN), CPT1-dependent fatty-acid oxidation, and lipid uptake pathways are under active translational investigation, particularly in combination with immunotherapy or ferroptosis-inducing strategies [135, 137]. Another major immunometabolic pathway involves extracellular adenosine generation through the CD39–CD73 axis, which converts ATP released from stressed tissues into immunosuppressive adenosine capable of inhibiting T-cell and NK cell function [138, 139]. Fig. 3 illustrates how metabolic reprogramming in the tumor microenvironment suppresses anti-tumor immunity by altering glycolysis, lipid, and amino-acid metabolism, leading to dysfunction of dendritic cells, T cells, NK cells, and expansion of immunosuppressive populations.
Fig. 3.

Metabolic reprogramming within the tumour microenvironment (TME) and its immune consequences. Tumor glycolysis and lactate export, together with altered amino-acid and lipid fluxes, reprogramme stromal and immune cells: lactate drives macrophage polarization toward an M2-like, pro-tumor phenotype and promotes invasion/angiogenesis (A). Metabolite accumulation also impairs antigen-presenting cell function and T-cell priming—lactate and tumor lipids blunt dendritic-cell maturation and cytokine outputs, while IDO-dependent tryptophan catabolism and arginase-mediated arginine depletion suppress effective CD8+ responses and favour tolerogenic programmes (B). Direct suppression of cytotoxic effectors is further mediated by lactate/hypoxia-driven reductions in NK cell IFN-γ and by CD36-dependent uptake of oxidized lipids that induce lipid peroxidation, p38 activation, and dysfunction in CD8+ tumour-infiltrating lymphocytes (C). Finally, tumour-exploited amino-acid pathways (kynurenine–AHR signalling, IDO/TDO activity, arginase, and methionine/serine metabolism) expand regulatory populations (Tregs/MDSCs), engage AHR-driven tolerogenic programmes and constrain T-cell proliferation—together creating an immunometabolic niche that sustains tumour persistence (D). These metabolic axes are actionable vulnerabilities—inhibitors of glycolysis/MCTs, glutaminase, FAO/CPT1, IDO/arginase, and CD39/CD73/adenosine signalling, as well as strategies targeting CD36-mediated lipid uptake or replenishing key amino acids, can rewire the TME to restore antitumour immunity and improve therapeutic responses
remain active areas for improvementDespite substantial progress, therapeutic targeting of tumor metabolism remains challenging due to marked metabolic heterogeneity, adaptive rewiring of metabolic pathways, systemic toxicities, and the lack of robust spatial biomarkers capable of identifying metabolically vulnerable tumor niches [123, 124]. Therapeutic approaches targeting metabolic reprogramming in the tumor microenvironment and their clinical relevance are summarized in Table 3. Current translational efforts increasingly focus on spatial metabolomics, isotope tracing technologies, and biomarker-guided combination therapies to identify actionable metabolic dependencies and optimize treatment timing [128, 140]. In parallel, advanced drug-delivery systems, including nanoparticle-based and platelet-guided platforms, are being developed to selectively modulate hostile metabolic niches while minimizing systemic toxicity [141, 142]. Collectively, these findings suggest that metabolic interventions are unlikely to achieve durable efficacy as standalone therapies but may substantially enhance immunotherapy and targeted treatment responses when incorporated into rationally designed combination strategies.
Table 3.
Therapeutic strategies targeting metabolic reprogramming in the TME
| Category | Sub-Category | Target/pathway | Example therapies (agent names) | Modality | Status (representative) | Mechanism | Representative studies/Ref | |
|---|---|---|---|---|---|---|---|---|
| Amino-acid metabolism | Tryptophan catabolism | IDO1/TDO → kynurenine axis | Epacadostat, navoximod, indoximod | Small molecules | Phase I–III | Reduces tryptophan depletion and kynurenine-mediated immune suppression; aims to restore T-cell activity | [143, 144] | |
| Arginine depletion | ASS1-deficient arginine auxotrophy; arginase axis | ADI-PEG20 (pegargiminase), pegzilarginase, INCB001158 | Enzyme therapy/arginase inhibitor | Phase I–II | Depletes extracellular arginine, starving arginine-auxotrophic tumors and reshaping myeloid/T-cell metabolism | [145–147] | ||
| Glutamine addiction | GLS/glutaminolysis | Telaglenastat (CB-839) | Small molecule | Phase I–II | Blocks glutamine-to-glutamate conversion, limiting anaplerosis and supporting antitumor immunity | [148–150] | ||
| Serine/glycine/one-carbon metabolism | PHGDH/SGOC pathway | PHGDH inhibitors (e.g., NCT-503), serine/glycine restriction | Small molecule/dietary/preclinical | Preclinical | Restricts nucleotide synthesis, redox buffering, and one-carbon flux; can increase stress sensitivity | [151–153] | ||
| Asparagine availability | ASNS/extracellular asparagine | L-asparaginase-based strategies, dual asparagine-depriving nanoparticles | Enzyme therapy/nanotherapy | Preclinical to early translational | Lowers asparagine supply and improves T-cell fitness under nutrient stress | [154–156] | ||
| Cystine/ferroptosis susceptibility | SLC7A11/xCT → glutathione/ferroptosis | Erastin-like agents, SLC7A11 inhibitors | Small molecules | Preclinical | Blocks cystine import, depletes glutathione, promotes ferroptosis and immune sensitization | [157–159] | ||
| Glucose metabolism | Glycolysis blockade | Hexokinase/glycolytic flux | 2-deoxy-D-glucose (2-DG) | Small molecule | Phase I | Competes with glucose and suppresses glycolytic ATP production | [160–162] | |
| Metabolic normalization | AMPK/mTOR-linked glucose signaling | Metformin | Repurposed biguanide | Phase II and translational clinical studies | Reduces tumor glycolysis and mTOR signaling, while improving immune fitness in some TME settings | [163–165] | ||
| Lactate export/acidification | MCT1/MCT4 | AZD3965, syrosingopine, MCT4 blockade strategies | Small molecules | Phase I/preclinical | Prevents lactate efflux, limits acidification, and reduces immunosuppressive lactate signaling | [129, 166, 167] | ||
| Lactate production | LDH-A/pyruvate-to-lactate | LDH inhibitors, pyridazine-based inhibitors, PDK-linked approaches | Small molecules | Preclinical | Lowers lactate generation and reverses lactate-driven immune dysfunction | [168–170] | ||
| Pyruvate oxidation/Warburg reversal | PDK/mitochondrial entry of pyruvate | Dichloroacetate (DCA) | Repurposed metabolic drug | Phase I | Inhibits PDK, promotes pyruvate oxidation, and can reverse tumor-associated immune dysfunction | [171–173]. | ||
| Lipid metabolism | De novo lipogenesis | FASN | TVB-2640, orlistat, C75 | Small molecule | Phase I/preclinical | Blocks fatty-acid synthesis, alters membrane biogenesis, and lipid-dependent immunosuppression | [174, 175] | |
| Fatty-acid oxidation | CPT1A/FAO | Etomoxir, perhexiline, CPT1A inhibitors | Small molecules | Preclinical | Restricts FAO-driven survival programs in MDSCs, TAMs, and tumor cells; can enhance therapy response | [176, 177] | ||
| Fatty-acid uptake | CD36-mediated lipid import | CD36-blocking approaches, anti-lipid uptake strategies | Antibody/small-molecule/preclinical | Preclinical | Reduces uptake of exogenous lipids from the TME and limits metastatic or immunosuppressive lipid programs | [178–180] | ||
| Cholesterol esterification | ACAT1/SOAT1 | Avasimibe and ACAT1-directed strategies | Small molecule | Preclinical | Increases cholesterol availability for antitumor T-cell signaling and improves antitumor immunity | [181–183] | ||
| COX-2/prostaglandin axis | COX-2/PGE2/mPGES1 | Celecoxib, mPGES1 inhibitors | NSAID/small molecule | Preclinical and translational combination rationale | Reduces PGE2-driven myeloid suppression, T-cell dysfunction, and PD-1 upregulation | [184, 185] | ||
| other | Adenosine pathway | CD39/CD73/A2A/A2B | Oleclumab, AZD4635, etrumadenant, taminadenant, AB928 | mAb/small molecule | Phase I–II/active clinical development | Blocks extracellular adenosine generation or receptor signaling, relieving strong TME immunosuppression | [186–188] | |
| Nucleotide metabolism | Purine/pyrimidine biosynthesis; DHODH/IMPDH/RNR | Leflunomide, brequinar, mycophenolate-like strategies | Small molecule | Preclinical to early translational | Restricts nucleotide supply, affecting tumor proliferation and immune-cell activation | [189–191] | ||
| NAD+ metabolism/redox | NAMPT/CD38/PARP/SIRT axis | FK866 (daporinad), CD38 antibodies, PARP inhibitors | Small molecule/antibody | Preclinical to early clinical | Depletes or rewires NAD+ metabolism, impairing stress adaptation and immunosuppressive signaling | [192–194] | ||
| Mitochondrial metabolism/OXPHOS | Complex I/ETC/mitochondrial fitness | Metformin, phenformin, IACS-010759, atovaquone, devimistat | Repurposed drug/small molecule | Preclinical to phase I/II | Suppresses mitochondrial respiration and rewires tumor and immune-cell bioenergetics | [195–197] | ||
| Ferroptosis/iron–lipid peroxidation | GPX4/lipid-ROS defense | RSL3, FIN56, ML162, sorafenib | Small molecule | Preclinical | Induces iron-dependent lipid peroxidation and ferroptotic death, potentially reshaping the TME | [198–200] | ||
| Creatine/energy buffering | Creatine transporter/phosphocreatine shuttle | Creatine supplementation, creatine-pathway modulation | Nutrient/metabolic support | Preclinical/translational | Supports bioenergetic buffering in CD8 T cells and may improve antitumor immunity | [201–203] | ||
| Tumor acidity/carbonic anhydrase | CAIX/CA XII | SLC-0111, CAIX-targeting inhibitors | Small molecule/targeted inhibitor | Preclinical to early clinical | Limits extracellular acidification that suppresses immune activity and supports invasion | [204–206] | ||
| Metabolic crosstalk via extracellular vesicles | EV-mediated nutrient and metabolite transfer | EV-targeted strategies; investigational | Experimental | Preclinical | Interrupts metabolic reprogramming transmitted by tumor-derived EVs to stromal and immune cells | [207–209] | ||
Abbreviations: 2-DG, 2-deoxy-D-glucose; A2A/A2B, adenosine receptor A2A/A2B; ACC, acetyl-CoA carboxylase; ACAT1, acyl-CoA:cholesterol acyltransferase 1; ADI-PEG20, pegylated arginine deiminase; AhR, aryl hydrocarbon receptor; AMPK, AMP-activated protein kinase; ASNS, asparagine synthetase; ASS1, argininosuccinate synthetase 1; ATP, adenosine triphosphate; CAIX, carbonic anhydrase IX; CA XII, carbonic anhydrase XII; CB-839, telaglenastat; CD36, cluster of differentiation 36; CD38, cluster of differentiation 38; CD39/CD73, ectonucleotidases; COX-2, cyclooxygenase-2; CPT1A, carnitine palmitoyltransferase 1A; DCA, dichloroacetate; DHODH, dihydroorotate dehydrogenase; ETC, electron transport chain; EV, extracellular vesicle; FAO, fatty-acid oxidation; FASN, fatty acid synthase; FK866, daporinad; GLS, glutaminase; GPX4, glutathione peroxidase 4; HK, hexokinase; IDO1, indoleamine 2,3-dioxygenase 1; IMPDH, inosine monophosphate dehydrogenase; LDH-A, lactate dehydrogenase A; mAb, monoclonal antibody; MCT1/MCT4, monocarboxylate transporter 1/4; MDSCs, myeloid-derived suppressor cells; mPGES1, microsomal prostaglandin E synthase-1; mTOR, mechanistic target of rapamycin; NAD+, nicotinamide adenine dinucleotide; NAMPT, nicotinamide phosphoribosyltransferase; NSAID, nonsteroidal anti-inflammatory drug; OXPHOS, oxidative phosphorylation; PARP, poly(ADP-ribose) polymerase; PDK, pyruvate dehydrogenase kinase; PD-1, programmed cell death protein 1; PFKFB3, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3; PGE2, prostaglandin E2; PHGDH, phosphoglycerate dehydrogenase; PKM2, pyruvate kinase M2; RNR, ribonucleotide reductase; SAM, S-adenosylmethionine; SGOC, serine–glycine-one-carbon; SIRT, sirtuin; SLC7A11/xCT, cystine/glutamate antiporter; SOAT1, sterol O-acyltransferase 1; TAMs, tumor-associated macrophages; TDO, tryptophan 2,3-dioxygenase; TME, tumor microenvironment
Spatial profiling and diagnostic biomarkers in the tumor microenvironment
Spatial profiling technologies have fundamentally reshaped TME research by enabling high-resolution mapping of cellular states, cell–cell interactions, and functional niches within intact tissue architecture, thereby overcoming the limitations of conventional bulk analyses [210, 211]. Spatial transcriptomics and integrated single-cell multiomic approaches now permit simultaneous characterization of gene-expression programs, stromal remodeling, immune exclusion, and malignant plasticity in situ, revealing clinically relevant spatial heterogeneity that is not detectable using standard sequencing or immunohistochemistry methods [212, 213].
In parallel, advanced spatial and single-cell profiling technologies, including multiplex immunofluorescence, imaging mass cytometry, CODEX-based imaging, digital spatial profiling, and integrated single-cell transcriptomic platforms, now enable simultaneous characterization of diverse cellular populations, signaling pathways, and protein-expression programs while preserving tissue architecture and spatial cellular interactions [214, 215]. These approaches have substantially refined the understanding of disease-associated microenvironments by identifying spatially organized cellular states and pathogenic signaling niches with strong prognostic and mechanistic relevance. In immuno-oncology, such platforms have revealed clinically important biomarkers, including CD8+ T-cell localization, macrophage PD-L1 expression, tertiary lymphoid structures, and immune-excluded stromal niches that predict therapeutic response more accurately than conventional bulk biomarkers [216]. Similarly, integrative bulk and single-cell transcriptomic analyses in vascular pathology have identified macrophage migration inhibitory factor (MIF)-driven signaling programs within vascular smooth muscle cells, linking AKT/mTOR-mediated autophagy suppression and phenotypic switching to aortic dissection progression. These findings further illustrate the broader translational utility of spatially resolved multi-omic approaches for identifying pathogenic cellular interactions and actionable therapeutic targets in complex inflammatory diseases [217].
Importantly, spatial analyses across colorectal cancer, breast cancer, hepatocellular carcinoma, and non-small-cell lung cancer have demonstrated that tumor behavior and treatment response are strongly determined by the spatial organization of malignant, stromal, vascular, and immune compartments rather than by single biomarker abundance alone. Spatial diagnostics, therefore, provide critical insight into why tumors with similar molecular profiles often exhibit markedly different clinical outcomes. Despite substantial progress, clinical implementation remains limited by assay complexity, computational burden, tissue variability, and the lack of standardized analytical pipelines. Consequently, current translational efforts increasingly focus on integrating spatial omics, multiplex imaging, AI-assisted pathology, and simplified biomarker panels to develop scalable diagnostic platforms for precision oncology, patient stratification, and immunotherapy selection.
Surgical disruption of microenvironment and implications for residual disease
Surgical resection remains the primary curative strategy for many solid tumors; however, surgery itself profoundly alters the tumor microenvironment through inflammatory activation, oxidative stress, hypoxia, and tissue remodeling, processes that may unintentionally support residual tumor survival and metastatic dissemination [218]. Recent studies further emphasize the importance of tumor microenvironment-responsive therapeutic strategies capable of modulating hypoxia and reactive oxygen species dynamics within residual tumor niches [219]. Moreover, tissue injury during tumor excision induces complex local and systemic responses, including growth-factor release, vascular remodeling, coagulation activation, and stress-mediated immune dysregulation, thereby establishing a postoperative microenvironment conducive to tumor recurrence and metastatic progression [220, 221].
Three major perioperative mechanisms have been implicated in postoperative tumor progression. First, intraoperative tumor manipulation can mechanically release circulating tumor cells (CTCs) and disseminate residual malignant cells into the bloodstream or surrounding tissues, with elevated postoperative CTC levels correlating with increased recurrence and metastatic risk across multiple cancer types [222, 223]. Second, surgical wound-healing responses induce robust pro-angiogenic and tissue-remodeling programs that may reactivate dormant micrometastatic niches. Removal of the primary tumor can disrupt systemic angiogenic homeostasis, including the loss of endogenous angiogenesis-suppressive signals, thereby facilitating angiogenic switching and metastatic outgrowth in distant tissues [224]. Third, surgery induces a transient but clinically significant immunosuppressive state characterized by catecholamine and glucocorticoid release, increased prostaglandin production, and inflammatory cytokine activation, which collectively impair antitumor immune surveillance and promote tumor-cell survival. At the molecular level, perioperative inflammatory signaling activates protumorigenic pathways such as NF-κB and STAT3, enhances vascular permeability, and increases tumor-cell motility, invasion, and metastatic potential [225–227].
Perioperative tissue injury creates a transient pro-metastatic niche characterized by coagulation activation, inflammatory signaling, and immune suppression. The detailed platelet-dependent mechanisms of tumor protection and immune evasion are discussed in Section “Tumor-platelet interactions” [228, 229]. Platelets are uniquely poised to exacerbate the perioperative metastatic cascade because they are activated by tissue injury, secrete growth and pro-angiogenic factors, cloak CTCs to prevent immune recognition, and cooperate with neutrophils to potentiate NETosis and local immunothrombosis at sites of residual disease [230, 231]. Collectively, these processes create a transient postoperative window of vulnerability characterized by enhanced tumor cell dissemination, a permissive stromal and vascular microenvironment, and impaired antitumor immunity, thereby increasing the likelihood that residual microscopic disease will persist and progress [224, 232]. Clinically, perioperative complications (for example, infection or major inflammatory events), blood transfusions, and certain anesthetic or analgesic regimens have been associated with worsened long-term oncologic outcomes in observational studies, reinforcing the translational relevance of perioperative biology to recurrence risk [233, 234]. Fig. 4 illustrates surgery-induced immunosuppression and inflammatory–thrombotic mechanisms that promote tumor escape and metastatic seeding in the postoperative period. In parallel, large-scale clinical and Mendelian randomization evidence demonstrates that circulating immune and hematologic parameters, including platelet and neutrophil indices, reflect systemic inflammatory burden and are associated with adverse clinical outcomes, highlighting the broader translational relevance of platelet-driven immune dysregulation beyond cancer biology [235]. A translational framework summarizing perioperative pro-metastatic mechanisms, associated measurable biomarkers, and potential therapeutic interventions is provided in Table 4.
Fig. 4.

Surgical trauma–driven systemic and local pathways that promote postoperative immunosuppression, tumor cell escape, and metastatic seeding. Resection of a solid tumor releases DAMPs and provokes a systemic stress response (HPA-axis activation with catecholamine/glucocorticoid release and PGE₂ production) and ischemia–reperfusion cytokine surges that together induce SIRS, expand immunoregulatory populations (tregs, MDSCs, M2-macrophages), suppress CTL/NK function and alter cytokine balance toward IL-6/IL-10–dominated, pro-tumor signaling—creating a transient window of postoperative immunosuppression that facilitates residual tumor outgrowth and recurrence. Concurrently, surgical manipulation and inflammation trigger NETosis and the formation of platelet–CTC–NET aggregates, which protect circulating tumor cells, remodel the extracellular matrix (e.g., by cleaving laminin), and awaken dormant cells to promote metastatic colonization. Perioperative measures (analgesia, normothermia, limiting transfusion/ischemia) and targeted pharmacologic strategies (β-adrenergic and COX-2 blockade, NET targeting, perioperative immunotherapy/vascular/ECM-modulation) are proposed to blunt these pathways and improve oncologic outcomes
Table 4.
Perioperative pro-metastatic mechanisms, measurable biomarkers, and candidate interventions—a translational framework
| # | Perioperative pro-metastatic mechanism | Biological rationale/pathway | Measurable biomarkers (assay) | Timing & sample type (periop window) | Typical periop change (direction) | Candidate perioperative interventions (timing & modality) | Proposed measurement in trials/endpoints | Ref. |
|---|---|---|---|---|---|---|---|---|
| 1 | Surgical wound-healing/inflammation (angiogenic switch) | Release of VEGF, PDGF, TGF-β, and prostaglandins supports angiogenesis and outgrowth of dormant micrometastases | Plasma VEGF-A, PDGF, TGF-β (ELISA); COX-2 expression; CRP; IL-6 | Baseline (pre-op), immediate postop (0–24 h), POD1–7; plasma/serum | ↑ VEGF, ↑ IL-6, ↑ CRP | Periop COX-2 inhibitors/NSAIDs, corticosteroid sparing, minimize tissue trauma (laparoscopy) | Change in VEGF/IL-6 area-under-curve; time-to-relapse | [236–238] |
| 2 | NETosis induced by surgical stress | Neutrophil activation → chromatin/NET release that traps CTCs, promotes adhesion & metastatic colonization | Plasma cell-free DNA bound to neutrophil markers, MPO-DNA complexes, citrullinated histone H3 (H3Cit) (ELISA); neutrophil elastase | Pre-op, immediate postop, POD1–3; plasma | ↑ MPO-DNA, ↑ H3Cit | DNase (periop, experimental), PAD4 inhibitors (preclinical), minimize postop infection/inflammation | Serial MPO-DNA/H3Cit; correlation with CTC seeding or early recurrence | [239] |
| 3 | Platelet–tumour cell interactions (cloaking) | Platelets adhere to CTCs, transfer pro-metastatic cargo (TGF-β), protect from NK cells and shear stress | Platelet count, mean platelet volume (MPV), platelet activation markers (P-selectin/CD62P), platelet–tumor RNA signatures; circulating platelet-tumor aggregates (flow cytometry) | Pre-op, intraop, immediate postop; peripheral blood | ↑ platelet activation, sometimes thrombocytosis | Periop aspirin/antiplatelet (careful bleeding risk), minimize transfusion, antiplatelet trials periop (experimental) | Platelet activation AUC; CTC–platelet aggregate frequency | [23, 240] |
| 4 | CTC release & dissemination from tumor manipulation | Mechanical shedding during resection; transient spike in CTCs increases seeding probability | Circulating tumor cells (EpCAM/CK+, or tumor-specific markers by CellSearch/CTC platforms); ctDNA by PCR/NGS | Baseline, intraop (venous sampling), immediate postop, POD1 | ↑ transient spike in CTC count/ctDNA | Gentle surgical technique, tumor isolation, avoid tumor fracture, intraop tumor cell clearance strategies (in development) | Peak intraop CTC count; persistence at POD1; relapse-free survival | [222] |
| 5 | Postoperative immunosuppression: NK cell dysfunction | Stress hormones + opioids reduce NK cytotoxicity → less elimination of disseminated cells | NK cell cytotoxicity assays (ex vivo), NK cell count (flow cytometry), IFN-γ levels | Pre-op, immediately postop, POD1–7; peripheral blood | ↓ NK function | Periop beta-blockade, reduce opioids (regional techniques), periop immunostimulants (TLR agonists) | NK cytotoxicity change; correlation with early metastatic events | [241, 242] |
| 6 | Neuroendocrine stress: catecholamines & cortisol | β-adrenergic signaling promotes invasion, MMP expression, immunosuppression | Plasma epinephrine/norepinephrine, cortisol; downstream cAMP signaling markers | Pre-op, intraop, immediate postop | ↑ catecholamines, ↑ cortisol | Periop β-blockers (non-selective propranolol in trials), anxiolysis, regional anesthesia | Plasma catecholamine AUC; disease-free survival in RCTs | [241, 243–245] |
| 7 | Coagulation & thrombin generation | Thrombin and fibrin promote tumor cell adhesion and extravasation; tumor-coagulant platelets | D-dimer, thrombin–antithrombin complexes, TAT, fibrinogen; tissue factor (TF) activity | Pre-op, immediate postop | ↑ thrombin generation, ↑ D-dimer | Periop anticoagulation strategies (risk–benefit); periop LMWH in select settings | Periop D-dimer/TAT; VTE vs recurrence endpoints | [23, 246] |
| 8 | Myeloid-derived suppressor cells (MDSCs) & Tregs expansion | Surgery mobilizes immunosuppressive myeloid cells that inhibit T/NK responses | Flow cytometry phenotyping (MDSC panels), Treg frequency (CD4+CD25+FOXP3+) | Pre-op, POD1–7 | ↑ MDSC, ↑ Tregs | Periop agents targeting MDSC (CSF-1 R inhibitors experimental), periop immunomodulation | MDSC/Treg frequency dynamics; immune function assays | [247] |
| 9 | Opioid-mediated immunosuppression & tumor promotion | Opioids can impair cellular immunity and may promote angiogenesis | Opioid dose records, NK assays, cytokine panels | Intraop & postop analgesia window | Dose-dependent ↓ NK function | Reduce systemic opioids (regional analgesia), multimodal analgesia | Analgesic strategy vs NK function and recurrence | [248] |
| 10 | Blood transfusion immunomodulation (TRIM) | Allogeneic transfusion induces immunosuppression that may increase recurrence | Transfusion exposure records; immune assays (NK, cytokines); storage-lesion markers | Periop transfusion events | Associated with immunosuppression | Restrictive transfusion strategies, cell-saver, leukoreduction | Compare recurrence in transfused vs non-transfused matched cohorts | [249, 250] |
| 11 | Hypothermia & metabolic stress | Hypothermia impairs immune cell function and wound healing; hyperglycemia promotes inflammation | Core temperature records; blood glucose; insulin resistance markers | Intraop & immediate postop | Hypothermia ↓ immune function; ↑ glucose | Maintain normothermia, strict glycemic control | Temperature maintenance vs immune endpoints | [251] |
Abbreviations: POD, Postoperative day; CTC, circulating tumor cell; ctDNA, circulating tumor DNA; NETs, neutrophil extracellular traps; MDSC, myeloid-derived suppressor cell
Perioperative tissue injury initiates a coordinated thromboinflammatory response characterized by coagulation activation, platelet recruitment, and systemic inflammatory signaling, all of which can inadvertently favor residual tumor survival rather than simply restoring hemostasis. In postoperative experimental models, platelet activation has been shown to enhance tumor-cell proliferation, migration, epithelial-to-mesenchymal transition-like behavior, and metastatic dissemination, while platelet–tumor cell interactions create a protective niche that facilitates immune evasion and early colonization of distant sites [252, 253]. Importantly, these findings are not merely mechanistic observations but also suggest actionable mitigation strategies. A biodegradable local delivery platform co-releasing doxorubicin-loaded microparticles and aspirin reduced platelet activation, inhibited platelet-promoted tumor progression, and suppressed postoperative recurrence and metastasis in a preclinical model, indicating that localized antiplatelet intervention may be preferable to systemic platelet inhibition when bleeding risk is a concern [252]. In parallel, surgery-induced NETs represent another postoperative pro-metastatic axis, as surgical stress can accelerate NET formation and promote metastatic growth in vivo; conversely, perioperative NET inhibition with DNase-based approaches has reduced surgery-enhanced tumor progression in mouse models [254]. These data support the concept that platelet-guided CAR-T delivery should be considered within a broader perioperative anti-thromboinflammatory framework, in which platelet modulation, NET blockade, and perioperative immune-supportive strategies may be combined to reduce residual-disease escape while preserving hemostatic safety [255]. Building on this microenvironmental context, platelets emerge as key mediators of postoperative niche formation and therapeutic targeting, as discussed in Section “Current solid tumor therapies and limitations”.
Because the perioperative period is both mechanistically distinct and temporally defined, it represents an important therapeutic window for targeted intervention. Current investigational strategies include perioperative inhibition of catecholamine and prostaglandin signaling, preservation of NK- and T-cell function, suppression of NETosis, carefully balanced antiplatelet or anticoagulant approaches, and short-term perioperative immunotherapies aimed at preventing residual tumor cell survival and dissemination [256]. Preclinical models provide proof-of-concept for many of these strategies (for example, NET inhibitors or platelet-targeting agents reduce surgery-associated metastasis in mice), and early clinical or translational trials and prospective perioperative biomarker studies are increasingly focused on validating whether such interventions can translate into lower recurrence rates in humans [254]. Finally, integrating perioperative strategies into routine oncologic care will demand (i) standardized perioperative biomarker panels (CTC dynamics, NET markers, cytokine profiles) to stratify risk and monitor effect, (ii) randomized trials that test pragmatic combinations (for example NSAID + beta-blocker ± immune adjuvant), and (iii) mechanistic correlative studies that map how interventions alter the wound-healing, coagulation, and immune axes that govern residual-disease fate.
Platelets in tumor biology and therapy
Platelet physiology
Platelets are anucleate cytoplasmic fragments produced by bone-marrow megakaryocytes through regulated thrombopoiesis, a process that sets baseline platelet mass and is controlled by thrombopoietin-MPL signaling and megakaryocyte maturation; human platelet production and basic kinetics are summarized in classic physiology chapters and recent reviews [257, 258]. In healthy adults, platelet counts typically range ~150–350 × 109/L and individual platelets circulate for roughly 7–10 days before clearance by splenic and hepatic mechanisms, with recent work refining molecular pathways of platelet aging and removal [259, 260]. The microvascular distribution of platelets is non-uniform, as they preferentially marginate toward the vessel wall in flowing blood. This hydrodynamic segregation, driven by red blood cell–platelet interactions, lift forces, and the formation of a near-wall cell-free layer, concentrates hemostatic activity at sites of endothelial injury [261, 262]. Computational and experimental studies show that margination is sensitive to hematocrit, channel geometry, RBC deformability, and shear rate, so that physiological and pathological alterations to blood rheology (for example, anemia, microvascular narrowing, or altered RBC stiffness) modulate platelet access to the vessel wall and hence thrombogenic potential [263, 264].
Upon vascular injury, a rapid, multistep cascade of tethering, rolling, firm adhesion, and activation secures platelets to subendothelial matrix: initial capture at high shear is primarily mediated by von Willebrand factor (vWF) binding to the platelet GPIb-IX-V complex, which slows platelets sufficiently to permit engagement of collagen receptors (notably GPVI) and subsequent signaling [265]. Engagement of GPVI and GPIb initiates intracellular signaling that activates integrins, principally αIIbβ3, induces actin cytoskeletal reorganization, and exposes pro-adhesive surfaces, thereby promoting fibrin binding and converting reversible platelet tethering into stable aggregate formation that further supports thrombin generation [266–268]. Activated platelets undergo pronounced shape change and degranulation, releasing three major granule types: α-granules, dense (δ) granules, and lysosomes. This process leads to the secretion of adhesive proteins such as von Willebrand factor (VWF) and fibrinogen, nucleotides including ADP, bioactive lipids such as thromboxane A2, growth factors including PDGF, VEGF, and TGF-β, as well as immunomodulatory mediators. Collectively, these factors amplify cellular recruitment and functionally integrate hemostasis with inflammation and tissue repair [269, 270]. Beyond soluble mediators, platelets also engage in membrane-bound and vesicular modes of intercellular communication, including surface receptor display, release of microparticles and extracellular vesicles, and horizontal transfer of RNAs and mitochondria. These mechanisms are increasingly recognized as important pathways through which platelets modulate endothelial, immune, and malignant cell behavior [271, 272]. Hemodynamic forces tightly modulate platelet behavior: shear magnitude alters the relative importance of GPIb–vWF versus integrin–ligand interactions, influences receptor conformation and signaling thresholds, and determines whether platelets form adhesive monolayers, compact three-dimensional thrombi, or become mechanically desensitized [262].
Tumor-platelet interactions
Within the postoperative microenvironment described in “Surgical disruption of microenvironment and implications for residual disease”, activated platelets establish direct molecular interactions with tumor cells [273, 274]. These perioperative platelets form heterotypic aggregates with tumor cells and leukocytes and serve as a nidus for NET formation, physically trapping CTCs and promoting their arrest and extravasation into permissive niches [273, 275]. Direct platelet–tumor cell interactions activate oncogenic signaling in carcinoma cells, particularly via platelet-derived TGF-β and related mediators, inducing EMT, enhanced motility, and increased metastatic competence of residual tumor cells in the postoperative setting [276–278]. Beyond contact-dependent signaling, platelets also transfer membranous and vesicular cargo (including RNA, proteins, and surface receptors) to tumor and stromal cells within the surgical bed, functionally reprogramming both compartments to support survival, migration, and resistance to anoikis [279].
Importantly, platelet activity in the tumor microenvironment is modulated by systemic neuroendocrine–immune states. Chronic psychological stress, through sustained hypothalamic–pituitary–adrenal axis activation, induces immune dysregulation, metabolic reprogramming, and inflammatory amplification that collectively promote tumor progression and may reinforce systemic conditions favoring tumor–platelet interactions [280]. These stress-associated pathways may therefore contribute indirectly to a pro-thrombotic and pro-tumorigenic milieu, particularly in perioperative and advanced disease contexts.
Platelets are a concentrated reservoir of pro-angiogenic mediators (VEGF, PDGF, FGF and others) that are released upon activation and locally stimulate endothelial proliferation, vascular remodeling, and neovascularization within healing tissues and residual tumor deposits, thereby facilitating the angiogenic switch that supports micrometastatic expansion [281–283]. In parallel, endothelial progenitor cell dysfunction and mitochondrial oxidative stress have emerged as important regulators of endothelial integrity and vascular repair capacity during pathological remodeling processes [284]. Concurrently, platelet secretomes contain immunomodulatory mediators (TGF-β, soluble CD40L, serotonin, and other bioactive effectors) and present self-signals (eg, CD47) that blunt NK- and T-cell-mediated cytotoxicity and mask tumor antigens, effectively cloaking residual tumor cells from immune clearance during the vulnerable perioperative interval [274, 285]. Clinically, perioperative biomarkers of platelet activation, including soluble CD40L, platelet-derived microparticles, and dynamic changes in platelet-CTC aggregates, have been associated with recurrence risk across multiple clinical series and are being explored as stratification tools to guide targeted perioperative interventions [286, 287]. Preclinical models and translational studies further indicate that blocking platelet activation, inhibiting platelet-tumor adhesion pathways, or preventing NET formation during the perioperative window reduces surgery-associated metastasis, collectively supporting a causal role for platelet-centric mechanisms in residual disease progression [275]. Fig. 5 highlights the role of platelets in supporting tumor progression, metastasis, and immune evasion.
Fig. 5.

Integrated roles of platelets in the tumour microenvironment, intravascular survival and metastatic progression. Platelets and tumor-educated platelets (TEPs) are recruited and instructed within the TME (and via systemic IL-6/TPO-driven thrombopoiesis) to release growth factors (VEGF, PDGF, FGF), cytokines, and pro-thrombotic mediators that promote local invasion, neoangiogenesis, and cancer-associated thrombosis (CAT), while platelet-derived extracellular vesicles and soluble factors create a paracrine loop that amplifies stromal remodelling and tumour cell dissemination (A). Platelets communicate with cancer cells via (i) contact-independent routes—secretion of ADP/ATP, TGF-β, VEGF, HMGB1 and release of microparticles/exosomes that deliver proteins, lipids and miRnas—and (ii) contact-dependent receptor–ligand interactions (e.g., CLEC-2–podoplanin, GPVI–collagen, P-selectin–PSGL-1, integrin αIIbβ3 engagements) that stabilize platelet–CTC aggregates, trigger intracellular signalling in tumour cells and promote intravascular survival (B). These inputs converge on tumour cell programs that drive EMT and invasion (platelet TGF-β/Smad and NF-κB signalling), augment DNA-repair and chemoresistance through PMP-mediated cargo transfer, rewire metabolism (including platelet→tumour mitochondrial transfer), and cooperate with NETosis and platelet–neutrophil interactions to remodel matrix and awaken dormant cells—collectively promoting metastatic seeding and therapeutic failure (C). Targeting these axes (antiplatelet/CLEC-2–podoplanin blockade, PMP/NET inhibition, anti-TGF-β or perioperative modulation of thrombosis/inflammation) is therefore a rational strategy to limit dissemination and improve outcomes
Platelets as natural cargo carriers
Platelets possess a unique set of biological properties, including a lipid bilayer enriched with adhesive receptors such as GPIb and integrins, a diverse repertoire of surface ligands and chemokines, and intrinsic tropism for sites of vascular injury, inflammation, and tumors. Collectively, these characteristics make them an attractive endogenous platform for therapeutic payload delivery [288, 289]. Building on these native properties, two principal strategies have emerged: (1) direct use or engineering of whole platelets or platelet-derived extracellular vesicles (PEVs) as drug carriers, and (2) biomimetic coating of synthetic nanoparticle cores with platelet membranes to create platelet-membrane-coated nanoparticles (PNPs) that combine biological targeting with tunable core functionality [27, 290]. Platelet-membrane cloaking has emerged as a well-established strategy in which purified platelet membranes are coated onto polymeric, lipid-based, or inorganic nanoparticle cores. This design transfers key platelet-derived properties, including immune evasion, prolonged circulation, and lesion-targeting capability, to the engineered core while maintaining flexibility for payload functionalization. The approach has been demonstrated in multiple preclinical models, including applications in targeted cancer therapy, vascular repair, and anti-infective drug delivery [26, 290]. Complementary to PNPs, PEVs, and microparticles provide a fully biological nanoscale carrier that is naturally internalized by endothelial and immune cells, can encapsulate small molecules or nucleic acids, and shows promise for topical and systemic delivery with favorable biocompatibility and intrinsic bioactivity [291, 292]. Platelet-based therapeutic strategies in cancer, including their mechanistic rationale and translational development status, are summarized in Table 5.
Table 5.
Platelet-based therapeutic strategies in cancer: a comprehensive mechanistic and translational overview
| No. | Category | Mechanism/Rationale | Therapeutic format(s)/Examples | Tumor models/indications | Key findings | Advantages | Limitations/safety concerns | Ref |
|---|---|---|---|---|---|---|---|---|
| 1 | Platelet-membrane-coated nanoparticles (PM@NPs) | Use native platelet membrane proteins (P-selectin, integrins, CD47, etc.) to confer tumor-homing, vascular adhesion, and immune evasion to synthetic NPs. | PLGA, liposomes, polymeric NPs cloaked with platelet membrane; drug-loaded (chemo, kinase inhibitors), theranostic NPs. | Broad: breast, lung, hepatic, glioma models (preclinical mice). | Enhanced tumor accumulation, prolonged circulation, improved efficacy, and lower off-target toxicity vs uncoated NPs. | Biomimicry improves targeting & reduces RES clearance; modular (many payloads). | Scale-up of membrane isolation; reproducibility; potential thrombogenicity; manufacturing complexity. | [290] |
| 2 | Platelet-tumor hybrid membrane-coated NPs | Hybrid membranes combine tumor homotypic recognition (cancer membrane) + platelet targeting/immune-evasion for dual specificity. | Hybrid membrane-coated hollow PLGA NPs carrying chemo agents or natural compounds. | Glioma model (preclinical), other solid tumors. | Improved immune evasion, homotypic targeting to tumor cells, and superior tumor growth inhibition vs single-membrane NPs. | Combines homotypic targeting + platelet tropism; potentially stronger tumor selectivity. | Complexity of sourcing two membranes; risk of carrying tumor antigens; regulatory concerns. | [293] |
| 3 | Platelets used as living drug carriers (drug/NP loading into platelets) | Platelets are naturally home to damaged vasculature/tumor microenvironment and can be loaded ex vivo with drugs/NPs that are released upon activation. | Drug-loaded platelets; platelet-loaded gold nanorods (PLT-AuNRs) for PTT; chemotherapeutic-loaded platelets. | Solid tumor models (breast, lung, orthotopic tumors). | Platelet carriers increase tumor retention of payload and reduce macrophage clearance; effective PTT/chemo delivery in mice. | Natural circulation profile, stimulus-responsive release at activation sites (surgical wound, exposed matrix). | Risk of platelet activation promoting metastasis or thrombosis; storage/viability of loaded platelets. | [294, 295] |
| 4 | Platelet-derived extracellular vesicles (PEVs/microparticles/exosomes) as carriers | PEVs (microvesicles, exosomes) carry proteins, miRNAs, and membrane receptors able to bind tumor cells; they can be loaded with small molecules/siRNA/miRNA. | Isolated PEVs loaded with drugs, miRNA mimics/antagonists, or conjugated to ligands. | Multiple tumor types have been investigated in vitro and in vivo models. | PEVs can deliver nucleic acids and small drugs, modulate tumor biology (apoptosis, anti-angiogenesis), and show tumor tropism. | Endogenous vesicles → low immunogenicity, natural targeting motifs. | EV heterogeneity, isolation/purification challenges, potential pro-tumor bioactive cargo if not well controlled. | [291, 296] |
| 5 | Engineered platelets presenting immune checkpoint molecules (PD-1/PD-L1) or antibody conjugates | Platelets bind to surgical wounds/exposed matrix and to micrometastases; platelets can be engineered to carry immune checkpoint inhibitors (antibodies) or to express PD-1 to locally boost checkpoint blockade. | Genetically engineered platelets or conjugation of anti-PD-1/anti-PD-L1 to platelets/PMVs. | Postsurgical residual disease models, solid tumors (mouse). | Local enrichment of checkpoint inhibitors in the tumor bed improved antitumor immunity, reduced systemic toxicity; improved survival in preclinical models. | Localized ICI delivery, reduce systemic immune-related AEs and exploits platelet wound tropism. | Platelet engineering complexity; platelet activation kinetics; potential for immune suppression or altered hemostasis. | [297, 298] |
| 6 | Platelet-conjugated/platelet-engineered cell therapies (platelet-CAR-T or platelet-adjuvanted ACT) | Conjugating platelets (or platelet factors) to adoptive cells or engineering platelets to present adjuvant signals promotes accumulation at wound sites/residual tumor and overcomes local immunosuppression. | Platelet-conjugated CAR-T (platelets attached to CAR-T or vice versa), platelet-boosted adoptive T cells (preclinical engineering). | Postsurgical residual tumor models, solid tumor PDX models. | Enhanced accumulation of CAR-T at surgical beds, improved persistence and local tumor control in mouse models. | Novel strategy to target residual disease; harnesses platelet tropism and local activation cues. | Early preclinical; safety and thrombosis risk require careful evaluation; manufacturing complexity for cell therapy. | [299] |
| 7 | Platelet-mimicking NPs for photothermal/photodynamic therapy (PTT/PDT) | Platelet membrane cloak increases retention of photothermal agents (AuNRs, nanostars) at tumor and reduces immune clearance; activation releases agents to the tumor. | Gold nanorods/stars coated with platelet membrane; platelet-mimicking photothermal NPs; PM@NPs carrying photosensitizers. | Localized solid tumor models (breast, melanoma, glioma). | Improved tumor accumulation, stronger local heating/PDT, better tumor ablation and reduced systemic toxicity vs uncoated NP. | Effective local ablation; synergy with immunotherapy via immunogenic cell death. | Potential off-target heating; platelet cloak may interact with coagulation; translation/laser accessibility. | [295, 300] |
| 8 | Platelet membrane NPs co-delivering chemo + immunotherapy (combination cargo) | Synchronized delivery of chemotherapeutic agents and immune modulators (e.g., anti-PD-1, metformin) using PM@NPs to convert cold tumors → hot. | PM-coated NPs loaded with chemo + ICI or chemo + immunomodulators; acid-sensitive release systems. | HCC, lung cancer, other solid tumors (preclinical models). | Better tumor control, improved immune activation, and lower systemic toxicity. | Enables rational combination therapy with tumor targeting. | Complexity of co-loading, ensuring correct pharmacokinetics of both cargos, and regulatory hurdles. | [301, 302] |
| 9 | Platelet-derived microparticles delivering nucleic acids (miRNA/siRNA) | PEVs and platelet microparticles carry small RNAs that can modulate tumor survival pathways (e.g., miR-24) or be loaded with therapeutic nucleic acids. | PEVs loaded with therapeutic RNAs; engineered microparticles with antitumor miRNA. | Varied in vitro and in vivo tumor models. | Evidence for apoptosis induction and anti-angiogenic effects by delivered miRNAs; potential for reversing drug resistance. | Natural carrier of nucleic acids; low immediate immunogenicity. | Heterogeneous cargo; risk of delivering pro-tumor signals if not purified/engineered correctly. | [303, 304] |
| 10 | Antiplatelet pharmacology as anti-metastatic therapy (aspirin, P2Y12 inhibitors, others) | Reduce platelet activation/aggregation that shields CTCs, thereby decreasing metastasis seeding and tumor-platelet interactions. | Low-dose aspirin, clopidogrel, ticagrelor, GPIIb/IIIa inhibitors in animal models and epidemiological/clinical studies. | Epidemiologic signals for reduced incidence/mortality in some cancers; preclinical metastasis models. | Aspirin associated with reduced incidence of some cancers in population studies; preclinical data support reduced metastasis with platelet inhibition but results are context-dependent. | Readily available drugs, low cost, potential chemoprevention/adjuvant benefit. | Conflicting data; bleeding risk; some regimens paradoxically increased progression in specific models—need tumor-type and timing-specific evaluation. | [305, 306] |
| 11 | Targeting platelet receptors/adhesion axes (P-selectin, PSGL-1, GPIb, GPVI, integrins) | Interfere with platelet–tumor cell adhesion (TCIPA), extravasation and metastatic seeding by blocking receptor–ligand interactions. | Antibodies, small molecules, selectin inhibitors, receptor biologics targeting P-selectin, GPIbα, GPVI, integrins. | Lung metastasis models, various solid tumor models. | P-selectin inhibition often reduces metastasis; GPIb/GPVI blockade sometimes reduces metastasis but in some contexts paradoxically increased dissemination—receptor biology is complex. | Mechanism-directed approach to block early metastatic steps. | Risk of bleeding/hemostatic dysfunction; receptor redundancy and compensatory mechanisms; context-dependent outcomes. | [307, 308] |
Mechanistically, platelet-based carriers leverage multiple synergistic targeting pathways, including adhesion receptor interactions (such as platelet P-selectin and GPIb binding to damaged endothelium or tumor-associated vasculature), chemokine display that recruits or modulates local immune cells, and partial evasion of opsonization. Together, these properties enable site-selective accumulation that, in preclinical models, often exceeds the performance of conventional untargeted delivery platforms [290, 309]. These approaches have been applied to deliver chemotherapeutics, siRNA, and CRISPR cargos (via platelet-membrane-coated metal-organic frameworks and polymeric cores), thrombolytics for clot dissolution, and anti-angiogenic agents for ocular disease, demonstrating both improved efficacy and reduced off-target toxicity in animal models [310]. Beyond passive delivery, platelets and platelet-mimetic systems can actively modulate immune responses: platelets serve as pathfinders that capture and guide leukocytes to sites of extravasation, present or transfer immunomodulatory signals, and can be engineered to display tumor antigens or immune-stimulatory molecules to enhance antitumor immunity when deployed as a cellular or membrane platform [25, 309]. Practical engineering advances, including hybrid membrane systems combining platelet membranes with leukocyte or cancer cell membranes, core–shell designs enabling stimulus-triggered payload release, and improved scalable isolation methods for clinical-grade PEVs, are increasingly overcoming earlier translational barriers and broadening the therapeutic applicability of platelet-based delivery platforms. Nevertheless, important safety and manufacturing challenges remain: the pro-thrombotic potential of intact platelets, batch heterogeneity of biologic membranes or vesicles, control of cargo loading/release kinetics, and the need for rigorous immunotoxicology and biodistribution studies before routine clinical use [311, 312]. Looking forward, the convergence of platelet biology, membrane engineering, and precision nanoparticle cores promises a versatile toolbox for targeted delivery and immune engineering; the field will benefit from standardized PEV characterization, head-to-head comparisons of platelet versus other cell-membrane coatings, and carefully designed early-phase clinical studies to define safety windows and disease niches where platelet carriers offer the greatest advantage [291, 313].
In summary, platelets act as natural cargo carriers that can be harnessed either directly or biomimetically to deliver a wide range of therapeutics with improved targeting and immunomodulatory capacity, positioning platelet-based strategies as a rapidly evolving and translationally promising class of nanomedicines [27].
Mechanistic rationale for platelet-mediated targeting
Platelets possess a dense repertoire of adhesion receptors, growth factors, and chemokines, together with an intrinsic tropism for sites of vascular injury and inflammation. These biological characteristics contribute to their preferential localization within surgical wounds and tumor-associated vasculature and provide the mechanistic basis for platelet-mediated targeting strategies [229, 314]. Surgical resection creates a permissive niche for residual tumor cells by exposing subendothelial matrix (collagen, von Willebrand factor, fibrin) and releasing inflammatory mediators that rapidly recruit and activate platelets, thereby producing adhesive docking surfaces and a pro-coagulant microenvironment that platelets naturally home to [315, 316]. Mechanistically, platelet-tumor cell interactions are mediated by direct receptor–ligand binding (e.g., platelet P-selectin to tumor cell PSGL-1, GPIb interactions with VWF/fibrin and integrin engagement), platelet-derived microparticles and cytokine transfer, all of which allow platelets to adhere to, cloak, and modulate residual malignant cells or CTCs and thereby provide a high-fidelity targeting axis for therapeutic cargo [294, 317]. This natural adhesion and cloaking can be exploited in two complementary engineering paradigms: (1) in-situ platelet modification or ex vivo loading of intact platelets to carry proteins, nucleic acids, or surface ligands that are delivered when the platelet binds a tumor cell or wound site, and (2) platelet-membrane-coated or platelet-derived vesicle carriers that transfer the platelet’s targeting and immune-evasive surface while using a tunable synthetic core for controlled drug release [318, 319]. These engineered systems provide several advantages for precision drug delivery, including prolonged circulation time and reduced opsonization through membrane cloaking, receptor-mediated accumulation at fibrin-rich surgical sites or tumor-associated microvasculature, and stimulus-responsive payload release triggered by local cues such as thrombin, acidic pH, or proteolytic enzymes. Collectively, these features enhance local drug concentration at sites of microscopic residual disease while minimizing systemic exposure [311, 318].
Importantly for the post-operative setting, platelets’ intrinsic hemostatic function can be harnessed simultaneously with antitumor payloads so that a single platelet-based platform both promotes local clot formation to prevent bleeding and delivers cytotoxic, immune-modulatory or anti-metastatic agents directly to the surgical margin, providing a logical strategy to reduce the twin risks of post-surgical hemorrhage and local recurrence [26, 294]. Proof-of-principle studies illustrate these concepts: in-situ modification of platelets with TRAIL markedly reduced viability of tumor cells in flow models and targeted CTCs, while platelet-membrane-cloaked nanoparticles have improved accumulation of doxorubicin or photothermal agents in tumor sites and reduced off-target toxicity in multiple preclinical models [318, 319]. Despite the mechanistic promise, there are clear translational hurdles that derive directly from platelet biology: intact platelet carriers retain a pro-thrombotic potential that must be controlled, biological membrane and vesicle preparations show batch heterogeneity and variable cargo loading, and immune or coagulation responses to engineered platelet products require careful toxicology and biodistribution profiling [26, 311]. To enable clinical translation of platelet-mediated targeting, several key requirements must be addressed, including standardized characterization of platelet-derived extracellular vesicles and membrane preparations, development of engineered control systems for platelet activation and stimulus-responsive cargo release, scalable GMP-compatible isolation and loading protocols, and rigorously designed early-phase clinical trials that assess both hemostatic safety and oncologic outcomes, such as recurrence [320].
Current solid tumor therapies and limitations
Surgery as primary treatment: scope, efficacy, and recurrence rates
Surgery remains the cornerstone of curative treatment for many localized solid tumors because it physically removes the macroscopic disease and regional lymphatic tissue, and it therefore defines the only treatment with immediate potential for cure in anatomic-confined cancers [321, 322]. The scope of oncologic surgery spans intent (curative wide resection, organ-sparing resection, cytoreductive/debulking procedures, and palliative operations), technique (open, minimally invasive, and image-guided approaches), and timing (upfront primary resection, staged resection for oligometastatic disease, or resection after neoadjuvant therapy), with each choice tailored to tumor biology, location, patient fitness, and multidisciplinary goals [323]. Efficacy is highly stage- and site-dependent: whereas some early-stage cancers treated with complete resection can achieve very high long-term survival, overall population-level 5-year relative survival for all cancers combined is substantially lower and heterogeneous by tumor type, reflecting that surgery’s absolute benefit varies across disease spectra [324, 325]. Despite the curative intent of resection, recurrence remains the main limitation of surgical therapy and a major cause of postoperative morbidity and cancer-specific mortality, widely considered the Achilles’ heel of locoregional treatment [4, 326].
The magnitude and timing of recurrence vary by disease: for example, modern high-quality registries and population studies show that recurrence after colorectal resection has fallen over recent decades but still affects a meaningful minority (with changes in 5-year cumulative incidence across calendar eras), reflecting improvements in staging, multimodality care, and surveillance [1]. Certain tumor types typify the limits of surgery: hepatocellular carcinoma (HCC) classically demonstrates very high post-resection relapse rates (many series report 5-year recurrence in the order of 50–70%), driven by both intrahepatic tumor biology (microscopic satellites and vascular invasion) and the diseased parenchymal substrate that predisposes to multicentric tumor emergence [327, 328]. Similarly, even after complete anatomic resection of thoracic malignancies, recurrence patterns vary by stage. Early-stage lung cancers generally demonstrate favorable recurrence-free survival compared with higher-stage disease; however, a significant proportion of patients still develop local or distant relapse within 3–5 years. This highlights the importance of stage-specific risk stratification to better predict and manage postoperative recurrence [3]. A major mechanistic cause of local recurrence is residual microscopic disease at the resection margin or in regional nodes that escapes detection by current imaging and pathology, and margin status remains one of the most consistent operative predictors of local control across tumor types [328]. Beyond oncologic control, surgical therapy is also limited by patient-related factors such as comorbidity burden, frailty, and reduced hepatic or cardiopulmonary reserve, as well as by procedure-related morbidity and mortality. In addition, functional impairment and quality-of-life consequences further influence outcomes [322, 329]. Methodological and translational limitations also temper the evidence base for many surgical questions: prospective randomized trials in surgery are difficult to design and accrue, heterogeneity in techniques and centers produces variable outcomes, and surgical innovation often outpaces rigorous comparative evaluation [326, 330]. Finally, contemporary priorities to extend the curative reach of surgery include better molecular and imaging-based risk stratification to select patients most likely to benefit, perioperative systemic or targeted therapies to eradicate occult microscopic disease, and standardized reporting of recurrence endpoints so that surgical advances can be reliably compared across trials and registries [1, 331].
Chemotherapy, radiotherapy, and adjuvant immunotherapy: partial solutions
Chemotherapy has long remained the backbone of systemic treatment for solid tumors, exerting antitumor effects through cytotoxic injury to rapidly proliferating cells or through targeted inhibition of oncogenic signaling pathways. Nevertheless, these approaches remain fundamentally constrained by a narrow therapeutic index, systemic toxicities, intratumoral heterogeneity, and the persistence of therapy-resistant cellular subpopulations that ultimately drive recurrence and metastatic progression [332, 333]. Even when systemic therapy achieves substantial tumor regression and enables curative-intent surgical resection in borderline or locally advanced disease, limited tumor specificity frequently results in off-target tissue injury, including myelosuppression, mucositis, neuropathy, and organ toxicities, thereby restricting dose intensity and long-term therapeutic sustainability [332, 334]. Moreover, the emergence of immunotherapy has introduced a distinct spectrum of immune-related adverse events, including severe hematologic complications such as immune thrombocytopenia associated with nivolumab therapy, further underscoring the complex balance between antitumor efficacy and systemic immune dysregulation in modern cancer treatment paradigms [335]. Mechanistically, chemoresistance arises from multiple, often coexisting pathways, including pre-existing or acquired genetic alterations, increased drug efflux, defective DNA damage sensing and repair pathways, phenotypic plasticity such as epithelial-to-mesenchymal transition, and protective interactions with the tumor microenvironment. Collectively, these mechanisms can render residual microscopic disease after surgery intrinsically less sensitive to systemic therapy, thereby rendering postoperative residual disease a major driver of tumor recurrence [336, 337]. Radiotherapy provides excellent locoregional control in many settings by delivering DNA-damaging ionizing radiation to tumor volumes, yet its efficacy is constrained by the dose tolerances of adjacent normal tissues, by hypoxic or radioresistant tumor subclones, and by late tissue toxicity that can cause permanent morbidity even when cure is achieved [338, 339]. Advances such as stereotactic radiotherapy and image-guided hypofractionation have improved convenience and, in some settings, local control, but they do not reliably prevent distant relapse and can increase the risk of severe toxicities (for example, radiation pneumonitis or late fibrosis) in anatomically delicate sites [339, 340]. A critical limitation shared by chemotherapy and radiotherapy is that both are most effective against bulk, proliferating disease, whereas microscopic residual cells after surgery may be slow-cycling or sheltered within supportive niches (perivascular spaces, fibrotic stroma) that blunt cytotoxic effects and facilitate eventual outgrowth [336].
Adjuvant immunotherapy, particularly immune checkpoint inhibitors (ICIs), has emerged as an important strategy for reducing recurrence risk in selected solid tumors. Randomized studies and pooled analyses have demonstrated improvements in disease-free survival and, in some settings, overall survival following surgical resection [341, 342]. Notable recent successes include adjuvant PD-1/PD-L1 blockade in resected renal cell carcinoma and subsets of non-small cell lung cancer, and checkpoint-containing perioperative regimens that improve event-free survival in carefully selected cohorts, yet these benefits are not universal and depend strongly on tumor biology (PD-L1 status, mismatch repair deficiency, tumor mutational burden) and patient selection [343, 344]. Mechanistically, the variable efficacy of immunotherapy in the adjuvant setting reflects tumor-intrinsic immune escape (low neoantigen load, defective antigen presentation), an immunosuppressive tumor microenvironment that persists after surgery, and the challenge of generating a durable systemic antitumor T-cell response from a minimal residual disease state [345]. Emerging therapeutic strategies increasingly focus on biologically rational combination approaches. These include combining radiotherapy or selected cytotoxic agents with ICIs to enhance antigen release and immune priming, as well as perioperative immunotherapy approaches designed to strengthen systemic antitumor immunity before or after surgery [346, 347]. Fig. 6 summarizes current cancer therapies, mechanisms of treatment failure, and strategies for early detection and prevention of recurrence.
Fig. 6.

Integrated overview of contemporary solid-tumour management, barriers to durable cure, and strategies for early interception and eradication of residual disease. This schematic juxtaposes current therapies (surgery, chemotherapy, radiotherapy, targeted and hormonal agents, immunotherapies, and cellular/APC-based approaches) with principal mechanisms of treatment failure—intratumoural genetic/phenotypic heterogeneity, growth-kinetic and quiescent cell reservoirs, undruggable drivers, therapy-imposed selective pressure, and immune- and stroma-mediated delivery barriers—and highlights how these factors underlie incomplete eradication of minimal residual disease (MRD), limited predictive biomarkers, and perioperative pro-metastatic windows. The panel also depicts advanced T-cell modalities (TIL, TCR-engineered and CAR T cells, and APC-based strategies) alongside a precision-surveillance paradigm in which real-time molecular monitoring (ctDNA-based MRD) enables earlier relapse detection and risk-adapted therapeutic escalation. Importantly, perioperative inflammation, platelet activation, and NETosis are shown to create protective niches for circulating and residual clones, and therefore represent actionable targets in combination with vascular/ECM normalization, anti-thrombotic/anti-NET interventions, and rational immunotherapy/targeted-therapy combinations to prevent metastatic seeding and improve long-term outcomes
Post-surgical recurrence: the major clinical complexity
Surgical resection, while often necessary and potentially curative for localized solid tumors, paradoxically opens a high-risk window during which microscopic residual disease and liberated tumor cells can exploit wound-healing and systemic responses to seed recurrence, a phenomenon increasingly recognized and studied in recent perioperative oncology literature [255, 348]. Clinically, a substantial fraction of patients who undergo apparently complete resection will later present with locoregional relapse or distant metastases, and the emergence of sensitive minimal-residual-disease (MRD) assays such as ctDNA now demonstrates that many recurrences are preceded by detectable molecular disease in the early postoperative period [349, 350]. As discussed in Section “Surgical disruption of microenvironment and implications for residual disease” and “Tumor-platelet interactions”, perioperative inflammatory and thromboinflammatory responses facilitate residual tumor survival. Direct mechanical effects of surgery also matter: manipulation of the primary tumor and regional tissues can release CTCs into the bloodstream and lymphatics, and higher postoperative levels of CTCs correlate with increased recurrence risk in multiple series [351, 352]. At the cellular level, NETs, immunothrombotic cascades, and platelet–tumor cell interactions generated by tissue injury and coagulation provide adhesive scaffolds and immune-evasive cloaks that promote tumor cell arrest, extravasation, and establishment of metastatic niches [239, 353].
Importantly, several modifiable perioperative factors, including blood transfusion, anesthetic and analgesic strategies, extent of surgical trauma, and perioperative nutrition or infection status, have been associated with changes in recurrence risk. These influences are thought to act through modulation of postoperative inflammation and immune competence during the critical days to weeks following surgery [234, 354]. From a diagnostic standpoint, early postoperative MRD detection (for example, via highly sensitive ctDNA assays) stratifies patients by recurrence risk, identifies those with occult systemic disease who might benefit from intensified adjuvant therapy, and offers a measurable biomarker to test perioperative interventions aimed at preventing relapse [349, 350]. Therapeutically, this mechanistic insight has spurred a twofold strategy: (1) develop perioperative interventions that blunt pro-metastatic inflammation, thromboinflammatory responses, and immune suppression (for example, targeted perioperative anti-inflammatories, NET inhibitors, platelet-modulating agents, or anaesthetic choices), and (2) pair more precise MRD-guided adjuvant therapies or vaccination approaches to eradicate residual clones before clinical relapse [255, 355].
In sum, post-surgical recurrence is a multifactorial clinical complexity driven by mechanical dissemination, a perioperative pro-metastatic microenvironment, and occult residual disease detectable by modern liquid biopsies; addressing it will require coordinated strategies that integrate perioperative immune and coagulation modulation, MRD surveillance, and targeted adjuvant interventions tested in robust clinical trials.
Limitations of current therapies in addressing residual tumor cells
Despite advances in surgical technique and systemic therapy, residual microscopic disease after resection remains the dominant cause of relapse because minimal residual tumor cells can persist in protected niches and exploit perioperative immune dysfunction to re-establish macroscopic disease [255, 356]. Many of these residual cells are slow-cycling or biologically dormant and occupy specialized microenvironments (for example, the perivascular or bone marrow niche) that confer resistance to cytotoxic chemotherapy and radiotherapy designed to kill proliferating tumor bulk [357, 358]. Residual microscopic disease can persist in protected niches and exploit the perioperative inflammatory, coagulation, and platelet-driven microenvironment to escape immune clearance and re-establish macroscopic disease [254, 359]. Modern liquid-biopsy studies further support the clinical relevance of these mechanisms, as postoperative ctDNA or MRD positivity is strongly associated with an increased risk of recurrence and may identify patients who could benefit from earlier therapeutic intervention [356, 360].
Recognizing these limitations, investigators have developed local immune modulation strategies, including intratumoral immunotherapies, depot-forming hydrogels, and oncolytic viruses administered into the resection cavity. These approaches aim to transform the postoperative surgical bed from a permissive microenvironment into a locally immunostimulatory niche capable of eliminating residual malignant cells [361, 362]. Pattern-recognition receptor agonists (STING, TLRs) and cytokine payloads delivered locally generate strong innate activation and antigen presentation, thereby recruiting and priming antitumor T cells, but their clinical translation is limited by delivery challenges, dose-limiting local inflammation, and rapid systemic clearance unless coupled to an optimized carrier [363, 364]. Sustained-release biomaterials, such as implantable or injectable hydrogels, have shown preclinical proof-of-principle that prolonged local release of immunomodulators or checkpoint inhibitors in the resection cavity can prevent local tumor regrowth and improve survival in animal models, and several translational reviews argue that these platforms are particularly well suited to the postsurgical setting [365, 366]. Supporting this concept, recent work using supramolecular hydrogels loaded with oncolytic adenoviruses demonstrated that immediate in situ postoperative delivery induced sustained type I interferon activation, enhanced innate and adaptive antitumor immunity, suppressed recurrence and metastasis, and significantly prolonged survival in murine models, highlighting the therapeutic potential of prolonged local immune activation initiated during surgery [367]. Oncolytic viruses offer a complementary local-first approach by lysing residual tumor cells while simultaneously stimulating innate and adaptive immunity; notable clinical successes (for example, engineered HSV variants) demonstrate activity, although intratumoral virotherapy still faces practical hurdles related to access to deep resection beds, antiviral immunity, and scalable administration [368, 369].
Despite encouraging signals, intratumoral and local-depot approaches confront several common limitations that blunt their ability to eliminate MRD: (1) heterogeneity of the residual cell population and antigenic landscape, (2) inconsistent drug penetration into complex surgical microcavities and fibrotic niches, (3) local toxicity and wound-healing interference at the resection site, and (4) the absence of prospectively validated biomarkers to select patients most likely to benefit [370]. Taken together, these limitations argue for an integrated pathway to make local immune modulation clinically effective, combining highly sensitive MRD assays to identify high-risk patients, biocompatible sustained-release carriers (hydrogels, scaffolds, or membrane-coated nanoparticles) that localize potent innate adjuvants or oncolytics to the surgical bed, and perioperative regimens that blunt pro-metastatic inflammation (for example, NET or platelet modulation) while preserving vaccine-like antigen presentation. In parallel, emerging computational frameworks capable of modeling complex drug–drug interactions and multidimensional molecular features may further improve the rational design, safety optimization, and translational scalability of multimodal perioperative combination therapies [371].
CAR-T cell therapy in solid tumors
Successes in hematologic malignancies
Chimeric antigen receptor (CAR) T-cell therapy has transformed the treatment paradigm for several B-cell and plasma-cell malignancies by converting a patient’s own T cells into potent, antigen-directed living drugs, and the first regulatory approvals in 2017 marked a historic inflection point for cancer immunotherapy [372, 373]. The pioneering CD19-directed product tisagenlecleucel (Kymriah) demonstrated striking single-dose activity in heavily pretreated pediatric and young-adult B-cell precursor acute lymphoblastic leukemia (ALL), leading to durable complete remissions in a substantial subset of patients and rapid FDA approval for relapsed/refractory disease [374, 375]. Shortly thereafter, CD19-directed CAR-T cell therapies targeting aggressive large B-cell lymphomas, most notably axicabtagene ciloleucel (axi-cel) within the ZUMA-1 trial program, demonstrated high overall and complete response rates in patients with refractory disease. Importantly, these therapies also achieved durable long-term remissions in a subset of patients who previously had extremely poor prognoses [376]. Longer follow-up of pivotal cohorts has reinforced that a proportion of patients experience sustained, treatment-free remissions consistent with potential cure, with recent multi-year analyses confirming durable benefit in a clinically meaningful minority and supporting broader adoption of CAR-T for select hematologic indications [377, 378]. Beyond CD19, BCMA-targeted CAR-T cells (for example, idecabtagene vicleucel and ciltacabtagene autoleucel) have produced deep and often rapid responses in heavily pretreated multiple myeloma, substantially improving progression-free outcomes versus historical salvage regimens and earning regulatory approvals or strong guideline support [379, 380].
Real-world analyses and comparative registry studies increasingly confirm that the efficacy observed in clinical trials translates into routine practice, while also highlighting product-specific differences in toxicity profiles, logistical complexity, and resource requirements that influence center selection and patient counseling [381, 382]. The clinical impact of CAR-T therapy has been partly constrained by characteristic toxicities, most notably CRS and ICANS. However, the development of consensus management algorithms, including interleukin-6 (IL-6) blockade with tocilizumab and carefully titrated corticosteroid administration, has rendered these complications largely manageable in experienced clinical centers [383]. The central biological importance of the IL-6/IL6R axis in immune-mediated pathology is further supported by integrative multi-omics and Mendelian randomization analyses identifying IL6R upregulation as a mechanistically relevant driver of inflammatory tissue injury and immune dysregulation in human disease [384], thereby reinforcing the translational rationale for IL-6-targeted interventions in CAR-T-associated toxicities [378]. Operational and scientific challenges nevertheless remain even in hematologic disease: manufacturing failures and vein-to-vein delays, high upfront costs, variable CAR-T persistence, antigen escape or lineage switching, and heterogeneity in durable responses mean that not all patients benefit and that access and long-term efficacy remain active areas for improvement [385, 386].
Challenges in solid tumors
CAR T-cell therapy, which has demonstrated transformative efficacy in hematologic malignancies, encounters a distinct and interconnected set of challenges in solid tumors. Chief among these are insufficient trafficking and infiltration of T cells into tumor masses, on-target off-tumor toxicity leading to systemic adverse effects, and a profoundly immunosuppressive TME that compromises the fitness, persistence, and effector function of engineered T cells [387, 388]. Poor trafficking and intratumoral penetration arise from multiple convergent biological barriers, including abnormal and heterogeneous tumor vasculature, elevated interstitial pressure, collagen crosslinking, dense glycosaminoglycan-rich extracellular matrices, and stromal entrapment, all of which restrict lymphocyte extravasation, migration, and direct contact with malignant cells [389, 390]. Consequently, several strategies, including chemokine receptor engineering (eg, CXCR2/CXCR1 modification), regional or intratumoral delivery, and matrix-remodeling approaches, are being explored to enhance CAR-T trafficking and tumor penetration, although these approaches have not yet become routine in clinical practice [390, 391]. Interestingly, similar delivery limitations have stimulated growing interest in alternative biologically active delivery systems, including bacteria-based tumor therapeutics, which exhibit intrinsic tumor tropism, preferential accumulation within hypoxic tumor regions, and deep intratumoral penetration capacities that may help overcome structural and metabolic barriers restricting immune-cell infiltration in solid tumors [392].
Preclinical and early clinical strategies to overcome these traffic problems (vascular normalization, matrix-degrading enzymes such as heparanase, chemokine receptor retargeting, and regional infusion approaches) show activity in models but raise safety and delivery complexity that must be balanced against the incremental benefit observed so far. A summary of key preclinical models and clinical trials evaluating CAR-T cell therapy in solid tumors, including efficacy and safety outcomes, is provided in Tables 6 and 7 [14, 393]. Off-target effects and systemic toxicity are a second, tightly coupled challenge: many solid-tumor antigens are shared at low levels with essential normal tissues, so high-affinity CARs can mediate on-target, off-tumor injury that ranges from organ-specific damage to life-threatening inflammation [394]. Clinical and preclinical studies have documented severe toxicities when target expression on healthy tissues is underestimated. As a result, several safety-engineering approaches have been developed, including inducible suicide switches, affinity-tuned CARs, logic-gated systems, and regional delivery strategies designed to reduce off-tumor toxicity [395, 396]. Moreover, systemic immune toxicities such as cytokine release syndrome and neurotoxicity can occur with vigorous CAR-T activation, and although management algorithms (IL-6 blockade, steroids) exist, these events limit dose escalation and complicate combination strategies with agents that might otherwise improve efficacy [397, 398].
Table 6.
Comprehensive overview of clinical studies evaluating CAR T-cell therapy in solid tumors
| Year | First author (journal) | Tumour type (indication) | Target antigen | CAR co-stim/construct notes | Phase/design | N (approx.) | Route | Key efficacy outcome (brief) | Major safety/notable events | Ref |
|---|---|---|---|---|---|---|---|---|---|---|
| 2014 | Beatty (Cancer Immunol Res) | Mesothelin-expressing solid malignancies | Mesothelin | Transient mRNA CAR; 4-1BB/CD3ζ | Early clinical case reports | 2 | IV | Feasible; early anti-tumor activity | NR | [399] |
| 2015 | Ahmed (J Clin Oncol) | Advanced HER2+ sarcoma | HER2 | CD28ζ HER2-CAR | Phase I/II dose-escalation | 19 | IV | Tumor-site detection in responders; clinical activity | No DLT; well tolerated | [400] |
| 2015 | Brown (Clin Cancer Res) | Recurrent glioblastoma | IL13Rα2 | First-generation IL13(E13Y)-zetakine CD8+ CTL | First-in-human pilot | 3 | Intracavitary/local brain delivery | Temporary anti-glioma activity | No serious adverse events | [401] |
| 2016 | Feng (Sci China Life Sci) | EGFR+ advanced relapsed/refractory NSCLC | EGFR | EGFR-CAR with expansion ex vivo | Phase I | 11 evaluable | IV | 2 PR, 5 SD (2–8 months) | Safe/feasible; no severe toxicity | [402] |
| 2017 | O’Rourke (Sci Transl Med) | Recurrent glioblastoma | EGFRvIII | EGFRvIII-directed CAR T | First-in-human phase I | 10 | Peripheral infusion (IV) | Antigen loss and adaptive resistance observed | No off-tumor toxicity or CRS; feasible and safe | [403] |
| 2018 | Feng (Protein Cell) | HER2+ advanced biliary tract cancers and pancreatic cancers | HER2 | HER2-CAR (CD28ζ) | Phase I | 11 | NR | 1 PR, 5 SD | Grade 3 fever; liver enzyme elevation; reversible GI bleeding in one case | [403] |
| 2018 | Beatty (Gastroenterology) | Pancreatic carcinoma metastases | Mesothelin | Mesothelin-CAR T | Phase I | 6 | IV | Early anti-tumor activity reported | Feasible and generally safe in report | [404] |
| 2020 | Liu (Cytotherapy) | Metastatic pancreatic carcinoma | EGFR | Anti-EGFR CAR T | Phase I | 14 evaluable | Systemic infusion | 4 PR; 8 SD; median PFS 3 mo; OS 4.9 mo | Reversible grade ≥ 3 fever, mucosal/cutaneous toxicity, pleural effusion, pulmonary interstitial exudation | [405] |
| 2020 | Shi (Clin Cancer Res) | Advanced hepatocellular carcinoma | GPC3 | CAR-GPC3 | Phase I trials | 13 | NR | Early anti-tumor activity; some responses | Pyrexia, decreased lymphocyte count, and CRS reported; one grade 5 CRS; no grade 3/4 neurotoxicity | [406] |
| 2021 | Adusumilli (Cancer Discovery) | Malignant pleural disease (MPM and metastatic lung/breast) | Mesothelin | Regional MSLN-CAR; later pembrolizumab combination | Phase I | 27 | Intrapleural | 2 complete metabolic responses; 5 PR; 4 SD among combination-treated MPM subset | No CAR T-related toxicities > grade 1; no on-target/off-tumor toxicity | [407] |
| 2022 | Qi (Nature Medicine) | Advanced GI cancers | CLDN18.2 | CLDN18.2-specific CAR T (satri-cel) | Phase I interim results | 37 (49 infused at data cutoff) | IV infusion | Promising efficacy in CLDN18.2+ GI cancers | Manageable safety profile | [408] |
| 2024 | Brown (Nature Medicine) | Recurrent high-grade glioma | IL13Rα2 | Locoregional IL13Rα2-CAR T | Completed phase I | 65 | Locoregional/intrathecal strategy | Largest completed clinical study; feasibility and safety emphasized | Safety and feasibility were primary objectives; manageable toxicities | [409] |
| 2024 | Hegde (Nature Medicine) | Advanced sarcoma | HER2 | Autologous HER2-CAR after lymphodepletion | Phase I | 13 individuals/14 enrollments | IV | Evidence of clinical activity in some patients | Safety acceptable after lymphodepletion | [410] |
| 2024 | Pinto (Clin Cancer Res) | Relapsed/refractory solid tumors | B7–H3 | Systemic B7–H3 CAR T | First-in-human phase I | NR | Systemic administration | Limited antitumor activity | Tolerable; no acute on-target/off-tumor toxicity | [411] |
| 2024 | Gargett (J Clin Oncol) | Metastatic melanoma and other solid cancers | GD2 | Third-generation GD2 CAR T | Phase I | 12 | NR | First report of GD2-targeted CAR T in adults with solid cancers | Safety/biological outcomes acceptable in phase I | [412] |
Abbreviations: ACS, acute coronary syndrome; CAR, chimeric antigen receptor; CRS, cytokine release syndrome; Cy, cyclophosphamide; EGFR, epidermal growth factor receptor; EGFRvIII, epidermal growth factor receptor variant III; GI, gastrointestinal; GPC3, glypican-3; HER2, human epidermal growth factor receptor 2; HGG, high-grade glioma; IL13Rα2, interleukin-13 receptor alpha 2; IV, intravenous; MPM, malignant pleural mesothelioma; MSLN, mesothelin; NSCLC, non-small cell lung cancer; NR, not reported; OS, overall survival; PFS, progression-free survival; PR, partial response; SD, stable disease
Table 7.
Comprehensive overview of experimental & preclinical studies evaluating CAR T-cell therapy in solid tumor models
| Year | First author (journal) | Tumour type/model | Target antigen | CAR co-stim/construct notes | Model/design | Route/delivery | Key efficacy outcome (brief) | Ref |
|---|---|---|---|---|---|---|---|---|
| 2016 | Katz (Cancer Gene Ther) | Peritoneal carcinomatosis models | CEA | Regional CAR-T | Preclinical comparison of delivery routes | Regional vs systemic | Regional infusion superior to systemic delivery | [413] |
| 2017 | Tchou (Cancer Immunol Res) | Metastatic breast cancer models | c-Met was expressed in ∼50% of breast tumors | Intratumoral CAR-T | Preclinical + translational | Intratumoral | Supported feasibility of local CAR-T for solid tumors | [414] |
| 2018 | Priceman (Clin Cancer Res) | HER2+ breast cancer brain metastasis models | HER2 | Regional HER2-CAR | Preclinical brain-metastasis study | Regional delivery | Effective against HER2+ brain metastasis | [415] |
| 2018 | Han (Am J Cancer Res) | Gastric cancer xenografts/peritoneal metastasis | HER2 | chA21-4-1BBζ CAR | Xenograft + metastasis models | Systemic/in vivo adoptive transfer | Strong cytolysis; tumor regression; prolonged survival; tumor homing | [416] |
| 2017 | Wei (Oncoimmunology) | NSCLC models | PSCA and MUC1 | Dual-target CAR-T | Preclinical dual-antigen targeting | In vitro/in vivo | Established PSCA/MUC1 as dual targets in NSCLC | [417] |
| 2019 | Wallstabe (JCI Insight) | Lung and breast cancer 3D microphysiologic models | ROR1 | ROR1-CAR | Advanced 3D model study | In vitro/3D culture | Effective in lung and breast cancer models | [418] |
| 2021 | Pang (Cancer Res) | Human solid tumor models | IL-7/CCL19 armoring | 7 × 19 CAR-T | Armored CAR-T | In vitro/xenograft | Enhanced antitumor activity versus conventional CAR-T | [419] |
| 2023 | Lu (Cell Biol Toxicol) | HCC models | GPC3 | GPC3-7–19 CAR-T | Armored CAR-T | In vivo HCC models | Improved killing, infiltration, and TCM/TEM features | [420] |
| 2023 | Wang (Cell Immunol) | Pancreatic cancer models | MUC1 + PSCA | Tandem CAR; anti-CTLA-4 combination | Dual-target combinatorial design | In vitro/in vivo | Improved antitumor efficacy and resistance control | [421] |
| 2024 | Barrett (Clin Cancer Res) | CLDN18.2+ gastric/pancreatic models | CLDN18.2 | Armored AZD6422 | Preclinical development | Patient-derived models | Potent activity in CLDN18.2+ models | [422] |
| 2024 | Shabaneh (J Immunother Cancer) | HER2+ solid tumor models | HER2 | Low-affinity HER2-CAR | Affinity-tuned CAR | Systemic preclinical evaluation | Tumor regression with improved safety window | [423] |
Abbreviations: CAR, chimeric antigen receptor; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; GPC3, glypican-3; HER2, human epidermal growth factor receptor 2; HCC, hepatocellular carcinoma; IL-7, interleukin-7; MUC1, mucin 1; NSCLC, non-small cell lung cancer; PSCA, prostate stem cell antigen; ROR1, receptor tyrosine kinase-like orphan receptor 1; TCM, central memory T cells; TEM, effector memory T cells; TME, tumor microenvironment; NR, not reported
The third major barrier is the immunosuppressive TME, where metabolic stress, MDSCs, regulatory T cells, and inhibitory cytokines (notably TGF-β and IL-10) conspire to exhaust or reprogram CAR-T cells and protect residual tumor cells [424, 425]. Emerging evidence further suggests that tumors can actively exploit exosome-mediated immune modulation to suppress inflammatory and cytotoxic immune responses through the transfer of immunoregulatory proteins and miRNAs that alter T-cell activation and cytokine signaling pathways, thereby reinforcing local immune tolerance and therapeutic resistance [426]. Myeloid populations and stromal elements also actively exclude or deactivate CAR-T cells via arginase/indoleamine pathways, reactive oxygen species, and checkpoint ligand expression, making single-agent CAR approaches unlikely to succeed in many solid-tumor contexts without concurrent modulation of these inhibitory axes [427]. Fig. 7 outlines the CAR-T manufacturing workflow and highlights key biological and translational barriers limiting its efficacy in solid tumors, including antigen heterogeneity, poor tumor infiltration, immunosuppression, and safety-related challenges. Taken together, these three problem domains are interdependent: poor trafficking amplifies the impact of local immunosuppression (fewer effector cells arriving to counter suppression), while efforts to increase potency through higher-affinity CARs or stronger costimulatory signaling simultaneously raise the risk of off-tumor toxicity unless coupled with precise control mechanisms or highly tumor-restricted antigen selection [389, 394].
Fig. 7.

Overview of the CAR-T cell manufacturing continuum and the principal biological barriers limiting its efficacy in solid tumors. A) The schematic depicts the standardized autologous production pipeline, including peripheral T-cell collection, ex vivo activation, genetic engineering via viral or non-viral platforms, controlled expansion, quality assessment, and reinfusion, together with the progressive evolution from first- to fifth-generation CAR constructs aimed at improving signaling strength, persistence, and functional stability. B) Despite these technological advances, several critical challenges continue to constrain therapeutic performance in solid malignancies. Prominent among these are antigen heterogeneity and tumor immune escape, which undermine durable target recognition; insufficient trafficking and limited intratumoral penetration resulting from abnormal vasculature and dense stromal architecture, and the profoundly immunosuppressive tumor microenvironment, characterized by inhibitory cellular subsets, suppressive cytokines, and metabolic stressors that collectively promote T-cell dysfunction and exhaustion. In addition, safety concerns—including on-target/off-tumor toxicity and cytokine release syndrome—remain major translational constraints. For each of these barriers, both established and emerging strategies have been proposed to enhance therapeutic precision, durability, and clinical applicability in solid tumor settings
Need for targeted, localized CAR T delivery
The disappointing and inconsistent results of systemically delivered CAR-T cells in most solid tumors have crystallized a clear rationale for shifting toward targeted, localized delivery strategies that place effector cells where microscopic residual disease and hostile microenvironments actually reside [18, 428]. A key driver of this limitation is impaired tumor trafficking following intravenous infusion. Abnormal tumor vasculature, dysregulated chemokine gradients, and a dense extracellular matrix often restrict CAR-T cell accumulation within the tumor parenchyma. Regional delivery strategies can help circumvent these physical and biological entry barriers, improving local cell delivery and retention [428, 429]. Equally important, the immunosuppressive TME, which can rapidly exhaust or inactivate CAR-T cells, is driven by factors such as TGF-β, hypoxia, suppressive myeloid cell populations, and metabolic constraints. This context supports strategies that achieve high local effector-to-target ratios in combination with local immunomodulatory adjuvants, rather than relying solely on limited systemic exposure of engineered T cells [14, 18].
Targeted locoregional approaches also address a core safety problem: by confining potent CAR-T activity to the resection cavity or tumor bed (or by regional infusion into a body cavity or feeding artery), clinicians can raise local doses while reducing systemic on-target/off-tumor toxicity and severe cytokine-mediated adverse events [429, 430]. Preclinical biomaterial platforms, including injectable hydrogels, porous scaffolds, and polymer–nanoparticle (PNP) depots, offer practical strategies to localize CAR-T cells at the surgical margin. These systems can simultaneously co-deliver cytokines or costimulatory signals and establish a transient pro-expansion microenvironment, thereby enhancing local CAR-T cell proliferation and improving tumor control in animal models [431]. In parallel, emerging stimulus-responsive nanoplatforms, such as ultrasound-triggered copper–calcium phosphate systems, demonstrate the broader feasibility of spatiotemporally controlled therapeutic release, where external physical cues enable localized, on-demand activation of cytotoxic mechanisms while minimizing off-target effects, further supporting the translational rationale for site-restricted therapeutic activation in oncology [432].
Clinical experience with regional and intratumoral CAR-T administration is growing and shows feasibility and early signals of activity across indications (intracavitary, intrapleural, intraperitoneal, and intra-arterial routes), supporting the translational potential of local delivery while also defining important procedural and patient-selection lessons [433, 434]. Very recent early-phase reports of intratumoral CAR-T dosing (for example, EPHB4-CAR intracavitary work and other site-directed trials) demonstrate that direct tumor-bed injection can be performed safely and can produce objective local responses, although durability and systemic control remain variable, and trial designs are still evolving [435]. Concurrently, materials-driven innovations (3D scaffolds, adhesive matrices, and immune-instructive hydrogels) are maturing from conceptual papers into translational toolkits that permit controlled cell release, protect transferred T cells from immediate suppression, and permit sustained exposure to low-dose cytokine adjuvants without systemic spillover [436, 437]. To realize the full potential of targeted CAR-T delivery, a coordinated translational framework is required. This should integrate MRD and ctDNA-guided patient selection, perioperative or intraoperative placement of biomaterial depots or regional infusion strategies, and next-generation engineered CAR constructs incorporating enhanced trafficking receptors and armored features to resist local immunosuppression. In parallel, early-phase clinical trials should comprehensively evaluate local tumor control, systemic immune activation, safety profiles, and functional clinical outcomes [438, 439].
An additional and highly attractive translational possibility is to combine platelet-guided delivery with in vivo CAR-T generation platforms. In vivo CAR-T engineering has recently emerged as a strategy to bypass the complexity, time, and cost of ex vivo CAR-T manufacture by delivering CAR-encoding nucleic acids directly to endogenous T cells, most commonly through targeted lipid nanoparticle systems designed to achieve T-cell transfection and transient, tunable CAR expression in situ [440, 441]. This concept is especially relevant to the postoperative setting, where a platelet-guided carrier could theoretically serve as a biologically selective depot that concentrates CAR-encoding cargo within fibrin-rich, inflamed, or wounded tissue, thereby increasing local payload exposure while reducing off-target systemic distribution. Such a strategy would be conceptually aligned with biomaterial-based approaches for in situ CAR-T generation and local immune-cell programming, which have already been explored as ways to improve cellular retention, expansion, and function in restricted anatomical niches [437, 442, 443]. Platelet-based nanocarriers are particularly appealing for this purpose because platelet membrane-coated or platelet-derived platforms have already demonstrated in vivo delivery of nucleic acids, including targeted gene-silencing payloads, while preserving the natural targeting behavior of platelets toward injured endothelium and inflammatory microenvironments [290, 444]. From a mechanistic standpoint, platelet-guided localization could therefore complement in vivo CAR-T generation by helping to confine CAR programming to the residual-disease niche, potentially improving safety, local effector concentration, and spatial precision in the postoperative bed. Nevertheless, this remains a hypothesis-generating concept rather than a clinically validated strategy, and it will require direct proof-of-principle studies addressing T-cell targeting efficiency, payload release kinetics, thrombogenic safety, and the feasibility of GMP-compatible manufacturing before translational adoption.
Platelet-guided CAR T cells: optimized strategy
Concept and design of platelet-CAR T conjugates
The concept of platelet-CAR-T conjugates is based on two complementary biological properties. First, platelets are naturally home to sites of vascular injury, inflammation, and tumor-associated tissue damage. Second, CAR-T cells provide potent antigen-specific cytotoxic activity. Combining these features may improve the localization of cellular therapy to sites of residual disease while potentially reducing systemic exposure and off-target toxicity [299]. The biological rationale is strong: platelets form intimate associations with circulating tumor cells and with fibrin-rich surgical beds, a behavior that has been exploited to target therapeutic payloads (for example, TRAIL or drug-loaded liposomes) to tumor cells in circulation and to inflamed vascular niches [319, 445]. Current design strategies can generally be divided into two major categories. The first involves cellular conjugates in which intact platelets are directly linked to CAR-T cells or engineered immune components. The second includes biomimetic membrane-based approaches that use platelet membranes to confer targeting and immune-evasive properties to engineered carriers [446, 447]. Several conjugation and bridging strategies have been described for platelet-based delivery systems, including receptor-directed antibody linkers, biotin–streptavidin coupling, thioether-based chemistry, and transient loading through platelet-targeted liposomes. These approaches have already been used in preclinical studies to attach TRAIL, cytotoxic agents, or other therapeutic payloads to platelets and to generate platelet-associated drug delivery platforms [319, 446].
From a translational standpoint, two major engineering paradigms can be distinguished. In cellular platelet–CAR-T conjugates, intact platelets are used as living carriers that retain native lesion-homing behavior and may thereby enhance local accumulation at injury- or tumor-associated sites; however, this biological authenticity also introduces important translational constraints, including inadvertent platelet activation, thrombogenicity, limited ex vivo stability, and technically demanding control of conjugation and release kinetics [311, 448]. By contrast, biomimetic platelet-membrane platforms transfer selected platelet surface features onto nanoparticles, vesicles, or other engineered carriers, thereby preserving immune evasion, prolonged circulation, and targeting capacity while improving modularity and manufacturability; their main limitations are the loss of living-platelet responsiveness, dependence on high-quality membrane isolation, and residual heterogeneity or stability issues during processing [291, 449]. Taken together, these two design paradigms should be viewed as complementary rather than competing: cellular conjugates maximize biological authenticity, whereas biomimetic membrane systems maximize manufacturability and platform versatility (See Table 8). Complementary to direct conjugation, implantable or injectable biomaterials (for example, pro-adhesive hydrogels and porous scaffolds) can be seeded with CAR-T cells and platelet components at the time of surgery to create a retained depot that sustains local expansion, provides costimulatory cytokines, and physically concentrates effector cells at the resection margin [447, 450].
Table 8.
Comparative overview of preparation strategies for platelet-guided CAR-T systems
| Feature | Cellular platelet–CAR-T conjugates | Biomimetic platelet-membrane platforms |
|---|---|---|
| Core design | Intact platelets are directly conjugated to CAR-T cells. | Platelet membranes are transferred onto nanoparticles or engineered carriers associated with CAR-T delivery. |
| Typical preparation methods | Chemical crosslinking, receptor-mediated tethering, biotin–streptavidin systems, bioorthogonal conjugation. | Membrane extraction, extrusion, sonication, vesicle fusion, nanoparticle cloaking. |
| Biological responsiveness | High; preserves native platelet activation and fibrin/injury sensing. | Moderate; retains selected surface ligands but lacks living platelet dynamics. |
| Tumor/lesion homing | Strong active homing to thrombotic and postoperative niches. | Efficient passive/biomimetic targeting through preserved membrane receptors. |
| Immune evasion capacity | Moderate; depends on platelet viability and circulation time. | High; platelet membranes reduce phagocytic clearance and prolong circulation. |
| CAR-T support function | May improve local CAR-T retention and immediate perioperative localization. | Mainly improves delivery efficiency and spatial targeting of CAR-T-associated systems. |
| Major advantages | Maximal biological fidelity; dynamic vascular responsiveness; strong local retention. | Better modularity, engineering flexibility, scalability, and GMP compatibility. |
| Major limitations | Platelet activation, thrombogenicity, short ex vivo stability, and batch variability. | Membrane heterogeneity, ligand loss during processing, and reduced biologic responsiveness. |
| Manufacturing complexity | High; requires viable platelet handling and controlled conjugation workflows. | Moderate-to-high; dependent on membrane isolation and nanoparticle standardization. |
| Storage stability | Limited because of intact platelet viability requirements. | Relatively improved compared with live platelet systems. |
| Safety concerns | Thromboinflammatory activation and unintended coagulation signaling. | Potential membrane-associated procoagulant signaling if purification is incomplete. |
| Translational suitability | Best suited for localized perioperative or residual-disease settings. | More attractive for scalable and reproducible clinical translation. |
| Current development stage | Early preclinical/conceptual | Advanced preclinical development across multiple nanomedicine platforms. |
Functionally, platelets offer three attractive technical advantages when paired with CAR-T cells: targeted adhesion to fibrin and vWF in damaged vessels, intrinsic hemostatic activity that can reduce perioperative bleeding, and a membrane coat rich in self-markers that can reduce opsonization and prolong local retention [451, 452]. Similarly, platelet-membrane cloaking with synthetic nanoparticles has been shown to confer immune evasion and prolonged circulation while enhancing tumor accumulation, suggesting that membrane-based strategies may likewise protect transferred lymphocytes or local depots from rapid clearance and non-specific activation [447, 451]. However, the biology that enables targeting also creates risks: platelets can shield tumor cells from immune recognition and have been reported to blunt the efficacy of some T-cell–recruiting therapies, so any platelet-based delivery system must be engineered to avoid promoting tumor immune evasion or unintended pro-metastatic effects [445, 453]. Manufacturing and clinical translation also present important challenges, including control of platelet activation during processing, batch-to-batch variability of membrane preparations, transfusion compatibility concerns, and the need for reproducible large-scale production workflows [454, 455].
Mechanistic advantages
Platelet-guided CAR-T delivery integrates two complementary biological functions: the natural homing behavior of platelets toward vascular injury and inflammatory niches, and the antigen-specific cytotoxic activity of CAR-T cells. Together, these properties provide a rationale for improving local immune-cell concentration within postoperative tumor beds and sites of minimal residual disease [299, 456]. First, platelets naturally adhere to exposed subendothelial matrix (collagen, von Willebrand factor, and fibrin) and to inflamed microvasculature created by surgery, which provides a robust biochemical anchor to retain a platelet-CAR-T conjugate at the resection margin where residual malignant cells are most likely to persist [299, 457]. Second, platelet membranes and intact platelets present a repertoire of adhesive receptors (P-selectin, GPIb–IX–V, integrins) and self-markers that promote selective interaction with damaged vascular niches while reducing immediate opsonization and nonspecific clearance, thereby increasing local residency time of attached CAR-T effectors compared with free systemic infusion [290, 458]. Third, by physically localizing CAR-T cells to the tumor bed or an implanted depot, platelet guidance enables much higher local effector: target ratios than are achievable safely via systemic dosing, which can amplify on-target tumor killing while limiting the systemic T-cell burden that drives cytokine release and off-tumor toxicity [299]. Fourth, platelet-based depots or membrane-cloaked carriers permit co-delivery of immune-stimulatory adjuvants (for example, TLR/STING agonists or low-dose cytokines) in the same local microenvironment as CAR-T cells, a spatial colocalization that fosters in-situ antigen presentation and CAR-T expansion without large systemic exposure to proinflammatory agents [431, 459]. Fifth, the intrinsic hemostatic function of platelets offers a practical perioperative advantage: platelet-CAR-T constructs or platelet-seeded scaffolds can both mitigate bleeding at the resection site and immediately deposit effector cells at the highest-risk locations for recurrence, combining haemostasis with immune surveillance in a single intervention [456, 460]. Sixth, biomimetic platelet membranes have already been shown to improve tumor accumulation and immune evasion of nanoparticle cargos in multiple preclinical models, supporting the idea that membrane-based cloaking of cellular depots could extend CAR-T persistence and reduce premature activation or clearance in the hostile postsurgical milieu [290, 461]. Seventh, material-assisted formulations, including adhesive hydrogels, porous scaffolds, or PNP depots seeded with platelet components and CAR-T cells, establish a tunable micro-niche that supports local proliferation, provides costimulatory signals, and enables the gradual release of cells into the wound cavity. This strategy has demonstrated improved local tumor control in multiple animal studies [431]. Eighth, by constraining CAR-T activity spatially, platelet guidance reduces the probability that CAR-T cells will engage low-level antigen on distant healthy tissues (on-target/off-tumor effects), and when combined with affinity-tuned receptors or safety switches, this localized approach offers a layered risk-mitigation strategy for high-risk antigen targets [462]. Fig. 8 summarizes platelet-guided CAR T engineering and its localized anti-tumor effects in the tumor resection site.
Fig. 8.

Engineering paradigms and localized therapeutic cascade of platelet-guided CAR T cells. (A) Design strategies for therapy delivery, including cellular conjugates utilizing intact platelets for living-system homing via diverse conjugation chemistries (A1), and (A2) biomimetic platforms leveraging platelet-membrane (PM) isolation for cloaking CAR T cells. (B) The multifaceted mechanistic cascade within the post-surgical resection cavity: (1) active homing and anchoring to the damaged microvasculature via native platelet receptors; (2) extended local residency and immune evasion through PM-mediated shielding; (3) localized activation and high effector-to-target (E:T) ratios; (4) synergistic in situ expansion driven by co-delivered adjuvants and cytokines; and (5) amplified tumor apoptosis coupled with concurrent hemostasis. This localized approach ensures robust prevention of recurrence while minimizing systemic toxicities
Preclinical evidence and proof-of-concept studies
Animal models of platelet-coupled immunotherapy
A rapidly expanding body of preclinical evidence, including flow-based circulating tumor cell (CTC) assays, syngeneic and xenograft metastasis models, and orthotopic post-resection recurrence systems, demonstrates that platelet-based targeting strategies can significantly modify the biodistribution of therapeutic payloads and reduce metastatic seeding in vivo [319, 445]. One of the clearest, reproducible findings across multiple laboratories is that platelets engineered to display cytotoxic ligands or conjugated with cytotoxic cargos markedly increase the killing of circulating tumor cells under physiological shear and reduce lung or systemic metastases in mouse models compared with untargeted controls [287, 319]. Complementary animal studies using PNPs or platelet-cloaked PLGA/MSN cores show enhanced accumulation in tumor beds and fibrin-rich vascular lesions, improved retention of small-molecule or biologic payloads, and superior anti-tumor effect and metastasis suppression versus uncoated particles in multiple rodent models [463, 464].
Importantly, several groups have translated the platelet-targeting concept into perioperative models: in postsurgical murine systems, platelet-conjugated immune modulators, including anti-PD-1/PD-L1-loaded platelet platforms, preferentially accumulate within surgical wound microenvironments and residual tumor niches, where they enhance local immune activation, suppress postoperative tumor recurrence, and prolong survival compared with surgery alone [297]. These findings are particularly noteworthy in light of emerging clinical evidence demonstrating that perioperative and neoadjuvant PD-1/PD-L1 blockade significantly improves pathological complete response, event-free survival, and overall survival in resectable solid tumors such as NSCLC, underscoring the translational relevance of localized immune checkpoint modulation during the perioperative period [465]. Complementary preclinical studies have shown that locoregional cellular depots, such as CAR-T cells delivered within transient stimulatory hydrogels or lymph node–mimetic scaffolds, significantly enhance local expansion, persistence, and antitumor efficacy in murine solid tumor models compared with systemic infusion. Together, these findings establish a translationally relevant framework that may be further optimized through the incorporation of platelet-derived components [431, 466]. At the same time, rigorous preclinical safety signals have been consistently reported. In several experimental models, platelets may paradoxically shield tumor cells from immune surveillance or facilitate thromboinflammatory cascades. Moreover, platelet-derived materials exhibit batch-to-batch variability in functional performance, which can influence both therapeutic efficacy and coagulation-related readouts. Collectively, these findings underscore the need for rigorous functional standardization and safety optimization before clinical translation [453, 467]. From a manufacturing and process perspective, animal studies have also informed practical choices: using platelet-derived supplements (human platelet lysate) can improve T-cell expansion phenotypes in vivo and ex vivo, while dosing and formulation studies in rodents have clarified activation thresholds, depot release kinetics, and local cytokine safety windows that may guide early translational tolerability assessment [468].
CAR T cell functional enhancement via platelet delivery
Platelet-guided approaches are emerging as a pragmatic strategy to enhance CAR-T efficacy in solid tumors by combining the innate wound- and lesion-homing behavior of platelets with biomaterial depots and engineered cellular payloads that concentrate and sustain effector function at sites of minimal residual disease [469]. A proof-of-concept preclinical study demonstrated that implantation of an immunotherapeutic hydrogel containing CAR-T cells, IL-15-loaded nanoparticles, and anti-PD-L1-conjugated platelets improved local tumor control and reduced postoperative recurrence in murine models [450]. From a manufacturing and cell-fitness perspective, platelet-derived supplements such as human platelet lysate can improve T-cell expansion phenotypes and functional persistence during ex vivo manufacture, offering a complementary production advantage when creating platelet-paired CAR-T products [468]. Operationally, membrane-based cellular products introduce quality-control challenges (lot-to-lot membrane composition, activation state control, and release criteria) that mirror but also extend the hurdles seen in membrane-coated nanoparticle and advanced cell-therapy manufacturing [37]. To enhance clinical signal strength and maximize patient benefit, platelet-guided CAR-T strategies should be integrated with modern MRD and ctDNA–based patient selection. This would enable intraoperative or early postoperative deployment of cellular depots specifically in patients with molecular evidence of residual disease. Such an approach is consistent with emerging evidence indicating that ctDNA-defined MRD status is a robust predictor of recurrence risk and serves to enrich the magnitude of benefit from adjuvant therapeutic interventions [470, 471]. A practical translational roadmap therefore emerges: (1) rigorous GLP toxicology and large-animal biodistribution studies focused on coagulation and immune endpoints, (2) standardized GMP workflows for platelet/membrane sourcing and activation control, (3) MRD-guided patient selection and adaptive early-phase trials that measure local persistence, ctDNA clearance and systemic spillover, and (4) integrated safety switches or affinity-tuning on CARs to reduce off-target risk.
Safety and hemostatic considerations
Platelet-guided CAR-T strategies operate at the interface of hemostasis and cellular immunity, offering a powerful targeting paradigm but simultaneously introducing a complex safety landscape in which pro-thrombotic, immunologic, and tumor-protective mechanisms must be rigorously evaluated [23, 472]. Leveraging platelet biology within the surgical bed or circulation may amplify coagulation cascades and increase the risk of venous or arterial thrombosis, consistent with the established pro-thrombotic role of tumor-activated platelets and reported thrombotic events following cellular immunotherapies [23, 473]. Although platelet-based depots may reduce perioperative bleeding, modification of platelet function or administration of membrane-rich products can disrupt systemic hemostatic equilibrium, potentially leading to consumptive coagulopathy or unintended platelet activation; therefore, design strategies must prioritize localized hemostatic benefit without systemic coagulation perturbation [474]. Native platelet–tumor interactions can shield malignant cells from immune surveillance and facilitate metastatic dissemination, raising the possibility that inadequately engineered platelet conjugates could protect residual tumor cells unless specifically designed to neutralize protumor signaling or deliver cytotoxic payloads [472, 475]. Importantly, the available literature on platelet-based delivery systems and thrombotic endpoints is still largely preclinical and is centered on thrombus-targeted applications rather than oncology-oriented constructs. In a carotid thrombosis model, platelet membrane-functionalized nanoparticles improved thrombus targeting and were associated with lower hemorrhagic risk during thrombolysis, while platelet membrane-coated r-SAK shortened recanalization time in a rabbit femoral artery thrombosis model. Likewise, a platelet-derived nanoplatform co-delivering recombinant hirudin and apixaban reduced deep vein thrombosis burden at a reduced dose, and a platelet membrane-coated nanoparticle formulation attenuated platelet activation and NET formation in a thrombo-inflammatory model [476–478]. Taken together, these findings indicate that platelet biomimicry can be engineered to treat thrombosis without necessarily increasing thrombotic burden, but they also reinforce the need for rigorous coagulation, platelet-function, and NETosis monitoring when platelet components are incorporated into CAR-T delivery systems.
Administration of platelet-derived materials, including intact platelets, platelet membranes, or extracellular vesicles, is associated with potential risks such as alloimmunization, febrile or allergic reactions, and transfusion-related inflammatory responses. These safety concerns underscore the necessity for antigen matching, leukoreduction, and rigorous product characterization to ensure consistency, reduce immunogenicity, and improve translational safety [479, 480]. Clinical experience with CAR-T therapies demonstrates that coagulopathy and bleeding events occur in a clinically meaningful subset of patients, often in association with systemic inflammation or cytokine release syndrome, necessitating integrated coagulation monitoring and predefined management algorithms when platelet components are incorporated [481, 482]. Injectable or implantable depots such as hydrogels and scaffolds may enhance local hemostasis; however, their blood-contact properties, degradation profiles, and release kinetics must be carefully validated to avoid unintended activation of platelets, complement, or contact pathways [483]. Given the biological complexity and batch-to-batch variability of platelet membranes and extracellular vesicles, translational development requires stringent release criteria. These should include comprehensive surface marker profiling, assessment of activation state, and evaluation of residual cytosolic content, alongside the implementation of validated analytical assays. Together, these measures are essential to ensure consistent product safety, reproducibility, and therapeutic potency [484]. Fig. 9 summarizes preclinical evidence and safety considerations of platelet-guided CAR T therapy, highlighting enhanced anti-tumor efficacy, reduced metastasis, and key thrombotic risks with proposed mitigation strategies.
Fig. 9.

Preclinical validation, functional enhancement mechanisms, and safety landscape of platelet-guided CAR T cell therapy. (A) Mechanistic orchestration within the surgical resection bed depot (e.g., hydrogel) involves the localized concentration of CAR T cells alongside IL-15-loaded nanoparticles and anti-PD-L1-conjugated platelets. These components selectively anchor to the exposed ECM and damaged microvasculature, facilitating robust in situ expansion, prevention of T-cell exhaustion, and a high local effector-to-target (E:T) ratio. (B) Preclinical evidence from mouse models demonstrates that systemic platelet-CAR T conjugates effectively bind circulating tumor cells (CTCs) under physiological shear to reduce metastatic burden (B1), while perioperative implantation of seeded scaffolds significantly prevents local recurrence and prolongs survival (B2). The integration of human platelet lysate (HPL) further enhances manufacturing by improving cell persistence in vivo. (C) The translational safety landscape identifies key risks such as venous/arterial thrombosis and disrupted systemic hemostasis, which are addressed through targeted mitigation strategies including CAR affinity-tuning, safety switches, and the localized co-delivery of antithrombotic agents like hirudin or apixaban. This integrated approach ensures potent anti-tumor efficacy while minimizing thrombo-immunologic complications and off-target risks
Translational challenges and opportunities
Manufacturing and scalability of platelet-CAR T conjugates
Successful translation of platelet-guided CAR-T products depends as much on robust, reproducible manufacturing and scalable supply-chain solutions as it does on the underlying biology, because these hybrid products combine cell-therapy complexity with membrane/membrane-derived materials that introduce new process steps and release criteria [485, 486]. At the process level, these constructs are hybrid biologics requiring two parallel manufacturing streams. First, standard GMP-grade CAR-T cell production, including cell collection, activation, genetic modification, expansion, and final formulation. Second, consistent production of platelet-derived materials, such as intact platelets, membrane isolates, or platelet-derived vesicles, using validated protocols for isolation, purification, and storage. Each component introduces distinct technical challenges and bottlenecks that must be addressed for scalable and reproducible manufacturing [487, 488]. A central early decision is platelet sourcing: autologous platelet material avoids alloimmunization but complicates logistics and per-patient cost, whereas allogeneic platelet or pooled membrane supplies simplify scheduling but demand stringent donor screening and rigorous testing for lot-to-lot variability and functional consistency [489, 490].
Storage stability is another major determinant of feasibility. Platelets have a relatively short shelf life, and platelet activation during processing can induce shape change, degranulation, and premature release of therapeutic cargo, thereby compromising carrier function. For this reason, preparation should be performed with strict attention to collection technique and handling conditions; immediate processing after blood draw, avoidance of unnecessary mechanical stress, and careful purification of platelet-rich plasma are important to minimize unintended activation. During centrifugation, anti-activation agents such as apyrase or prostaglandin E1 (PGE1) can be used to suppress platelet activation and improve the consistency of the final product [491, 492]. Batch variability is an additional challenge because platelet functional state can vary across donors and across processing runs. One strategy to reduce this problem is the use of pooled platelet starting material, which has been proposed to limit batch-to-batch variability in platelet extracellular vesicle manufacturing. In parallel, PEVs may offer a more practical alternative for some applications because they have been reported to exhibit greater storage stability and a longer usable shelf life than intact platelets, while retaining key platelet-associated biological properties relevant to targeting and delivery [493–495].
Quality-control and release testing must therefore be broadened beyond standard sterility/potency to include membrane identity, residual procoagulant activity, platelet activation markers, aggregate/particle profiling, and validated thrombogenicity assays (for example, D-dimer, thrombin-generation, and platelet-function panels) as part of lot release [496]. Preclinical and GLP packages must therefore prioritize biodistribution (local retention vs systemic spillover), thrombo-inflammatory readouts, and large-animal studies that recapitulate human coagulation physiology, because small-rodent safety models do not reliably predict thrombosis or platelet-mediated immunologic effects in humans [497, 498]. A practical, de-risked development pathway is to begin with tightly controlled, small-scale autologous feasibility runs (intraoperative or depot use), concurrently develop a GMP-grade membrane-manufacturing stream and standardized potency/thrombogenicity assays, and only move to larger allogeneic or off-the-shelf formats after robust comparability and stability data are demonstrated [489].
Comparative perspective: platelet-based delivery versus lipid nanoparticles, hydrogels, and cell-based carriers
Platelet-based delivery systems occupy a distinct translational niche because they combine endogenous wound-homing, receptor-mediated adhesion, immune evasion, and biologically meaningful interactions with fibrin-rich, inflamed, or tumor-associated niches. In contrast to conventional synthetic nanocarriers, platelet-derived membranes can confer both targeting competence and a self-like surface phenotype, which may improve circulation and lesion recognition while preserving compatibility with biologically active payloads. At the same time, the field still faces important barriers, including platelet activation control, membrane heterogeneity, thrombogenic risk, and manufacturing standardization [499, 500]. To position platelet-guided CAR-T within the broader therapeutic landscape, Table 9 compares it with traditional systemic CAR-T, locoregional immunotherapy, and platelet-based drug delivery systems across efficacy, applicable tumor types, adverse effects, manufacturing complexity, and clinical positioning.
Table 9.
Comparative translational landscape of platelet-guided CAR-T therapy and current targeted immunotherapeutic strategies for solid tumors
| Modality | Efficacy | Tumor setting | Key limitations/adverse effects | Manufacturing/logistics | Clinical positioning |
|---|---|---|---|---|---|
| Systemic CAR-T | Highly effective in hematologic malignancies; limited in most solid tumors. | Blood cancers and solid tumors remain investigational. | Poor trafficking, antigen heterogeneity, CRS/ICANS, and off-tumor toxicity. | High-cost, autologous, GMP-intensive. | Benchmark platform for blood cancers; limited solid-tumor utility. |
| Locoregional immunotherapy | Improves local exposure and may enhance tumor-bed control. | Resection cavities, pleural/peritoneal spaces, accessible lesions. | Procedure-dependent; limited systemic coverage; local inflammation possible. | Moderate complexity; requires image-guided or device-based delivery. | Best for spatially confined residual disease. |
| Platelet-based drug delivery | Strong homing to inflamed, fibrin-rich, and tumor-associated niches. | Post-injury beds, metastatic niches, platelet-recruiting tumors. | Activation control, thrombogenic risk, membrane heterogeneity. | Moderate-to-high; biologic sourcing and QC remain challenging. | Delivery platform, not antigen-specific therapy. |
| Platelet-guided CAR-T | Proposed hybrid strategy to raise local effector density and specificity. | Most relevant for postoperative MRD and fibrin-rich surgical beds. | Still preclinical/conceptual; thromboinflammatory risk and off-tumor toxicity need validation. | Highest complexity; combines CAR-T GMP workflow with platelet engineering/QC. | Precision perioperative platform; complementary to systemic and locoregional approaches. Inference. |
When compared with lipid nanoparticles (LNPs), platelet-based platforms are generally less mature from a regulatory and manufacturing perspective, but they may offer superior biologic specificity for postoperative or injury-associated sites because their targeting is driven by native platelet adhesion pathways rather than only physicochemical optimization or ligand decoration. LNPs remain the most clinically advanced non-viral nucleic-acid delivery system and are particularly attractive for scalable formulation, reproducibility, and efficient cargo protection; however, their clinical translation is still constrained by limited targeting specificity, biodistribution control, and scale-up-related challenges. Thus, LNPs are best viewed as highly mature delivery vehicles, whereas platelet-based carriers are better suited to contexts where active homing to a wound-like microenvironment is a central therapeutic requirement [501–503].
Compared with hydrogels, platelet-guided systems offer a different mode of localization. Hydrogels are exceptionally useful as postsurgical depots because they can be placed directly into the resection bed, maintain prolonged local residence, and generate high drug concentrations at the lesion site. Their main limitation is that they are primarily passive retention platforms: they localize to their implantation site, but they do not intrinsically seek out dispersed residual cells outside the implantation zone. By contrast, platelet-based carriers can actively home to activated endothelium, fibrin, and inflamed tissue, making them especially attractive when microscopic residual disease may extend beyond the immediate surgical cavity. For this reason, hydrogels and platelet systems should not be considered competing technologies, but rather potentially complementary ones, with hydrogels serving as local depots and platelets serving as biologically guided targeting modules [362, 504].
Compared with living cell-based carriers, platelet-derived systems are operationally simpler and potentially easier to translate. Living cell platforms, including leukocytes and other circulating cells, can exploit innate disease sensing, migration, and homing behavior, and they offer the conceptual advantage of dynamic responsiveness to the tumor microenvironment. However, they also introduce substantial complexity related to cell viability, cargo loading, batch consistency, potency testing, storage stability, and regulatory oversight. Platelet-based systems retain several of the targeting advantages of cell-mediated delivery while avoiding some of the difficulties associated with maintaining viable therapeutic cells, since platelets are anucleate and can be engineered as membrane-coated nanoparticles, vesicles, or conjugates without depending on long-term cellular function [505–508]. Accordingly, platelet-guided delivery may represent a more practical bridge between biologic precision and manufacturable translational design. Overall, platelet-guided delivery occupies an intermediate translational niche: it is more actively lesion-targeted than conventional LNPs, more site-homing than passive hydrogels, and less burdensome than viable cell carriers, but it still requires stringent control of thrombogenicity, activation state, and membrane heterogeneity before routine clinical application.
Potential clinical trial designs
Designing first-in-human and early-phase clinical trials for platelet-guided CAR-T constructs requires an integrated translational strategy that aligns therapeutic delivery with surgical timing, optimizes dosing and scheduling, and applies stringent patient selection criteria. Each of these elements should be informed by perioperative tumor biology, MRD-based stratification, and the specific thrombo-immunologic risks associated with platelet-derived components. This coordinated framework is essential to ensure both safety and meaningful biological activity in early clinical evaluation [509, 510].
Timing relative to surgery
Delivering platelet-guided CAR-T at the time of tumor resection (intraoperative placement of a CAR-T–loaded hydrogel or scaffold into the resection cavity) capitalizes on immediate access to the highest-risk anatomical niche for residual disease and avoids many trafficking barriers faced by systemic infusion; preclinical and early translational reports describe feasible intraoperative depot placement with improved local retention and tumor control [428, 511]. However, the immediate perioperative period is biologically complex. Surgical intervention induces inflammation, transient immune dysregulation, and wound-healing responses that may either enhance or impair immunotherapeutic efficacy. Therefore, intraoperative or perioperative treatment protocols should carefully consider potential effects on wound healing, immune-related adverse events, and overall perioperative management [509, 512]. An alternative strategy is early postoperative delivery (for example, within 2–8 weeks after resection), which allows partial wound healing and baseline immune recovery while still targeting residual disease before clinical relapse; retrospective and prospective adjuvant data in other modalities suggest that a 4–8 week window often balances surgical recovery with adjuvant efficacy, making it a pragmatic starting interval for dose-finding cohorts [513]. Neoadjuvant or preoperative administration (giving CAR-T before resection) could prime systemic anti-tumor immunity and reduce intraoperative dissemination, but raises logistical and safety tradeoffs for surgical timing and tumor downstaging that require careful coordination with the surgical team and perioperative anesthetic plans [428]. A staged, adaptive design that compares intraoperative versus early-postoperative placement (or includes both as separate cohorts) while measuring short-term surgical endpoints (hemostasis, wound complications), local pharmacodynamics (tissue CAR-T persistence), and early MRD/ctDNA signals will provide the clearest translational readout to choose an optimal timing strategy.
Dose optimization
Dose escalation for platelet-guided CAR-T should embrace modern, efficient designs (Bayesian CRM or modified continual reassessment methods) rather than relying solely on 3 + 3 rules, because these adaptive frameworks identify biologically active windows and balance safety with the desire to reach effective local effector: target ratios while minimizing thrombotic and inflammatory harms [514, 515]. Because local depots fundamentally change exposure (high local cell density with limited systemic spillover), dose definition must consider both local cell burden (cells per scaffold or per mL depot) and systemic cell equivalent (estimated circulating cells released over time), and early cohorts should incorporate intensive biodistribution and systemic cytokine monitoring to inform escalation rules [516]. Key pharmacodynamic endpoints to guide dose-finding include: (A) local CAR-T persistence assessed through tissue or intracavitary sampling, (B) serial ctDNA or MRD clearance as an early biomarker of therapeutic efficacy, and (C) sensitive coagulation and thrombo-inflammatory readouts, including D-dimer levels, thrombin generation assays, platelet function testing, and NET assays, to detect subclinical thrombo-inflammatory activity at low doses. Collectively, these parameters enable a more informative, biomarker-driven approach to dose selection than reliance on toxicity endpoints alone [438, 517]. Practical escalation schemas for locoregional CAR-T can combine single-dose intraoperative cohorts with planned repeat-dosing cohorts (for example, one intraoperative depot ± one scheduled booster infusion at 2–6 weeks) so trials can characterize whether a single sustained depot suffices or whether timed repeat dosing improves durability without increasing systemic toxicity [518, 519]. Dose-finding strategies should also account for co-delivered immunomodulatory adjuvants, such as low-dose IL-15 or STING/TLR agonists incorporated within the depot, as these agents can significantly influence CAR-T cell expansion and the extent of systemic cytokine exposure. Early clinical cohorts should therefore employ structured escalation designs, such as factorial or staggered dose-escalation schemes, to delineate interaction effects between cellular and adjuvant components and to define a safe and biologically effective therapeutic window [428, 520]. Finally, statistical plans should prespecify pharmacodynamic stopping rules (for adverse coagulation signal or persistent ctDNA rise) and incorporate adaptive borrowing across cohorts to accelerate identification of the minimally effective and maximally safe local dosing strategy [514].
Patient selection criteria
MRD-guided enrollment (requiring postoperative ctDNA positivity or other validated MRD markers) enriches trials for patients most likely to harbor actionable residual disease and therefore maximizes the chance to see a treatment effect while limiting exposure of low-risk patients to novel thrombo-immunologic hazards; multiple ctDNA-directed adjuvant studies provide a validated precedent for this strategy [521, 522]. Tumor type and anatomic considerations should prioritize indications with (A) a well-defined resection cavity that is accessible intraoperatively (pancreatic adenocarcinoma, high-risk colorectal metastasectomy, glioblastoma) or (B) cavity-based disease amenable to regional infusion (pleural/peritoneal malignancies), because these settings combine high local relapse risk with pragmatic access for depot placement [513, 518]. Eligibility must explicitly screen for hemostatic and thrombotic risk: baseline platelet count and function, recent thromboembolic events, active coagulopathy, and anticoagulant use are rational exclusion criteria (or stratification variables) given the novel platelet biology in the product and the known interactions between cancer, platelets, and thrombosis [517, 523]. Biomarker selection beyond MRD should include validated target antigen expression in residual tissue (to limit on-target/off-tumor risk), assessment of tumor immune contexture (TILs, myeloid signatures) to anticipate responsiveness, and optional germline or pharmacogenomic features that affect bleeding or inflammatory risk [428, 510]. Practical trial inclusion strategies may combine a high-risk MRD-positive cohort for proof-of-mechanism and an exploratory MRD-negative cohort (with closer safety monitoring) to evaluate potential preventive benefits and to define the product’s risk: benefit across molecular strata [438, 524]. Fig. 10 summarizes the translational pathway, manufacturing, and clinical trial design of platelet-guided CAR T therapy.
Fig. 10.

Integrated translational roadmap, comparative positioning, and clinical trial design for platelet-guided CAR T cell therapy. (A) The GMP manufacturing workflow integrates specialized platelet sourcing (autologous or allogeneic) with anti-activation stabilization (e.g., PGE1, apyrase) to formulate cellular conjugates or biomimetic carriers within closed, automated systems. Product quality is rigorously controlled via a critical release criteria panel—encompassing membrane identity, procoagulant activity, and thrombogenic potential—prior to GLP toxicology and biodistribution assessment in large-animal models. (B) In the comparative landscape, platelet-guided systems occupy a unique clinical niche for active lesion homing to wound-like or postoperative microenvironments, offering superior biological targeting compared to passive hydrogels or synthetic lipid nanoparticles (LNPs) while maintaining lower complexity than viable living cell carriers. (C) Proposed early-phase clinical trial designs prioritize MRD-guided stratification (e.g., ctDNA status) and Bayesian adaptive dose-optimization streams to monitor systemic cell equivalents and sensitive thrombo-inflammatory markers. The timing of administration—ranging from neoadjuvant priming to intraoperative and early postoperative windows—is strategically aligned with perioperative biology to leverage the highest-risk anatomical niches and ensure a de-risked pathway toward clinical application
Future directions and perspectives
Combining platelet-guided CAR T therapy with other modalities
Future development of platelet-guided CAR-T approaches will likely depend on integration with complementary therapeutic modalities. Combination strategies may help overcome the trafficking, persistence, and immunosuppressive barriers that currently limit CAR-T efficacy in solid tumors [14]. Platelet guidance already demonstrates the capacity to concentrate payloads in fibrin-rich surgical beds and inflamed vasculature, making it an attractive platform to co-deliver or colocate immune modulators that would otherwise cause intolerable systemic toxicity [450].
Checkpoint inhibitor combinations may enhance local immune activation but also introduce safety concerns related to cytokine-driven inflammation. This risk is consistent with evidence that immune-cell–derived extracellular vesicles can amplify inflammatory signaling within tissue niches; for example, lung M2 macrophage–derived extracellular vesicles activate group 2 innate lymphoid cells (ILC2s), increasing IL-5 and IL-13 production and promoting pathological airway inflammation through RNA-mediated metabolic reprogramming [525]. This highlights how intercellular vesicular communication can amplify local immune cascades, reinforcing the risk of excessive cytokine amplification in combination immunotherapy. In this context, localized platelet-mediated delivery provides a strategy to spatially confine immune activation, reducing systemic exposure while maintaining high local therapeutic concentration within the tumor or surgical microenvironment [450, 526]. Finally, successful translation will depend on precise control of sequencing and dosing. Intraoperative or early postoperative deployment may maximize access to residual disease but must be balanced against perioperative immunosuppression and wound-healing dynamics. Accordingly, future trials should incorporate immune monitoring, tissue repair assessment, and early safety biomarkers alongside efficacy endpoints [255].
Oncolytic viruses or nanoparticles
Oncolytic viruses (OVs) are natural partners for CAR-T because OVs create an inflamed, antigen-rich microenvironment that increases T-cell recruitment, antigen presentation, and the likelihood of epitope spreading; conversely, CAR-T cells can be used to deliver or amplify OV activity, producing a bidirectional synergy [527, 528]. Pairing platelet-guided CAR-T with OVs could provide site-directed viral amplification: platelets (or platelet-membrane carriers) can increase local retention of viral payloads or CAR-T depots in the cavity, potentially improving oncolytic spread within the resection bed while keeping systemic viral exposure low [26, 527]. Nanoparticle-based systems, particularly platelet-membrane-coated nanoparticles, may provide an effective platform for co-delivery of cytokines, immune adjuvants, or microenvironment-modulating agents within the same localized niche as CAR-T cells [26, 529]. Preclinical studies already show that platelet-membrane cloaking improves nanoparticle tumor accumulation and payload retention, and that combining depot-based CAR-T delivery with controlled local adjuvants (for example, IL-15-loaded nanoparticles) enhances expansion and local tumor control versus CAR-T alone in surgical models [529, 530]. Nevertheless, combining modalities multiplies safety and regulatory complexity: thrombogenic potential, inflammatory overshoot, viral shedding (for OVs), and nanoparticle biodistribution must be prospectively measured, and combination regimens will require stepwise escalation with sensitive coagulation, immune, and viral-safety endpoints [528]. Across all combinations, success depends on four translational enablers: robust local pharmacodynamic biomarkers (tissue CAR-T counts, ctDNA/MRD kinetics), engineered safety layers (affinity tuning, suicide switches, local release control), GMP-grade platelet/membrane manufacturing with validated thrombogenicity assays, and adaptive trial designs that preserve patient safety while rapidly identifying biologically active regimens [26, 531].
Biomarker-driven personalization
Effective personalization of platelet-guided CAR-T therapy will depend on a layered biomarker strategy that (A) identifies patients who truly harbor minimal residual disease after surgery, (B) defines the immune and spatial context of the resection bed that will determine local CAR-T fitness, and (C) monitors early pharmacodynamic signals of local activity and systemic safety so therapy can be adapted in real time [360, 532]. The first and most tractable biomarker axis is ctDNA. Postoperative ctDNA assessment, whether as a single landmark measurement or through serial surveillance, has consistently been shown to detect molecular residual disease and to strongly predict short- and mid-term recurrence risk. Accordingly, ctDNA represents a rational and clinically grounded criterion for patient enrollment and enrichment in trials evaluating intraoperative or early postoperative platelet-guided CAR-T depot strategies [350, 360]. Beyond binary MRD status, quantitative ctDNA kinetics, including time to clearance and the magnitude of changes in variant allele fraction, can serve as early pharmacodynamic endpoints during treatment. Rapid reductions in ctDNA driven by local depot activity may provide near real-time evidence of biological response, enabling adaptive clinical decisions such as dose escalation, redosing, or incorporation of additional local adjuvants [533, 534]. Complementary circulating biomarkers, including tumor-derived extracellular vesicles, circulating tumor cells, and immune-inflammatory markers such as cytokine panels, NET-associated signatures, and platelet activation assays, may play an important role in predicting thromboinflammatory risk associated with platelet-based components and in detecting early systemic inflammatory activation [535, 536].
A second important component is tissue-based characterization of the resection margin. Spatially resolved approaches, including spatial transcriptomics, spatial proteomics, multiplex immunofluorescence, and single-cell RNA sequencing, can provide detailed information about local immune architecture, suppressive myeloid populations, tertiary lymphoid structures, and stromal barriers. These features may help predict whether locally delivered CAR-T cells are likely to expand and persist within the postoperative microenvironment or become rapidly suppressed [537, 538]. Integrating spatial signatures with routine pathology and molecular tests (PD-L1, TMB/neoantigen load, and specific actionable mutations) will allow a composite local fitness score to triage patients: those with inflamed, antigen-rich beds and ctDNA positivity would be prioritized for aggressive intraoperative CAR-T+platelet depots, whereas highly fibrotic or myeloid-dominant beds might require combined stromal-modulating pretreatment [539, 540]. Neoantigen and TMB profiling remain useful for selecting adjunct systemic or local adjuvants (for example, whether to combine depot CAR-T with checkpoint blockade or with in-situ vaccination strategies), because high neoantigen burden increases the likelihood of epitope spreading from a local CAR-T stimulus, while low-TMB tumours may need stronger local innate adjuvants [541, 542].
Practically, biomarker workflows must be rapid and perioperatively compatible: assays used for enrollment or intraoperative decision-making require fast turnaround (same-day or within a few days for ctDNA and targeted genomics) and validated thresholds that have been pre-specified in trial protocols to avoid ad hoc selection bias [543, 544]. Importantly, biomarker strategy should be dynamic and adaptive: early-phase cohorts should embed serial ctDNA, local tissue biopsies (when safe), intracavitary fluid sampling, and multiplex immune monitoring so that correlates of local persistence, ctDNA clearance, and adverse coagulation/inflammation are mapped and used to refine inclusion criteria and dosing rules [350, 536]. From a translational perspective, harmonization and standardization will be essential for clinical implementation. Analytic validation of ctDNA assays, standardized spatial-omics workflows, and consensus definitions for MRD positivity thresholds will be necessary to enable reliable data integration across multicenter trials and to support consistent evaluation of benefit-risk profiles across clinical sites [545].
Expanding beyond surgical residual tumor to metastasis control
Expanding the platelet-guided CAR-T paradigm from local adjuvant therapy toward active interception of metastatic seeds requires a coordinated strategy that (1) captures or disperses CTC clusters, (2) renders pre-metastatic niches hostile to colonization, and (3) preserves local hemostasis while minimizing systemic thrombogenic risk [546, 547]. A practical first pillar is to exploit tumour-educated platelet (TEP) biology not only as a targeting vehicle but also as a sensitive liquid-biopsy biomarker: platelet RNA and proteomic signatures reflect tumour presence and evolution and can therefore be used both to select MRD-positive patients and to serially monitor response to perioperative interception strategies [546, 548]. At the device level, engineered platelet decoys that preserve tumour-binding receptors while lacking pro-hemostatic granule content can function as phenotype-independent capture agents for CTCs; when coupled to magnetic cores or affinity handles, these decoys permit ex vivo retrieval or in vivo neutralization of circulating clusters with high yield [549]. A complementary therapeutic axis couples a fibrinolytic opening depot with a colocated CAR-T/platelet scaffold: the fibrinolytic component enzymatically loosens the protective fibrin-rich microarchitecture around CTC clusters and micrometastatic niches, permitting locally retained CAR-T cells to access, recognize, and eradicate exposed tumour cells [437]. To mitigate the thrombosis risk associated with platelet-targeted platforms, bioresponsive hydrogels that release anticoagulant or fibrinolytic agents in response to thrombin or other coagulation-related signals offer a self-regulating strategy. This approach enables preservation of localized hemostatic activity when required while simultaneously limiting pathological clot propagation, thereby improving the safety profile of these therapeutic systems [550, 551].
Engineering these smart surgical depots around thrombin-sensitive linkers or heparin-releasing chemistries allows spatially precise control: adhesive matrices retain CAR-T and platelet elements at the resection margin, while thrombin-triggered release reduces local procoagulant burden if clotting markers exceed predefined thresholds [552, 553]. Patient-level personalization should integrate multi-omic predictors: high-throughput T-cell receptor (TCR) repertoire sequencing provides dynamic information on the host antitumour clonotypes, while tumour and plasma metabolomic profiling (notably amino-acid signatures) reveal metabolic constraints that will shape CAR-T fitness and local persistence [554, 555]. Combining TCR repertoire metrics with spatial transcriptomic/proteomic mapping of the resection margin enables a composite local-fitness score that can triage patients into immediate intraoperative depot placement, early postoperative dosing, or a strategy that first reconditions the niche (for example, with stromal-modulating agents) before cell delivery [556, 557]. Economic and access considerations must be addressed in parallel; process innovations (automation, non-viral gene transfer, and point-of-care or decentralized manufacture) substantially affect unit cost and scalability and should be modelled early to ensure any platelet-CAR-T platform can be deployed beyond single academic centres [558, 559].
We propose three high-priority near-term experiments (12–18 months) to de-risk the metastasis-control concept: (1) a head-to-head orthotopic murine study comparing a fibrinolytic + CAR-T depot versus CAR-T depot alone with endpoints of CTC burden and ctDNA; (2) engineering and in-vitro/in-vivo validation of thrombin-responsive heparin-releasing hydrogels to define safety windows for local anticoagulation; and (3) GLP-format porcine tolerability and biodistribution studies focused on thrombo-inflammatory endpoints and depot retention kinetics. If these steps demonstrate safety and mechanistic efficacy, the translational pathway becomes clear: a staged program that begins with MRD-enriched intraoperative feasibility cohorts, proceeds through iterative optimization of depot chemistry and CAR-T dosing, and culminates in randomized studies that evaluate metastasis-free survival and cost-effectiveness
Conclusion and limitations
Platelet-guided CAR-T cell strategies represent an emerging and conceptually compelling approach for addressing several persistent limitations of CAR-T therapy in solid tumors, including poor trafficking, limited intratumoral persistence, perioperative residual disease, and the highly immunosuppressive postoperative microenvironment. By leveraging the intrinsic tumor-homing, thromboinflammatory, and vascular-interactive properties of platelets, these hybrid bioengineering platforms may offer new opportunities for improving regional CAR-T localization and enhancing therapeutic precision within surgically altered tumor niches.
At the same time, the field remains at an early developmental stage, and substantial biological, translational, and manufacturing challenges must still be addressed before clinical implementation can be realistically considered. Important unresolved issues include the risk of unintended thromboinflammatory activation, platelet-mediated immune dysregulation, off-target vascular effects, variability in platelet source material, manufacturing reproducibility, scalability of engineered constructs, and regulatory standardization. In addition, the dynamic heterogeneity of the tumor microenvironment and the complexity of perioperative immune responses may significantly influence therapeutic behavior and patient-specific outcomes.
Current evidence supporting platelet-guided CAR-T systems remains predominantly preclinical, with many proposed mechanisms and therapeutic advantages still requiring rigorous experimental validation. Future investigations should therefore focus on the mechanistic dissection of platelet–immune interactions, optimization of bioengineering strategies, safety-oriented platform design, biomarker-guided patient stratification, and carefully designed translational studies capable of evaluating pharmacology, biodistribution, toxicity, and long-term therapeutic efficacy in clinically relevant models.
Rather than representing an immediately translatable therapeutic modality, platelet-guided CAR-T therapy should presently be viewed as a promising investigational framework at the intersection of tumor immunology, thrombosis biology, perioperative oncology, and cellular bioengineering. Continued interdisciplinary collaboration and systematic translational validation will ultimately determine whether these strategies can evolve into clinically feasible approaches for improving outcomes in patients with solid tumors.
Acknowledgements
None.
Abbreviations
- AhR
Aryl hydrocarbon receptor
- APC
Antigen-presenting cell
- ASS1
Argininosuccinate synthase-1
- CAR
Chimeric antigen receptor
- CAR-T
Chimeric antigen receptor T cells
- CPT1
Carnitine palmitoyltransferase-1
- CRS
Cytokine release syndrome
- CTC
Circulating tumor cell
- ctDNA
Circulating tumor DNA
- ECM
Extracellular matrix
- FAO
Fatty-acid oxidation
- FASN
Fatty-acid synthase
- FGF
Fibroblast growth factor
- GLP
Good Laboratory Practice
- GLS
Glutaminase
- GMP
Good Manufacturing Practice
- GM-CSF
Granulocyte-macrophage colony-stimulating factor
- HAP
Hypoxia-activated prodrug
- HER2
Human epidermal growth factor receptor-2
- HIF
Hypoxia-inducible factor
- HK2
Hexokinase-2
- ICI
Immune checkpoint inhibitor
- IDO1
Indoleamine-2,3-dioxygenase-1
- IFN-γ
Interferon-gamma
- IL-6
Interleukin-6
- LDH/LDHA
Lactate dehydrogenase A
- MCT
Monocarboxylate transporter
- MRD
Minimal residual disease
- NET
Neutrophil extracellular trap
- NK
Natural killer
- NSAID
Non-steroidal anti-inflammatory drug
- OXPHOS
Oxidative phosphorylation
- PD-1
Programmed cell death protein-1
- PDGF
Platelet-derived growth factor
- PEV
Platelet-derived extracellular vesicle
- PHGDH
Phosphoglycerate dehydrogenase
- PMP
Platelet-derived microparticle
- PNP
Platelet-membrane-coated nanoparticle
- ROS
Reactive oxygen species
- SHMT
Serine hydroxymethyltransferase
- SSP
Serine synthesis pathway
- TCR
T-cell receptor
- TGF-β
Transforming growth factor-beta
- TIL
Tumor-infiltrating lymphocyte
- TME
Tumor microenvironment
- TPO
Thrombopoietin
- TRAIL
TNF-related apoptosis-inducing ligand
- VEGF
Vascular endothelial growth factor
- VWF
Von Willebrand factor
Dr. Jalal Naghinezhad
is a researcher specializing in hematology, immunology, thrombosis, cancer biology, and nanomedicine. His research focuses on the interplay between platelet biology, immune regulation, cardiovascular diseases, and cancer progression, with a particular emphasis on developing innovative nanoparticle-based therapeutic strategies. Through interdisciplinary and translational research, he aims to advance precision medicine approaches for the treatment of hematologic disorders, metastatic cancer, and thromboinflammatory diseases.
Author contributions
F.H. conceived the study, designed the research framework, performed data analysis, and drafted the original manuscript. S.F., HZ, and MMV contributed to data collection, interpretation of results, provided overall scientific guidance, and manuscript revision. M.D. assisted in methodology development and data validation. S.A. contributed to experimental procedures and data curation. F.F. supported data analysis and manuscript editing. J.N. served as the primary corresponding author, supervised the project, contributed to study design, and critically revised the manuscript. M.R.H., as co-corresponding author, contributed to conceptual development, critical revision of the manuscript, and provided expert insight.
Funding
This project was funded by a research grant awarded to Dr. Jalal Naghinezhad from Mazandaran University of Medical Sciences. The authors gratefully acknowledge Mazandaran University of Medical Sciences, Iran, for supporting this research project. This study was conducted within the framework of a research project proposed by Dr. Jalal Naghinezhad and officially registered under project code 26291.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Consent to publish
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Farshad Heydari and Hoda Zendehdel contributed equally to this work.
Contributor Information
Michael R. Hamblin, Email: hamblin.lab@gmail.com
Jalal Naghinezhad, Email: jalalalinaghinezhad@gmail.com.
References
- 1.Nors J, Iversen LH, Erichsen R, Gotschalck KA, Andersen CL. Incidence of recurrence and time to recurrence in stage I to III colorectal cancer: a nationwide Danish cohort study. JAMA Oncol. 2024;10(1):54–62. 10.1001/jamaoncol.2023.5098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Poolakkil P, Pareekutty NM, Balasubramanian S, Nethrakare A, Anilkumar B. Real-world data on the pattern of recurrence of colorectal cancer at a tertiary cancer center in South India: a retrospective observational study. Cancer Res Stat Treat. 2024;7(1):36–44. 10.4103/crst.crst_209_23. [Google Scholar]
- 3.Rajaram R, Huang Q, Li RZ, Chandran U, Zhang Y, Amos TB, et al. Recurrence-free survival in patients with surgically resected non-small cell lung cancer: a systematic literature review and meta-analysis. Chest. 2024;165(5):1260–70. 10.1016/j.chest.2023.11.042. [DOI] [PubMed] [Google Scholar]
- 4.Aliperti LA, Predina JD, Vachani A, Singhal S. Local and systemic recurrence is the achilles heel of cancer surgery. Ann Surg Oncol. 2011;18(3):603–07. 10.1245/s10434-010-1442-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Balboa-Barreiro V, Pértega-Díaz S, García-Rodríguez T, González-Martín C, Pardeiro-Pértega R, Yáñez-González-Dopeso L, et al. Colorectal cancer recurrence and its impact on survival after curative surgery: an analysis based on multistate models. Digestive Liver Disease. 2024;56(7):1229–36. 10.1016/j.dld.2023.11.041. [DOI] [PubMed] [Google Scholar]
- 6.Aguiar-Ibáñez R, Mbous YPV, Sharma S, Chawla E, Pawar D. Clinical, humanistic and economic burden associated with recurrence among patients with early-stage cancers: a systematic literature review. Front Oncol. 2025;15:1575813. 10.3389/fonc.2025.1575813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Garaszczuk R, Yong JHE, Sun Z, de Oliveira C. The economic burden of cancer in Canada from a societal perspective. Curr Oncol. 2022;29(4):2735–48. 10.3390/curroncol29040223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Lee JM, Wang R, Johnson A, Ogale S, Kent M, Lee JS. Economic burden of recurrence among resected medicare patients with early stage NSCLC. JTO Clin Res Rep. 2023;4(4):100487. 10.1016/j.jtocrr.2023.100487. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bonis A, Detterbeck F, Figueroa PU, Chen H, Osarogiagbon R, Dell’amore A, et al. Classification of recurrence patterns in surgically treated non-small cell lung cancer – a systematic review and a call for standardization. Eur J Surg Oncol. 2025;51(11):110425. 10.1016/j.ejso.2025.110425. [DOI] [PubMed] [Google Scholar]
- 10.Renehan AG, Egger M, Saunders MP, O’Dwyer ST. Impact on survival of intensive follow up after curative resection for colorectal cancer: systematic review and meta-analysis of randomised trials. BMJ. 2002;324(7341):813. 10.1136/bmj.324.7341.813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tjandra JJ, Chan MKY. Follow-up after curative resection of colorectal cancer: a meta-analysis. Dis Colon Rectum. 2007;50(11):1783–99. 10.1007/s10350-007-9030-5. [DOI] [PubMed] [Google Scholar]
- 12.Johnson A, Townsend M, O’Neill K. Tumor microenvironment immunosuppression: a roadblock to CAR T-Cell advancement in solid tumors. Cells. 2022;11(22):3626. 10.3390/cells11223626. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kong Y, Li J, Zhao X, Wu Y, Chen L. CAR-T cell therapy: developments, challenges and expanded applications from cancer to autoimmunity. Front Immunol. 2024;15:1519671. 10.3389/fimmu.2024.1519671. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Escobar G, Berger TR, Maus MV. CAR-T cells in solid tumors: challenges and breakthroughs. Cell Rep Med. 2025;6(11):102353. 10.1016/j.xcrm.2025.102353. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Andreou T, Neophytou C, Kalli M, Mpekris F, Stylianopoulos T. Breaking barriers: enhancing CAR-armored T cell therapy for solid tumors through microenvironment remodeling. Front Immunol. 2025;16:1638186. 10.3389/fimmu.2025.1638186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Vo MC, Tran VDH, Nguyen VT, Ruzimurodov N, Trung DT, Kim SK, et al. Challenges and limitations of chimeric antigen receptor T-cell therapies in solid tumors: Why are approvals restricted to hematologic malignancies? J Hematol Oncol. 2025;18(1):91. 10.1186/s13045-025-01744-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kong Y, Tang L, You Y, Li Q, Zhu X. Analysis of causes for poor persistence of CAR-T cell therapy in vivo. Front Immunol. 2023;14:1063454. 10.3389/fimmu.2023.1063454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Smirnov S, Zaritsky Y, Silonov S, Gavrilova A, Fonin A. Advancing CAR-T therapy for solid tumors: from barriers to clinical progress. Biomolecules. 2025;15(10):1407. 10.3390/biom15101407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Liang X. CAR T-cell therapy failure in solid tumors as a problem of temporal signal integration. J Educ Chang High Sch Sci. 2026;10(1). 10.64336/001c.155592.
- 20.Rehorst P, Kros A. A general logic-gating framework for CAR-T and nanocarrier cancer therapies: a cross-platform comparative analysis of logic architectures and their molecular implementation strategies. J Control Release. 2025;391:114583. 10.1016/j.jconrel.2025.114583. [DOI] [PubMed] [Google Scholar]
- 21.Rafiq S, Hackett CS, Brentjens RJ. Engineering strategies to overcome the current roadblocks in CAR T cell therapy. Nat Rev Clin Oncol. 2020;17(3):147–67. 10.1038/s41571-019-0297-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Contursi A, Tacconelli S, Di Berardino S, De Michele A, Patrignani P. Platelets as crucial players in the dynamic interplay of inflammation, immunity, and cancer: unveiling new strategies for cancer prevention. Front Pharmacol. 2024;15:1520488. 10.3389/fphar.2024.1520488. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Raskov H, Orhan A, Agerbæk M, Gögenur I. The impact of platelets on the metastatic potential of tumour cells. Heliyon. 2024;10(14):e34361. 10.1016/j.heliyon.2024.e34361. [DOI] [PMC free article] [PubMed]
- 24.Guo J, Cui B, Zheng J, Yu C, Zheng X, Yi L, et al. Platelet-derived microparticles and their cargos: the past, present and future. Asian J Pharm Sci. 2024;19(2):100907. 10.1016/j.ajps.2024.100907. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Cacic D, Hervig T, Reikvam H. Platelets for advanced drug delivery in cancer. Expert Opin Drug Delivery. 2023;20(5):673–88. 10.1080/17425247.2023.2217378. [DOI] [PubMed] [Google Scholar]
- 26.Safdar A, Wang P, Muhaymin A, Nie G, Li S. From bench to bedside: platelet biomimetic nanoparticles as a promising carriers for personalized drug delivery. J Control Release. 2024;373:128–44. 10.1016/j.jconrel.2024.07.013. [DOI] [PubMed] [Google Scholar]
- 27.Dai Z, Zhao T, Song N, Pan K, Yang Y, Zhu X, et al. Platelets and platelet extracellular vesicles in drug delivery therapy: a review of the current status and future prospects. Front Pharmacol. 2022;13:1026386. 10.3389/fphar.2022.1026386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Fatima S. Tumor microenvironment: a complex landscape of cancer development and drug resistance. Cureus. 2025;17(4):e82090. 10.7759/cureus.82090. [DOI] [PMC free article] [PubMed]
- 29.Nikfar M, Mi H, Gong C, Kimko H, Popel AS. Quantifying intratumoral heterogeneity and immunoarchitecture generated in-silico by a spatial quantitative systems pharmacology model. Cancers (Basel). 2023;15(10):2750. 10.3390/cancers15102750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Fu YC, Liang SB, Luo M, Wang XP. Intratumoral heterogeneity and drug resistance in cancer. Cancer Cell Int. 2025;25(1):103. 10.1186/s12935-025-03734-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yu H, Li J, Peng S, Liu Q, Chen D, He Z, et al. Tumor microenvironment: nurturing cancer cells for immunoevasion and druggable vulnerabilities for cancer immunotherapy. Cancer Lett. 2025;611:217385. 10.1016/j.canlet.2024.217385. [DOI] [PubMed] [Google Scholar]
- 32.Luk CK, Sutherland RM. Influence of growth phase, nutrition and hypoxia on heterogeneity of cellular buoyant densities in in vitro tumor model systems. Intl J Cancer. 1986;37(6):883–90. 10.1002/ijc.2910370614. [DOI] [PubMed] [Google Scholar]
- 33.Huang J, Zhang L, Wan D, Zhou L, Zheng S, Lin S, et al. Extracellular matrix and its therapeutic potential for cancer treatment. Sig Transduct Target Ther. 2021;6(1):153. 10.1038/s41392-021-00544-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Leone P, Malerba E, Susca N, Favoino E, Perosa F, Brunori G, et al. Endothelial cells in tumor microenvironment: insights and perspectives. Front Immunol. 2024;15:1367875. 10.3389/fimmu.2024.1367875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Zhang M, Zhang B. Extracellular matrix stiffness: mechanisms in tumor progression and therapeutic potential in cancer. Exp Hematol Oncol. 2025;14(1):54. 10.1186/s40164-025-00647-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Finger AM, Hendley AM, Figueroa D, Gonzalez H, Weaver VM. Tissue mechanics in tumor heterogeneity and aggression. Trends Cancer. 2025;11(8):806–24. 10.1016/j.trecan.2025.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Enjalbert R, Köry J, Krüger T, Bernabeu MO. Abnormal vasculature reduces overlap between drugs and oxygen in a tumour computational model: implications for therapeutic efficacy. PLoS Comput Biol. 2025;21(12):e1013801. 10.1371/journal.pcbi.1013801. [DOI] [PMC free article] [PubMed]
- 38.Tzeng HT, Huang YJ. Tumor vasculature as an emerging pharmacological target to promote anti-tumor immunity. IJMS. 2023;24(5):4422. 10.3390/ijms24054422. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Aguilar-Cazares D, Perez-Medina M, Benito-Lopez JJ, Galicia-Velasco M, Meneses-Flores M, Camarena A, et al. The tumor microenvironment: adding pieces to the puzzle. Front Immunol. 2026;16:1731338. 10.3389/fimmu.2025.1731338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Liu JS, Cai YX, He YZ, Xu J, Tian SF, Li ZQ. Spatial and temporal heterogeneity of tumor immune microenvironment between primary tumor and brain metastases in NSCLC. BMC Cancer. 2024;24(1):123. 10.1186/s12885-024-11875-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Yuan Y. Spatial heterogeneity in the tumor microenvironment. Cold Spring Harb Perspect Med. 2016;6(8):a026583. 10.1101/cshperspect.a026583. [DOI] [PMC free article] [PubMed]
- 42.Keenan BP, Yadav M, Ansstas G, Fabrizio D, Murugesan K, Montesion M, et al. Intratumoral heterogeneity and immunotherapy resistance: clinical implications. Ann Oncol. 2026;37(3):314–28. 10.1016/j.annonc.2025.10.1239. [DOI] [PubMed] [Google Scholar]
- 43.Cheng R, Santos HA. Smart nanoparticle-based platforms for regulating tumor microenvironment and cancer immunotherapy. Adv Healthcare Mater. 2023;12(8):e2202063. 10.1002/adhm.202202063. [DOI] [PMC free article] [PubMed]
- 44.Bożyk A, Wojas-Krawczyk K, Krawczyk P, Milanowski J. Tumor microenvironment-a short review of cellular and interaction diversity. Biol (Basel). 2022;11(6). 10.3390/biology11060929. [DOI] [PMC free article] [PubMed]
- 45.Mayer S, Milo T, Isaacson A, Halperin C, Miyara S, Stein Y, et al. The tumor microenvironment shows a hierarchy of cell-cell interactions dominated by fibroblasts. Nat Commun. 2023;14(1):5810. 10.1038/s41467-023-41518-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Pe’er D, Ogawa S, Elhanani O, Keren L, Oliver TG, Wedge D. Tumor heterogeneity. Cancer Cell. 2021;39(8):1015–17. 10.1016/j.ccell.2021.07.009. [DOI] [PubMed] [Google Scholar]
- 47.Pan D, Jia D. Application of single-cell multi-omics in Dissecting cancer cell plasticity and tumor heterogeneity. Front Mol Biosci. 2021;8:757024. 10.3389/fmolb.2021.757024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Janesick A, Shelansky R, Gottscho AD, Wagner F, Williams SR, Rouault M, et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Commun. 2023;14(1):8353. 10.1038/s41467-023-43458-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Yang D, Liu J, Qian H, Zhuang Q. Cancer-associated fibroblasts: from basic science to anticancer therapy. Exp Mol Med. 2023;55(7):1322–32. 10.1038/s12276-023-01013-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Sahai E, Astsaturov I, Cukierman E, DeNardo DG, Egeblad M, Evans RM, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174–86. 10.1038/s41568-019-0238-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Chitty JL, Cox TR. The extracellular matrix in cancer: from understanding to targeting. Trends Cancer. 2025;11(9):839–49. 10.1016/j.trecan.2025.05.003. [DOI] [PubMed] [Google Scholar]
- 52.Fromme JE, Zigrino P. The role of extracellular matrix remodeling in skin tumor progression and therapeutic resistance. Front Mol Biosci. 2022;9:864302. 10.3389/fmolb.2022.864302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Yang D, Guo P, He T, Powell CA. Role of endothelial cells in tumor microenvironment. Clin Transl Med. 2021;11(6):e450. 10.1002/ctm2.450. [DOI] [PMC free article] [PubMed]
- 54.Wu B, Zhang B, Li B, Wu H, Jiang M. Cold and hot tumors: from molecular mechanisms to targeted therapy. Sig Transduct Target Ther. 2024;9(1):274. 10.1038/s41392-024-01979-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Wang L, Geng H, Liu Y, Liu L, Chen Y, Wu F, et al. Hot and cold tumors: immunological features and the therapeutic strategies. MedComm (2020). 2023;4(5):e343. 10.1002/mco2.343. [DOI] [PMC free article] [PubMed]
- 56.Bied M, Ho WW, Ginhoux F, Blériot C. Roles of macrophages in tumor development: a spatiotemporal perspective. Cell Mol Immunol. 2023;20(9):983–92. 10.1038/s41423-023-01061-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Xiao L, Wang Q, Peng H. Tumor-associated macrophages: new insights on their metabolic regulation and their influence in cancer immunotherapy. Front Immunol. 2023;14:1157291. 10.3389/fimmu.2023.1157291. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Erdogan B, Ao M, White LM, Means AL, Brewer BM, Yang L, et al. Cancer-associated fibroblasts promote directional cancer cell migration by aligning fibronectin. J Cell Biol. 2017;216(11):3799–816. 10.1083/jcb.201704053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Liu T, Zhou L, Li D, Andl T, Zhang Y. Cancer-associated fibroblasts Build and secure the tumor microenvironment. Front Cell Dev Biol. 2019;7:60. 10.3389/fcell.2019.00060. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Afik R, Zigmond E, Vugman M, Klepfish M, Shimshoni E, Pasmanik-Chor M, et al. Tumor macrophages are pivotal constructors of tumor collagenous matrix. J Exp Med. 2016;213(11):2315–31. 10.1084/jem.20151193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Madsen DH, Jürgensen HJ, Siersbæk MS, Kuczek DE, Grey Cloud L, Liu S, et al. Tumor-associated macrophages derived from circulating inflammatory monocytes degrade collagen through cellular uptake. Cell Rep. 2017;21(13):3662–71. 10.1016/j.celrep.2017.12.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Albrengues J, Shields MA, Ng D, Park CG, Ambrico A, Poindexter ME, et al. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science. 2018;361(6409):6409. 10.1126/science.aao4227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Kuczek DE, Larsen AMH, Thorseth ML, Carretta M, Kalvisa A, Siersbæk MS, et al. Collagen density regulates the activity of tumor-infiltrating T cells. J Immunother Cancer. 2019;7(1):68. 10.1186/s40425-019-0556-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Fuller AM, Pruitt HC, Liu Y, Irizarry-Negron VM, Pan H, Song H, et al. Oncogene-induced matrix reorganization controls CD8+ T cell function in the soft-tissue sarcoma microenvironment. J Clin Investigation. 2024;134(11). 10.1172/JCI167826. [DOI] [PMC free article] [PubMed]
- 65.Bunting MD, Vyas M, Requesens M, Langenbucher A, Schiferle EB, Manguso RT, et al. Extracellular matrix proteins regulate NK cell function in peripheral tissues. Sci Adv. 2022;8(11):eabk 3327. 10.1126/sciadv.abk3327. [DOI] [PMC free article] [PubMed]
- 66.Cordo Russo RI, Ernst G, Lompardía, Blanco G, Álvarez É, Garcia MG, et al. Increased hyaluronan levels and decreased dendritic cell activation are associated with tumor invasion in murine lymphoma cell lines. Immunobiology. 2012;217(9):842–50. 10.1016/j.imbio.2011.12.006. [DOI] [PubMed] [Google Scholar]
- 67.Rizzo M, Bayo J, Piccioni F, Malvicini M, Fiore E, Peixoto E, et al. Low molecular weight hyaluronan-pulsed human dendritic cells showed increased migration capacity and induced resistance to tumor chemoattraction. PLoS ONE. 2014;9(9):e107944. 10.1371/journal.pone.0107944. [DOI] [PMC free article] [PubMed]
- 68.Maishi N, Ohba Y, Akiyama K, Ohga N, Hamada J, Nagao-Kitamoto H, et al. Tumour endothelial cells in high metastatic tumours promote metastasis via epigenetic dysregulation of biglycan. Sci Rep. 2016;6(1):28039. 10.1038/srep28039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Yamamoto K, Ohga N, Hida Y, Maishi N, Kawamoto T, Kitayama K, et al. Biglycan is a specific marker and an autocrine angiogenic factor of tumour endothelial cells. Br J Cancer. 2012;106(6):1214–23. 10.1038/bjc.2012.59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Vymola P, Garcia-Borja E, Cervenka J, Balaziova E, Vymolova B, Veprkova J, et al. Fibrillar extracellular matrix produced by pericyte-like cells facilitates glioma cell dissemination. Brain Pathol. 2024;34(6):e13265. 10.1111/bpa.13265. [DOI] [PMC free article] [PubMed]
- 71.Tan J, Yu Z, Pi R, Lin Y, Wang W, Chen M, et al. Tumor-associated Pericytes: tumorigenicity and targeting for cancer therapy. CVP. 2026;24(1):3–19. 10.2174/0115701611365339250213101338. [DOI] [PubMed] [Google Scholar]
- 72.Lim EJ, Suh Y, Kim S, Kang SG, Lee SJ. Force-mediated proinvasive matrix remodeling driven by tumor-associated mesenchymal stem-like cells in glioblastoma. BMB Rep. 2018;51(4):182–87. 10.5483/BMBRep.2018.51.4.185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Ghosh D, Mejia Pena C, Quach N, Xuan B, Lee AH, Dawson MR. Senescent mesenchymal stem cells remodel extracellular matrix driving breast cancer cells to a more-invasive phenotype. J Cell Sci. 2020;133(2). 10.1242/jcs.232470. [DOI] [PMC free article] [PubMed]
- 74.Iyengar P, Espina V, Williams TW, Lin Y, Berry D, Jelicks LA, et al. Adipocyte-derived collagen VI affects early mammary tumor progression in vivo, demonstrating a critical interaction in the tumor/stroma microenvironment. J Clin Invest. 2005;115(5):1163–76. 10.1172/JCI23424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Park J, Scherer PE. Adipocyte-derived endotrophin promotes malignant tumor progression. J. Clin. Invest. 2012;122(11):4243–56. 10.1172/JCI63930. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Maltby S, Khazaie K, McNagny KM. Mast cells in tumor growth: angiogenesis, tissue remodelling and immune-modulation. Biochim et Biophys Acta (BBA) - Rev Cancer. 2009;1796(1):19–26. 10.1016/j.bbcan.2009.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Zhang Y, Manouchehri Doulabi E, Herre M, Cedervall J, Qiao Q, Miao Z, et al. Platelet-derived PDGFB promotes recruitment of cancer-associated fibroblasts, deposition of extracellular matrix and Tgfβ signaling in the tumor microenvironment. Cancers (Basel). 2022;14(8):1947. 10.3390/cancers14081947. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Lodi F, Vanmassenhove S, Chen D, Boeckx B, Ferreira Maciel L, Peeters F, et al. Decoding tumor heterogeneity: a spatially informed pan-cancer analysis of the tumor microenvironment. Cell Rep. Med. 2025;6(10):102416. 10.1016/j.xcrm.2025.102416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.de Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41(3):374–403. 10.1016/j.ccell.2023.02.016. [DOI] [PubMed] [Google Scholar]
- 80.Winkler J, Abisoye-Ogunniyan A, Metcalf KJ, Werb Z. Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat Commun. 2020;11(1):5120. 10.1038/s41467-020-18794-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Magnussen AL, Mills IG. Vascular normalisation as the stepping stone into tumour microenvironment transformation. Br J Cancer. 2021;125(3):324–36. 10.1038/s41416-021-01330-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Mai Z, Lin Y, Lin P, Zhao X, Cui L. Modulating extracellular matrix stiffness: a strategic approach to boost cancer immunotherapy. Cell Death Dis. 2024;15(5):307. 10.1038/s41419-024-06697-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Boaru DL, De Leon-Oliva D, Fraile-Martinez O, De Castro-Martinez P, Garcia-Montero C, Ferrara-Coppola C, et al. The role of the LOX family in cancer. Eur J Cell Biol. 2026;105(1):151527. 10.1016/j.ejcb.2025.151527. [DOI] [PubMed] [Google Scholar]
- 84.Zhang J, Han W, Zhang M, Yi Y, Long M. Targeting tumor microenvironmental barriers to enhance immunogenic cell death in solid tumors. Front Immunol. 2025;16:1672601. 10.3389/fimmu.2025.1672601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Choi Y, Jung K. Normalization of the tumor microenvironment by harnessing vascular and immune modulation to achieve enhanced cancer therapy. Exp Mol Med. 2023;55(11):2308–19. 10.1038/s12276-023-01114-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Gong P, Wang F, Hua Y, Ying J, Chen J, Qiao Y. Collagenase-mediated extracellular matrix targeting for enhanced drug penetration and therapeutic efficacy in nanoscale delivery systems for cancer therapy. J Nanobiotechnol. 2025;23(1):733. 10.1186/s12951-025-03815-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Yu P, Wang Y, Yuan D, Sun Y, Qin S, Li T. Vascular normalization: reshaping the tumor microenvironment and augmenting antitumor immunity for ovarian cancer. Front Immunol. 2023;14:1276694. 10.3389/fimmu.2023.1276694. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Zhou F, Mu G, Bi H, Chen L, Zha Z, Jin Y, et al. Targeted hyaluronan degradation enhanced tumor growth inhibition in gastrointestinal cancer models. Cancers (Basel). 2025;17(21):3411. 10.3390/cancers17213411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Seki T, Saida Y, Kishimoto S, Lee J, Otowa Y, Yamamoto K, et al. PEGPH20, a PEGylated human hyaluronidase, induces radiosensitization by reoxygenation in pancreatic cancer xenografts. A molecular imaging study. Neoplasia. 2022;30:100793. 10.1016/j.neo.2022.100793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Wang Y, Zhang F, Qian Z, Jiang Y, Wu D, Liu L, et al. Targeting collagen to optimize cancer immunotherapy. Exp Hematol Oncol. 2025;14(1):101. 10.1186/s40164-025-00691-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Provenzano PP, Inman DR, Eliceiri KW, Knittel JG, Yan L, Rueden CT, et al. Collagen density promotes mammary tumor initiation and progression. BMC Med. 2008;6(1):11. 10.1186/1741-7015-6-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Levental KR, Yu H, Kass L, Lakins JN, Egeblad M, Erler JT, et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell. 2009;139(5):891–906. 10.1016/j.cell.2009.10.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 93.Brisson BK, Mauldin EA, Lei W, Vogel LK, Power AM, Lo A, et al. Type III collagen directs stromal organization and limits metastasis in a murine model of breast cancer. Am J Pathol. 2015;185(5):1471–86. 10.1016/j.ajpath.2015.01.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Di Martino JS, Nobre AR, Mondal C, Taha I, Farias EF, Fertig EJ, et al. A tumor-derived type III collagen-rich ECM niche regulates tumor cell dormancy. Nat Cancer. 2021;3(1):90–107. 10.1038/s43018-021-00291-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Zeng ZS, Cohen AM, Guillem JG. Loss of basement membrane type IV collagen is associated with increased expression of metalloproteinases 2 and 9 (MMP-2 and MMP-9) during human colorectal tumorigenesis. Carcinogenesis. 1999;20(5):749–55. 10.1093/carcin/20.5.749. [DOI] [PubMed] [Google Scholar]
- 96.Park J, Morley TS, Scherer PE. Inhibition of endotrophin, a cleavage product of collagen VI, confers cisplatin sensitivity to tumours. EMBO Mol Med. 2013;5(6):935–48. 10.1002/emmm.201202006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Sil H, Sen T, Chatterjee A. Fibronectin-integrin (α5β1) modulates migration and invasion of murine melanoma cell line B16F10 by involving MMP-9. Oncol Res. 2011;19(7):335–48. 10.3727/096504011X13079697132925. [DOI] [PubMed] [Google Scholar]
- 98.Baba Y, Iyama KI, Hirashima K, Nagai Y, Yoshida N, Hayashi N, et al. Laminin-332 promotes the invasion of oesophageal squamous cell carcinoma via PI3K activation. Br J Cancer. 2008;98(5):974–80. 10.1038/sj.bjc.6604252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Kim BG, An HJ, Kang S, Choi YP, Gao MQ, Park H, et al. Laminin-332-rich tumor microenvironment for tumor invasion in the interface zone of breast cancer. Am J Pathol. 2011;178(1):373–81. 10.1016/j.ajpath.2010.11.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Hamilton SR, Fard SF, Paiwand FF, Tolg C, Veiseh M, Wang C, et al. The hyaluronan receptors CD44 and rhamm (CD168) form complexes with ERK1, 2 that sustain high basal motility in breast cancer cells. J Biol Chem. 2007;282(22):16667–80. 10.1074/jbc.M702078200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Chauhan VP, Boucher Y, Ferrone CR, Roberge S, Martin JD, Stylianopoulos T, et al. Compression of pancreatic tumor blood vessels by hyaluronan is caused by solid stress and not interstitial fluid pressure. Cancer Cell. 2014;26(1):14–15. 10.1016/j.ccr.2014.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102.Oskarsson T, Acharyya S, Zhang XH-F, Vanharanta S, Tavazoie SF, Morris PG, et al. Breast cancer cells produce tenascin C as a metastatic niche component to colonize the lungs. Nat Med. 2011;17(7):867–74. 10.1038/nm.2379. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 103.Bao S, Ouyang G, Bai X, Huang Z, Ma C, Liu M, et al. Periostin potently promotes metastatic growth of colon cancer by augmenting cell survival via the Akt/PKB pathway. Cancer Cell. 2004;5(4):329–39. 10.1016/S1535-6108(04)00081-9. [DOI] [PubMed] [Google Scholar]
- 104.Wang Z, Xiong S, Mao Y, Chen M, Ma X, Zhou X, et al. Periostin promotes immunosuppressive premetastatic niche formation to facilitate breast tumour metastasis. The J Pathol. 2016;239(4):484–95. 10.1002/path.4747. [DOI] [PubMed] [Google Scholar]
- 105.Singhal H, Bautista DS, Tonkin KS, O’Malley FP, Tuck AB, Chambers AF, et al. Elevated plasma osteopontin in metastatic breast cancer associated with increased tumor burden and decreased survival. Clin Cancer Res. 1997;3(4):605–11. [PubMed] [Google Scholar]
- 106.Jia R, Liang Y, Chen R, Liu G, Wang H, Tang M, et al. Osteopontin facilitates tumor metastasis by regulating epithelial–mesenchymal plasticity. Cell Death Dis. 2016;7(12):e 2564. 10.1038/cddis.2016.422. [DOI] [PMC free article] [PubMed]
- 107.Gao D, Joshi N, Choi H, Ryu S, Hahn M, Catena R, et al. Myeloid progenitor cells in the premetastatic lung promote metastases by inducing mesenchymal to epithelial transition. Cancer Res. 2012;72(6):1384–94. 10.1158/0008-5472.CAN-11-2905. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 108.Dos Reis DC, Damasceno KA, de Campos Cb, Veloso ES, Pêgas, Kraemer LR, et al. Versican and tumor-associated macrophages promotes tumor progression and metastasis in canine and murine models of breast carcinoma. Front Oncol. 2019;9:577. 10.3389/fonc.2019.00577. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 109.Moscatello DK, Santra M, Mann DM, McQuillan DJ, Wong AJ, Iozzo RV. Decorin suppresses tumor cell growth by activating the epidermal growth factor receptor. J Clin Invest. 1998;101(2):406–12. 10.1172/JCI846. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Patel S, Santra M, McQuillan DJ, Iozzo RV, Thomas AP. Decorin activates the epidermal growth factor receptor and elevates cytosolic Ca2+ in A431 carcinoma cells. J Biol Chem. 1998;273(6):3121–24. 10.1074/jbc.273.6.3121. [DOI] [PubMed] [Google Scholar]
- 111.Cong L, Maishi N, Annan DA, Young MF, Morimoto H, Morimoto M, et al. Inhibition of stromal biglycan promotes normalization of the tumor microenvironment and enhances chemotherapeutic efficacy. Breast Cancer Res. 2021;23(1):51. 10.1186/s13058-021-01423-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 112.Sharma B, Handler M, Eichstetter I, Whitelock JM, Nugent MA, Iozzo RV. Antisense targeting of perlecan blocks tumor growth and angiogenesis in vivo. J Clin Invest. 1998;102(8):1599–608. 10.1172/JCI3793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Jiang X, Couchman JR. Perlecan and tumor angiogenesis. J Histochem Cytochem. 2003;51(11):1393–410. 10.1177/002215540305101101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 114.Jagroop R, Martin CJ, Moorehead RA. Nidogen 1 regulates proliferation and migration/invasion in murine claudin‑low mammary tumor cells. Oncol Lett. 2020;21(1):52. 10.3892/ol.2020.12313. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 115.Rokavec M, Jaeckel S, Hermeking H. Nidogen-1/NID1 function and regulation during progression and metastasis of colorectal cancer. Cancers (Basel). 2023;15(22):5316. 10.3390/cancers15225316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 116.Robinet A, Fahem A, Cauchard JH, Huet E, Vincent L, Lorimier S, et al. Elastin-derived peptides enhance angiogenesis by promoting endothelial cell migration and tubulogenesis through upregulation of MT1-MMP. J Cell Sci. 2005;118(2):343–56. 10.1242/jcs.01613. [DOI] [PubMed] [Google Scholar]
- 117.Sangaletti S, Stoppacciaro A, Guiducci C, Torrisi MR, Colombo MP. Leukocyte, rather than tumor-produced SPARC, determines stroma and collagen type IV deposition in mammary carcinoma. J Exp Med. 2003;198(10):1475–85. 10.1084/jem.20030202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 118.Arnold SA, Rivera LB, Miller AF, Carbon JG, Dineen SP, Xie Y, et al. Lack of host SPARC enhances vascular function and tumor spread in an orthotopic murine model of pancreatic carcinoma. Disease Model Mechanisms. 2010;3(1–2):57–72. 10.1242/dmm.003228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.Cox TR, Bird D, Baker AM, Barker HE, Ho MW-Y, Lang G, et al. LOX-mediated collagen crosslinking is responsible for fibrosis-enhanced metastasis. Cancer Res. 2013;73(6):1721–32. 10.1158/0008-5472.CAN-12-2233. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120.Hrabec E, Strek M, Nowak D, Greger J, Suwalski M, Hrabec Z. Activity of type IV collagenases (MMP-2 and MMP-9) in primary pulmonary carcinomas: a quantitative analysis. J Cancer ResearchClin Oncol. 2002;128(4):197–204. 10.1007/s00432-001-0320-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 121.Hulett MD, Freeman C, Hamdorf BJ, Baker RT, Harris MJ, Parish CR. Cloning of mammalian heparanase, an important enzyme in tumor invasion and metastasis. Nat Med. 1999;5(7):803–09. 10.1038/10525. [DOI] [PubMed] [Google Scholar]
- 122.Vlodavsky I, Goldshmidt O, Zcharia E, Atzmon R, Rangini-Guatta Z, Elkin M, et al. Mammalian heparanase: involvement in cancer metastasis, angiogenesis and normal development. Semin Cancer Biol. 2002;12(2):121–29. 10.1006/scbi.2001.0420. [DOI] [PubMed] [Google Scholar]
- 123.Zhang T, Gao P, Sun L. Targeting metabolic reprogramming in tumor: from mechanisms to precision immunotherapies. MedComm – Oncol. 2025;4(4):e70045. 10.1002/mog2.70045.
- 124.Liu Y, Zhao Y, Song H, Li Y, Liu Z, Ye Z, et al. Metabolic reprogramming in tumor immune microenvironment: impact on immune cell function and therapeutic implications. Cancer Lett. 2024;597:217076. 10.1016/j.canlet.2024.217076. [DOI] [PubMed] [Google Scholar]
- 125.Dong Z, Yuan Z, Jin T, Gao C, Wang X, Xu F. Lactate at the crossroads of tumor metabolism and immune escape: a new frontier in cancer therapy. J Transl Med. 2025;23(1):1239. 10.1186/s12967-025-07272-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 126.Wang Z, Gu Z, Mo W, Zhang H. Lactate metabolic checkpoint in immuno-oncology: mechanisms and therapeutic implications. Cancer Lett. 2025;633:218038. 10.1016/j.canlet.2025.218038. [DOI] [PubMed] [Google Scholar]
- 127.Gu XY, Yang JL, Lai R, Zhou ZJ, Tang D, Hu L, et al. Impact of lactate on immune cell function in the tumor microenvironment: mechanisms and therapeutic perspectives. Front Immunol. 2025;16:1563303. 10.3389/fimmu.2025.1563303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 128.Li S, Zhang Y, Tong H, Sun H, Liao H, Li Q, et al. Metabolic regulation of immunity in the tumor microenvironment. Cell Rep. 2025;44(11):116463. 10.1016/j.celrep.2025.116463. [DOI] [PubMed] [Google Scholar]
- 129.Halford S, Veal GJ, Wedge SR, Payne GS, Bacon CM, Sloan P, et al. A phase I dose-escalation study of AZD3965, an Oral monocarboxylate transporter 1 inhibitor, in patients with advanced cancer. Clin Cancer Res. 2023;29(8):1429–39. 10.1158/1078-0432.CCR-22-2263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 130.Halford SE, Walter H, McKay P, Townsend W, Linton K, Heinzmann K, et al. Phase I expansion study of the first-in-class monocarboxylate transporter 1 (MCT1) inhibitor AZD3965 in patients with diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL). Wolters Kluwer Health; 2021. [Google Scholar]
- 131.Jin J, Byun JK, Choi YK, Park KG. Targeting glutamine metabolism as a therapeutic strategy for cancer. Exp Mol Med. 2023;55(4):706–15. 10.1038/s12276-023-00971-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 132.Pan S, Fan M, Liu Z, Li X, Wang H. Serine, glycine and one‑carbon metabolism in cancer (review). Int J Oncol. 2020;58(2):158–70. 10.3892/ijo.2020.5158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 133.Spada M, Piras C, Leoni VP, Casula M, Simbula G, Noto A, et al. Glutaminase inhibitor CB-839 causes metabolic adjustments in colorectal cancer cells. Sci Rep. 2025;15(1):37028. 10.1038/s41598-025-20528-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 134.Gouda M, Voss M, Tawbi H, Gordon M, Tykodi S, Lam E, et al. A phase I/II study of the safety and efficacy of telaglenastat (CB-839) in combination with nivolumab in patients with metastatic melanoma, renal cell carcinoma, and non-small-cell lung cancer. ESMO Open. 2025;10(5):104536. 10.1016/j.esmoop.2025.104536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Terry AR, Hay N. Emerging targets in lipid metabolism for cancer therapy. Trends Pharmacological Sci. 2024;45(6):537–51. 10.1016/j.tips.2024.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 136.Liu W, Wang Z, Li Z, Li S, Shi X, Xu Y, et al. Lipid metabolic reprogramming in the tumor microenvironment and its mechanistic role in immunosuppressive cells. Front Immunol. 2025;16:1728354. 10.3389/fimmu.2025.1728354. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 137.Zhang W, Wang P, Yuan G, Liu F, Jin G, Zhang J. Fatty acid metabolic reprogramming in the tumor microenvironment: unraveling mechanisms and therapeutic prospects. Genes Dis. 2026;13(3):101772. 10.1016/j.gendis.2025.101772. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 138.Yin X, Yang Y, Liu S. Cancer immunotherapy by adenosinergic CD39 and CD73 as emerging immune checkpoints. Discov Oncol. 2025;16(1):2153. 10.1007/s12672-025-04001-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 139.Bi C, Patel JS, Liang SH. Development of CD73 inhibitors in tumor immunotherapy and opportunities in imaging and combination therapy. J Med Chem. 2025;68(7):6860–69. 10.1021/acs.jmedchem.4c02151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 140.Li M, Wang T, Zhang Z, Dongye Y. Metabolic reprogramming in the post-metastatic tumor microenvironment: multi-omics insights into determinants of immunotherapy response. Front Immunol. 2026;16:1742855. 10.3389/fimmu.2025.1742855. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 141.Heydari F, Hamblin MR, Naghinezhad J. Hijacking the helpers: platelet and neutrophil trafficking in AML and therapeutic exploitation. Exp Hematol Oncol. 2026;15(1):13. 10.1186/s40164-026-00744-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 142.Heydari F, Hamblin MR, Naghinezhad J. From inflammation to targeted therapy: leveraging neutrophil, platelet, RBC, and macrophage biology for nanocarrier in Atherosclerosis. Curr Atheroscler Rep. 2026;28(1):26. 10.1007/s11883-026-01394-3. [DOI] [PubMed] [Google Scholar]
- 143.Beatty GL, O’Dwyer PJ, Clark J, Shi JG, Bowman KJ, Scherle PA, et al. First-in-human phase I study of the Oral inhibitor of indoleamine 2, 3-dioxygenase-1 epacadostat (INCB024360) in patients with advanced solid malignancies. Clin Cancer Res. 2017;23(13):3269–76. 10.1158/1078-0432.CCR-16-2272. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 144.Nayak-Kapoor A, Hao Z, Sadek R, Dobbins R, Marshall L, Vahanian NN, et al. Phase Ia study of the indoleamine 2, 3-dioxygenase 1 (IDO1) inhibitor navoximod (GDC-0919) in patients with recurrent advanced solid tumors. J Immunother Cancer. 2018;6(1):61. 10.1186/s40425-018-0351-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 145.Glazer ES, Piccirillo M, Albino V, Di Giacomo R, Palaia R, Mastro AA, et al. Phase II study of pegylated arginine deiminase for nonresectable and metastatic hepatocellular carcinoma. JCO. 2010;28(13):2220–26. 10.1200/JCO.2009.26.7765. [DOI] [PubMed] [Google Scholar]
- 146.Chang KY, Chiang NJ, Wu SY, Yen CJ, Chen SH, Yeh YM, et al. Phase 1b study of pegylated arginine deiminase (ADI-PEG 20) plus pembrolizumab in advanced solid cancers. Oncoimmunology. 2021;10(1):1943253. 10.1080/2162402X.2021.1943253. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Agnello G, Alters SE, Rowlinson SW. Preclinical safety and antitumor activity of the arginine-degrading therapeutic enzyme pegzilarginase, a PEGylated, cobalt-substituted recombinant human arginase 1. Transl Res. 2020;217:11–22. 10.1016/j.trsl.2019.12.005. [DOI] [PubMed] [Google Scholar]
- 148.Harding JJ, Telli M, Munster P, Voss MH, Infante JR, DeMichele A, et al. A phase I dose-escalation and expansion study of Telaglenastat in patients with advanced or metastatic solid tumors. Clin Cancer Res. 2021;27(18):4994–5003. 10.1158/1078-0432.CCR-21-1204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 149.Meric-Bernstam F, Tannir NM, Iliopoulos O, Lee RJ, Telli ML, Fan AC, et al. Telaglenastat plus Cabozantinib or everolimus for advanced or metastatic renal cell carcinoma: an Open-label phase I trial. Clin Cancer Res. 2022;28(8):1540–48. 10.1158/1078-0432.CCR-21-2972. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 150.Varghese S, Pramanik S, Williams LJ, Hodges HR, Hudgens CW, Fischer GM, et al. The glutaminase inhibitor CB-839 (Telaglenastat) enhances the antimelanoma activity of T-Cell–mediated immunotherapies. Mol Cancer Ther. 2021;20(3):500–11. 10.1158/1535-7163.MCT-20-0430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 151.Mullarky E, Lucki NC, Beheshti Zavareh R, Anglin JL, Gomes AP, Nicolay BN, et al. Identification of a small molecule inhibitor of 3-phosphoglycerate dehydrogenase to target serine biosynthesis in cancers. Proc Natl Acad Sci USA. 2016;113(7):1778–83. 10.1073/pnas.1521548113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 152.Saha S, Ghosh M, Li J, Wen A, Galluzzi L, Martinez LA, et al. Serine depletion promotes antitumor immunity by activating mitochondrial DNA-Mediated cGAS-STING signaling. Cancer Res. 2024;84(16):2645–59. 10.1158/0008-5472.CAN-23-1788. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 153.Van de Gucht M, Dufait I, Kerkhove L, Corbet C, de Mey S, Jiang H, et al. Inhibition of phosphoglycerate dehydrogenase radiosensitizes human colorectal cancer cells under hypoxic conditions. Cancers (Basel). 2022;14(20):5060. 10.3390/cancers14205060. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Gnanaprakasam JNR, Kushwaha B, Liu L, Chen X, Kang S, Wang T, et al. Asparagine restriction enhances CD8+ T cell metabolic fitness and antitumoral functionality through an NRF2-dependent stress response. Nat Metab. 2023;5(8):1423–39. 10.1038/s42255-023-00856-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Shen Y, Wang H, Guo D, Liu J, Sun J, Chen N, et al. Dual asparagine-depriving nanoparticles against solid tumors. Nat Commun. 2025;16(1):5675. 10.1038/s41467-025-60798-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 156.Chang HC, Tsai CY, Hsu CL, Tai TS, Cheng ML, Chuang YM, et al. Asparagine deprivation enhances T cell antitumour response in patients via ROS-mediated metabolic and signal adaptations. Nat Metab. 2025;7(5):918–27. 10.1038/s42255-025-01245-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 157.Zhao Y, Li Y, Zhang R, Wang F, Wang T, Jiao Y. The role of Erastin in ferroptosis and its prospects in cancer therapy. OTT. 2020, 13: 5429–41. 10.2147/OTT.S254995. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 158.Lang X, Green MD, Wang W, Yu J, Choi JE, Jiang L, et al. Radiotherapy and immunotherapy promote tumoral lipid oxidation and ferroptosis via synergistic repression of SLC7A11. Cancer Discov. 2019;9(12):1673–85. 10.1158/2159-8290.CD-19-0338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 159.Koppula P, Zhang Y, Shi J, Li W, Gan B. The glutamate/cystine antiporter SLC7A11/xCT enhances cancer cell dependency on glucose by exporting glutamate. J Biol Chem. 2017;292(34):14240–49. 10.1074/jbc.M117.798405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 160.Raez LE, Papadopoulos K, Ricart AD, Chiorean EG, Dipaola RS, Stein MN, et al. A phase I dose-escalation trial of 2-deoxy-D-glucose alone or combined with docetaxel in patients with advanced solid tumors. Cancer Chemother Pharmacol. 2013;71(2):523–30. 10.1007/s00280-012-2045-1. [DOI] [PubMed] [Google Scholar]
- 161.Stein M, Lin H, Jeyamohan C, Dvorzhinski D, Gounder M, Bray K, et al. Targeting tumor metabolism with 2-deoxyglucose in patients with castrate-resistant prostate cancer and advanced malignancies. The Prostate. 2010;70(13):1388–94. 10.1002/pros.21172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 162.Simons AL, Ahmad IM, Mattson DM, Dornfeld KJ, Spitz DR. 2-deoxy-D-glucose combined with cisplatin enhances cytotoxicity via metabolic oxidative stress in human head and neck cancer cells. Cancer Res. 2007;67(7):3364–70. 10.1158/0008-5472.CAN-06-3717. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 163.Wang S, Lin Y, Xiong X, Wang L, Guo Y, Chen Y, et al. Low-dose metformin reprograms the tumor immune microenvironment in human esophageal cancer: results of a phase II clinical trial. Clin Cancer Res. 2020;26(18):4921–32. 10.1158/1078-0432.CCR-20-0113. [DOI] [PubMed] [Google Scholar]
- 164.Wu Z, Zhang C, Najafi M. Targeting of the tumor immune microenvironment by metformin. J Cell Commun Signal. 2022;16(3):333–48. 10.1007/s12079-021-00648-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 165.Eikawa S, Nishida M, Mizukami S, Yamazaki C, Nakayama E, Udono H. Immune-mediated antitumor effect by type 2 diabetes drug, metformin. Proc Natl Acad Sci USA. 2015;112(6):1809–14. 10.1073/pnas.1417636112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 166.Benjamin D, Robay D, Hindupur SK, Pohlmann J, Colombi M, El-Shemerly MY, et al. Dual inhibition of the lactate transporters MCT1 and MCT4 is synthetic lethal with metformin due to NAD+ depletion in cancer cells. Cell Rep. 2018;25(11):3047–58.e4. 10.1016/j.celrep.2018.11.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 167.Babl N, Decking SM, Voll F, Althammer M, Sala-Hojman A, Ferretti R, et al. MCT4 blockade increases the efficacy of immune checkpoint blockade. J Immunother Cancer. 2023;11(10):e007349. 10.1136/jitc-2023-007349. [DOI] [PMC free article] [PubMed]
- 168.Di Magno L, Coluccia A, Bufano M, Ripa S, La Regina G, Nalli M, et al. Discovery of novel human lactate dehydrogenase inhibitors: structure-based virtual screening studies and biological assessment. Eur J Med Chem. 2022;240:114605. 10.1016/j.ejmech.2022.114605. [DOI] [PubMed] [Google Scholar]
- 169.Yeung C, Gibson AE, Issaq SH, Oshima N, Baumgart JT, Edessa LD, et al. Targeting glycolysis through inhibition of lactate dehydrogenase impairs tumor growth in preclinical models of Ewing Sarcoma. Cancer Res. 2019;79(19):5060–73. 10.1158/0008-5472.CAN-19-0217. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 170.Ohashi T, Akazawa T, Aoki M, Kuze B, Mizuta K, Ito Y, et al. Dichloroacetate improves immune dysfunction caused by tumor-secreted lactic acid and increases antitumor immunoreactivity. Intl J Cancer. 2013;133(5):1107–18. 10.1002/ijc.28114. [DOI] [PubMed] [Google Scholar]
- 171.Chu QS-C, Sangha R, Spratlin J, Vos LJ, Mackey JR, McEwan AJB, et al. A phase I open-labeled, single-arm, dose-escalation, study of dichloroacetate (DCA) in patients with advanced solid tumors. Invest New Drugs. 2015;33(3):603–10. 10.1007/s10637-015-0221-y. [DOI] [PubMed] [Google Scholar]
- 172.Dunbar EM, Coats BS, Shroads AL, Langaee T, Lew A, Forder JR, et al. Phase 1 trial of dichloroacetate (DCA) in adults with recurrent malignant brain tumors. Invest New Drugs. 2014;32(3):452–64. 10.1007/s10637-013-0047-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 173.Michelakis ED, Webster L, Mackey JR. Dichloroacetate (DCA) as a potential metabolic-targeting therapy for cancer. Br J Cancer. 2008;99(7):989–94. 10.1038/sj.bjc.6604554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 174.Falchook G, Infante J, Arkenau HT, Patel MR, Dean E, Borazanci E, et al. First-in-human study of the safety, pharmacokinetics, and pharmacodynamics of first-in-class fatty acid synthase inhibitor TVB-2640 alone and with a taxane in advanced tumors. EClinicalMedicine. 2021;34:100797. 10.1016/j.eclinm.2021.100797. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 175.Zaytseva YY, Rychahou PG, Le AT, Scott TL, Flight RM, Kim JT, et al. Preclinical evaluation of novel fatty acid synthase inhibitors in primary colorectal cancer cells and a patient-derived xenograft model of colorectal cancer. Oncotarget. 2018;9(37):24787–800. 10.18632/oncotarget.25361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 176.Hossain F, Al-Khami AA, Wyczechowska D, Hernandez C, Zheng L, Reiss K, et al. Inhibition of fatty acid oxidation modulates immunosuppressive functions of myeloid-derived suppressor cells and enhances cancer therapies. Cancer Immunol Res. 2015;3(11):1236–47. 10.1158/2326-6066.CIR-15-0036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 177.Liu Z, Liu W, Wang W, Ma Y, Wang Y, Drum DL, et al. CPT1A-mediated fatty acid oxidation confers cancer cell resistance to immune-mediated cytolytic killing. Proc Natl Acad Sci USA. 2023;120(39):e2302878120. 10.1073/pnas.2302878120. [DOI] [PMC free article] [PubMed]
- 178.Watt MJ, Clark AK, Selth LA, Haynes VR, Lister N, Rebello R, et al. Suppressing fatty acid uptake has therapeutic effects in preclinical models of prostate cancer. Sci Transl Med. 2019;11(478). 10.1126/scitranslmed.aau5758. [DOI] [PubMed]
- 179.Takaichi M, Tachinami H, Takatsuka D, Yonesi A, Sakurai K, Rasul MI, et al. Targeting CD36-mediated lipid metabolism by selective inhibitor-augmented antitumor immune responses in Oral cancer. IJMS. 2024;25(17):9438. 10.3390/ijms25179438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 180.Ruan C, Meng Y, Song H. CD36: an emerging therapeutic target for cancer and its molecular mechanisms. J Cancer ResearchClin Oncol. 2022;148(7):1551–58. 10.1007/s00432-022-03957-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 181.Schmidt NM, Wing PAC, Diniz MO, Pallett LJ, Swadling L, Harris JM, et al. Targeting human acyl-CoA: cholesterol acyltransferase as a dual viral and T cell metabolic checkpoint. Nat Commun. 2021;12(1):2814. 10.1038/s41467-021-22967-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 182.Sun T, Xiao X. Targeting ACAT1 in cancer: from threat to treatment. Front Oncol. 2024;14:1395192. 10.3389/fonc.2024.1395192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 183.O’Keefe S, Wang Q. ACAT1 regulates tertiary lymphoid structures: a target for enhancing immunotherapy in non–small cell lung cancer. J Clin Investigation. 2025;135(7). 10.1172/JCI191094. [DOI] [PMC free article] [PubMed]
- 184.Jin K, Qian C, Lin J, Liu B. Cyclooxygenase-2-prostaglandin E2 pathway: a key player in tumor-associated immune cells. Front Oncol. 2023;13:1099811. 10.3389/fonc.2023.1099811. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 185.Pu D, Yin L, Huang L, Qin C, Zhou Y, Wu Q, et al. Cyclooxygenase-2 inhibitor: a potential combination strategy with immunotherapy in cancer. Front Oncol. 2021;11:637504. 10.3389/fonc.2021.637504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 186.Bendell J, LoRusso P, Overman M, Noonan AM, Kim DW, Strickler JH, et al. First-in-human study of oleclumab, a potent, selective anti-CD73 monoclonal antibody, alone or in combination with durvalumab in patients with advanced solid tumors. Cancer Immunol Immunother. 2023;72(7):2443–58. 10.1007/s00262-023-03430-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 187.Lim EA, Bendell JC, Falchook GS, Bauer TM, Drake CG, Choe JH, et al. Phase Ia/b, Open-label, multicenter study of AZD4635 (an adenosine A2A receptor antagonist) as monotherapy or combined with durvalumab, in patients with solid tumors. Clin Cancer Res. 2022;28(22):4871–84. 10.1158/1078-0432.CCR-22-0612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 188.Seitz L, Jin L, Leleti M, Ashok D, Jeffrey J, Rieger A, et al. Safety, tolerability, and pharmacology of AB928, a novel dual adenosine receptor antagonist, in a randomized, phase 1 study in healthy volunteers. Invest New Drugs. 2019;37(4):711–21. 10.1007/s10637-018-0706-6. [DOI] [PubMed] [Google Scholar]
- 189.Greene S, Watanabe K, Braatz-Trulson J, Lou L. Inhibition of dihydroorotate dehydrogenase by the immunosuppressive agent leflunomide. Biochemical Pharmacol. 1995;50(6):861–67. 10.1016/0006-2952(95)00255-X. [DOI] [PubMed] [Google Scholar]
- 190.Takebe N, Cheng X, Fandy TE, Srivastava RK, Wu S, Shankar S, et al. IMP dehydrogenase inhibitor mycophenolate mofetil induces caspase-dependent apoptosis and cell cycle inhibition in multiple myeloma cells. Mol Cancer Ther. 2006;5(2):457–66. 10.1158/1535-7163.MCT-05-0340. [DOI] [PubMed] [Google Scholar]
- 191.Mullen NJ, Shukla SK, Thakur R, Kollala SS, Wang D, Chaika N, et al. DHODH inhibition enhances the efficacy of immune checkpoint blockade by increasing cancer cell antigen presentation. bioRxiv. 2024. [DOI] [PMC free article] [PubMed]
- 192.Gehrke I, Bouchard EDJ, Beiggi S, Poeppl AG, Johnston JB, Gibson SB, et al. On-target effect of FK866, a nicotinamide phosphoribosyl transferase inhibitor, by apoptosis-mediated death in chronic lymphocytic leukemia cells. Clin Cancer Res. 2014;20(18):4861–72. 10.1158/1078-0432.CCR-14-0624. [DOI] [PubMed] [Google Scholar]
- 193.Schuster S, Penke M, Gorski T, Gebhardt R, Weiss TS, Kiess W, et al. FK866-induced NAMPT inhibition activates AMPK and downregulates mTOR signaling in hepatocarcinoma cells. Biochem Bioph Res Co. 2015;458(2):334–40. 10.1016/j.bbrc.2015.01.111. [DOI] [PubMed] [Google Scholar]
- 194.Chen L, Diao L, Yang Y, Yi X, Rodriguez BL, Li Y, et al. CD38-mediated immunosuppression as a mechanism of tumor cell escape from PD-1/PD-L1 blockade. Cancer Discov. 2018;8(9):1156–75. 10.1158/2159-8290.CD-17-1033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 195.Molina JR, Sun Y, Protopopova M, Gera S, Bandi M, Bristow C, et al. An inhibitor of oxidative phosphorylation exploits cancer vulnerability. Nat Med. 2018;24(7):1036–46. 10.1038/s41591-018-0052-4. [DOI] [PubMed] [Google Scholar]
- 196.Yap TA, Daver N, Mahendra M, Zhang J, Kamiya-Matsuoka C, Meric-Bernstam F, et al. Complex I inhibitor of oxidative phosphorylation in advanced solid tumors and acute myeloid leukemia: phase I trials. Nat Med. 2023;29(1):115–26. 10.1038/s41591-022-02103-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 197.Saito A, Kitayama J, Horie H, Koinuma K, Ohzawa H, Yamaguchi H, et al. Metformin changes the immune microenvironment of colorectal cancer in patients with type 2 diabetes mellitus. Cancer Sci. 2020;111(11):4012–20. 10.1111/cas.14615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 198.Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060–72. 10.1016/j.cell.2012.03.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 199.Lachaier E, Louandre C, Godin C, Saidak Z, Baert M, Diouf M, et al. Sorafenib induces ferroptosis in human cancer cell lines originating from different solid tumors. Anticancer Res. 2014;34(11):6417–22. [PubMed] [Google Scholar]
- 200.Sui X, Zhang R, Liu S, Duan T, Zhai L, Zhang M, et al. RSL3 drives ferroptosis through GPX4 inactivation and ROS production in colorectal cancer. Front Pharmacol. 2018;9:1371. 10.3389/fphar.2018.01371. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 201.Di Biase S, Ma X, Wang X, Yu J, Wang YC, Smith DJ, et al. Creatine uptake regulates CD8 T cell antitumor immunity. J Exp Med. 2019;216(12):2869–82. 10.1084/jem.20182044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 202.Peng Z, Saito S. Creatine supplementation enhances anti-tumor immunity by promoting adenosine triphosphate production in macrophages. Front Immunol. 2023;14:1176956. 10.3389/fimmu.2023.1176956. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 203.Samborska B, Roy DG, Rahbani JF, Hussain MF, Ma EH, Jones RG, et al. Creatine transport and creatine kinase activity is required for CD8+ T cell immunity. Cell Rep. 2022;38(9):110446. 10.1016/j.celrep.2022.110446. [DOI] [PubMed] [Google Scholar]
- 204.McDonald PC, Chia S, Bedard PL, Chu Q, Lyle M, Tang L, et al. A phase 1 study of SLC-0111, a novel inhibitor of carbonic anhydrase IX, in patients with advanced solid tumors. Am J Clin Oncol. 2020;43(7):484–90. 10.1097/COC.0000000000000691. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 205.Chafe SC, McDonald PC, Saberi S, Nemirovsky O, Venkateswaran G, Burugu S, et al. Targeting hypoxia-induced carbonic anhydrase IX enhances immune-checkpoint blockade locally and Systemically. Cancer Immunol Res. 2019;7(7):1064–78. 10.1158/2326-6066.CIR-18-0657. [DOI] [PubMed] [Google Scholar]
- 206.Andreucci E, Ruzzolini J, Peppicelli S, Bianchini F, Laurenzana A, Carta F, et al. The carbonic anhydrase IX inhibitor SLC-0111 sensitises cancer cells to conventional chemotherapy. J Enzym Inhib Med Chem. 2019;34(1):117–23. 10.1080/14756366.2018.1532419. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 207.Palviainen M, Laukkanen K, Tavukcuoglu Z, Velagapudi V, Kärkkäinen O, Hanhineva K, et al. Cancer alters the metabolic fingerprint of extracellular vesicles. Cancers (Basel). 2020;12(11):3292. 10.3390/cancers12113292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 208.Lee YJ, Seo CW, Chae S, Lee CY, Kim SS, Shin YH, et al. Metabolic reprogramming into a glycolysis phenotype induced by extracellular vesicles derived from prostate cancer cells. Mol Cellular Proteomics. 2025;24(4):100944. 10.1016/j.mcpro.2025.100944. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 209.Yu L, Ding T, Olofsson Bagge R, Zheng L, Hao X. Proteomics of prostate cancer tissue small extracellular vesicles reveal alteration of metabolism. Proteomics. 2026;26(2–3):121–28. 10.1002/pmic.70081. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 210.Zhang L, Chen D, Song D, Liu X, Zhang Y, Xu X, et al. Clinical and translational values of spatial transcriptomics. Sig Transduct Target Ther. 2022;7(1):111. 10.1038/s41392-022-00960-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 211.Semba T, Ishimoto T. Spatial analysis by current multiplexed imaging technologies for the molecular characterisation of cancer tissues. Br J Cancer. 2024;131(11):1737–47. 10.1038/s41416-024-02882-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 212.Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78–82. 10.1126/science.aaf2403. [DOI] [PubMed] [Google Scholar]
- 213.Wu SZ, Al-Eryani G, Roden DL, Junankar S, Harvey K, Andersson A, et al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet. 2021;53(9):1334–47. 10.1038/s41588-021-00911-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 214.Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014;11(4):417–22. 10.1038/nmeth.2869. [DOI] [PubMed] [Google Scholar]
- 215.Merritt CR, Ong GT, Church SE, Barker K, Danaher P, Geiss G, et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol. 2020;38(5):586–99. 10.1038/s41587-020-0472-9. [DOI] [PubMed] [Google Scholar]
- 216.Williams HL, Frei AL, Koessler T, Berger MD, Dawson H, Michielin O, et al. The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition. NPJ Precis Onc. 2024;8(1):178. 10.1038/s41698-024-00671-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 217.Pu Y, Zhou Y, Guo T, Pan X, Sun X, Yang G, et al. MIF promotes phenotypic switching of VSMCs via AKT/mTOR-mediated autophagy regulation in aortic dissection. Faseb J. 2025;39(19):e71079. 10.1096/fj.202501761R. [DOI] [PubMed]
- 218.Naghinezhad J, Zarifi N, Soleymani S, Kandovan MN, Chermehini NY, Hamblin MR, et al. Smart DNA hydrogels for post-surgical hemostasis and tumor recurrence prevention: bridging bioengineering and clinical translation. Cancer Nanotechnol. 2026;17(1). 10.1186/s12645-025-00355-w.
- 219.Wang Y, Xu Y, Song J, Liu X, Liu S, Yang N, et al. Tumor cell-targeting and tumor microenvironment–responsive nanoplatforms for the multimodal imaging-guided Photodynamic/Photothermal/Chemodynamic treatment of cervical cancer. IJN. 2024;19:5837–58. 10.2147/IJN.S466042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 220.Alieva M, van Rheenen J, Broekman MLD. Potential impact of invasive surgical procedures on primary tumor growth and metastasis. Clin Exp Metastasis. 2018;35(4):319–31. 10.1007/s10585-018-9896-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 221.Predina J, Eruslanov E, Judy B, Kapoor V, Cheng G, Wang LC, et al. Changes in the local tumor microenvironment in recurrent cancers may explain the failure of vaccines after surgery. Proc Natl Acad Sci USA. 2013;110(5):E415–24. 10.1073/pnas.1211850110. [DOI] [PMC free article] [PubMed]
- 222.Jiao Y, Lv Q. Does primary tumor resection induce Accelerated metastasis in breast cancer? A review. J Retailing Surg Res. 2023;283:1005–17. 10.1016/j.jss.2022.11.064. [DOI] [PubMed] [Google Scholar]
- 223.Feng Q, Ni C, Zhou F, Zhu J, Sun X, Qin Y, et al. Clinical relevance of perioperative changes in circulating tumor cells in resectable colorectal cancer. BMC Cancer. 2025;25(1):1689. 10.1186/s12885-025-15087-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 224.Tohme S, Simmons RL, Tsung A. Surgery for cancer: a trigger for metastases. Cancer Res. 2017;77(7):1548–52. 10.1158/0008-5472.CAN-16-1536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 225.Choi H, Hwang W. Perioperative inflammatory response and cancer recurrence in lung cancer surgery: a narrative review. Front. Surg. 2022;9:888630. 10.3389/fsurg.2022.888630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 226.Onuma AE, Zhang H, Gil L, Huang H, Tsung A. Surgical stress promotes tumor progression: a focus on the impact of the immune response. JCM. 2020;9(12):4096. 10.3390/jcm9124096. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 227.Zhao H, Wu L, Yan G, Chen Y, Zhou M, Wu Y, et al. Inflammation and tumor progression: signaling pathways and targeted intervention. Sig Transduct Target Ther. 2021;6(1):263. 10.1038/s41392-021-00658-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 228.Cools-Lartigue J, Spicer J, McDonald B, Gowing S, Chow S, Giannias B, et al. Neutrophil extracellular traps sequester circulating tumor cells and promote metastasis. J Clin Invest. 2013;123(8):3446–58. 10.1172/JCI67484. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 229.Liao K, Zhang X, Liu J, Teng F, He Y, Cheng J, et al. The role of platelets in the regulation of tumor growth and metastasis: the mechanisms and targeted therapy. MedComm (2020). 2023;4(5):e350. 10.1002/mco2.350. [DOI] [PMC free article] [PubMed]
- 230.Haemmerle M, Stone RL, Menter DG, Afshar-Kharghan V, Sood AK. The platelet lifeline to cancer: challenges and opportunities. Cancer Cell. 2018;33(6):965–83. 10.1016/j.ccell.2018.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 231.Olsson AK, Cedervall J. The pro-inflammatory role of platelets in cancer. Platelets. 2018;29(6):569–73. 10.1080/09537104.2018.1453059. [DOI] [PubMed] [Google Scholar]
- 232.Tang F, Tie Y, Tu C, Wei X. Surgical trauma-induced immunosuppression in cancer: recent advances and the potential therapies. Clin Transl Med. 2020;10(1):199–223. 10.1002/ctm2.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 233.Kim R. Effects of surgery and anesthetic choice on immunosuppression and cancer recurrence. J Transl Med. 2018;16(1):8. 10.1186/s12967-018-1389-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 234.Cata JP, Wang H, Gottumukkala V, Reuben J, Sessler DI. Inflammatory response, immunosuppression, and cancer recurrence after perioperative blood transfusions. Br J Anaesth. 2013;110(5):690–701. 10.1093/bja/aet068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 235.Yao YL, Xu Z, Xiao R, Li EH, Zhang YJ, Zhou LY, et al. Elevated neutrophil, lymphocyte, and platelet counts as early biomarkers of preeclampsia risk: a retrospective cohort study. Am J Rep Immunol. 2025;94(2):e70137. 10.1111/aji.70137. [DOI] [PubMed]
- 236.Townsend EC, Cheong JZA, Radzietza M, Fritz B, Malone M, Bjarnsholt T, et al. What is slough? Defining the proteomic and microbial composition of slough and its implications for wound healing. Wound Repair Regener. 2024;32(6):783–98. 10.1111/wrr.13170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 237.Murthy BL, Thomson CS, Dodwell D, Shenoy H, Mikeljevic JS, Forman D, et al. Postoperative wound complications and systemic recurrence in breast cancer. Br J Cancer. 2007;97(9):1211–17. 10.1038/sj.bjc.6604004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 238.Kinoshita T, Goto T. Links between inflammation and postoperative cancer recurrence. JCM. 2021;10(2):228. 10.3390/jcm10020228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 239.Zeng F, Shao Y, Wu J, Luo J, Yue Y, Shen Y, et al. Tumor metastasis and recurrence: the role of perioperative NETosis. Cancer Lett. 2025;611:217413. 10.1016/j.canlet.2024.217413. [DOI] [PubMed] [Google Scholar]
- 240.Wan S, Lai Y, Myers RE, Li B, Hyslop T, London J, et al. Preoperative platelet count associates with survival and distant metastasis in surgically resected colorectal cancer patients. J Gastrointest Cancer. 2013;44(3):293–304. 10.1007/s12029-013-9491-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 241.Shi X, Wang X, Yao W, Shi D, Shao X, Lu Z, et al. Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives. Sig Transduct Target Ther. 2024;9(1):192. 10.1038/s41392-024-01885-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 242.Market M, Tennakoon G, Auer RC. Postoperative natural killer cell dysfunction: the prime suspect in the case of metastasis following curative cancer surgery. IJMS. 2021;22(21):11378. 10.3390/ijms222111378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 243.Acharya K, Rout DK, Kapadia NA, Caroicar YS, Karunakaran NB, Patel P, et al. Surgical stress response: a physiological review of the endocrine, immune, and metabolic changes. Cureus. 2025;17(12):e100101. 10.7759/cureus.100101. [DOI] [PMC free article] [PubMed]
- 244.Prete A, Yan Q, Al-Tarrah K, Akturk HK, Prokop LJ, Alahdab F, et al. The cortisol stress response induced by surgery: a systematic review and meta-analysis. Clin Endocrinol (Oxford). 2018;89(5):554–67. 10.1111/cen.13820. [DOI] [PubMed] [Google Scholar]
- 245.Carnet Le Provost K, Kepp O, Kroemer G, Bezu L. Trial watch: beta-blockers in cancer therapy. Oncoimmunology. 2023;12(1):2284486. 10.1080/2162402X.2023.2284486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 246.Marchetti M, Giaccherini C, Masci G, Verzeroli C, Russo L, Celio L, et al. Thrombin generation predicts early recurrence in breast cancer patients. J Thromb Haemostasis. 2020;18(9):2220–31. 10.1111/jth.14891. [DOI] [PubMed] [Google Scholar]
- 247.Zhu S, Zhao Y, Quan Y, Ma X. Targeting myeloid-derived suppressor cells derived from surgical stress: the key to prevent post-surgical metastasis. Front. Surg. 2021;8:783218. 10.3389/fsurg.2021.783218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 248.Zhou Y, Li W, Chen Y, Hu X, Miao C. Research progress on the impact of opioids on the tumor immune microenvironment (review). Mol Clin Oncol. 2025;22(6):1–11. 10.3892/mco.2025.2848. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 249.Abdolmohammadi K, Mahmoudi T, Jafari-Koshki T, Hassan ZM, Pourfathollah AA. Immunomodulatory effects of blood transfusion on tumor size, metastasis, and survival in experimental fibrosarcoma. Indian J Hematol Blood Transfus. 2018;34(4):697–702. 10.1007/s12288-018-0962-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 250.Goubran H, Sheridan D, Radosevic J, Burnouf T, Seghatchian J. Transfusion-related immunomodulation and cancer. Tranfus Apheresis Sci. 2017;56(3):336–40. 10.1016/j.transci.2017.05.019. [DOI] [PubMed] [Google Scholar]
- 251.Sandbank E, Matzner P, Eckerling A, Sorski L, Rossene E, Nachmani I, et al. Perioperative hypothermia and stress jeopardize antimetastatic immunity and TLR-9 immune activation: potential mediating mechanisms (experimental studies). Int J Surg. 2024;110(11):6941–52. 10.1097/JS9.0000000000002021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 252.Li J, Li J, Yao Y, Yong T, Bie N, Wei Z, et al. Biodegradable electrospun nanofibrous platform integrating antiplatelet therapy-chemotherapy for preventing postoperative tumor recurrence and metastasis. Theranostics. 2022;12(7):3503–17. 10.7150/thno.69795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 253.Lin Z, Hua G, Hu X. Lipid metabolism associated crosstalk: the bidirectional interaction between cancer cells and immune/stromal cells within the tumor microenvironment for prognostic insight. Cancer Cell Int. 2024;24(1):295. 10.1186/s12935-024-03481-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 254.Haykal T, Yang R, Tohme C, He Z, Liu S, Geller DA, et al. Surgery-induced neutrophil extracellular traps promote tumor metastasis by reprogramming cancer cell lipid metabolism. Cancer Res. 2025;85(24):4995–5014. 10.1158/0008-5472.CAN-24-3393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 255.Bezu L, Akçal Öksüz D, Bell M, Buggy D, Diaz-Cambronero O, Enlund M, et al. Perioperative immunosuppressive factors during cancer surgery: an updated review. Cancers (Basel). 2024;16(13):2304. 10.3390/cancers16132304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 256.Bakos O, Lawson C, Rouleau S, Tai LH. Combining surgery and immunotherapy: turning an immunosuppressive effect into a therapeutic opportunity. J Immunother Cancer. 2018;6(1):86. 10.1186/s40425-018-0398-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 257.Huang L, Xu J, Zhang H, Wang M, Zhang Y, Lin Q. Application and investigation of thrombopoiesis-stimulating agents in the treatment of thrombocytopenia. Ther Adv Hematol. 2023;14:20406207231152746. 10.1177/20406207231152746. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 258.Boudreaux MK, Christopherson PW. Thrombopoiesis. In: Schalm’s veterinary hematology. 2022. p. 649–57.
- 259.Gremmel T, Frelinger Iii AL, Michelson AD, editors. Platelet physiology. Seminars in thrombosis and hemostasis. Thieme Medical Publishers; 2016. [DOI] [PubMed]
- 260.An O, Deppermann C. Platelet lifespan and mechanisms for clearance. Curr Opin Hematol. 2024;31(1):6–15. 10.1097/MOH.0000000000000792. [DOI] [PubMed] [Google Scholar]
- 261.Sugihara-Seki M, Takinouchi N. Margination of platelet-sized particles in the red blood cell suspension flow through square Microchannels. Micromachines (Basel). 2021;12(10):1175. 10.3390/mi12101175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 262.Kotsalos C, Raynaud F, Lätt J, Dutta R, Dubois F, Zouaoui Boudjeltia K, et al. Shear induced diffusion of platelets revisited. Front Physiol. 2022;13:985905. 10.3389/fphys.2022.985905. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 263.Elhanafy A, Elsagheer S, Nada S. Numerical simulation of red blood cells migration and platelets margination for blood flow in micro-vessels with fusiform aneurysms. Sci Rep. 2025;15(1):37350. 10.1038/s41598-025-22429-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 264.Dynar M, Ez-Zahraouy H, Misbah C, Abbasi M. Platelet margination dynamics in blood flow: the role of lift forces and red blood cells aggregation. Phys Rev Fluids. 2024;9(8):083603. 10.1103/PhysRevFluids.9.083603. [Google Scholar]
- 265.Li Z, Delaney MK, O’Brien KA, Du X. Signaling during platelet adhesion and activation. ATVB. 2010;30(12):2341–49. 10.1161/ATVBAHA.110.207522. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 266.Constantinescu-Bercu A, Wang YA, Woollard KJ, Mangin P, Vanhoorelbeke K, Crawley JTB, et al. The GPIbα intracellular tail - role in transducing VWF- and collagen/GPVI-mediated signaling. Haematologica. 2022;107(4):933–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 267.Rabie T, Varga-Szabo D, Bender M, Pozgaj R, Lanza F, Saito T, et al. Diverging signaling events control the pathway of GPVI down-regulation in vivo. Blood. 2007;110(2):529–35. 10.1182/blood-2006-11-058107. [DOI] [PubMed] [Google Scholar]
- 268.Naghinezhad J, Mohajerian A, Ahadi S, Khodakarim N, Hamblin M, Rezaeeyan H. When GPVI Goes rogue: pathogenesis and therapeutic horizons in ITP. Expert Rev Mol Med. 2026;28:1–41. 10.1017/erm.2026.10047. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 269.Heijnen H, van der Sluijs P. Platelet secretory behaviour: as diverse as the granules … or not? J Thromb Haemostasis. 2015;13(12):2141–51. 10.1111/jth.13147. [DOI] [PubMed] [Google Scholar]
- 270.Scridon A. Platelets and their role in hemostasis and thrombosis—from physiology to pathophysiology and therapeutic implications. IJMS. 2022;23(21):12772. 10.3390/ijms232112772. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 271.Allan HE, Dark N, Vulliamy P, Crescente M, Maffucci T, Armstrong PC, et al. Platelet mitochondrial transfer via extracellular vesicles modulates neutrophil phenotype and function. J Thromb Haemostasis. 2025;23(11):3665–77. 10.1016/j.jtha.2025.08.004. [DOI] [PubMed] [Google Scholar]
- 272.Mussbacher M, Pirabe A, Brunnthaler L, Schrottmaier WC, Assinger A. Horizontal MicroRNA transfer by platelets – evidence and implications. Front Physiol. 2021;12:678362. 10.3389/fphys.2021.678362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 273.Ren J, He J, Zhang H, Xia Y, Hu Z, Loughran P, et al. Platelet TLR4-ERK5 axis facilitates NET-Mediated capturing of circulating tumor cells and distant metastasis after surgical stress. Cancer Res. 2021;81(9):2373–85. 10.1158/0008-5472.CAN-20-3222. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 274.Ward MP, Ek L, An L, Mohamed BM, Kelly T, Bates M, et al. Platelets, immune cells and the coagulation cascade; Friend or foe of the circulating tumour cell? Mol Cancer. 2021;20(1):59. 10.1186/s12943-021-01347-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 275.Lucotti S, Muschel RJ. Platelets and metastasis: new implications of an old interplay. Front Oncol. 2020;10:1350. 10.3389/fonc.2020.01350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 276.Labelle M, Begum S, Hynes RO. Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis. Cancer Cell. 2011;20(5):576–90. 10.1016/j.ccr.2011.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 277.Zhong C, Wang W, Yao Y, Lian S, Xie X, Xu J, et al. TGF-β secreted by cancer cells-platelets interaction activates cancer metastasis potential by inducing metabolic reprogramming and bioenergetic adaptation. J Cancer. 2025;16(4):1310–23. 10.7150/jca.103757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 278.Naghinezhad J, Moradpanah S, Khodakarim N, Hamblin MR, Rezaeeyan H. Nanodrug-based modulation of platelet–leukocyte interactions in ovarian cancer: a new frontier in targeted therapy. Cancer Nanotechnol. 2025;16(1):37. 10.1186/s12645-025-00340-3. [Google Scholar]
- 279.Zhou L, Zhang Z, Tian Y, Li Z, Liu Z, Zhu S. The critical role of platelet in cancer progression and metastasis. Eur J Med Res. 2023;28(1):385. 10.1186/s40001-023-01342-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 280.Huang B, An H, Wu H, Qiu Y, Su Y, Chen L, et al. Chronic psychological stress in oncogenesis: multisystem crosstalk and multimodal interventions. Res (Wash D C). 2025;8:0948. 10.34133/research.0948. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 281.Farhid F, Rezaeeyan H, Habibi R, Kamali Yazdi E, Hamblin MR, Naghinezhad J. When the victim becomes the villain: platelets as drivers of immune dysregulation in ITP. J Transl Autoimmun. 2025;11:100309. 10.1016/j.jtauto.2025.100309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 282.Heydari F, Chermehini NY, Safari Z, Hamblin MR, Naghinezhad J. Targeting glycoprotein VI (GPVI) in cancer: mechanistic insights and therapeutic strategies to suppress metastasis and thrombosis. Cancer Nanotechnol. 2026. 10.1186/s12645-026-00375-0.
- 283.Naghinezhad J, Yazdi E, Mohajerian A, Chermehini N, Ghanavati P, Motamedi H, et al. The tumor–platelet–immune Interface: driving metastasis, pre-metastatic niche formation and therapeutic vulnerabilities. Expert Rev Mol Med. 2026;28:1–34. 10.1017/erm.2026.10043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 284.Lin L, Sun B, Hu Y, Yang W, Li J, Wang D, et al. Rhynchophylline as an agonist of sirtuin 3 ameliorates endothelial dysfunction via antagonizing mitochondrial damage of endothelial progenitor cells. Br J Pharmacol. 2025;182(15):3476–502. 10.1111/bph.70032. [DOI] [PubMed] [Google Scholar]
- 285.Stanger BZ, Kahn ML. Platelets and tumor cells: a new form of border control. Cancer Cell. 2013;24(1):9–11. 10.1016/j.ccr.2013.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 286.Scripcariu DV, Huzum B, Mircea C, Tesoi DF, Badulescu OV. Systemic impact of platelet activation in abdominal surgery: from oxidative and inflammatory pathways to postoperative complications. IJMS. 2025;26(15):7150. 10.3390/ijms26157150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 287.Yang J, Xu P, Zhang G, Wang D, Ye B, Wu L. Advances and potentials in platelet-circulating tumor cell crosstalk. Am J Cancer Res. 2025;15(2):407–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 288.Tyagi T, Jain K, Gu SX, Qiu M, Gu VW, Melchinger H, et al. A guide to molecular and functional investigations of platelets to bridge basic and clinical sciences. Nat Cardiovasc Res. 2022;1(3):223–37. 10.1038/s44161-022-00021-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 289.Lam FW, Vijayan KV, Rumbaut RE. Platelets and their interactions with other immune cells. Compr Physiol. 2015;5(3):1265–80. 10.1002/j.2040-4603.2015.tb00649.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 290.Wang S, Duan Y, Zhang Q, Komarla A, Gong H, Gao W, et al. Drug targeting via platelet membrane–coated nanoparticles. Small Struct. 2020;1(1). 10.1002/sstr.202000018. [DOI] [PMC free article] [PubMed]
- 291.Yao C, Wang C. Platelet-derived extracellular vesicles for drug delivery. Biomater Sci. 2023;11(17):5758–68. 10.1039/D3BM00893B. [DOI] [PubMed] [Google Scholar]
- 292.Tao SC, Guo SC, Zhang CQ. Platelet-derived extracellular vesicles: an emerging therapeutic approach. Int J Biol Sci. 2017;13(7):828–34. 10.7150/ijbs.19776. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 293.Wu L, Li Q, Deng J, Shen J, Xu W, Yang W, et al. Platelet-tumor cell hybrid membrane-Camouflaged nanoparticles for enhancing therapy efficacy in glioma. IJN. 2021;16:8433–46. 10.2147/IJN.S333279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 294.Li X, Hu L, Tan C, Wang X, Ran Q, Chen L, et al. Platelet-promoting drug delivery efficiency for inhibition of tumor growth, metastasis, and recurrence. Front Oncol. 2022;12:983874. 10.3389/fonc.2022.983874. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 295.Kim MW, Lee G, Niidome T, Komohara Y, Lee R, Park YI. Platelet-like Gold nanostars for cancer therapy: the ability to treat cancer and evade immune Reactions. Front Bioeng Biotechnol. 2020;8:133. 10.3389/fbioe.2020.00133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 296.Zhuang T, Wang S, Yu X, He X, Guo H, Ou C. Current status and future perspectives of platelet-derived extracellular vesicles in cancer diagnosis and treatment. Biomark Res. 2024;12(1):88. 10.1186/s40364-024-00639-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 297.Wang Y, Li W, Li Z, Mo F, Chen Y, Iida M, et al. Active recruitment of anti–PD-1–conjugated platelets through tumor-selective thrombosis for enhanced anticancer immunotherapy. Sci Adv. 2023;9(13):eadf 6854. 10.1126/sciadv.adf6854. [DOI] [PMC free article] [PubMed]
- 298.Zhang X, Wang J, Chen Z, Hu Q, Wang C, Yan J, et al. Engineering PD-1-presenting platelets for cancer immunotherapy. Nano Lett. 2018;18(9):5716–25. 10.1021/acs.nanolett.8b02321. [DOI] [PubMed] [Google Scholar]
- 299.Wang Y, Ogunnaike E, Yang H, Yuan S, Hong R, Barrett A, et al. Platelet-engineered CAR-T cells as adjuvant therapy after cancer surgery. Proc Natl Acad Sci USA. 2025;122(51):e2522020122. 10.1073/pnas.2522020122. [DOI] [PMC free article] [PubMed]
- 300.Rao L, Bu LL, Ma L, Wang W, Liu H, Wan D, et al. Platelet-facilitated photothermal therapy of head and neck squamous cell carcinoma. Angew Chem Int Ed Engl. 2018;57(4):986–91. 10.1002/anie.201709457. [DOI] [PubMed] [Google Scholar]
- 301.Cai C, Liu Y, Lu R, Fan X, Zeng S, Gan P. Platelets in cancer and immunotherapy: functional dynamics and therapeutic opportunities. Exp Hematol Oncol. 2025;14(1):83. 10.1186/s40164-025-00676-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 302.Zhang Y, Wang Z, Wang J, Lin Y, Gao H, Wang P, et al. Immunomodulating platelet-mimicking nanoparticles for AIE-based enhanced photodynamic immunotherapy against lung cancer. Mater Today Bio. 2025;32:101683. 10.1016/j.mtbio.2025.101683. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 303.Muttiah B, Ng SL, Lokanathan Y, Ng MH, Law JX. Beyond blood clotting: the many Roles of platelet-derived extracellular vesicles. Biomedicines. 2024;12(8):1850. 10.3390/biomedicines12081850. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 304.Leng Q, Ding J, Dai M, Liu L, Fang Q, Wang DW, et al. Insights into platelet-derived MicroRNAs in Cardiovascular and oncologic diseases: potential predictor and therapeutic target. Front Cardiovasc Med. 2022;9:879351. 10.3389/fcvm.2022.879351. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 305.Tao DL, Tassi Yunga S, Williams CD, McCarty OJT. Aspirin and antiplatelet treatments in cancer. Blood. 2021;137(23):3201–11. 10.1182/blood.2019003977. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 306.Leader A, Zelikson-Saporta R, Pereg D, Spectre G, Rozovski U, Raanani P, et al. The effect of combined aspirin and clopidogrel treatment on cancer incidence. Am J Med. 2017;130(7):826–32. 10.1016/j.amjmed.2017.01.022. [DOI] [PubMed] [Google Scholar]
- 307.Shamay Y, Elkabets M, Li H, Shah J, Brook S, Wang F, et al. P-selectin is a nanotherapeutic delivery target in the tumor microenvironment. Sci Transl Med. 2016;8(345):345ra87. 10.1126/scitranslmed.aaf7374. [DOI] [PMC free article] [PubMed]
- 308.Qi Y, Chen W, Liang X, Xu K, Gu X, Wu F, et al. Novel antibodies against GPIbα inhibit pulmonary metastasis by affecting vWF-GPIbα interaction. J Hematol Oncol. 2018;11(1):117. 10.1186/s13045-018-0659-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 309.Zuchtriegel G, Uhl B, Puhr-Westerheide D, Pörnbacher M, Lauber K, Krombach F, et al. Platelets guide leukocytes to their sites of extravasation. PLoS Biol. 2016;14(5):e1002459. 10.1371/journal.pbio.1002459. [DOI] [PMC free article] [PubMed]
- 310.Liu GS, Chen HA, Chang CY, Chen YJ, Wu YY, Widhibrata A, et al. Platelet-derived extracellular vesicle drug delivery system loaded with kaempferol for treating corneal neovascularization. Biomaterials. 2025;319:123205. 10.1016/j.biomaterials.2025.123205. [DOI] [PubMed] [Google Scholar]
- 311.Fang M, Liu R, Fang Y, Zhang D, Kong B. Emerging platelet-based drug delivery systems. Biomed Pharmacother. 2024;177:117131. 10.1016/j.biopha.2024.117131. [DOI] [PubMed] [Google Scholar]
- 312.Taus F, Meneguzzi A, Castelli M, Minuz P. Platelet-derived extracellular vesicles as target of antiplatelet agents. What is the evidence? Front Pharmacol. 2019;10:1256. 10.3389/fphar.2019.01256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 313.Mitchell MJ, Billingsley MM, Haley RM, Wechsler ME, Peppas NA, Langer R. Engineering precision nanoparticles for drug delivery. Nat Rev Drug Discov. 2021;20(2):101–24. 10.1038/s41573-020-0090-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 314.Josefsson EC. Platelets and megakaryocytes in cancer. J Thromb Haemostasis. 2025;23(3):804–16. 10.1016/j.jtha.2024.12.016. [DOI] [PubMed] [Google Scholar]
- 315.Liu Y, Zhang Y, Ding Y, Zhuang R. Platelet-mediated tumor metastasis mechanism and the role of cell adhesion molecules. Crit Rev Oncol/Hematol. 2021;167:103502. 10.1016/j.critrevonc.2021.103502. [DOI] [PubMed] [Google Scholar]
- 316.Xue J, Deng J, Qin H, Yan S, Zhao Z, Qin L, et al. The interaction of platelet-related factors with tumor cells promotes tumor metastasis. J Transl Med. 2024;22(1):371. 10.1186/s12967-024-05126-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 317.Li Y, Wang H, Zhao Z, Yang Y, Meng Z, Qin L. Effects of the interactions between platelets with other cells in tumor growth and progression. Front Immunol. 2023;14:1165989. 10.3389/fimmu.2023.1165989. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 318.Wu L, Xie W, Zan HM, Liu Z, Wang G, Wang Y, et al. Platelet membrane-coated nanoparticles for targeted drug delivery and local chemo-photothermal therapy of orthotopic hepatocellular carcinoma. J Mater Chem B. 2020;8(21):4648–59. 10.1039/D0TB00735H. [DOI] [PubMed] [Google Scholar]
- 319.Ortiz-Otero N, Marshall JR, Lash BW, King MR. Platelet mediated TRAIL delivery for efficiently targeting circulating tumor cells. Nanoscale Adv. 2020;2(9):3942–53. 10.1039/D0NA00271B. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 320.Liu B, Wang Y, Gong W, Han S, Lv Z, Zhang Z, et al. Natural, engineered, and hybrid platelet membrane–based nanotherapeutics for inflammatory diseases. IJN. 2025;20:14149–84. 10.2147/IJN.S558928. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 321.Sasako M. Role of surgery in multidisciplinary treatment for solid cancers. Int J Clin Oncol. 2004;9(5):346–51. 10.1007/s10147-004-0429-x. [DOI] [PubMed] [Google Scholar]
- 322.Mee T, Kirkby NF, Defourny NN, Kirkby KJ, Burnet NG. The use of radiotherapy, surgery and chemotherapy in the curative treatment of cancer: results from the FORTY (favourable outcomes from RadioTherapY) project. The Br J Radiol. 2023;96(1152):20230334. 10.1259/bjr.20230334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 323.Sabel MS, Diehl KM, Chang AE. Principles of surgical therapy in oncology. In: Oncology: an evidence-based approach. Springer; 2006. p. 58–72.
- 324.Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clinicians. 2024;74(1):12–49. 10.3322/caac.21820. [DOI] [PubMed] [Google Scholar]
- 325.He W, Li Z, Xie Q, Han Y, Peng L, Wang C, et al. Long-term survival outcomes of NCRT with surgery vs surgery with adjuvant therapy for ESCC: a single-center prospective phase 3 randomized clinical trial. JAMA Netw Open. 2026;9(1):e2550307–e. 10.1001/jamanetworkopen.2025.50307. [DOI] [PMC free article] [PubMed]
- 326.Balch CM, Nelson H, Niederhuber JE. Limitations of prospective surgical oncology trials — a US view. Nat Rev Clin Oncol. 2016;13(1):6–8. 10.1038/nrclinonc.2015.212. [DOI] [PubMed] [Google Scholar]
- 327.Kim J, Kang W, Sinn DH, Gwak GY, Paik YH, Choi MS, et al. Substantial risk of recurrence even after 5 recurrence-free years in early-stage hepatocellular carcinoma patients. Clin Mol Hepatol. 2020;26(4):516–28. 10.3350/cmh.2020.0016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 328.Zhou KQ, Sun YF, Cheng JW, Du M, Ji Y, Wang PX, et al. Effect of surgical margin on recurrence based on preoperative circulating tumor cell status in hepatocellular carcinoma. EBioMedicine. 2020;62:103107. 10.1016/j.ebiom.2020.103107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 329.Cheung KYK, Parks RM, Giza D, Cheung K-L. Surgery in older cancer patients. Curr Oncol Rep. 2026;28(1):1. 10.1007/s11912-026-01740-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 330.Bobin JY, Delay E, Rivoire M. [Role of surgery in the treatment of cancer. Surgical oncology]. Bull Cancer. 1995;82 Suppl 2( Suppl, 2):113s–26. [PubMed]
- 331.Ruivo A, Oliveira RC, Silva-Vaz P, Tralhão JG. Colorectal cancer Liver metastasis—state-of-the-art and future perspectives. Gastrointestinal Disord. 2023;5(4):580–608. 10.3390/gidisord5040046. [Google Scholar]
- 332.Wagle NS, Nogueira L, Devasia TP, Mariotto AB, Yabroff KR, Islami F, et al. Cancer treatment and survivorship statistics, 2025. CA Cancer J Clinicians. 2025;75(4):308–40. 10.3322/caac.70011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 333.Heydari F, Feli M, Maleknia M, Habibi Z, Sohrabi A, Hamblin MR, et al. Beyond remission: the cardiotoxic and thrombotic shadow of AML chemotherapy. Cardiovasc Toxicol. 2026;26(3):31. 10.1007/s12012-026-10104-z. [DOI] [PubMed] [Google Scholar]
- 334.Chen EY, Rai M, Tadikonda Y, Roy P, Nollner DW, Chitkara A, et al. Trends in complexity of single-agent and combination therapies for solid tumor cancers approved by the US food and drug administration. Oncologist. 2025;30(3):oyae302. 10.1093/oncolo/oyae302. [DOI] [PMC free article] [PubMed]
- 335.Peng L, Wu Z, Sun W, Wang C. Clinical characteristics, treatment, and outcomes of nivolumab induced immune thrombocytopenia. Invest New Drugs. 2024;42(5):575–80. 10.1007/s10637-024-01472-w. [DOI] [PubMed] [Google Scholar]
- 336.Zafar A, Khatoon S, Khan MJ, Abu J, Naeem A. Advancements and limitations in traditional anti-cancer therapies: a comprehensive review of surgery, chemotherapy, radiation therapy, and hormonal therapy. Discov Oncol. 2025;16(1):607. 10.1007/s12672-025-02198-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 337.Althafar ZM. Engineered immune cell therapies for solid tumors: pharmacological advances, clinical outcomes, and future directions. Front Pharmacol. 2025;16:1614325. 10.3389/fphar.2025.1614325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 338.Verginadis II, Citrin DE, Ky B, Feigenberg SJ, Georgakilas AG, Hill-Kayser CE, et al. Radiotherapy toxicities: mechanisms, management, and future directions. Lancet. 2025;405(10475):338–52. 10.1016/S0140-6736(24)02319-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 339.Tan M, Li L, Tan B, Yang J. Innovations in modern low-LET radiotherapy regimens for locally advanced non-small cell lung cancer: a meta-analysis and systematic review of high-dose-rate brachytherapy, stereotactic body radiotherapy, and hypofractionated proton therapy. BMC Cancer. 2025;25(1):942. 10.1186/s12885-025-14328-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 340.Reddy S, Upadhyay R, Klamer B, Singh R, Konieczkowski D, Cripe T, et al. Local control and toxicity outcomes after stereotactic body radiation therapy for metastatic osteosarcoma in pediatric patients. Pediatr Blood Cancer. 2025;72(9):e31834. 10.1002/pbc.31834. [DOI] [PubMed]
- 341.Zhang J, Yang P, Zhao D, Yang T, Sun M, Li Y, et al. Efficacy and safety of postoperative adjuvant immunotherapy for esophageal cancer: a systematic review and meta-analysis. J Thorac Dis. 2025;17(8):5575–87. 10.21037/jtd-2025-423. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 342.Donia M, Jespersen H, Jalving M, Lee R, Eriksson H, Hoeller C, et al. Adjuvant immunotherapy in the modern management of resectable melanoma: current status and outlook to 2028. ESMO Open. 2025;10(3):104295. 10.1016/j.esmoop.2025.104295. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 343.Huang H, Li L, Tong L, Luo H, Luo H, Zhang Q. Perioperative PD-1/PD-L1 inhibitors for resectable non-small cell lung cancer: a meta-analysis based on randomized controlled trials. PLoS ONE. 2024;19(9):e0310808. 10.1371/journal.pone.0310808. [DOI] [PMC free article] [PubMed]
- 344.Beckermann KE, Johnson DB, Sosman JA. PD-1/PD-L1 blockade in renal cell cancer. Expert Rev Clin Immunol. 2017;13(1):77–84. 10.1080/1744666X.2016.1214575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 345.Zhang Z, Lin Y, Chen S. Efficacy of neoadjuvant, adjuvant, and perioperative immunotherapy in non-small cell lung cancer across different PD-L1 expression levels: a systematic review and meta-analysis. Front Immunol. 2025;16:1569864. 10.3389/fimmu.2025.1569864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 346.Ji J, Liu Y, Bao Y, Men Y, Wang J, Hui Z. Review of radiotherapy combined with systemic therapy for advanced esophageal cancer: from chemotherapy to immunotherapy era. Vis Cancer Med. 2025;6:5. 10.1051/vcm/2025004. [Google Scholar]
- 347.Wang Y, Zhang L, Zhao X, Sun D, Zhou H, Zhang Y, et al. Efficacy and safety of perioperative, adjuvant and neoadjuvant chemoimmunotherapy stratified by clinical stage and PD-L1 expression in resectable non-small cell lung cancer: a systematic review and network meta-analysis. Transl Lung Cancer Res. 2025;14(9):3378–95. 10.21037/tlcr-2025-426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 348.Cheng X, Zhang H, Hamad A, Huang H, Tsung A. Surgery-mediated tumor-promoting effects on the immune microenvironment. Semin Cancer Biol. 2022;86(Pt 3):408–19. 10.1016/j.semcancer.2022.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 349.Zhu L, Xu R, Yang L, Shi W, Zhang Y, Liu J, et al. Minimal residual disease (MRD) detection in solid tumors using circulating tumor DNA: a systematic review. Front Genet. 2023;14:1172108. 10.3389/fgene.2023.1172108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 350.Sim ES, Rhoades J, Xiong K, Walsh L, Crnjac A, Blewett T, et al. Early postoperative minimal residual disease detection with MAESTRO is associated with recurrence and worse survival in patients with head and neck cancer. Clin Cancer Res. 2025;31(16):3494–502. 10.1158/1078-0432.CCR-25-0307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 351.Tsai KY, Huang PS, Chu PY, Nguyen TNA, Hung HY, Hsieh CH, et al. Current applications and future directions of circulating tumor cells in colorectal cancer recurrence. Cancers (Basel). 2024;16(13):2316. 10.3390/cancers16132316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 352.Galizia G, Gemei M, Orditura M, Romano C, Zamboli A, Castellano P, et al. Postoperative detection of circulating tumor cells predicts tumor recurrence in colorectal cancer patients. J Gastrointestinal Surg. 2013;17(10):1809–18. 10.1007/s11605-013-2258-6. [DOI] [PubMed] [Google Scholar]
- 353.Kwak SB, Kim SJ, Kim J, Kang YL, Ko CW, Kim I, et al. Tumor regionalization after surgery: roles of the tumor microenvironment and neutrophil extracellular traps. Exp Mol Med. 2022;54(6):720–29. 10.1038/s12276-022-00784-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 354.Choi H, Hwang W. Anesthetic approaches and their impact on cancer recurrence and metastasis: a comprehensive review. Cancers (Basel). 2024;16(24):4269. 10.3390/cancers16244269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 355.Xiao M, Wang L, Tang Q, Yang Q, Yang X, Zhu G, et al. Postoperative tumor treatment strategies: from basic research to clinical therapy. View. 2024;5(3):20230117. 10.1002/VIW.20230117. [Google Scholar]
- 356.Zheng J, Qin C, Wang Q, Tian D, Chen Z. Circulating tumour DNA-Based molecular residual disease detection in resectable cancers: a systematic review and meta-analysis. EBioMedicine. 2024;103:105109. 10.1016/j.ebiom.2024.105109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 357.Talukdar S, Bhoopathi P, Emdad L, Das S, Sarkar D, Fisher PB. Dormancy and cancer stem cells: an enigma for cancer therapeutic targeting. Adv Cancer Res. 2019;141:43–84. [DOI] [PubMed] [Google Scholar]
- 358.Ghajar CM, Peinado H, Mori H, Matei IR, Evason KJ, Brazier H, et al. The perivascular niche regulates breast tumour dormancy. Nat Cell Biol. 2013;15(7):807–17. 10.1038/ncb2767. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 359.Zhang L, Yuan Y, Deng Y, Wang L, Chen F. Platelet‑circulating tumor cell crosstalk: a pivotal target in cancer diagnosis and therapy (Review). Oncol Rep. 2025;55(1):1–14. 10.3892/or.2025.9007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 360.Nakamura Y, Watanabe J, Akazawa N, Hirata K, Kataoka K, Yokota M, et al. ctDNA-based molecular residual disease and survival in resectable colorectal cancer. Nat Med. 2024;30(11):3272–83. 10.1038/s41591-024-03254-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 361.Silihe Kamga K, Fiering S. Intratumoral immunotherapy prior to cancer surgery, a promising therapeutic approach. Front Immunol. 2025;16:1545000. 10.3389/fimmu.2025.1545000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 362.Zhong Z, Gan L, Feng Z, Wang W, Pan X, Wu C, et al. Hydrogel local drug delivery systems for postsurgical management of tumors: status quo and perspectives. Mater Today Bio. 2024;29:101308. 10.1016/j.mtbio.2024.101308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 363.Huang C, Shao N, Huang Y, Chen J, Wang D, Hu G, et al. Overcoming challenges in the delivery of STING agonists for cancer immunotherapy: a comprehensive review of strategies and future perspectives. Mater Today Bio. 2023;23:100839. 10.1016/j.mtbio.2023.100839. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 364.Bhatnagar S, Revuri V, Shah M, Larson P, Shao Z, Yu D, et al. Combination of STING and TLR 7/8 agonists as vaccine adjuvants for cancer immunotherapy. Cancers (Basel). 2022;14(24):6091. 10.3390/cancers14246091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 365.Park CG, Hartl CA, Schmid D, Carmona EM, Kim H-J, Goldberg MS. Extended release of perioperative immunotherapy prevents tumor recurrence and eliminates metastases. Sci Transl Med. 2018;10(433):eaar1916. 10.1126/scitranslmed.aar1916. [DOI] [PubMed]
- 366.Qian Q, Song J, Chen C, Pu Q, Liu X, Wang H. Recent advances in hydrogels for preventing tumor recurrence. Biomater Sci. 2023;11(8):2678–92. 10.1039/D3BM00003F. [DOI] [PubMed] [Google Scholar]
- 367.Guo C, Gao T, Xue B, Zhang L, Wang S, Xiao R, et al. In situ extended immune activation instantly after tumor resection by oncolytic virus controls postoperative tumor recurrence. Cell Rep Med. 2025;6(10):102399. 10.1016/j.xcrm.2025.102399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 368.Todo T, Ito H, Ino Y, Ohtsu H, Ota Y, Shibahara J, et al. Intratumoral oncolytic herpes virus G47∆ for residual or recurrent glioblastoma: a phase 2 trial. Nat Med. 2022;28(8):1630–39. 10.1038/s41591-022-01897-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 369.Chen L, Zuo M, Zhou Q, Wang Y. Oncolytic virotherapy in cancer treatment: challenges and optimization prospects. Front Immunol. 2023;14:1308890. 10.3389/fimmu.2023.1308890. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 370.Trédan O, Galmarini CM, Patel K, Tannock IF. Drug resistance and the solid tumor microenvironment. JNCI J Natl Cancer Inst. 2007;99(19):1441–54. 10.1093/jnci/djm135. [DOI] [PubMed] [Google Scholar]
- 371.Wang S, Yang C, Chen L. LSA-DDI: learning stereochemistry-aware drug interactions via 3D Feature fusion and contrastive cross-attention. IJMS. 2025;26(14):6799 p. 10.3390/ijms26146799. [DOI] [PMC free article] [PubMed]
- 372.Bayat M, Nahand JS. CAR-engineered cell therapies: current understandings and future perspectives. Mol Biomed. 2026;7(1):7. 10.1186/s43556-025-00401-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 373.First-ever CAR T-cell therapy approved in US. Cancer Discov. 2017;7(10):OF1–1. 10.1158/2159-8290.CD-NB2017-126. [DOI] [PubMed]
- 374.O’Leary MC, Lu X, Huang Y, Lin X, Mahmood I, Przepiorka D, et al. FDA approval summary: tisagenlecleucel for treatment of patients with relapsed or refractory B-cell precursor acute lymphoblastic leukemia. Clin Cancer Res. 2019;25(4):1142–46. 10.1158/1078-0432.CCR-18-2035. [DOI] [PubMed] [Google Scholar]
- 375.Sanderson R. Restoring chimeric antigen receptor (CAR) T cell function in chronic lymphocytic leukaemia. CLL: Queen Mary University of London; 2020. [Google Scholar]
- 376.Neelapu SS, Locke FL, Bartlett NL, Lekakis LJ, Miklos DB, Jacobson CA, et al. Axicabtagene ciloleucel CAR T-Cell therapy in refractory large B-Cell lymphoma. N Engl J Med. 2017;377(26):2531–44. 10.1056/NEJMoa1707447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 377.Westin JR, Oluwole OO, Kersten MJ, Miklos DB, Perales M-A, Ghobadi A, et al. Survival with axicabtagene ciloleucel in large B-cell lymphoma. N Engl J Med. 2023;389(2):148–57. 10.1056/NEJMoa2301665. [DOI] [PubMed] [Google Scholar]
- 378.Cappell KM, Kochenderfer JN. Long-term outcomes following CAR T cell therapy: what we know so far. Nat Rev Clin Oncol. 2023;20(6):359–71. 10.1038/s41571-023-00754-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 379.Rodriguez-Otero P, Ailawadhi S, Arnulf B, Patel K, Cavo M, Nooka AK, et al. Ide-cel or standard regimens in relapsed and refractory multiple myeloma. N Engl J Med. 2023;388(11):1002–14. 10.1056/NEJMoa2213614. [DOI] [PubMed] [Google Scholar]
- 380.San-Miguel J, Dhakal B, Yong K, Spencer A, Anguille S, Mateos MV, et al. Cilta-cel or standard care in lenalidomide-refractory multiple myeloma. N Engl J Med. 2023;389(4):335–47. 10.1056/NEJMoa2303379. [DOI] [PubMed] [Google Scholar]
- 381.Deschênes-Simard X, Bromberg M, Devlin SM, Gonen M, Beyar-Katz O, Ip A, et al. Comparative real-world outcomes of CD19-directed CAR T-cell therapies in large B-cell lymphoma. Blood Adv. 2025;9(21):5571–84. 10.1182/bloodadvances.2025016778. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 382.Gagelmann N, Bishop M, Ayuk F, Bethge W, Glass B, Sureda A, et al. Axicabtagene ciloleucel versus Tisagenlecleucel for relapsed or refractory large B cell lymphoma: a systematic review and meta-analysis. Transplant Cellular Ther. 2024;30(6):.e584.1–.13. 10.1016/j.jtct.2024.01.074. [DOI] [PMC free article] [PubMed]
- 383.Jain MD, Smith M, Shah NN. How I treat refractory CRS and ICANS following CAR T-cell therapy. Blood. 2023;141(20):2430–42. 10.1182/blood.2022017414. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 384.Ma W, Gu Z, Lin Q, Cao M, Zhong J, Li X, et al. Integrative genetic and multi-omics analysis reveals the interleukin-6 receptor’s role in recurrent spontaneous abortion. Front Immunol. 2025;16:1659251. 10.3389/fimmu.2025.1659251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 385.Rafii S, Mukherji D, Komaranchath AS, Khalil C, Iqbal F, Abdelwahab SI, et al. Advancing CAR T-Cell therapy in solid tumors: current landscape and future directions. Cancers (Basel). 2025;17:2898. 10.3390/cancers17172898. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 386.Kasar GN, Rasal PB, Jagtap MN, Surana KR, Mahajan SK, Sonawane DD, et al. CAR T-cell structure, manufacturing, applications, and challenges in the management of communityacquired diseases and disorders. Community Acquir Infect. 2025;12:12. 10.54844/cai.2024.0780. [Google Scholar]
- 387.Wagner J, Wickman E, DeRenzo C, Gottschalk S. CAR T cell therapy for solid tumors: bright future or Dark reality? Mol Ther. 2020;28(11):2320–39. 10.1016/j.ymthe.2020.09.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 388.Zugasti I, Espinosa-Aroca L, Fidyt K, Mulens-Arias V, Diaz-Beya M, Juan M, et al. CAR-T cell therapy for cancer: current challenges and future directions. Sig Transduct Target Ther. 2025;10(1):210. 10.1038/s41392-025-02269-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 389.Donnadieu E, Dupré L, Pinho LG, Cotta-de-Almeida V. Surmounting the obstacles that impede effective CAR T cell trafficking to solid tumors. J Leukoc Biol. 2020;108(4):1067–79. 10.1002/JLB.1MR0520-746R. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 390.Klabukov I, Kabakov AE, Yakimova A, Baranovskii D, Sosin D, Atiakshin D, et al. Tumor-associated extracellular matrix obstacles for CAR-T cell therapy: approaches to overcoming. Curr Oncol. 2025;32(2):79. 10.3390/curroncol32020079. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 391.Zhang Y, Sun H, Zhao L, Zhao N, Chen Z, He L, et al. Barriers and strategies to enhance CAR-T cell infiltration in solid tumours: a systematic review. Immunology. 2026;178(1):62–78. 10.1111/imm.70094. [DOI] [PubMed] [Google Scholar]
- 392.Guo L, Ding J, Zhou W. Harnessing bacteria for tumor therapy: current advances and challenges. Chin Chem Lett. 2024;35(2):108557. 10.1016/j.cclet.2023.108557. [Google Scholar]
- 393.Wang S, Du X, Zhao S, Nie Y. Strategies and challenges in promoting chimeric antigen receptor T cells trafficking and infiltration of solid tumors. Chin Med J. 2025;138(19):2411–20. 10.1097/CM9.0000000000003803. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 394.Flugel CL, Majzner RG, Krenciute G, Dotti G, Riddell SR, Wagner DL, et al. Overcoming on-target, off-tumour toxicity of CAR T cell therapy for solid tumours. Nat Rev Clin Oncol. 2023;20(1):49–62. 10.1038/s41571-022-00704-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 395.Gargett T, Brown MP. The inducible caspase-9 suicide gene system as a “safety switch” to limit on-target, off-tumor toxicities of chimeric antigen receptor T cells. Front Pharmacol. 2014;5:235. 10.3389/fphar.2014.00235. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 396.Laborda E, Young TS. Strategies to control CAR-T cell therapy: perspective on next-generation CARs. Cell Gene Ther Insights. 2018;4(4):275–85. 10.18609/cgti.2018.028. [Google Scholar]
- 397.Xiao X, Huang S, Chen S, Wang Y, Sun Q, Xu X, et al. Mechanisms of cytokine release syndrome and neurotoxicity of CAR T-cell therapy and associated prevention and management strategies. J Exp Clin Cancer Res. 2021;40(1):367. 10.1186/s13046-021-02148-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 398.Zhang Y, Han W. Management of cytokine release syndrome (CRS) following CAR T-cell therapy: a comprehensive review. Clin Cancer Bull. 2025;4(1):1–9. 10.1007/s44272-025-00044-0. [Google Scholar]
- 399.Beatty GL, Haas AR, Maus MV, Torigian DA, Soulen MC, Plesa G, et al. Mesothelin-specific chimeric antigen receptor mRNA-engineered T cells induce antitumor activity in solid malignancies. Cancer Immunol Res. 2014;2(2):112–20. 10.1158/2326-6066.CIR-13-0170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 400.Ahmed N, Brawley VS, Hegde M, Robertson C, Ghazi A, Gerken C, et al. Human epidermal growth factor receptor 2 (HER2) –specific chimeric antigen receptor–modified T cells for the immunotherapy of HER2-positive sarcoma. JCO. 2015;33(15):1688–96. 10.1200/JCO.2014.58.0225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 401.Brown CE, Badie B, Barish ME, Weng L, Ostberg JR, Chang WC, et al. Bioactivity and safety of IL13Rα2-redirected chimeric antigen receptor CD8+ T cells in patients with recurrent glioblastoma. Clin Cancer Res. 2015;21(18):4062–72. 10.1158/1078-0432.CCR-15-0428. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 402.Feng K, Guo Y, Dai H, Wang Y, Li X, Jia H, et al. Chimeric antigen receptor-modified T cells for the immunotherapy of patients with EGFR-expressing advanced relapsed/refractory non-small cell lung cancer. Sci China Life Sci. 2016;59(5):468–79. 10.1007/s11427-016-5023-8. [DOI] [PubMed] [Google Scholar]
- 403.O’Rourke DM, Nasrallah MP, Desai A, Melenhorst JJ, Mansfield K, Morrissette JJD, et al. A single dose of peripherally infused EGFRvIII-directed CAR T cells mediates antigen loss and induces adaptive resistance in patients with recurrent glioblastoma. Sci Transl Med. 2017;9(399). 10.1126/scitranslmed.aaa0984. [DOI] [PMC free article] [PubMed]
- 404.Beatty GL, O’Hara MH, Lacey SF, Torigian DA, Nazimuddin F, Chen F, et al. Activity of Mesothelin-specific chimeric antigen receptor T cells against pancreatic carcinoma metastases in a phase 1 trial. Gastroenterology. 2018;155(1):29–32. 10.1053/j.gastro.2018.03.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 405.Liu Y, Guo Y, Wu Z, Feng K, Tong C, Wang Y, et al. Anti-EGFR chimeric antigen receptor-modified T cells in metastatic pancreatic carcinoma: a phase I clinical trial. Cytotherapy. 2020;22(10):573–80. 10.1016/j.jcyt.2020.04.088. [DOI] [PubMed] [Google Scholar]
- 406.Shi D, Shi Y, Kaseb AO, Qi X, Zhang Y, Chi J, et al. Chimeric antigen receptor-glypican-3 T-Cell therapy for advanced hepatocellular carcinoma: results of phase I trials. Clin Cancer Res. 2020;26(15):3979–89. 10.1158/1078-0432.CCR-19-3259. [DOI] [PubMed] [Google Scholar]
- 407.Adusumilli PS, Zauderer MG, Rivière I, Solomon SB, Rusch VW, O’Cearbhaill RE, et al. A phase I trial of regional Mesothelin-targeted CAR T-cell therapy in patients with malignant pleural disease, in combination with the anti–PD-1 agent pembrolizumab. Cancer Discov. 2021;11(11):2748–63. 10.1158/2159-8290.CD-21-0407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 408.Qi C, Gong J, Li J, Liu D, Qin Y, Ge S, et al. Claudin18.2-specific CAR T cells in gastrointestinal cancers: phase 1 trial interim results. Nat Med. 2022;28(6):1189–98. 10.1038/s41591-022-01800-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 409.Brown CE, Hibbard JC, Alizadeh D, Blanchard MS, Natri HM, Wang D, et al. Locoregional delivery of IL-13Rα2-targeting CAR-T cells in recurrent high-grade glioma: a phase 1 trial. Nat Med. 2024;30(4):1001–12. 10.1038/s41591-024-02875-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 410.Hegde M, Navai S, DeRenzo C, Joseph SK, Sanber K, Wu M, et al. Autologous HER2-specific CAR T cells after lymphodepletion for advanced sarcoma: a phase 1 trial. Nat Cancer. 2024;5(6):880–94. 10.1038/s43018-024-00749-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 411.Pinto N, Albert CM, Taylor MR, Ullom HB, Wilson AL, Huang W, et al. STRIvE-02: a first-in-human phase I study of Systemically administered B7-H3 chimeric antigen receptor T cells for patients with Relapsed/Refractory solid tumors. JCO. 2024;42(35):4163–72. 10.1200/JCO.23.02229. [DOI] [PubMed] [Google Scholar]
- 412.Gargett T, Truong NTH, Gardam B, Yu W, Ebert LM, Johnson A, et al. Safety and biological outcomes following a phase 1 trial of GD2-specific CAR-T cells in patients with GD2-positive metastatic melanoma and other solid cancers. J Immunother Cancer. 2024;12(5):e008659. 10.1136/jitc-2023-008659. [DOI] [PMC free article] [PubMed]
- 413.Katz SC, Point GR, Cunetta M, Thorn M, Guha P, Espat NJ, et al. Regional CAR-T cell infusions for peritoneal carcinomatosis are superior to systemic delivery. Cancer Gene Ther. 2016;23(5):142–48. 10.1038/cgt.2016.14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 414.Tchou J, Zhao Y, Levine BL, Zhang PJ, Davis MM, Melenhorst JJ, et al. Safety and efficacy of intratumoral injections of chimeric antigen receptor (CAR) T cells in metastatic breast cancer. Cancer Immunol Res. 2017;5(12):1152–61. 10.1158/2326-6066.CIR-17-0189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 415.Priceman SJ, Tilakawardane D, Jeang B, Aguilar B, Murad JP, Park AK, et al. Regional delivery of chimeric antigen receptor–engineered T cells effectively targets HER2+ breast cancer metastasis to the brain. Clin Cancer Res. 2018;24(1):95–105. 10.1158/1078-0432.CCR-17-2041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 416.Han Y, Liu C, Li G, Li J, Lv X, Shi H, et al. Antitumor effects and persistence of a novel HER2 CAR T cells directed to gastric cancer in preclinical models. Am J Cancer Res. 2018;8(1):106–19. [PMC free article] [PubMed] [Google Scholar]
- 417.Wei X, Lai Y, Li J, Qin L, Xu Y, Zhao R, et al. PSCA and MUC1 in non-small-cell lung cancer as targets of chimeric antigen receptor T cells. Oncoimmunology. 2017;6(3):e1284722. 10.1080/2162402X.2017.1284722. [DOI] [PMC free article] [PubMed]
- 418.Wallstabe L, Göttlich C, Nelke LC, Kühnemundt J, Schwarz T, Nerreter T, et al. ROR1-CAR T cells are effective against lung and breast cancer in advanced microphysiologic 3D tumor models. JCI Insight. 2019;4(18). 10.1172/jci.insight.126345. [DOI] [PMC free article] [PubMed]
- 419.Pang N, Shi J, Qin L, Chen A, Tang Y, Yang H, et al. IL-7 and CCL19-secreting CAR-T cell therapy for tumors with positive glypican-3 or mesothelin. J Hematol Oncol. 2021;14(1):118. 10.1186/s13045-021-01128-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 420.Lu LL, Xiao SX, Lin ZY, Bai JJ, Li W, Song ZQ, et al. GPC3-IL7-CCL19-CAR-T primes immune microenvironment reconstitution for hepatocellular carcinoma therapy. Cell Biol Toxicol. 2023;39(6):3101–19. 10.1007/s10565-023-09821-w. [DOI] [PubMed] [Google Scholar]
- 421.Wang A, Lv T, Song Y. Tandem CAR-T cells targeting MUC1 and PSCA combined with anti-PD-1 antibody exhibit potent preclinical activity against non-small cell lung cancer. Cellular Immunol. 2023;391-392:391–2. 104760. 10.1016/j.cellimm.2023.104760. [DOI] [PubMed] [Google Scholar]
- 422.Barrett AM, Britton ZT, Carrasco RA, Breen S, Broggi MAS, Hatke AL, et al. Preclinical evaluation of AZD6422, an armored chimeric antigen receptor T cell targeting CLDN18.2 in gastric, pancreatic, and esophageal cancers. Clin Cancer Res. 2024;30(23):5413–29. 10.1158/1078-0432.CCR-24-1853. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 423.Shabaneh TB, Stevens AR, Stull SM, Shimp KR, Seaton BW, Gad EA, et al. Systemically administered low-affinity HER2 CAR T cells mediate antitumor efficacy without toxicity. J Immunother Cancer. 2024;12(2):e008566. 10.1136/jitc-2023-008566. [DOI] [PMC free article] [PubMed]
- 424.Krishnamoorthy M, Gerhardt L, Maleki Vareki S. Immunosuppressive effects of myeloid-derived suppressor cells in cancer and immunotherapy. Cells. 2021;10(5):1170. 10.3390/cells10051170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 425.Wang H, Zhou F, Qin W, Yang Y, Li X, Liu R. Metabolic regulation of myeloid-derived suppressor cells in tumor immune microenvironment: targets and therapeutic strategies. Theranostics. 2025;15(6):2159–84. 10.7150/thno.105276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 426.Yang J, Zhu J, Lu S, Qin H, Zhou W. Transdermal psoriasis treatment inspired by tumor microenvironment-mediated immunomodulation and advanced by exosomal engineering. J Control Release. 2025;382:113664. 10.1016/j.jconrel.2025.113664. [DOI] [PubMed] [Google Scholar]
- 427.Abdalsalam NMF, Ibrahim A, Saliu MA, Liu TM, Wan X, Yan D. MDSC: a new potential breakthrough in CAR-T therapy for solid tumors. Cell Commun Signal. 2024;22(1):612. 10.1186/s12964-024-01995-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 428.Tang Y, Yang X, Hu H, Jiang H, Xiong W, Mei H, et al. Elevating the potential of CAR-T cell therapy in solid tumors: exploiting biomaterials-based delivery techniques. Front Bioeng Biotechnol. 2024;11:1320807. 10.3389/fbioe.2023.1320807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 429.Cherkassky L, Hou Z, Amador-Molina A, Adusumilli PS. Regional CAR T cell therapy: an ignition key for systemic immunity in solid tumors. Cancer Cell. 2022;40(6):569–74. 10.1016/j.ccell.2022.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 430.Sridhar P, Petrocca F. Regional delivery of chimeric antigen receptor (CAR) T-Cells for cancer therapy. Cancers (Basel). 2017;9(7):92. 10.3390/cancers9070092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 431.Grosskopf AK, Labanieh L, Klysz DD, Roth GA, Xu P, Adebowale O, et al. Delivery of CAR-T cells in a transient injectable stimulatory hydrogel niche improves treatment of solid tumors. Sci Adv. 2022;8(14):eabn 8264. 10.1126/sciadv.abn8264. [DOI] [PMC free article] [PubMed]
- 432.Qin J, Jiang Z. Ultrasound-responsive Calcium Copper phosphate nanomaterials induce tumor cell death via the synergistic release of Copper and Calcium. IJMS. 2026;27(4):2016 p. 10.3390/ijms27042016. [DOI] [PMC free article] [PubMed]
- 433.Sagnella SM, White AL, Yeo D, Saxena P, van Zandwijk N, Rasko JEJ. Locoregional delivery of CAR-T cells in the clinic. Pharmacological Res. 2022;182:106329. 10.1016/j.phrs.2022.106329. [DOI] [PubMed] [Google Scholar]
- 434.Park S, Maus MV, Choi BD. CAR-T cell therapy for the treatment of adult high-grade gliomas. NPJ Precis Onc. 2024;8(1):279. 10.1038/s41698-024-00753-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 435.Ito Y, Suzuki T, Shimomura M, Takenouchi K, Ohnuki K, Shoda K, et al. Feasibility of intratumoral administration with EPHB4-CAR-T cells for the treatment of Oral squamous cell carcinoma. Cancer Sci. 2025;116(5):1227–38. 10.1111/cas.70023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 436.Lin Y, Chen Y, Luo Z, Wu YL. Recent advances in biomaterial designs for assisting CAR-T cell therapy towards potential solid tumor treatment. Nanoscale. 2024;16(7):3226–42. 10.1039/D3NR05768B. [DOI] [PubMed] [Google Scholar]
- 437.Huang S, Zhao Y, Shen J. Advances in integrating biomaterials with CAR-T cells for enhancing solid tumor therapy. Chin J Cancer Res. 2025;37(5):742–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 438.Fiorenza S, Dowling MR. ctDNA MRD after CAR-T. Blood Adv. 2025;9(21):5568–70. 10.1182/bloodadvances.2025017507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 439.Han J, Yang Z, Zhao H. Lung cancer immunotherapy in 2025: Where we stand and what comes next? Front Immunol. 2026;16:1728163. 10.3389/fimmu.2025.1728163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 440.Billingsley MM, Gong N, Mukalel AJ, Thatte AS, El-Mayta R, Patel SK, et al. In vivo mRNA CAR T cell engineering via targeted ionizable lipid nanoparticles with extrahepatic tropism. Small. 2024;20(11):e2304378. 10.1002/smll.202304378. [DOI] [PubMed]
- 441.Bimbo JF, van Diest E, Murphy DE, Ashoti A, Evers MJW, Narayanavari SA, et al. T cell-specific non-viral DNA delivery and in vivo CAR-T generation using targeted lipid nanoparticles. J Immunother Cancer. 2025;13(7):e011759. 10.1136/jitc-2025-011759. [DOI] [PMC free article] [PubMed]
- 442.Qin YT, Li YP, He XW, Wang X, Li WY, Zhang YK. Biomaterials promote in vivo generation and immunotherapy of CAR-T cells. Front Immunol. 2023;14:1165576. 10.3389/fimmu.2023.1165576. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 443.Pandit S, Agarwalla P, Song F, Jansson A, Dotti G, Brudno Y. Implantable CAR T cell factories enhance solid tumor treatment. Biomaterials. 2024;308:122580. 10.1016/j.biomaterials.2024.122580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 444.Zhuang J, Gong H, Zhou J, Zhang Q, Gao W, Fang RH, et al. Targeted gene silencing in vivo by platelet membrane–coated metal-organic framework nanoparticles. Sci Adv. 2020;6(13):eaaz 6108. 10.1126/sciadv.aaz6108. [DOI] [PMC free article] [PubMed]
- 445.Li J, Ai Y, Wang L, Bu P, Sharkey CC, Wu Q, et al. Targeted drug delivery to circulating tumor cells via platelet membrane-functionalized particles. Biomaterials. 2016;76:52–65. 10.1016/j.biomaterials.2015.10.046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 446.Yang Y, Wang Y, Yao Y, Wang S, Zhang Y, Dotti G, et al. T cell-mimicking platelet-drug conjugates. Matter. 2023;6(7):2340–55. 10.1016/j.matt.2023.04.026. [Google Scholar]
- 447.Zhang S, Zhang X, Gao H, Zhang X, Sun L, Huang Y, et al. Cell membrane-coated biomimetic nanoparticles in cancer treatment. Pharmaceutics. 2024;16(4):531. 10.3390/pharmaceutics16040531. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 448.Chen Y, Wolter T, Gu Z, Hu Q. Engineering platelets as cancer therapeutics. Nat Rev Clin Oncol. 2026;23(5):323–40. 10.1038/s41571-026-01122-5. [DOI] [PubMed] [Google Scholar]
- 449.Kunde SS, Wairkar S. Platelet membrane camouflaged nanoparticles: Biomimetic architecture for targeted therapy. Int J Multiling Pharm. 2021;598:120395. 10.1016/j.ijpharm.2021.120395. [DOI] [PubMed] [Google Scholar]
- 450.Hu Q, Li H, Archibong E, Chen Q, Ruan H, Ahn S, et al. Inhibition of post-surgery tumour recurrence via a hydrogel releasing CAR-T cells and anti-PDL1-conjugated platelets. Nat Biomed Eng. 2021;5(9):1038–47. 10.1038/s41551-021-00712-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 451.Han H, Bártolo R, Li J, Shahbazi MA, Santos HA. Biomimetic platelet membrane-coated nanoparticles for targeted therapy. Eur J Criminol Pharm Biopharmaceutics. 2022;172:1–15. 10.1016/j.ejpb.2022.01.004. [DOI] [PubMed] [Google Scholar]
- 452.Wu Q, Zhu C, Li Y, Zhang B, Zheng H, Wang F, et al. Adoptive platelet transfer for metastasis treatment via circulating tumor cells targeted conveyance of oncolytic virus. Cell Biomater. 2026;2(4):100225. 10.1016/j.celbio.2025.100225. [Google Scholar]
- 453.Lutz MS, Klimovich B, Maurer S, Heitmann JS, Märklin M, Zekri L, et al. Platelets subvert antitumor efficacy of T cell-recruiting bispecific antibodies. J Immunother Cancer. 2022;10(2):e003655. 10.1136/jitc-2021-003655. [DOI] [PMC free article] [PubMed]
- 454.Torres Chavez A, Mk M, Canestrari E, Dann CT, Ramos CA, Lulla P, et al. Expanding CAR T cells in human platelet lysate renders T cells with in vivo longevity. J Immunother Cancer. 2019;7(1):330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 455.Jo T, Yoshihara K, Ri M, Tsukada N, Mimura N, Fujii K, et al. Low platelet counts and low CD4/CD8 ratios at apheresis increase the risk of CAR T-cell manufacturing failure in myeloma. Blood Neoplasia. 2025;2(1):100051. 10.1016/j.bneo.2024.100051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 456.Menter DG, Kopetz S, Hawk E, Sood AK, Loree JM, Gresele P, et al. Platelet “first responders” in wound response, cancer, and metastasis. Cancer Metastasis Rev. 2017;36(2):199–213. 10.1007/s10555-017-9682-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 457.Patmore S, Dhami SPS, O’Sullivan JM. Von Willebrand factor and cancer; metastasis and coagulopathies. J Thromb Haemostasis. 2020;18(10):2444–56. 10.1111/jth.14976. [DOI] [PubMed] [Google Scholar]
- 458.Li S, Liu J, Sun M, Wang J, Wang C, Sun Y. Cell membrane-Camouflaged nanocarriers for cancer diagnostic and therapeutic. Front Pharmacol. 2020;11:24. 10.3389/fphar.2020.00024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 459.Bahmani B, Gong H, Luk BT, Haushalter KJ, DeTeresa E, Previti M, et al. Intratumoral immunotherapy using platelet-cloaked nanoparticles enhances antitumor immunity in solid tumors. Nat Commun. 2021;12(1):1999. 10.1038/s41467-021-22311-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 460.Zhang K, Li H, Ma Z, Zhong W, Yu Y, Zhao Y. Engineered platelets for cancer therapy. Aggregate. 2025;6(2):e704. 10.1002/agt2.704.
- 461.Pei W, Huang B, Chen S, Wang L, Xu Y, Niu C. Platelet-mimicking drug delivery nanoparticles for enhanced chemo-photothermal therapy of breast cancer. IJN. 2020: 10151–67. 15 10.2147/IJN.S285952. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 462.Zhang Z, Cui D, Wang H, Wu L, Liu X. Unleashing CAR-T potential in solid tumors: overcoming intrinsic and extrinsic hurdles to improve therapy. Cancer Immunol Immunother. 2026;75(2):37. 10.1007/s00262-025-04278-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 463.Li B, Chu T, Wei J, Zhang Y, Qi F, Lu Z, et al. Platelet-membrane-coated nanoparticles enable vascular disrupting agent combining anti-angiogenic drug for improved tumor vessel impairment. Nano Lett. 2021;21(6):2588–95. 10.1021/acs.nanolett.1c00168. [DOI] [PubMed] [Google Scholar]
- 464.Wang H, Wu J, Williams GR, Fan Q, Niu S, Wu J, et al. Platelet-membrane-biomimetic nanoparticles for targeted antitumor drug delivery. J Nanobiotechnol. 2019;17(1):60. 10.1186/s12951-019-0494-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 465.Ergun Y. Neoadjuvant or perioperative immunotherapy in resectable non-small cell lung cancer: pooled analysis of subgroups in randomized controlled trial. EJMO. 2024;8(4):460–70. 10.14744/ejmo.2024.80504. [Google Scholar]
- 466.Liao Z, Jiang J, Wu W, Shi J, Wang Y, Yao Y, et al. Lymph node-biomimetic scaffold boosts CAR-T therapy against solid tumor. Natl Sci Rev. 2024;11(4):nwae018. 10.1093/nsr/nwae018. [DOI] [PMC free article] [PubMed]
- 467.Raskov H, Orhan A, Agerbæk MØ, Gögenur I. The impact of platelets on the metastatic potential of tumour cells. Heliyon. 2024;10(14):e34361. 10.1016/j.heliyon.2024.e34361. [DOI] [PMC free article] [PubMed]
- 468.Mo F, Tsai CT, Zheng R, Cheng C, Heslop HE, Brenner MK, et al. Human platelet lysate enhances in vivo activity of CAR-Vδ2 T cells by reducing cellular senescence and apoptosis. Cytotherapy. 2024;26(8):858–68. 10.1016/j.jcyt.2024.03.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 469.Li Z, Hu S, Cheng K. Platelets and their biomimetics for regenerative medicine and cancer therapies. J Mater Chem B. 2018;6(45):7354–65. 10.1039/C8TB02301H. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 470.Tie J, Cohen JD, Lahouel K, Lo SN, Wang Y, Kosmider S, et al. Circulating tumor DNA analysis guiding adjuvant therapy in stage II colon cancer. N Engl J Med. 2022;386(24):2261–72. 10.1056/NEJMoa2200075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 471.Peng Y, Mei W, Ma K, Zeng C. Circulating tumor DNA and minimal residual disease (MRD) in solid tumors: current horizons and future perspectives. Front Oncol. 2021;11:763790. 10.3389/fonc.2021.763790. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 472.Schmied L, Höglund P, Meinke S. Platelet-mediated protection of cancer cells from immune surveillance – possible implications for cancer immunotherapy. Front Immunol. 2021;12:640578. 10.3389/fimmu.2021.640578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 473.Lee EC, Cameron SJ. Cancer and thrombotic risk: the platelet paradigm. Front. Cardiovasc. Med. 2017;4:67. 10.3389/fcvm.2017.00067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 474.Galli E, Sorà F, Rossi E. Coagulopathy in CAR-T: Critical concern or mere blip? Br J Haematol. 2024;205(2):409–10. 10.1111/bjh.19619. [DOI] [PubMed] [Google Scholar]
- 475.Tang D, Kang R. Platelet activation: a barrier to effective antitumor immunity. Cancer Gene Ther. 2025;32(7):741–43. 10.1038/s41417-025-00915-7. [DOI] [PubMed] [Google Scholar]
- 476.Wang S, Wang R, Meng N, Guo H, Wu S, Wang X, et al. Platelet membrane-functionalized nanoparticles with improved targeting ability and lower hemorrhagic risk for thrombolysis therapy. J Control Release. 2020;328:78–86. 10.1016/j.jconrel.2020.08.030. [DOI] [PubMed] [Google Scholar]
- 477.Hua R, Li M, Lin Q, Dong M, Gong X, Lin Z, et al. Platelet membrane-coated r-SAK improves thrombolytic efficacy by targeting thrombus. ACS Appl Mater Interface. 2024;16(17):21438–49. 10.1021/acsami.3c18402. [DOI] [PubMed] [Google Scholar]
- 478.Xiao H, Meng X, Songtao L, Li Z, Fang S, Wang Y, et al. Combined drug anti-deep vein thrombosis therapy based on platelet membrane biomimetic targeting nanotechnology. Biomaterials. 2024;311:122670. [DOI] [PubMed] [Google Scholar]
- 479.Jackman RP, Thomas KA. Immune response to platelet transfusions. Curr Opin Hematol. 2025;32(6):357–63. 10.1097/MOH.0000000000000888. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 480.Kiefel V. Reactions induced by platelet transfusions. Transfus Med Hemother. 2008;35(5):354–58. 10.1159/000151350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 481.Johnsrud A, Craig J, Baird J, Spiegel J, Muffly L, Zehnder J, et al. Incidence and risk factors associated with bleeding and thrombosis following chimeric antigen receptor T-cell therapy. Blood Adv. 2021;5(21):4465–75. 10.1182/bloodadvances.2021004716. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 482.Bindal P, Patell R, Chiasakul T, Lauw MN, Ko A, Wang TF, et al. A meta-analysis to assess the risk of bleeding and thrombosis following chimeric antigen receptor T-cell therapy: communication from the ISTH SSC subcommittee on hemostasis and malignancy. J Thromb Haemostasis. 2024;22(7):2071–80. 10.1016/j.jtha.2024.03.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 483.Zawaneh PN, Singh SP, Padera RF, Henderson PW, Spector JA, Putnam D. Design of an injectable synthetic and biodegradable surgical biomaterial. Proc Natl Acad Sci USA. 2010;107(24):11014–19. 10.1073/pnas.0811529107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 484.Yuan S, Hu D, Gao D, Butch CJ, Wang Y, Zheng H, et al. Recent advances of engineering cell membranes for nanomedicine delivery across the blood–brain barrier. J Nanobiotechnol. 2025;23(1):493. 10.1186/s12951-025-03572-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 485.Abou-El-Enein M, Elsallab M, Feldman SA, Fesnak AD, Heslop HE, Marks P, et al. Scalable manufacturing of CAR T cells for cancer immunotherapy. Blood Cancer Discov. 2021;2(5):408–22. 10.1158/2643-3230.BCD-21-0084. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 486.Agency EM. Nanotechnology-based medicinal products for human use: EU horizon scanning report. 2025. European Medicines Agency Amsterdam.
- 487.Liu H, Su YY, Jiang XC, Gao JQ. Cell membrane-coated nanoparticles: a novel multifunctional biomimetic drug delivery system. Drug Deliv Transl Res. 2023;13(3):716–37. 10.1007/s13346-022-01252-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 488.Zewail MB, Yang G, Fan Y, Hui Y, Zhao CX, Liu Y. Cell membrane-coated lipid nanoparticles for drug delivery. Aggregate. 2025;6(7):e70054. 10.1002/agt2.70054.
- 489.Cheng K, Kalluri R. Guidelines for clinical translation and commercialization of extracellular vesicles and exosomes based therapeutics. Extracell Vesicle. 2023;2:100029. 10.1016/j.vesic.2023.100029. [Google Scholar]
- 490.Laermans J, Van Remoortel H, Scheers H, Avau B, Georgsen J, Nahirniak S, et al. Cost effectiveness of different platelet preparation, storage, selection and dosing methods in platelet transfusion: a systematic review. Pharmacoeconomics Open. 2023;7(5):679–708. 10.1007/s41669-023-00427-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 491.Gianazza E, Brioschi M, Baetta R, Mallia A, Banfi C, Tremoli E. Platelets in healthy and disease states: from biomarkers Discovery to drug targets Identification by Proteomics. IJMS. 2020;21(12):4541. 10.3390/ijms21124541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 492.Dhurat R, Sukesh M. Principles and methods of preparation of platelet-rich plasma: a review and author′s perspective. J Cutan Aesthet Surg. 2014;7(4):189–97. 10.4103/0974-2077.150734. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 493.Wu YW, Lee DY, Lu YL, Delila L, Nebie O, Barro L, et al. Platelet extracellular vesicles are efficient delivery vehicles of doxorubicin, an anti-cancer drug: preparation and in vitro characterization. Platelets. 2023;34(1):2237134. 10.1080/09537104.2023.2237134. [DOI] [PubMed] [Google Scholar]
- 494.Zahid AA, Patel M, Paul A. Cell membrane-engineered biomimetic nanoparticles: exploiting the nano-biointerface for medical applications. Cell Biomater. 2026;100444. 10.1016/j.celbio.2026.100444.
- 495.Burnouf T, Chou ML, Lundy DJ, Chuang EY, Tseng CL, Goubran H. Expanding applications of allogeneic platelets, platelet lysates, and platelet extracellular vesicles in cell therapy, regenerative medicine, and targeted drug delivery. J Biomed Sci. 2023;30(1):79. 10.1186/s12929-023-00972-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 496.Thakur A, Rai D. Global requirements for manufacturing and validation of clinical grade extracellular vesicles. J Sport Hist Liquid Biopsy. 2024;6:100278. 10.1016/j.jlb.2024.100278. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 497.van der Zee M, de Vries C, Masa M, Morales M, Rayo M, Hegger I. Regulatory aspects of a nanomaterial for imaging therapeutic cells. Drug Deliv Transl Res. 2023;13(11):2693–703. 10.1007/s13346-023-01359-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 498.Humbert C, Cordier C, Drut I, Hamrick M, Wong J, Bellamy V, et al. GMP-Compliant process for the manufacturing of an extracellular vesicles-enriched Secretome product derived from Cardiovascular progenitor cells suitable for a phase I clinical trial. J Extracell Vesicle. 2025;14(8):e70145. 10.1002/jev2.70145. [DOI] [PMC free article] [PubMed]
- 499.Coradduzza D, Vecciu B, Cadoni MPL, Azara EG, Carru C, Medici S. Platelet-derived membranes as biomimetic interfaces for engineering functional nanocarriers in targeted drug delivery and diagnostics: a systematic review. Biomater Sci. 2026;14(2):377–92. 10.1039/D5BM00511F. [DOI] [PubMed] [Google Scholar]
- 500.Zhu Y, Xu L, Kang Y, Cheng Q, He Y, Ji X. Platelet-derived drug delivery systems: pioneering treatment for cancer, cardiovascular diseases, infectious diseases, and beyond. Biomaterials. 2024;306:122478. 10.1016/j.biomaterials.2024.122478. [DOI] [PubMed] [Google Scholar]
- 501.Jia Y, Wang X, Li L, Li F, Zhang J, Liang XJ. Lipid nanoparticles optimized for targeting and release of nucleic acid. Adv Mater. 2024;36(4):e2305300. 10.1002/adma.202305300. [DOI] [PubMed]
- 502.Zhi D, Halemaimaiti A, Dai B, Li H, Wang H, Zhang Z, et al. Advances in lipids design for LNP-mediated DNA and RNA delivery. Adv Colloid Interface Sci. 2026;354:103897. 10.1016/j.cis.2026.103897. [DOI] [PubMed] [Google Scholar]
- 503.Dilliard SA, Siegwart DJ. Passive, active and endogenous organ-targeted lipid and polymer nanoparticles for delivery of genetic drugs. Nat Rev Mater. 2023;8(4):282–300. 10.1038/s41578-022-00529-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 504.Pareek A, Bhatt B, Parmar V, Alasiri G, Alsaidan OA, Kapoor DU, et al. pH-sensitive hydrogels for breast cancer therapy: targeted drug delivery and controlled release approaches. Int J Multiling Pharm. 2025;682:125899. 10.1016/j.ijpharm.2025.125899. [DOI] [PubMed] [Google Scholar]
- 505.Liu W, Cheng G, Cui H, Tian Z, Li B, Han Y, et al. Theoretical basis, state and challenges of living cell-based drug delivery systems. Theranostics. 2024;14(13):5152–83. 10.7150/thno.99257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 506.Su Y, Xie Z, Kim GB, Dong C, Yang J. Design strategies and applications of circulating cell-mediated drug delivery systems. ACS Biomater Sci Eng. 2015;1(4):201–17. 10.1021/ab500179h. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 507.Chen Y, Qin D, Zou J, Li X, Guo XD, Tang Y, et al. Living leukocyte-based drug delivery systems. Adv Mater. 2023;35(17):e2207787. 10.1002/adma.202207787. [DOI] [PubMed]
- 508.He A, Huang Y, Cao C, Li X. Advances in drug delivery systems utilizing blood cells and their membrane-derived microvesicles. Drug Deliv. 2024;31(1):2425156. 10.1080/10717544.2024.2425156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 509.Sandbank E, Eckerling A, Margalit A, Sorski L, Ben-Eliyahu S. Immunotherapy during the immediate perioperative period: a promising approach against metastatic disease. Curr Oncol. 2023;30(8):7450–77. 10.3390/curroncol30080540. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 510.Xie L, Gan F, Hu Y, Zheng Y, Lan J, Liu Y, et al. From blood to therapy: the revolutionary application of platelets in cancer-targeted drug delivery. JFB. 2025;16(1):15. 10.3390/jfb16010015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 511.Logun MT, Begley SL, Hicks K, Park J, Zhang L, Binder ZA, et al. CAR-T cells locally delivered in porcine decellularized matrix hydrogels enhance survival in post-resection glioblastoma. bioRxiv. 2025.02.11.637648.
- 512.Ni J, Huang M, Zhang L, Wu N, Bai CX, Chen LA, et al. Clinical recommendations for perioperative immunotherapy-induced adverse events in patients with non-small cell lung cancer. Thorac Cancer. 2021;12(9):1469–88. 10.1111/1759-7714.13942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 513.Dabo-Trubelja A, Gottumukkala V. Review of cancer therapies for the perioperative physician. Perioper Med. 2023;12(1):25. 10.1186/s13741-023-00315-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 514.Chen X, He R, Chen X, Jiang L, Wang F. Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges. Front Pharmacol. 2023;14:1261312. 10.3389/fphar.2023.1261312. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 515.Liao JJZ, Asatiani E, Liu Q, Hou K. Three steps toward dose optimization for oncology dose finding. Contemp Clin Trials Commun. 2024;40:101329. 10.1016/j.conctc.2024.101329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 516.Rotte A, Frigault MJ, Ansari A, Gliner B, Heery C, Shah B. Dose–response correlation for CAR-T cells: a systematic review of clinical studies. J Immunother Cancer. 2022;10(12):e005678. 10.1136/jitc-2022-005678. [DOI] [PMC free article] [PubMed]
- 517.Radomski A, Jurasz P, Alonso-Escolano D, Drews M, Morandi M, Malinski T, et al. Nanoparticle-induced platelet aggregation and vascular thrombosis. Br J Pharmacol. 2005;146(6):882–93. 10.1038/sj.bjp.0706386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 518.Restle D, Amador-Molina A, Misawa K, Banerjee S, Ku G, Adusumilli PS. Intraperitoneal CAR T-cell therapy for peritoneal carcinomatosis from gastroesophageal cancer: preclinical investigations to a phase I clinical trial (NCT06623396). J Immunother Cancer. 2025;13(9):e012292. 10.1136/jitc-2025-012292. [DOI] [PMC free article] [PubMed]
- 519.Lei S, Gao Y, Wang K, Wu S, Zhu M, Chen X, et al. An implantable Double-layered spherical scaffold depositing gene and cell agents to facilitate collaborative cancer immunotherapy. ACS Nano. 2025;19(18):17653–73. 10.1021/acsnano.5c01366. [DOI] [PubMed] [Google Scholar]
- 520.Yan Y, Chen Y, Huang L, Cai M, Yin X, Zhu YZ, et al. Bioengineered in situ-forming hydrogels as Smart drug delivery systems for postoperative breast cancer immunotherapy: from material innovation to clinical translation. JFB. 2025;16(10):381. 10.3390/jfb16100381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 521.Verschoor N, Bos MK, Oomen-de Hoop E, Martens JW, Sleijfer S, Jager A, et al. A review of trials investigating ctDNA-guided adjuvant treatment of solid tumors: the importance of trial design. Eur J Cancer. 2024;207:114159. [DOI] [PubMed] [Google Scholar]
- 522.Burley N, Lee Y, Liu L, Gangi A, Nasseri Y, Atkins K, et al. ctDNA-guided adjuvant immunotherapy in colorectal cancer. Immunotherapy. 2024;16(20–22):1197–202. 10.1080/1750743X.2024.2430941. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 523.Xu L, Zhou Y, Li N, Yang A, Qi H. Platelet membrane encapsulated curcumin nanomaterial-mediated specific thrombolysis and anti-thrombotic treatment among pregnant women. Biomater Sci. 2024;12(12):3163–74. 10.1039/D4BM00149D. [DOI] [PubMed] [Google Scholar]
- 524.Theocharopoulos C, Machairas N, Ziogas IA, Mungo B, Del Chiaro M, Glantzounis GK, et al. Personalizing treatment for pancreatic ductal adenocarcinoma: the emerging role of minimal residual disease in perioperative decision-making. Cancers. 2025;18(1):94. 10.3390/cancers18010094. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 525.Lv K, Zhang Y, Yin G, Li X, Zhong M, Zhu X, et al. Extracellular vesicles derived from lung M2 macrophages enhance group 2 innate lymphoid cells function in allergic airway inflammation. Exp Mol Med. 2025;57(6):1202–15. 10.1038/s12276-025-01465-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 526.Chen Y, Chen Q, Ma Y, Zhang C, Xu J, Li K, et al. In situ self-assembled cell reservoir hydrogel for maneuvering multistage radioimmunotherapy. Nat Commun. 2026;17(1). 10.1038/s41467-026-68490-5. [DOI] [PMC free article] [PubMed]
- 527.Conte M, Xella A, Woodall RT, Cassady KA, Branciamore S, Brown CE, et al. CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma. Math Biosci. 2025:109531. [DOI] [PMC free article] [PubMed]
- 528.Ponterio E, Haas TL, De Maria R. Oncolytic virus and CAR-T cell therapy in solid tumors. Front Immunol. 2024;15:1455163. 10.3389/fimmu.2024.1455163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 529.Gao R, Lin P, Yang W, Fang Z, Gao C, Cheng B, et al. Bio-inspired Nanodelivery platform: platelet membrane-cloaked genistein nanosystem for targeted lung cancer therapy. IJN. 2024;19:10455–78. 10.2147/IJN.S479438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 530.Song B, Na Y-G, Kim BJ, Jin M, Song YH, Kim D-E, et al. Platelet membrane-coated poly (lactic-co-glycolic acid) nanoparticles as a targeting drug delivery System for multidrug-resistant breast cancer. IJN. 2025;Volume 20:8529–45. 10.2147/IJN.S517753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 531.Zhang X, Fang Q, Yuan J, Dai L, Luo R, Huang T, et al. Evaluation of ctDNA-guided adjuvant therapy de-escalation in head and neck squamous cell carcinoma: a comparative cohort study. Front Immunol. 2025;16:1576042. 10.3389/fimmu.2025.1576042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 532.Hitchen N, Shahnam A, Tie J. Circulating tumor DNA: a Pan-cancer biomarker in solid tumors with prognostic and predictive value. Annu Rev Med. 2025;76(1):207–23. 10.1146/annurev-med-100223-090016. [DOI] [PubMed] [Google Scholar]
- 533.Valenza C, Saldanha EF, Gong Y, De Placido P, Gritsch D, Ortiz H, et al. Circulating tumor DNA clearance as a predictive biomarker of pathologic complete response in patients with solid tumors treated with neoadjuvant immune checkpoint inhibitors: a systematic review and meta-analysis. Ann Oncol. 2025;36(7):726–36. 10.1016/j.annonc.2025.03.019. [DOI] [PubMed] [Google Scholar]
- 534.Negro S, Pulvirenti A, Trento C, Indraccolo S, Ferrari S, Scarpa M, et al. Circulating tumor DNA as a real-time biomarker for minimal residual disease and recurrence prediction in stage II colorectal cancer: a systematic review and meta-analysis. IJMS. 2025;26(6):2486. 10.3390/ijms26062486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 535.Olejarz W, Sadowski K, Szulczyk D, Basak G. Advancements in personalized CAR-T therapy: comprehensive overview of biomarkers and therapeutic targets in hematological malignancies. IJMS. 2024;25(14):7743. 10.3390/ijms25147743. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 536.Galli E, Patelli G, Villa F, Gri N, Mazzarelli C, Mangoni I, et al. Circulating blood biomarkers for minimal residual disease in hepatocellular carcinoma: a systematic review. Cancer Treat Rev. 2025;135:102908. 10.1016/j.ctrv.2025.102908. [DOI] [PubMed] [Google Scholar]
- 537.Aung TN, Monkman J, Warrell J, Vathiotis I, Bates KM, Gavrielatou N, et al. Spatial signatures for predicting immunotherapy outcomes using multi-omics in non-small cell lung cancer. Nat Genet. 2025;57(10):2482–93. 10.1038/s41588-025-02351-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 538.Liu W, Puri A, Fu D, Chen L, Wang C, Kellis M, et al. Dissecting the tumor microenvironment in response to immune checkpoint inhibitors via single-cell and spatial transcriptomics. Clin Exp Metastasis. 2024;41(4):313–32. 10.1007/s10585-023-10246-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 539.Wang X, Lamberti G, Di Federico A, Alessi J, Ferrara R, Sholl ML, et al. Tumor mutational burden for the prediction of PD-(L)1 blockade efficacy in cancer: challenges and opportunities. Ann Oncol. 2024;35(6):508–22. 10.1016/j.annonc.2024.03.007. [DOI] [PubMed] [Google Scholar]
- 540.Zhang J, Song Z, Zhang Y, Zhang C, Xue Q, Zhang G, et al. Recent advances in biomarkers for predicting the efficacy of immunotherapy in non-small cell lung cancer. Front Immunol. 2025;16:1554871. 10.3389/fimmu.2025.1554871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 541.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(1):84. 10.1186/s13045-025-01732-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 542.Gandara DR, Agarwal N, Gupta S, Klempner SJ, Andrews MC, Mahipal A, et al. Tumor mutational burden and survival on immune checkpoint inhibition in >8000 patients across 24 cancer types. J Immunother Cancer. 2025;13(2):e010311. 10.1136/jitc-2024-010311. [DOI] [PMC free article] [PubMed]
- 543.Lawler M, Keeling P, Kholmanskikh O, Minnaard W, Moehlig-Zuttermeister H, Normanno N, et al. Empowering effective biomarker-driven precision oncology: a call to action. Eur J Cancer. 2024;209:114225. 10.1016/j.ejca.2024.114225. [DOI] [PubMed] [Google Scholar]
- 544.Iafolla MAJ, Yang C, Dashner S, Xu W, Hansen AR, Bedard PL, et al. Bespoke circulating tumor DNA (ctDNA) analysis as a predictive biomarker in solid tumor patients (pts) treated with single-agent pembrolizumab (P). JCO. 2019;37(15_suppl):2542–2542. 10.1200/JCO.2019.37.15_suppl.2542. [Google Scholar]
- 545.Esposito Abate R, Pasquale R, Sacco A, Simeon V, Maiello MR, Frezzetti D, et al. Harmonization of tumor mutation burden testing with comprehensive genomic profiling assays: an IQN path initiative. J Immunother Cancer. 2024;12(2):e007800. 10.1136/jitc-2023-007800. [DOI] [PMC free article] [PubMed]
- 546.Razzaghi H, Khabbazpour M, Heidary Z, Heiat M, Shirzad Moghaddam Z, Derogar P, et al. Emerging role of tumor-educated platelets as a New liquid Biopsy tool for colorectal cancer. Arch Iran Med. 2023;26(8):447–54. 10.34172/aim.2023.68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 547.Zhang L, Zhu Y, Wei X, Chen X, Li Y, Zhu Y, et al. Nanoplateletsomes restrain metastatic tumor formation through decoy and active targeting in a preclinical mouse model. Acta Pharmaceutica Sin. B. 2022;12(8):3427–47. 10.1016/j.apsb.2022.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 548.Antunes-Ferreira M, D’Ambrosi S, Arkani M, Post E, In ‘t Veld S, Ramaker J, et al. Tumor-educated platelet blood tests for non-small cell lung cancer detection and management. Sci Rep. 2023;13(1):9359. 10.1038/s41598-023-35818-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 549.Morris K, Li C, Sarangi S, Papa AL. Phenotype independent capture of circulating tumor cell using magnetic platelet decoys. J Sport Hist Liquid Biopsy. 2025;10:100326. 10.1016/j.jlb.2025.100326. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 550.Wang Y, Wu H, Zhou Z, Maitz MF, Liu K, Zhang B, et al. A thrombin-triggered self-regulating anticoagulant strategy combined with anti-inflammatory capacity for blood-contacting implants. Sci Adv. 2022;8(9):eabm3378. 10.1126/sciadv.abm3378. [DOI] [PMC free article] [PubMed]
- 551.Maitz MF, Zitzmann J, Hanke J, Renneberg C, Tsurkan MV, Sperling C, et al. Adaptive release of heparin from anticoagulant hydrogels triggered by different blood coagulation factors. Biomaterials. 2017;135:53–61. 10.1016/j.biomaterials.2017.04.044. [DOI] [PubMed] [Google Scholar]
- 552.Maitz MF, Kaiser DPO, Cuberi A, Weich Hernández R, Mühl-Benninghaus R, Tomori T, et al. Enhancing thromboresistance of neurovascular nickel-titanium devices with responsive heparin hydrogel coatings. J NeuroIntervent Surg. 2025;17(6):625–31. 10.1136/jnis-2024-021836. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 553.Mantry S, Das PK, Sankaraiah J, Panda S, Silakabattini K, Reddy Devireddy AK, et al. Advancements on heparin-based hydrogel/scaffolds in biomedical and tissue engineering applications: delivery carrier and pre-clinical implications. Int J Multiling Pharm. 2025;679:125733. 10.1016/j.ijpharm.2025.125733. [DOI] [PubMed] [Google Scholar]
- 554.Chiffelle J, Genolet R, Michielin O, Harari A. Harnessing TCR repertoires: predictive insights and therapeutic monitoring in cancer immunotherapy. Immuno-Oncol Technol. 2025;28:101076. 10.1016/j.iotech.2025.101076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 555.Zhu M, Hu Y, Gu Y, Lin X, Jiang X, Gong C, et al. Role of amino acid metabolism in tumor immune microenvironment of colorectal cancer. Am J Cancer Res. 2025;15(1):233–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 556.Frank ML, Lu K, Erdogan C, Han Y, Hu J, Wang T, et al. T-Cell receptor repertoire sequencing in the era of cancer immunotherapy. Clin Cancer Res. 2023;29(6):994–1008. 10.1158/1078-0432.CCR-22-2469. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 557.Elayeh E, Aleidi SM, Aboud O, Semreen MH, Bustanji YK, Dahabiyeh LA. Integrated multi-omics approaches for predicting immune checkpoint inhibitor response in NSCLC – insights from genomics, proteomics, and metabolomics. LCTT. 2025;16:167–98. 10.2147/LCTT.S539777. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 558.Abdo L, Batista-Silva LR, Bonamino MH. Cost-effective strategies for CAR-T cell therapy manufacturing. Mol Ther Oncol. 2025;33(2):200980. 10.1016/j.omton.2025.200980. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 559.Weltin AL, De Graaf Y, Goudarzi A, Müller M, Herbst L, Nießing B, et al. Industrializing CAR-T cell therapy: impact of automation on cost and space efficiency of manufacturing facilities. Front Bioeng Biotechnol. 2026;13:1612248. 10.3389/fbioe.2025.1612248. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No datasets were generated or analysed during the current study.
