Next Article in Journal
Adipo-Modulation by Turmeric Bioactive Phenolic Components: From Curcuma Plant to Effects
Next Article in Special Issue
How Can Plant-Derived Natural Products and Plant Biotechnology Help Against Emerging Viruses?
Previous Article in Journal
Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Strategic Advances in Targeted Delivery Carriers for Therapeutic Cancer Vaccines

State Key Laboratory of Drug Regulatory Sciences, National Institutes for Food and Drug Control, Beijing 102629, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2025, 26(14), 6879; https://doi.org/10.3390/ijms26146879
Submission received: 20 June 2025 / Revised: 11 July 2025 / Accepted: 16 July 2025 / Published: 17 July 2025
(This article belongs to the Special Issue Molecular Insights in Antivirals and Vaccines)

Abstract

Cancer is one of the major global health burdens, and more effective treatments are needed. At present, there are surgery, targeted therapy, and immunotherapy for the treatment of tumors, but due to the limitations of diagnostic technology and drug resistance, surgery and targeted therapy have little effect. Active immunization in the field of immunotherapy can mobilize host immunity, trigger tumor-specific T-cell responses, and produce targeted cytotoxicity. Its efficacy largely depends on the targeted delivery efficiency of cancer vaccines. Although immunotherapy is more durable than other approaches, immunosuppression in the tumor microenvironment and immune evasion by malignant cells limit the therapeutic efficacy of cancer vaccines. To overcome these challenges, this review summarizes key strategies for improving vaccine vector targeting, as well as recent advances and trends in delivery systems.

1. Introduction

Cancer is a leading cause of global mortality, with over 20 million new cases and 9.7 million deaths reported in 2022. In the U.S., it ranks as the second leading cause of death, with projections estimating 2.04 million new cases and 610,000 deaths by 2025 [1,2]. Current treatments include surgery, radiotherapy, chemotherapy, and immunotherapy. Surgery is mainly aimed at early-stage tumors, but it is limited by diagnostic methods. Chemotherapy and radiotherapy are common treatments, but further progression is limited by systemic toxic effects and limited efficacy in advanced or recurrent disease [3,4,5]. The evolution of cancer treatments—from radical surgery and cytotoxic chemotherapy to immune-based therapies—has been well documented by the American Cancer Society (ACS) [6], providing the historical context for the emergence of therapeutic cancer vaccines. The limitations of these approaches have shifted attention to immunotherapy strategies, particularly cancer vaccines that are designed to stimulate tumor-specific immune responses.
The concept of cancer vaccination can be traced back to the late 19th century, when Dr. William B. Coley observed tumor regression in sarcoma patients following streptococcal infections. He hypothesized that the infection-induced immune activation was responsible for the antitumor effects [7]. In the mid-20th century, the introduction of the theory of immune surveillance provided a mechanistic basis for immune-based tumor control. This period saw a surge in research and clinical trials of immune strategies, laying the groundwork for the development of therapeutic cancer vaccines, and in 2010, the FDA approved the first therapeutic cancer vaccine, Sipuleucel-T, a milestone that catalyzed a new era of personalized cancer immunotherapy [8,9]. The cancer vaccines can be classified into preventive vaccines and therapeutic vaccines. The former prevents the occurrence of virus-related tumors by inducing antiviral immunity; the latter aims to activate tumor antigen-specific T cells to clear the formed tumors [10]. Preventive vaccines mainly achieve tumor prevention through antiviral effects, such as human papillomavirus (HPV)- and hepatitis B virus (HBV)-related cancers. In contrast, therapeutic vaccines rely on the activation of antigen-presenting cells (APCs), especially dendritic cells, to promote effector and memory T-cell responses. For example, Sipuleucel-T, which has been approved for the treatment of prostate cancer, is a typical example of therapeutic vaccines [9,11,12]. Despite their specificity, safety, and long-term memory induction, the clinical efficacy of cancer vaccines remains limited by several factors. These factors include an immunosuppressive tumor microenvironment (TME), delayed onset of immune activation, and strong dependence on adjuvants and delivery platforms. Cancer vaccines can be broadly categorized into four platforms based on the mode of antigen presentation: cellular vaccines, protein/peptide vaccines, whole-tumor-cell vaccines, and nucleic acid vaccines. Cellular vaccines (including dendritic cell vaccines) are highly personalized but are complex, costly, and time-consuming to produce. Protein/peptide vaccines are usually safe and easy to synthesize but are weakly immunogenic and susceptible to enzymatic degradation in vivo. Whole-tumor-cell vaccines have a broad antigenic spectrum but present challenges in quality control and batch standardization. Nucleic acid vaccines, especially mRNA-based formulations, offer a high degree of flexibility and the potential for rapid design, but their efficacy is closely linked to efficient intracellular delivery and strong innate immune activation. Among these platforms, nucleic acid vaccines—particularly mRNA-based formulations—stand out for their rapid design flexibility and multi-antigen encoding capability. However, their therapeutic efficacy remains critically dependent on efficient delivery systems and immunostimulatory adjuvants. Therefore, the development of robust, targeted delivery platforms has become central to improving nucleic acid vaccine performance. The following sections of this review will discuss recent advances in such delivery systems, which can be broadly categorized into viral and non-viral types [13,14,15,16,17].
In addition to the influence of vector, vaccine efficacy is affected by many factors. For example, antigen selection determines specificity and immunogenicity, and vector and adjuvant designs affect the degree of antigen presentation and response [18]. These aspects, including antigen design, immunogenicity optimization, production cost, and quality control, have been extensively reviewed elsewhere [17,19,20] and are not the focus of this review. Here, we focus specifically on delivery strategies to enhance the therapeutic efficacy of cancer vaccines. At the same time, immunosuppression in the TME, immune evasion mechanisms such as antigenic heterogeneity and high mutational burden, and inefficient delivery systems remain major obstacles to clinical translation [10,19,21,22,23,24]. Targeted vectors are an important strategy to overcome these obstacles. Properly designed vectors can also reprogram tumor tissues and organs and synergize with approaches against tumor immune escape. Systematic evaluation of vector platforms and the mechanistic basis of their targeting is essential to advance the development of next-generation vaccines.
This review focuses on recent advances in targeting strategies for viral and non-viral vaccine delivery platforms. These include active targeting approaches—such as antibody conjugation, genetic ligand expression, and biomimetic engineering—as well as passive strategies involving physicochemical property modulation. As illustrated in Figure 1, these strategies aim to enhance vaccine accumulation within the TME and promote engagement with antigen-presenting cells and T lymphocyte subsets. This conceptual framework provides a mechanistic basis for subsequent discussions of vector design and tumor specificity.

2. Viral Vector Vaccines

Viral vectors are an important part of cancer vaccine delivery due to their efficient gene transfer and immunogenicity. Viral platforms such as adenovirus (Ad), poxvirus, and adeno-associated virus (AAV) have been genetically engineered to encode tumor-associated or tumor-specific antigens (TAA/TSA), enabling precise in situ antigen expression [25]. In addition to antigen payload, recent studies have focused on modifying viral capsid or envelope proteins to improve the cell tropism and tissue-specific uptake of vectors, thereby enhancing the ability of vectors to target lymphoid organs or TME. These advances not only enhance the antigen presentation ability of dendritic cells but also facilitate large-scale production and clinical translation [26].

2.1. Adenoviral (Ad) Vectors

Ad are non-enveloped, double-stranded DNA viruses with icosahedral symmetry and high transduction efficiency. Their genomes remain episomal, minimizing the risk of insertional mutagenesis. Since the 1990s, Ad vectors have been widely employed in gene therapy, vaccine development, and genome editing [27,28,29,30]. However, several limitations hinder clinical application.
The prevalence of pre-neutralizing antibodies against common Ad serotypes in the population reduces vaccine efficacy. To address this problem, alternative serotypes with low seroprevalence have been developed, such as those from non-primate hosts, or variants that have undergone capsid design to evade immune memory [31,32,33]. In addition, Ads exhibit a strong hepatotropism, leading to liver accumulation and toxicity, thereby affecting treatment efficiency. In 2020, Lu Z.H. and colleagues reported that wild-type gorilla adenovirus (GAd) exhibits intrinsic lung tropism. By adding a myeloid cell-binding peptide (MBP) to the coat of the virus, they changed the tissue distribution of the virus, reduced the lung targeting, and allowed the vector to be used in more scenarios [34]. Combining natural vector tropism with ligand-based modification of capsid proteins is a strategy to circumvent immunological barriers and improve tissue-specific delivery.
Among adenovirus platforms, recombinant Ad vectors, both replication-deficient and conditionally replicating, have attracted the attention of researchers. The natural tumor tropism of conditionally replicating adenoviruses (oncolytic adenoviruses, OAds) has been a key tool in the design of cancer vaccines [35]. For example, capsid fusion with a soluble TRAIL ligand containing a leucine zipper has been shown to enhance OAd targeting of TRAIL-expressing tumors, improving therapeutic specificity and potency [36]. Although most cancer vaccines focus on encoding TAA or TSA to elicit T-cell responses, vector targeting remains a critical factor for immunogenicity and safety. Local enrichment of Ad vectors within the tumor could enhance site-specific immunity while minimizing systemic toxicity.
Strategies to enhance Ad targeting can be divided into two broad categories: capsid protein modification and genetic engineering. Approaches based on capsid-protein modification include conjugated targeting ligands, incorporation of tumor chemokines, or structural changes in fibrin to increase tissue-specific affinity [37,38,39]. Conversely, genetic engineering may delete essential replicating genes to limit viral spread to tumor cells or insert genes encoding tumor-targeting molecules [40,41,42,43]. Physical modifications of coat proteins, such as covalent binding, provide rapid and transient anti-tumor immunity to vectors, offering a flexible strategy for short-term immunotherapy and exploratory applications. In contrast, genetically engineered surface modifications allow for sustained, controlled expression of the ligand and are therefore more suitable for long-term tumor immunotherapy. In recent years, hybrid delivery platforms embedding Ad vectors in liposomes or nanoparticles have attracted the interest of researchers, which utilize the advantages of both viral and non-viral vectors to reduce the required dose, improve pharmacokinetics, and enable additional surface modifications as a means of enhancing targeting and immune activation [38,44,45,46,47]. Future research could focus on optimizing the release kinetics of such strategies, improving their intracellular transport, and reducing off-target effects to further enhance the therapeutic capabilities of vaccines.
Ad vectors possess strong immunogenicity, episomal stability, and high transduction efficiency, making them effective platforms for therapeutic cancer vaccines. Capsid engineering and hybrid formulations have expanded their tissue tropism and reduced immunodominance. Ad-based vectors are especially suitable for immunogenic tumors (e.g., melanoma), virus-associated cancers (e.g., HPV-positive cervical cancer), and solid tumors with defined antigens such as prostate cancer [48,49]. Nonetheless, pre-existing neutralizing antibodies, hepatotropism, and systemic toxicity remain barriers. Advanced vector modification and localized delivery approaches are addressing these challenges.

2.2. Adeno-Associated Virus (AAV)

AAV is a non-enveloped, single-stranded DNA virus that was first isolated from adenovirus preparations in 1966. It is mainly composed of three viral proteins—VP1, VP2, and VP3 [50,51]. AAV offers multiple advantages as a gene-delivery vector, including high cargo volume, lack of pathogenicity, low toxicity, sustained gene expression, and tissue complement-dependent serotypes [52,53,54]. Since its first use for gene transfer in 1987, AAV has become a central platform for gene therapy research [55,56]. The immunogenicity of AAV is relatively low because the virus is nonpathogenic and latent in the absence of a helper virus such as adenovirus. However, residual epitopes associated with adenovirus may elicit immune recognition that limits repeated dosing [57,58]. Although AAV is currently primarily used for gene therapy, its durable transgene expression ability and inherent safety profile make it useful as a tumor vaccine vector. More importantly, capsid proteins can be rationally designed by genetic engineering or surface modification to improve their targeting to lymphoid tissue or tumor-resident immune cells.
Targeting strategies for AAV vectors can be divided into two main categories: genetic engineering and modification of capsid proteins. Given the limited natural tropism of AAV to tumors, these strategies were designed to enhance delivery specificity and functional expression in the immunosuppressive microenvironment. Among them, the former involves modulation of tissue tropism through amino acid sequence changes or insertion of functional elements, such as TLR9 inhibitory keynotes or PD-1 decoy constructs, to enhance gene expression and immune activation in a specific microenvironment [59,60]. The latter involves the generation of heritable capsid protein variants, the insertion of genes into targeting ligands (e.g., antibodies or peptides), or chemical conjugation to achieve cell-type specificity [61,62,63,64,65].
Although both AAV and Ad show promise for inducing anti-tumor immunity, their efficacy is still constrained by a number of factors, including immunosuppressive elements in the immunosuppressive tumor microenvironment, pre-existing neutralizing antibodies, and differences in the expression of tumor antigens in patients with HLA restriction. To address these limitations, efforts have been made to enhance targeting, stabilize vectors, and stimulate sustained antigen expression in the tumor microenvironment. Through these strategies, the aim is to achieve precision therapy, promote durable T-cell immune responses, and ultimately enhance the therapeutic efficacy of viral vector-based cancer vaccines.
AAV vectors feature low immunogenicity, long-term gene expression, and favorable safety profiles. They are well-suited for prophylactic or low-burden tumors, particularly glioblastoma or early-stage prostate cancer, where durable immune surveillance is required [66,67,68]. While their small size facilitates deep tissue penetration, weak immunogenicity and susceptibility to neutralizing antibodies limit their standalone efficacy. Heterologous prime–boost regimens and immunostimulatory co-delivery can enhance response, and capsid redesign continues to improve targeting. Table 1 summarizes some cases for improving the targeting efficiency of Ad and AAV vectors, including examples of gene reprogramming and combinatorial delivery systems in tumor immunotherapy.

2.3. Poxvirus Vectors

Poxviruses are double-stranded DNA viruses that infect vertebrates and insects and comprise at least 46 subfamilies. Of these, varicella virus, the causative agent of smallpox, is best known as the poxvirus [77,78]. The successful development of a poxvirus-based smallpox vaccine played a central role in the global eradication of smallpox [79]. Poxviruses have unique biological advantages for vaccine delivery, including large volumes of foreign genes, natural tissue tropism, and lack of risk of genomic integration [80]. Wild-type poxviruses exhibit intrinsic tumor-selective replication, which can be genetically modified to further improve tumor targeting and immunogenicity [81,82].
Physical and chemical surface modifications are commonly used to enhance targeting. In 2018, Ylosmaki et al. conjugated tumor-specific therapeutic peptides to viral envelopes, which facilitated antigen uptake by APCs and elicited tumor-specific T-cell responses [83]. In 2021, Carlisle’s group at the University of Oxford demonstrated that encapsulation of poxvirus in an amphiphilic polymer shielded it from neutralizing antibodies, followed by targeted delivery of anti-MUC1 antibodies to MUC1-expressing tumor cells by conjugation of anti-MUC1 antibodies to the surface of the ampholymer [84]. Although this change is not inherited, it is technically simple, time-saving, and suitable for time-sensitive personalized clinical applications.
Poxviruses have become an important platform for constructing oncolytic viruses (OV) and mediating oncolytic immunotherapy. Targeted engineering strategies include deletion of toxic genes, such as thymidine kinase (TK), A35, or A49, and insertion of tumor-specific elements to enhance tissue selectivity and antitumor potency [85,86,87,88,89,90,91]. In 2018, Ricordel et al. developed a poxvirus strain with broad tumor infectivity through iterative screening and added a suicide gene (FCU1) along with TK deletion, thereby enhancing tumor targeting [92]. In addition, cell-based delivery systems have been explored to overcome systemic clearance and improve delivery efficiency. A notable example is the “Trojan horse” approach proposed by Draganov and colleagues in 2019, in which oncolytic poxvirus is encapsulated in mesenchymal stem cells (MSCs) and delivered to the tumor site to evade immune surveillance using the tumor-homing ability of the MSCS [93]. A common strategy to enhance the tumor-selective replication of poxviral vectors is to delete the TK gene, the absence of which restricts the ability of poxviruses to replicate in non-dividing normal tissues, while allowing the virus to preferentially replicate in rapidly proliferating tumor cells. The higher transgenic capacity and safety of poxviruses compared to Ad vectors make them ideal platforms for complex immunotherapy. However, the clinical translation of poxviruses faces some limitations, such as host immune memory affecting repeat vaccination and immunosuppressive factors in the immunosuppressive tumor microenvironment leading to reduced vaccine therapeutic efficacy [90]. Current research on poxviruses is focused on enhancing targeting capabilities while combining poxvirus vaccines with checkpoint inhibitors, radiotherapy, or other combination therapies to improve the overall efficacy of vaccines in cancer immunotherapy [94].
Poxviruses provide large genetic capacity and inherent tumor tropism, making them well-suited for encoding multi-antigenic or immunomodulatory payloads. They are particularly promising in tumors with high antigenic heterogeneity, such as renal cell carcinoma and prostate cancer [95,96]. Although effective in heterologous prime–boost or adjuvant settings, challenges including particle size, innate immune activation, and immune memory to the vector limit repeated use. Advances in encapsulation, surface engineering, and delivery integration are helping to improve precision and reduce systemic reactivity.

2.4. Oncolytic Virus (OVs)

In addition to the several viruses described above, a variety of other viruses, such as herpes simplex virus (HSV), measles virus (MV), and Newcastle disease virus (NDV), have been engineered as oncolytic vectors that selectively infect tumor cells and initiate antitumor immune responses [97]. Concomitant combination with immune adjuvants or checkpoint inhibitors increases T-cell activation and limits immune escape [98,99,100,101].
Structural modifications of the virus further improved targeting and replication specificity [102,103,104,105]. Tumor-targeting is enhanced by vector surface integration of ligands with affinity for tumor-associated receptors [106,107,108]. Capsid modification improves intratumoral viral delivery by means of tumor-targeting peptides conjugated to the viral surface [83,109,110,111]. Loss of immune-evasion genes has also been shown to enhance T-cell responses by promoting dendritic cell uptake and enhancing antigen presentation [111]. Overall, these approaches improve vaccine selectivity and immunogenicity, supporting the development of oncolytic virus-targeted vaccine platforms.
Immune memory against viral vectors, particularly adaptive immunity mediated by neutralizing antibodies, constitutes a major obstacle to repeated dosing, leading to diminished antigen delivery and reduced vaccine efficacy. To reduce the impact of neutralizing antibodies, a heterologous primary immunization-boosting strategy has been developed, whereby different viral vectors are used sequentially to maintain memory and effector T-cell responses. This strategy has been validated in SARS-CoV-2 vaccine studies and is currently being translated into cancer vaccine applications [112,113,114]. In addition, the immune activation strength and specificity of the primary-booster strategy can be further enhanced by improving vector targeting, providing new optimization directions for tumor vaccine design. OVs combine direct tumor lysis with in situ immune activation, functioning as self-amplifying cancer vaccines. They are particularly effective in tumors with impaired antiviral defenses or those amenable to local injection, such as melanoma, glioblastoma, and hepatocellular carcinoma [115,116]. Structural modifications and surface functionalization improve tropism and immune activation. However, their systemic application is hindered by pre-existing immunity, rapid clearance, and limited intratumoral spread. Rational vector engineering, delivery via carrier systems, and combination with immunotherapies may broaden their clinical efficacy. Figure 2 summarizes the engineering strategies and immune mechanisms of viral vectors.

3. Non-Viral Vector Vaccines

Non-viral vectors avoid gene integration and treatment risks and provide greater design flexibility and higher biosafety for cancer vaccine development. Non-viral vector platforms can be divided into nanoparticle vectors, cell-based delivery platforms, biofilm-derived vectors, bacterial vectors, and so on [20,117,118,119]. Among them, nanoparticle-based systems have become the core of non-viral delivery of cancer immunotherapy.

3.1. Nanoparticle Delivery Systems

3.1.1. Lipid Nanoparticles (LNPs)

Lipid nanoparticles (LNPs) offer enhanced biocompatibility, low toxicity, and improved tissue penetration compared to traditional carriers [120,121]. They have been widely used in RNA-based delivery, such as mRNA COVID-19 vaccines [122,123,124]. Standard formulations consist of phospholipids, cholesterol, polyethylene glycol (PEG)—lipids, and ionizable lipids [125].
The reduced blood flow through the liver and spleen sinusoids leads to the accumulation of LNPs in the liver, thereby limiting extrahepatic delivery [126]. Passive, endogenous, and active targeting strategies have been developed to address this problem [127,128]. Solid lipid nanoparticles (SLNs) loaded with curcumin or carvacrol achieve passive targeting through the enhanced permeability and retention (EPR) effect—a process driven by tumor-induced angiogenesis, in which newly formed vasculature exhibits high permeability and impaired lymphatic drainage, resulting in preferential accumulation of nanocarriers within tumor tissue [129,130,131], but tumor heterogeneity and irregular vasculature compromise precision.
Endogenous targeting is dependent on the pattern of serum protein adsorption, which in turn is regulated by lipid composition [128,132]. A restricted lipid identified by Lokugamage et al. is selectively delivered to splenic T cells [133]. Further adjustments in lipid ratios have improved biodistribution to non-hepatic sites [134,135,136,137,138,139,140,141,142,143,144]. Table 2 lists representative lipid modification methods that can improve tissue selectivity.
Active targeting uses ligand or antibody binding to direct LNPs to specific sites [145,146,147,148,149,150]. Recent studies have developed high-density lipoprotein (HDL)—like LNPs for lymph nodes and antibody-functionalized mRNA-LNPs for HER2-positive (HER2+) breast tumors [151,152]. Meanwhile, mRNA optimization techniques such as circular RNA construction can extend translation time, providing new ideas for the development of next-generation LNP tumor vaccines [153,154,155]. However, the LNP platform still faces a number of challenges, including PEG-related pseudoallergy [156], endosomal escape mechanisms that are still not fully understood, and instability during transport [157]. It is worth noting that optimization of vector targeting not only helps to improve the specificity of antigen delivery and immune activation efficiency but also reduces the accumulation in non-specific tissues and thus side effects, which ultimately improves the overall safety and efficacy of the vaccine. Therefore, the combination of active targeting strategies and endogenous targeting strategies (e.g., systemic component modulation) is expected to promote the further development of LNP clinical translation.
Lipid nanoparticles represent a clinically validated, modular platform for RNA-based tumor vaccines. Their favorable biocompatibility, tunable biodistribution, and low immunogenicity have enabled successful mRNA delivery in both infectious and cancer settings. LNPs are particularly effective in melanoma and liver cancers, benefiting from enhanced immunogenicity and hepatic tropism [158,159]. However, challenges such as PEG-induced hypersensitivity, suboptimal endosomal escape, and off-target liver accumulation limit broader application. Combining endogenous lipid tuning with ligand-based active targeting may improve selectivity, reduce systemic toxicity, and expand use across diverse solid tumors.
Table 2. Representative LNP targeting strategies.
Table 2. Representative LNP targeting strategies.
Targeting MechanismStrategySpecific ApproachReference
Passive3-component formulation (3-Comp)Cholesterol removal enhances pulmonary tropism[160]
Cholesterol removal combined with miR-122/142-modified mRNA for dual organ/cell targeting[161]
Selective Organ Targeting (SORT)Addition of charged lipids enables organ-specific tropism[162]
Anionic lipids promote splenic accumulation[163]
Component replacementBile acid substitution for cholesterol enhances splenic targeting[164]
Ionizable lipid screeningLipid library screened for lung-specific delivery[165]
Hydrophobic tail optimizationBranched chains increase ovarian tumor selectivity[166]
Ionizable lipid + phospholipid tuningT-cell targeting achieved via phospholipid enrichment and cholesterol reduction[167]
pH-responsive lipidsCL4H6 lipid (a synthetic ionizable lipid) enables delivery to tumor-associated macrophages[168]
ActiveAntibody conjugation + chemotactic cueSurface anti-PECAM-1 and cationic lipid chemotaxis enhance lung targeting[169]
Surface peptide conjugationD-peptide–PEG conjugates direct LNPs to PD-L1+ tumor cells[170]
Pardaxin-modified LNPs facilitate endoplasmic reticulum (ER)-specific delivery[171]
Ganglioside insertionCD169 targeting enabled via ganglioside incorporation[172]
Dendritic cell (DC) membrane coatingDC membrane-coated LNPs target TME[173]

3.1.2. Polymeric Nanoparticles (PNP)

Polymeric nanoparticles (PNPs) have become ideal platforms for cancer vaccine delivery due to their tunable surface chemistry, biocompatibility, and biodegradability [174,175]. PNPs were first described by Speiser in 1969 and later developed by Robert Langer. Compared with liposomes, the structural and component flexibility of PNPs enables them to better match the physicochemical requirements of the TME, thus enabling passive and active targeting strategies [176,177].
Passive targeting strategies exploit intrinsic properties of the TME, such as acidity and hypoxia, or intrinsic properties of nanoparticles, such as differences in permeability due to particle size and composition [178,179,180]. In 2023, Zhou et al. designed ionizable, pH-sensitive PNPs that can release c-di-GMP (CDG) in acidic endosomes and activate stimulators of interferon gene (STING) signaling to achieve the purpose of tumor treatment [181]. The proportion of polyethylene glycol (PEG) on the surface of polymeric nanoparticles (PNPs) critically modulates their in vivo biodistribution. By tuning PEG density, organ tropism can be selectively altered, thereby enhancing target-site accumulation while reducing off-target toxicities—an essential parameter in optimizing PNP-based vaccine delivery [182,183]. In 2024, Tian et al. used polyvinyl alcohol (PVA)-modified poly (lactic-co-glycolic acid) (PLGA) nanoparticles that were stable in macrophages and responsive to acidic TME to achieve site-specific release and mimic natural delivery, providing a new idea for subsequent research [184].
Active targeted binding of functionalized ligands, such as small molecules or monoclonal antibodies [185,186,187,188,189,190,191,192,193,194,195,196,197,198,199]. In 2024, Wang’s group achieved tumor-targeted delivery in multiple preclinical models using albumin/polyester nanoparticles conjugated to PDL1, 4-1BB, and NKG2A (or TIGIT) antibodies [200]. In addition, genetically engineered modifications of surface ligands or receptors have also been used to enhance binding affinity with receptor-mediated uptake [201].
The development of PNP platforms is currently undergoing a gradual shift from single-targeted strategies to multi-mechanism synergistic delivery. Passive targeting and active targeting are common single-targeting strategies, but passive targeting is limited by tumor heterogeneity and local barriers, compared to active targeting, which is more precise and flexible in design and closer to clinical translational needs. With the advancement of technology, the integration of active and passive targeting strategies is a major trend in optimizing PNP delivery systems. For example, through the use of environmentally responsive polymers to improve spatial and temporal release control, and the superimposition of specific ligands to promote local uptake and immune activation in tumors. Such composite carriers can improve the bioavailability and safety of vaccines in complex tumor environments and are expected to break through existing delivery bottlenecks. Future research should focus on the screening of highly selective targeting proxies and the validation of the composite delivery mechanism to promote the clinical application of PNPs in therapeutic tumor vaccines.
PNP offer versatile physicochemical tunability, surface functionalization, and environmental responsiveness, making them suitable for vaccine delivery in complex TME. They support both passive (e.g., pH/hypoxia-triggered) and active (e.g., ligand-directed) targeting. PNPs are especially effective in tumors with acidic or heterogeneous profiles, such as colorectal and breast cancers [202,203]. Despite these advantages, clinical translation is constrained by tumor heterogeneity, variable release kinetics, and formulation complexity. Future advances should focus on multistage targeting systems integrating environmental triggers and multi-ligand engagement to enhance precision and efficacy. Table 3 below shows the advantages and disadvantages of common polymer particles and their common application ranges.

3.1.3. Inorganic Nanoparticle Carriers

Inorganic nanoparticles, including semiconductors and metal-organic frameworks, are used as cancer vaccine carriers due to their unique physical and chemical properties. These systems typically rely on EPR effects for passive tumor targeting. Some engineered variants show better immunomodulatory capabilities. For example, Cu2+-doped titanium dioxide (TiO2) nanoparticles have been shown to transiently hyperactivate dendritic cells, thereby enhancing DC uptake and improving antigen presentation [215]. Trail-loaded periodic mesoporous organosilicon (PMO) particles combine passive and active targeting to enhance therapeutic efficacy [216,217,218,219,220,221]. However, high doses of inorganic carriers can easily lead to off-target accumulation and systemic toxicity [222]. The limitations of synthesis uniformity and pore size-dependent degradation further limit the clinical use of inorganic nanoparticles. Compared with organic nanoparticles, inorganic nanomaterials are easier for surface modification and compositional modulation, but non-targeted toxicity at high doses and difficulty in preparation remain barriers to translational applications. Future optimization of inorganic nanoplatforms should take into account immune activation and controlled degradation capabilities, and prioritize targeting and safety dynamics.
Inorganic nanoparticles offer structural rigidity, high surface modifiability, and programmable immune stimulation. Their EPR-based passive targeting and functionalized surface modifications support multi-modal vaccine delivery. These systems are particularly useful in tumors requiring enhanced antigen presentation, such as breast and colorectal cancers [223,224]. However, their clinical potential is limited by synthetic heterogeneity, poor biodegradability, and off-target accumulation at high doses. Prioritizing immune-responsive, degradable nanoscaffolds with controllable release may help address safety and translational challenges. Table 4 summarizes the compositions, sizes, morphologies, key applications, and associated challenges of three representative inorganic nanoparticle carriers. Additionally, Figure 3 summarizes the targeting strategies and mechanisms employed by nanoparticle-based delivery platforms, using lipid nanoparticles as an example.
Nanoparticles are more promising than viral systems, but the complexity of formulation poses challenges for production and purification [232]. While focusing on targeting, clinical feasibility should be considered to avoid off-target effects and inflammation [233].
With the maturity of artificial intelligence-assisted formulation screening and high-throughput functional evaluation systems, nanomaterials are expected to make breakthroughs in precise structural design, tissue selectivity, and immune response modulation and to become important delivery tools for the next generation of therapeutic tumor vaccines.

3.2. Cell-Based Delivery Platforms

Cell-based delivery systems leverage vesicle-mediated signaling and surface ligand expression for targeted delivery. In the context of mRNA vaccines, the genetic engineering to express patient-specific surface markers or transport receptors allows precise cancer treatment [234].

3.2.1. Dendritic Cells (DCs)

DCs have been widely used due to their strong antigen presentation ability and high mRNA transfection efficiency [235]. In 2021, Harris et al. enhanced DC activation and improved passive targeting by knocking down AIM2, a type I interferon-induced sensor [236]. However, dendritic cell vaccines are clinically challenged by insufficient T-cell activation, an obstacle that has prompted researchers to shift from passive targeting to strategies that enhance active targeting and co-stimulatory signaling through cell surface functionalization. For example, Yang et al. used copper-free click chemistry and metabolic glycoengineering to functionalize the DC surface, improving DC-T-cell interactions and anti-tumor activity [237]. Targeting strategies for dendritic cell vaccines include both passive and active approaches to optimize T-cell activation and function [238,239]. Clinical translation of DC vaccines is still limited by manufacturing costs, technical complexity, and inter-individual immunophenotypic differences. DC vaccines leverage potent antigen presentation and have demonstrated clinical promise in tumors that require strong T-cell priming, such as melanoma and prostate cancer [9,20]. Their advantages include high mRNA transfection efficiency and ex vivo manipulability, enabling surface engineering to enhance targeting and co-stimulatory signaling. However, challenges such as complex manufacturing, inter-patient immunophenotypic variability, and limited in vivo expansion restrict their scalability. Standardizing off-the-shelf engineered DC platforms with active targeting ligands may help overcome these barriers and broaden their translational potential in solid tumors.

3.2.2. Engineered Immune Cells

Advances in gene editing technology and the success of CAR-T therapies have fueled the progress of engineered immune cells as a delivery platform. Unlike other platforms, immune cells inherently possess some tumor-homing ability. To enhance their specific delivery capacity, researchers have targeted modulation by introducing chimeric antigen receptors or artificial receptors, etc. In 2023, Zhao et al. introduced chimeric antigen receptors into NK cells for targeting B7-H3 in solid tumors [240]. In 2024, Wang et al. added HER2-binding affinities to macrophages to enhance the response to HER2+ tumor selective delivery [241]. In addition to exogenously engineered modifications, the tumor-homing ability of immune cells offers additional possibilities for targeted delivery.
Although engineering means can well enhance vector targeting performance, limited autologous cell sources, ex vivo and in vivo expansion efficiency, and individual differences hinder further clinical development. Engineered immune cells, such as CAR-NK cells and macrophages with synthetic receptors, offer both active tumor homing and programmable surface targeting. These vectors are particularly well-suited for solid tumors with defined antigen expression and accessible microenvironments, including HER2+ breast cancer and B7-H3+ neuroblastoma [242,243]. Despite their advantages, challenges such as limited scalability and patient-to-patient variability restrict widespread use. The development of universal, off-the-shelf immune cell platforms with modular targeting capabilities is a promising direction toward overcoming these barriers and enabling broader clinical translation [244,245,246].

3.2.3. Stem Cells

Stem cells are emerging as a platform for tumor vaccines due to their plasticity, tumor affinity, and wide range of sources [247,248,249,250,251]. For example, Kotlevsky’s group used neural stem cells and their exosomes to deliver vaccines to hypoxic glioma regions [252]. It was shown that their tumor localization ability was further enhanced by surface modification of specific ligands or selection of specific tumor-homing strong cell types [253,254,255,256]. Mesenchymal stem cells (MSCs) and their derived exosomes have shown particular promise in solid tumors with rich stromal components—such as glioblastoma, pancreatic, and breast cancers—where they support efficient cargo delivery and can be engineered to co-deliver immunomodulatory signals or differentiate into dendritic-like cells to enhance immunogenicity [257,258,259]. However, under certain conditions, stem cells may promote tumor development [260]. Moreover, variability in donor sources and the complexity of manufacturing continue to impede standardization. Therefore, cell-based platforms may be most suitable for tumors requiring localized, cell-guided delivery and sustained immune modulation. Future optimization should prioritize the selection of defined subpopulations and the clarification of tumor-tropic mechanisms to improve translational readiness.

3.3. Membrane-Derived Vesicular Carriers

Exosome-based delivery systems are derived from biofilms, and their inherent tissue tropism and biocompatibility make them an important delivery platform in tumor immunotherapy [261,262,263,264]. In 2022, Le et al. functionalized erythrocyte-derived exosomes with epidermal growth factor receptor (EGFR)-specific antibodies to target EGFR-positive tumor cells [265]. In 2024, Yu et al. utilized peptide-based surface engineering to direct exosomes to the ER, enhancing the therapeutic response [266]. Exosome-based systems can be further refined by genetic engineering, ligand binding, or encapsulation to improve targeting and payload control. However, large-scale production, structural stability, and translational efficiency are still limitations for large-scale clinical applications [261,264,267,268]. These vectors may be particularly effective for tumors with well-defined surface antigens (e.g., EGFR-overexpressing carcinomas) or elevated ER stress, where targeted delivery improves therapeutic precision [269]. Nonetheless, their heterogeneous content and immunomodulatory potential necessitate rigorous standardization for clinical application.
Membrane-wrapped nanoparticles combine the biological targeting properties of cell membranes with the tunability of synthetic nanocarriers [270]. It shows the characteristics of prolonged systemic circulation, immune evasion, and tissue-specific homing in membrane cells [271,272,273,274,275,276]. For example, tumor-associated macrophage membrane mimetic coatings combined with hyaluronic acid and α4β1 integrin have been used to achieve dual-mode targeting and improve immunogenicity [277]. In addition, the different targeting properties of different biofilms and the potential immunosuppressive or tumor-promoting effects pose great risks. In addition, the complexity and cost of membrane separation and fusion processes limit the wide application of such carriers [278,279].
Membrane-coated vectors are particularly suited for immunosuppressive tumors, such as pancreatic and breast cancers, where extended circulation and immune evasion are essential for effective delivery [280]. Nevertheless, the heterogeneity of membrane sources and the risk of incorporating pro-tumoral signals present significant safety and reproducibility challenges. To advance the application of membrane-derived vectors, future work should prioritize (1) the selection of membrane sources with natural tumor-homing ability for targeting needs, (2) the rigorous assessment of the risk of membrane-mediated immunosuppression, and (3) the development of high-throughput and controllable platforms for the preparation of biofilm-derived vectors.

3.4. Plant Virus-Derived Nanoparticles for Cancer Vaccine Delivery

Virus-like particles (VLPs) are multimeric protein assemblies that structurally mimic native viruses or bacteriophages yet lack viral genetic material, rendering them non-infectious [281]. The hollow capsid architecture of VLPs enables encapsulation of diverse cargos, including nucleic acids, peptides, and proteins, making them attractive candidates for precision delivery in cancer vaccine platforms. Their intrinsic immunogenicity further supports their role as self-adjuvanted carriers [282,283,284]. First identified by Baruch S. Blumberg in the 1960s, VLPs have since been exploited in the development of prophylactic vaccines against oncogenic viruses [285].
Among the VLP platforms, plant virus-derived VLPs—commonly referred to as plant virus nanoparticles (PVNPs)—have garnered considerable interest owing to their favorable safety profiles, low production costs, and structural stability [286,287]. Beyond serving as delivery vehicles, PVLPs and PVNPs can act as adjuvants to potentiate immune responses [288,289]. Commonly employed plant viruses include tobacco mosaic virus (TMV), potato virus X (PVX), and cowpea mosaic virus (CPMV), each with distinct structural and immunologic properties [290].
PVNPs exhibit a modular architecture conducive to genetic and surface engineering. For instance, covalent coupling of the immunostimulatory protein S100A9 to CPMV nanoparticles, as reported by Chung et al. in 2021, elicited both prophylactic and therapeutic responses in murine models of pulmonary metastatic melanoma and breast carcinoma [291]. Standard bioconjugation and click chemistry approaches have been employed to install targeting moieties, while surface passivation with inert polymers has been used to minimize nonspecific accumulation [292]. Genetic modifications—such as insertion, deletion, or substitution within the capsid protein—enable fine-tuning of PVNP tropism, although such alterations must preserve capsid self-assembly. In addition, physicochemical parameters, including particle size, surface charge, and hydrophilicity, substantially influence passive targeting behavior. PEGylation has been employed to extend systemic circulation time and enhance in vivo persistence [293,294,295].
While PVNPs offer a unique combination of safety, scalability, and tunable targeting, challenges remain. Capsid self-assembly is sensitive to genetic alterations, limiting the feasibility of aggressive genome engineering strategies. As a result, research has focused on external conjugation of targeting ligands [290]. Furthermore, the presence of plant-derived glycoproteins may pose immunogenicity concerns in human subjects, necessitating thorough clinical evaluation. Despite their relatively low manufacturing costs, progress in clinical translation has been slow, partially due to limited commercial engagement [295]. Given their self-adjuvanted properties and lymphoid trafficking capacity, PVNP-based vaccines promise to enhance immune responses effective in tumors with low baseline immunogenicity, such as pancreatic and ovarian cancers [289,296]. In addition, their favorable safety profile supports repeated administration, making them well-suited for tumor types requiring long-term immune surveillance [297,298]. Moving forward, leveraging plant-based expression platforms for scalable production, optimizing surface engineering for enhanced specificity, and integrating PVNPs into combination immunotherapeutic regimens may accelerate their adoption in the cancer vaccine field.

3.5. Bacterial Vectors

The unique immune stimulation, tumor localization ability, and pathogen-associated molecular patterns of bacteria provide promising platforms for cancer vaccine delivery [299,300,301,302,303,304,305]. Its genome is easy to engineer and easy to produce on a large scale—a major advantage for clinical translation. In 2024, Arpaia et al. engineered Escherichia coli Nissle 1917 to enhance its tropism to the TME and jointly transmit neoantigen-specific signals [306]. Nguyen’s group designed Salmonella strains carrying dual payloads that enhanced anti-tumor responses in mouse models [307].
In addition to Escherichia coli [308], various bacterial species have been evaluated for natural or engineered targeting capabilities. Lactobacillus and recombinant Bifidobacteria exploit mucosal adhesion for site-specific delivery [309,310]. Other bacteria, including Listeria monocytogenes [311] and photosynthetic bacteria, are also commonly used bacterial vectors [312,313]. In addition, bacterial extracellular vesicles have also become the target vector of choice because of their unique potential for intercellular signaling and immune activation [314,315,316,317].
Bacterial platforms have advantages over viral vectors in terms of operability, scale-up, and system safety, and engineered bacteria such as Salmonella and Listeria monocytogenes have been shown to have tumor-specific colonization and antigen delivery capabilities. However, some clinical challenges remain. Certain strains, such as Helicobacter pylori, may have an oncogenic risk under certain conditions, and the mechanisms behind this are unclear [318]. In addition, limited understanding of the mechanisms and poor patient compliance have hindered widespread clinical use [319]. Bacterial delivery platforms are particularly advantageous in hypoxic or necrotic tumor regions—such as pancreatic and colorectal cancers—where conventional vectors often fail to penetrate. Moreover, the intrinsic immunostimulatory properties of bacterial components render them well-suited for immunologically “cold” tumors [320,321]. Nevertheless, heterogeneity in colonization efficiency across tumor types and potential safety concerns necessitate rigorous strain selection and platform standardization.
In order to fully exploit the potential of bacterial vectors for therapeutic tumor vaccines, future efforts should focus on (1) eliminating oncogenic factors and improving vector predictability through genetic engineering; (2) developing programmable targeting strategies, such as the addition of specific ligands to the bacterial surface for tumor-specific localization; and (3) generating reliable clinical trial data to support the regulatory process. Among them, rational targeting strategies will help bacterial vector systems overcome current limitations and enhance clinical translation.

4. Perspective

The future of therapeutic cancer vaccines lies in multimodal vector delivery platforms that integrate spatial collaborative targeting, immune cell-specific binding, and payload release in response to the TME. For example, Chen et al. applied orthogonal cross-linking chemistry to design vectors that could target multiple immune cell subsets in the TME to improve efficacy [322]. Some of these combination strategies include combining vaccine vectors with immune checkpoint inhibitors and “Trojan horses”, in which specific membrane-coated nanoparticles are used to improve delivery precision while controlling toxicity [323,324].
Increasingly, emerging strategies incorporate multiple design features to integrate active targeting, passive stockpiling, and TME response into a unified system. Precise and long-lasting delivery in response to multiple signals. Triple-combination designs of linker vectors, immunostimulants, and checkpoint blockade are being evaluated for their potential for synergistic delivery and immune enhancement [325,326].
Advances in artificial intelligence and data-driven modeling are accelerating rational vector design. Artificial intelligence-driven modeling supports rapid screening of nanoparticle components, while machine learning facilitates target prediction and optimization of active targeting ligands. The integration of clinical data supports a stratified treatment approach, making the vector targeting strategy more specific and in line with precision treatment [327]. Together, these innovations have facilitated the development of targeting capabilities to advance personalized medicine.

Author Contributions

Q.H. participated in the conception and design of the review article and was responsible for funding acquisition. J.W. and J.L. were responsible for writing the manuscript and designing the figures. Y.Z. (Yuan Zhang), C.D., D.T., H.W. and Y.Z. (Yiyang Zheng) reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This review was supported by the State Key Laboratory of Drug Regulatory Sciences (research on the nonclinical efficacy evaluation study of precision therapeutic cancer vaccines with tumor neo-genic antigen mRNA binding liposome polymer nano-delivery vector, 2023SKLDRS0110; Study on key techniques for preclinical pharmacodynamic evaluation of tumor neoantigen mRNA therapeutic cancer vaccines, GJJS-2022-6-2). Discipline Construction Project of Institute for Chemical Drug Control (Exploration and Research on a Novel Composite Adjuvant Combining Aluminum Adjuvant and Toll-like Receptor Agonist, 2024HYZX09 and Research on the Immunogenicity Evaluation Method of Tumor Therapeutic mRNA Vaccines Delivered by Lipid Nanoparticles, 2024HYZX08).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2024, 74, 229–263. [Google Scholar] [CrossRef] [PubMed]
  2. Siegel, R.L.; Kratzer, T.B.; Giaquinto, A.N.; Sung, H.; Jemal, A. Cancer Statistics, 2025. CA Cancer J. Clin. 2025, 75, 10–45. [Google Scholar] [CrossRef] [PubMed]
  3. Vergati, M.; Intrivici, C.; Huen, N.-Y.; Schlom, J.; Tsang, K.Y. Strategies for Cancer Vaccine Development. J. Biomed. Biotechnol. 2010, 2010, 596432. [Google Scholar] [CrossRef] [PubMed]
  4. Arruebo, M.; Vilaboa, N.; Sáez-Gutierrez, B.; Lambea, J.; Tres, A.; Valladares, M.; González-Fernández, Á. Assessment of the Evolution of Cancer Treatment Therapies. Cancers 2011, 3, 3279–3330. [Google Scholar] [CrossRef] [PubMed]
  5. Moses, M.A.; Brem, H.; Langer, R. Advancing the Field of Drug Delivery: Taking Aim at Cancer. Cancer Cell 2003, 4, 337–341. [Google Scholar] [CrossRef] [PubMed]
  6. The History of Cancer. First Cancer Diagnosis. Available online: https://www.cancer.org/cancer/understanding-cancer/history-of-cancer.html (accessed on 4 July 2025).
  7. Coley, W.B. Contribution to the Knowledge of Sarcoma. Ann. Surg. 1891, 14, 199–220. [Google Scholar] [CrossRef] [PubMed]
  8. Devaraja, K.; Singh, M.; Sharan, K.; Aggarwal, S. Coley’s Toxin to First Approved Therapeutic Vaccine—A Brief Historical Account in the Progression of Immunobiology-Based Cancer Treatment. Biomedicines 2024, 12, 2746. [Google Scholar] [CrossRef] [PubMed]
  9. Kantoff, P.W.; Higano, C.S.; Shore, N.D.; Berger, E.R.; Small, E.J.; Penson, D.F.; Redfern, C.H.; Ferrari, A.C.; Dreicer, R.; Sims, R.B.; et al. Sipuleucel-T Immunotherapy for Castration-Resistant Prostate Cancer. N. Engl. J. Med. 2010, 363, 411–422. [Google Scholar] [CrossRef] [PubMed]
  10. Sellars, M.C.; Wu, C.J.; Fritsch, E.F. Cancer Vaccines: Building a Bridge over Troubled Waters. Cell 2022, 185, 2770–2788. [Google Scholar] [CrossRef] [PubMed]
  11. Nguyen, M.H.; Wong, G.; Gane, E.; Kao, J.-H.; Dusheiko, G. Hepatitis B Virus: Advances in Prevention, Diagnosis, and Therapy. Clin. Microbiol. Rev. 2020, 33, e00046-19. [Google Scholar] [CrossRef] [PubMed]
  12. Garbuglia, A.R.; Lapa, D.; Sias, C.; Capobianchi, M.R.; Del Porto, P. The Use of Both Therapeutic and Prophylactic Vaccines in the Therapy of Papillomavirus Disease. Front. Immunol. 2020, 11, 188. [Google Scholar] [CrossRef] [PubMed]
  13. Aurisicchio, L.; Ciliberto, G. Genetic Cancer Vaccines: Current Status and Perspectives. Expert Opin. Biol. Ther. 2012, 12, 1043–1058. [Google Scholar] [CrossRef] [PubMed]
  14. Igarashi, Y.; Sasada, T. Cancer Vaccines: Toward the next Breakthrough in Cancer Immunotherapy. J. Immunol. Res. 2020, 2020, 5825401. [Google Scholar] [CrossRef] [PubMed]
  15. Jorritsma, S.H.T.; Gowans, E.J.; Grubor-Bauk, B.; Wijesundara, D.K. Delivery Methods to Increase Cellular Uptake and Immunogenicity of DNA Vaccines. Vaccine 2016, 34, 5488–5494. [Google Scholar] [CrossRef] [PubMed]
  16. Hollingsworth, R.E.; Jansen, K. Turning the Corner on Therapeutic Cancer Vaccines. npj Vaccines 2019, 4, 7. [Google Scholar] [CrossRef] [PubMed]
  17. Zhou, Y.; Wei, Y.; Tian, X.; Wei, X. Cancer Vaccines: Current Status and Future Directions. J. Hematol. Oncol. J Hematol. Oncol. 2025, 18, 18. [Google Scholar] [CrossRef] [PubMed]
  18. He, Q.; Gao, H.; Tan, D.; Zhang, H.; Wang, J. mRNA Cancer Vaccines: Advances, Trends and Challenges. Acta Pharm. Sin. B 2022, 12, 2969–2989. [Google Scholar] [CrossRef]
  19. Saxena, M.; van der Burg, S.H.; Melief, C.J.M.; Bhardwaj, N. Therapeutic Cancer Vaccines. Nat. Rev. Cancer 2021, 21, 360–378. [Google Scholar] [CrossRef] [PubMed]
  20. Fan, T.; Zhang, M.; Yang, J.; Zhu, Z.; Cao, W.; Dong, C. Therapeutic Cancer Vaccines: Advancements, Challenges and Prospects. Signal Transduct. Target. Ther. 2023, 8, 450. [Google Scholar] [CrossRef]
  21. Pounraj, S.; Chen, S.; Ma, L.; Mazzieri, R.; Dolcetti, R.; Rehm, B.H.A. Targeting Tumor Heterogeneity with Neoantigen-Based Cancer Vaccines. Cancer Res. 2024, 84, 353–363. [Google Scholar] [CrossRef] [PubMed]
  22. Hegde, P.S.; Chen, D.S. Top 10 Challenges in Cancer Immunotherapy. Immunity 2020, 52, 17–35. [Google Scholar] [CrossRef] [PubMed]
  23. Kutzler, M.A.; Weiner, D.B. DNA Vaccines: Ready for Prime Time? Nat. Rev. Genet. 2008, 9, 776–788. [Google Scholar] [CrossRef] [PubMed]
  24. Liu, C.; Shi, Q.; Huang, X.; Koo, S.; Kong, N.; Tao, W. mRNA-Based Cancer Therapeutics. Nat. Rev. Cancer 2023, 23, 526–543. [Google Scholar] [CrossRef] [PubMed]
  25. Sasso, E.; D’Alise, A.M.; Zambrano, N.; Scarselli, E.; Folgori, A.; Nicosia, A. New Viral Vectors for Infectious Diseases and Cancer. Chall. Vaccinol. 2020, 50, 101430. [Google Scholar] [CrossRef] [PubMed]
  26. Schlom, J. Therapeutic Cancer Vaccines: Current Status and Moving Forward. JNCI J. Natl. Cancer Inst. 2012, 104, 599–613. [Google Scholar] [CrossRef] [PubMed]
  27. Scarsella, L.; Ehrke-Schulz, E.; Paulussen, M.; Thal, S.C.; Ehrhardt, A.; Aydin, M. Advances of Recombinant Adenoviral Vectors in Preclinical and Clinical Applications. Viruses 2024, 16, 377. [Google Scholar] [CrossRef] [PubMed]
  28. Nadeau, I.; Kamen, A. Production of Adenovirus Vector for Gene Therapy. Biotechnol. Adv. 2003, 20, 475–489. [Google Scholar] [CrossRef] [PubMed]
  29. Sato-Dahlman, M.; LaRocca, C.J.; Yanagiba, C.; Yamamoto, M. Adenovirus and Immunotherapy: Advancing Cancer Treatment by Combination. Cancers 2020, 12, 1295. [Google Scholar] [CrossRef] [PubMed]
  30. Majhen, D.; Calderon, H.; Chandra, N.; Fajardo, C.A.; Rajan, A.; Alemany, R.; Custers, J. Adenovirus-Based Vaccines for Fighting Infectious Diseases and Cancer: Progress in the Field. Hum. Gene Ther. 2014, 25, 301–317. [Google Scholar] [CrossRef] [PubMed]
  31. Hofmann, C.; Löser, P.; Cichon, G.; Arnold, W.; Both, G.W.; Strauss, M. Ovine Adenovirus Vectors Overcome Preexisting Humoral Immunity against Human Adenoviruses In Vivo. J. Virol. 1999, 73, 6930–6936. [Google Scholar] [CrossRef] [PubMed]
  32. Roberts, D.M.; Nanda, A.; Havenga, M.J.E.; Abbink, P.; Lynch, D.M.; Ewald, B.A.; Liu, J.; Thorner, A.R.; Swanson, P.E.; Gorgone, D.A.; et al. Hexon-Chimaeric Adenovirus Serotype 5 Vectors Circumvent Pre-Existing Anti-Vector Immunity. Nature 2006, 441, 239–243. [Google Scholar] [CrossRef] [PubMed]
  33. O’Riordan, C.R.; Lachapelle, A.; Delgado, C.; Parkes, V.; Wadsworth, S.C.; Smith, A.E.; Francis, G.E. PEGylation of Adenovirus with Retention of Infectivity and Protection from Neutralizing Antibody in Vitro and in Vivo. Hum. Gene Ther. 1999, 10, 1349–1358. [Google Scholar] [CrossRef] [PubMed]
  34. Lu, Z.H.; Dmitriev, I.P.; Brough, D.E.; Kashentseva, E.A.; Li, J.; Curiel, D.T. A New Gorilla Adenoviral Vector with Natural Lung Tropism Avoids Liver Toxicity and Is Amenable to Capsid Engineering and Vector Retargeting. J. Virol. 2020, 94, e00265-20. [Google Scholar] [CrossRef] [PubMed]
  35. Watanabe, M.; Nishikawaji, Y.; Kawakami, H.; Kosai, K. Adenovirus Biology, Recombinant Adenovirus, and Adenovirus Usage in Gene Therapy. Viruses 2021, 13, 2502. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, Z.; Liu, W.; Wang, L.; Gao, P.; Li, Z.; Wu, J.; Zhang, H.; Wu, H.; Kong, W.; Yu, B.; et al. Enhancing the Antitumor Activity of an Engineered TRAIL-Coated Oncolytic Adenovirus for Treating Acute Myeloid Leukemia. Signal Transduct. Target. Ther. 2020, 5, 40. [Google Scholar] [CrossRef]
  37. Wienen, F.; Nilson, R.; Allmendinger, E.; Graumann, D.; Fiedler, E.; Bosse-Doenecke, E.; Kochanek, S.; Krutzke, L. Affilin-Based Retargeting of Adenoviral Vectors to the Epidermal Growth Factor Receptor. Biomater. Adv. 2023, 144, 213208. [Google Scholar] [CrossRef] [PubMed]
  38. Yoon, A.-R.; Hong, J.; Li, Y.; Shin, H.C.; Lee, H.; Kim, H.S.; Yun, C.-O. Mesenchymal Stem Cell–Mediated Delivery of an Oncolytic Adenovirus Enhances Antitumor Efficacy in Hepatocellular Carcinoma. Cancer Res. 2019, 79, 4503–4514. [Google Scholar] [CrossRef] [PubMed]
  39. Sun, Y.; Zou, X.; Guo, X.; Yang, C.; Hung, T.; Lu, Z. CELO Fiber1 Knob Is a Promising Candidate to Modify the Tropism of Adenoviral Vectors. Genes 2022, 13, 2316. [Google Scholar] [CrossRef] [PubMed]
  40. Chen, Y.; Wang, J.; Huang, Y.; Wu, J.; Wang, Y.; Chen, A.; Guo, Q.; Zhang, Y.; Zhang, S.; Wang, L.; et al. An Oncolytic System Produces Oxygen Selectively in Pancreatic Tumor Cells to Alleviate Hypoxia and Improve Immune Activation. Pharmacol. Res. 2024, 199, 107053. [Google Scholar] [CrossRef] [PubMed]
  41. Su, Y.; Li, J.; Ji, W.; Wang, G.; Fang, L.; Zhang, Q.; Ang, L.; Zhao, M.; Sen, Y.; Chen, L.; et al. Triple-Serotype Chimeric Oncolytic Adenovirus Exerts Multiple Synergistic Mechanisms against Solid Tumors. J. Immunother. Cancer 2022, 10, e004691. [Google Scholar] [CrossRef] [PubMed]
  42. Freedman, J.D.; Duffy, M.R.; Lei-Rossmann, J.; Muntzer, A.; Scott, E.M.; Hagel, J.; Campo, L.; Bryant, R.J.; Verrill, C.; Lambert, A.; et al. An Oncolytic Virus Expressing a T-Cell Engager Simultaneously Targets Cancer and Immunosuppressive Stromal Cells. Cancer Res. 2018, 78, 6852–6865. [Google Scholar] [CrossRef] [PubMed]
  43. Kuryk, L.; Møller, A.W.; Garofalo, M.; Cerullo, V.; Pesonen, S.; Alemany, R.; Jaderberg, M. Antitumor-specific T-cell Responses Induced by Oncolytic Adenovirus ONCOS-102 (AdV5/3-D24-GM-CSF) in Peritoneal Mesothelioma Mouse Model. J. Med. Virol. 2018, 90, 1669–1673. [Google Scholar] [CrossRef] [PubMed]
  44. Zhang, W.; Zhang, J.; Zhang, J.; Chu, J.; Zhang, Z. Novel Combination Therapy Using Recombinant Oncolytic Adenovirus Silk Hydrogel and PD-L1 Inhibitor for Bladder Cancer Treatment. J. Nanobiotechnol. 2024, 22, 638. [Google Scholar] [CrossRef] [PubMed]
  45. Huang, C.-H.; Dong, T.; Phung, A.T.; Shah, J.R.; Larson, C.; Sanchez, A.B.; Blair, S.L.; Oronsky, B.; Trogler, W.C.; Reid, T.; et al. Full Remission of CAR-Deficient Tumors by DOTAP-Folate Liposome Encapsulation of Adenovirus. ACS Biomater. Sci. Eng. 2022, 8, 5199–5209. [Google Scholar] [CrossRef]
  46. Qiao, H.; Chen, X.; Wang, Q.; Zhang, J.; Huang, D.; Chen, E.; Qian, H.; Zhong, Y.; Tang, Q.; Chen, W. Tumor Localization of Oncolytic Adenovirus Assisted by pH-Degradable Microgels with JQ1-Mediated Boosting Replication and PD-L1 Suppression for Enhanced Cancer Therapy. Biomater. Sci. 2020, 8, 2472–2480. [Google Scholar] [CrossRef] [PubMed]
  47. Wu, M.; Li, H.; Zhang, C.; Wang, Y.; Zhang, C.; Zhang, Y.; Zhong, A.; Zhang, D.; Liu, X. Silk-Gel Powered Adenoviral Vector Enables Robust Genome Editing of PD-L1 to Augment Immunotherapy across Multiple Tumor Models. Adv. Sci. Weinh. Baden-Wurtt. Ger. 2023, 10, e2206399. [Google Scholar] [CrossRef] [PubMed]
  48. Chekaoui, A.; Garofalo, M.; Gad, B.; Staniszewska, M.; Chiaro, J.; Pancer, K.; Gryciuk, A.; Cerullo, V.; Salmaso, S.; Caliceti, P.; et al. Cancer Vaccines: An Update on Recent Achievements and Prospects for Cancer Therapy. Clin. Exp. Med. 2024, 25, 24. [Google Scholar] [CrossRef] [PubMed]
  49. Yang, K.; Feng, S.; Luo, Z. Oncolytic Adenovirus, a New Treatment Strategy for Prostate Cancer. Biomedicines 2022, 10, 3262. [Google Scholar] [CrossRef] [PubMed]
  50. Wu, Z.; Asokan, A.; Samulski, R.J. Adeno-Associated Virus Serotypes: Vector Toolkit for Human Gene Therapy. Mol. Ther. 2006, 14, 316–327. [Google Scholar] [CrossRef] [PubMed]
  51. Atchison, R.W.; Casto, B.C.; Hammon, W.M. Electron Microscopy of Adenovirus-Associated Virus (AAV) in Cell Cultures. Virology 1966, 29, 353–357. [Google Scholar] [CrossRef] [PubMed]
  52. Grimm, D.; Kay, M. From Virus Evolution to Vector Revolution: Use of Naturally Occurring Serotypes of Adeno-Associated Virus (AAV) as Novel Vectors for Human Gene Therapy. Curr. Gene Ther. 2003, 3, 281–304. [Google Scholar] [CrossRef] [PubMed]
  53. Basar, E.; Mead, H.; Shum, B.; Rauter, I.; Ay, C.; Skaletz-Rorowski, A.; Brockmeyer, N.H. Biological Barriers for Drug Delivery and Development of Innovative Therapeutic Approaches in HIV, Pancreatic Cancer, and Hemophilia a/B. Pharmaceutics 2024, 16, 1207. [Google Scholar] [CrossRef] [PubMed]
  54. Li, C.; Samulski, R.J. Engineering Adeno-Associated Virus Vectors for Gene Therapy. Nat. Rev. Genet. 2020, 21, 255–272. [Google Scholar] [CrossRef] [PubMed]
  55. Samulski, R.J.; Chang, L.S.; Shenk, T. A Recombinant Plasmid from Which an Infectious Adeno-Associated Virus Genome Can Be Excised in Vitro and Its Use to Study Viral Replication. J. Virol. 1987, 61, 3096–3101. [Google Scholar] [CrossRef] [PubMed]
  56. Keeler, A.M.; Flotte, T.R. Recombinant Adeno-Associated Virus Gene Therapy in Light of Luxturna (and Zolgensma and Glybera): Where Are We, and How Did We Get Here? Annu. Rev. Virol. 2019, 6, 601–621. [Google Scholar] [CrossRef] [PubMed]
  57. McCarty, D.M.; Young, S.M.; Samulski, R.J. Integration of Adeno-Associated Virus (AAV) and Recombinant AAV Vectors. Annu. Rev. Genet. 2004, 38, 819–845. [Google Scholar] [CrossRef] [PubMed]
  58. Dagotto, G.; Fisher, J.L.; Li, D.; Li, Z.; Jenni, S.; Li, Z.; Tartaglia, L.J.; Abbink, P.; Barouch, D.H. Identification of a Novel Neutralization Epitope in Rhesus AAVs. Mol. Ther. Methods Clin. Dev. 2024, 32, 101350. [Google Scholar] [CrossRef] [PubMed]
  59. Huang, K.C.-Y.; Lai, C.-Y.; Hung, W.-Z.; Chang, H.-Y.; Lin, P.-C.; Chiang, S.-F.; Ke, T.-W.; Liang, J.-A.; Shiau, A.-C.; Yang, P.-C.; et al. A Novel Engineered AAV-Based Neoantigen Vaccine in Combination with Radiotherapy Eradicates Tumors. Cancer Immunol. Res. 2023, 11, 123–136. [Google Scholar] [CrossRef] [PubMed]
  60. Krotova, K.; Day, A.; Aslanidi, G. An Engineered AAV6-Based Vaccine Induces High Cytolytic Anti-Tumor Activity by Directly Targeting DCs and Improves Ag Presentation. Mol. Ther. Oncolytics 2019, 15, 166–177. [Google Scholar] [CrossRef] [PubMed]
  61. Franke, A.-C.; Hardet, R.; Prager, L.; Bentler, M.; Demeules, M.; John-Neek, P.; Jäschke, N.M.; Ha, T.C.; Hacker, U.T.; Adriouch, S.; et al. Capsid-Modified Adeno-Associated Virus Vectors as Novel Vaccine Platform for Cancer Immunotherapy. Mol. Ther. Methods Clin. Dev. 2023, 29, 238–253. [Google Scholar] [CrossRef] [PubMed]
  62. Olarewaju, O.; Held, F.; Curtis, P.; Kenny, C.H.; Maier, U.; Panavas, T.; du Plessis, F. αFAP-Specific Nanobodies Mediate a Highly Precise Retargeting of Modified AAV2 Capsids Thereby Enabling Specific Transduction of Tumor Tissues. Mol. Ther. Methods Clin. Dev. 2024, 32, 101378. [Google Scholar] [CrossRef] [PubMed]
  63. Strecker, M.I.; Wlotzka, K.; Strassheimer, F.; Roller, B.; Ludmirski, G.; König, S.; Röder, J.; Opitz, C.; Alekseeva, T.; Reul, J.; et al. AAV-Mediated Gene Transfer of a Checkpoint Inhibitor in Combination with HER2-Targeted CAR-NK Cells as Experimental Therapy for Glioblastoma. Oncoimmunology 2022, 11, 2127508. [Google Scholar] [CrossRef] [PubMed]
  64. Martino, R.A.; Edwin C Fluck, I.I.I.; Murphy, J.; Wang, Q.; Hoff, H.; Pumroy, R.A.; Lee, C.Y.; Sims, J.J.; Roy, S.; Moiseenkova-Bell, V.Y.; et al. Context-Specific Function of the Engineered Peptide Domain of PHP.B. J. Virol. 2021, 95, e01164. [Google Scholar] [CrossRef] [PubMed]
  65. Krotova, K.; Kuoch (Yoshitomi), H.; Caine, C.; Aslanidi, G. Tumor Antigen-Loaded AAV Vaccine Drives Protective Immunity in a Melanoma Animal Model. Mol. Ther. Methods Clin. Dev. 2023, 28, 301–311. [Google Scholar] [CrossRef] [PubMed]
  66. Mulcrone, P.L.; Herzog, R.W.; Xiao, W. Adding Recombinant AAVs to the Cancer Therapeutics Mix. Mol. Ther. Oncolytics 2022, 27, 73–88. [Google Scholar] [CrossRef] [PubMed]
  67. Santiago-Ortiz, J.L.; Schaffer, D.V. Adeno-Associated Virus (AAV) Vectors in Cancer Gene Therapy. J. Control. Release Off. J. Control. Release Soc. 2016, 240, 287–301. [Google Scholar] [CrossRef] [PubMed]
  68. Hensel, J.A.; Khattar, V.; Ashton, R.; Ponnazhagan, S. Recombinant AAV-CEA Tumor Vaccine in Combination with an Immune Adjuvant Breaks Tolerance and Provides Protective Immunity. Mol. Ther. Oncolytics 2018, 12, 41–48. [Google Scholar] [CrossRef] [PubMed]
  69. Flickinger, J.C., Jr.; Singh, J.; Carlson, R.; Leong, E.; Baybutt, T.R.; Barton, J.; Caparosa, E.; Pattison, A.; Rappaport, J.A.; Roh, J.; et al. Chimeric Ad5.F35 Vector Evades Anti-Adenovirus Serotype 5 Neutralization Opposing GUCY2C-Targeted Antitumor Immunity. J. Immunother. Cancer 2020, 8, e001046. [Google Scholar] [CrossRef] [PubMed]
  70. Daradoumis, J.; Ragonnaud, E.; Skandorff, I.; Nielsen, K.N.; Bermejo, A.V.; Andersson, A.-M.; Schroedel, S.; Thirion, C.; Neukirch, L.; Holst, P.J. An Endogenous Retrovirus Vaccine Encoding an Envelope with a Mutated Immunosuppressive Domain in Combination with Anti-PD1 Treatment Eradicates Established Tumours in Mice. Viruses 2023, 15, 926. [Google Scholar] [CrossRef] [PubMed]
  71. Rosewell Shaw, A.; Porter, C.; Biegert, G.; Jatta, L.; Suzuki, M. HydrAd: A Helper-Dependent Adenovirus Targeting Multiple Immune Pathways for Cancer Immunotherapy. Cancers 2022, 14, 2769. [Google Scholar] [CrossRef] [PubMed]
  72. Palmer, C.D.; Rappaport, A.R.; Davis, M.J.; Hart, M.G.; Scallan, C.D.; Hong, S.-J.; Gitlin, L.; Kraemer, L.D.; Kounlavouth, S.; Yang, A.; et al. Individualized, Heterologous Chimpanzee Adenovirus and Self-Amplifying mRNA Neoantigen Vaccine for Advanced Metastatic Solid Tumors: Phase 1 Trial Interim Results. Nat. Med. 2022, 28, 1619–1629. [Google Scholar] [CrossRef] [PubMed]
  73. Molina, E.; Tejero, M.; Duzenli, O.F.; Kuoch, H.; Caine, C.; Krotova, K.; Paulaitis, M.; Aslanidi, G. Insights in AAV-Mediated Antigen-Specific Immunity and a Strategy for AAV Vaccine Dose Reduction through AAV-Extracellular Vesicle Association. Mol. Ther. Methods Clin. Dev. 2024, 32, 101358. [Google Scholar] [CrossRef] [PubMed]
  74. Wang, J.; Guo, C.; Wang, X.-Y.; Yang, H. “Double-Punch” Strategy for Delivery of Viral Immunotherapy with Prolonged Tumor Retention and Enhanced Transfection Efficacy. J. Control. Release Off. J. Control. Release Soc. 2021, 329, 328–336. [Google Scholar] [CrossRef] [PubMed]
  75. Mathlouthi, S.; Kuryk, L.; Prygiel, M.; Lupo, M.G.; Zasada, A.A.; Pesce, C.; Ferri, N.; Rinner, B.; Salmaso, S.; Garofalo, M. Extracellular Vesicles Powered Cancer Immunotherapy: Targeted Delivery of Adenovirus-Based Cancer Vaccine in Humanized Melanoma Model. J. Control. Release 2024, 376, 777–793. [Google Scholar] [CrossRef] [PubMed]
  76. Tan, Z.; Chiu, M.S.; Yan, C.W.; Man, K.; Chen, Z. Eliminating Mesothelioma by AAV-Vectored, PD1-Based Vaccination in the Tumor Microenvironment. Mol. Ther. Oncolytics 2021, 20, 373–386. [Google Scholar] [CrossRef] [PubMed]
  77. Lewis-Jones, S. Zoonotic Poxvirus Infections in Humans. Curr. Opin. Infect. Dis. 2004, 17, 81–89. [Google Scholar] [CrossRef] [PubMed]
  78. Moss, B. Poxviridae. In Fields Virology; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2013. [Google Scholar]
  79. Volz, A.; Sutter, G. Chapter Five—Modified Vaccinia Virus Ankara: History, Value in Basic Research, and Current Perspectives for Vaccine Development. In Advances in Virus Research; Kielian, M., Mettenleiter, T.C., Roossinck, M.J., Eds.; Academic Press: Cambridge, MA, USA, 2017; Volume 97, pp. 187–243. [Google Scholar]
  80. Liu, M.A. Immunologic Basis of Vaccine Vectors. Immunity 2010, 33, 504–515. [Google Scholar] [CrossRef] [PubMed]
  81. McFadden, G. Poxvirus Tropism. Nat. Rev. Microbiol. 2005, 3, 201–213. [Google Scholar] [CrossRef] [PubMed]
  82. de Vries, C.R.; Monken, C.E.; Lattime, E.C. The Addition of Recombinant Vaccinia HER2/Neu to Oncolytic Vaccinia-GMCSF given into the Tumor Microenvironment Overcomes MDSC-Mediated Immune Escape and Systemic Anergy. Cancer Gene Ther. 2015, 22, 154–162. [Google Scholar] [CrossRef] [PubMed]
  83. Ylösmäki, E.; Malorzo, C.; Capasso, C.; Honkasalo, O.; Fusciello, M.; Martins, B.; Ylösmäki, L.; Louna, A.; Feola, S.; Paavilainen, H.; et al. Personalized Cancer Vaccine Platform for Clinically Relevant Oncolytic Enveloped Viruses. Mol. Ther. 2018, 26, 2315–2325. [Google Scholar] [CrossRef] [PubMed]
  84. Hill, C.; Grundy, M.; Bau, L.; Wallington, S.; Balkaran, J.; Ramos, V.; Fisher, K.; Seymour, L.; Coussios, C.; Carlisle, R. Polymer Stealthing and Mucin-1 Retargeting for Enhanced Pharmacokinetics of an Oncolytic Vaccinia Virus. Mol. Ther. Oncolytics 2021, 21, 47–61. [Google Scholar] [CrossRef] [PubMed]
  85. Lv, C.; Su, Q.; Liang, Y.; Hu, J.; Yuan, S. Oncolytic Vaccine Virus Harbouring the IL-24 Gene Suppresses the Growth of Lung Cancer by Inducing Apoptosis. Biochem. Biophys. Res. Commun. 2016, 476, 21–28. [Google Scholar] [CrossRef] [PubMed]
  86. Xuan, Y.; Yan, W.; Wang, R.; Wang, X.; Guo, Y.; Dun, H.; Huan, Z.; Xu, L.; Han, R.; Sun, X.; et al. GM-CSF and IL-21-Armed Oncolytic Vaccinia Virus Significantly Enhances Anti-Tumor Activity and Synergizes with Anti-PD1 Immunotherapy in Pancreatic Cancer. Front. Immunol. 2025, 15, 1506632. [Google Scholar] [CrossRef] [PubMed]
  87. White, M.; Freistaedter, A.; Jones, G.J.B.; Zervos, E.; Roper, R.L. Development of Improved Therapeutic Mesothelin-Based Vaccines for Pancreatic Cancer. PLoS ONE 2018, 13, e0193131. [Google Scholar] [CrossRef] [PubMed]
  88. Kochneva, G.; Sivolobova, G.; Tkacheva, A.; Grazhdantseva, A.; Troitskaya, O.; Nushtaeva, A.; Tkachenko, A.; Kuligina, E.; Richter, V.; Koval, O. Engineering of Double Recombinant Vaccinia Virus with Enhanced Oncolytic Potential for Solid Tumor Virotherapy. Oncotarget 2016, 7, 74171–74188. [Google Scholar] [CrossRef] [PubMed]
  89. Cao, F.; Nguyen, P.; Hong, B.; DeRenzo, C.; Rainusso, N.C.; Rodriguez Cruz, T.; Wu, M.-F.; Liu, H.; Song, X.-T.; Suzuki, M.; et al. Engineering Oncolytic Vaccinia Virus to Redirect Macrophages to Tumor Cells. Adv. Cell Gene Ther. 2021, 4, e99. [Google Scholar] [CrossRef] [PubMed]
  90. Guo, Z.S.; Lu, B.; Guo, Z.; Giehl, E.; Feist, M.; Dai, E.; Liu, W.; Storkus, W.J.; He, Y.; Liu, Z.; et al. Vaccinia Virus-Mediated Cancer Immunotherapy: Cancer Vaccines and Oncolytics. J. Immunother. Cancer 2019, 7, 6. [Google Scholar] [CrossRef] [PubMed]
  91. Roy, D.G.; Geoffroy, K.; Marguerie, M.; Khan, S.T.; Martin, N.T.; Kmiecik, J.; Bobbala, D.; Aitken, A.S.; de Souza, C.T.; Stephenson, K.B.; et al. Adjuvant Oncolytic Virotherapy for Personalized Anti-Cancer Vaccination. Nat. Commun. 2021, 12, 2626. [Google Scholar] [CrossRef] [PubMed]
  92. Ricordel, M.; Foloppe, J.; Antoine, D.; Findeli, A.; Kempf, J.; Cordier, P.; Gerbaud, A.; Grellier, B.; Lusky, M.; Quemeneur, E.; et al. Vaccinia Virus Shuffling: deVV5, a Novel Chimeric Poxvirus with Improved Oncolytic Potency. Cancers 2018, 10, 231. [Google Scholar] [CrossRef] [PubMed]
  93. Draganov, D.D.; Santidrian, A.F.; Minev, I.; Nguyen, D.; Kilinc, M.O.; Petrov, I.; Vyalkova, A.; Lander, E.; Berman, M.; Minev, B.; et al. Delivery of Oncolytic Vaccinia Virus by Matched Allogeneic Stem Cells Overcomes Critical Innate and Adaptive Immune Barriers. J. Transl. Med. 2019, 17, 100. [Google Scholar] [CrossRef] [PubMed]
  94. Mirbahari, S.N.; Silva, M.D.; Zúñiga, A.I.M.; Zamani, N.K.; St-Laurent, G.; Totonchi, M.; Azad, T. Recent Progress in Combination Therapy of Oncolytic Vaccinia Virus. Front. Immunol. 2024, 15, 1272351. [Google Scholar] [CrossRef] [PubMed]
  95. Amato, R.J.; Hawkins, R.E.; Kaufman, H.L.; Thompson, J.A.; Tomczak, P.; Szczylik, C.; McDonald, M.; Eastty, S.; Shingler, W.H.; de Belin, J.; et al. Vaccination of Metastatic Renal Cancer Patients with MVA-5T4: A Randomized, Double-Blind, Placebo-Controlled Phase III Study. Clin. Cancer Res. 2010, 16, 5539–5547. [Google Scholar] [CrossRef] [PubMed]
  96. Singh, P.; Pal, S.K.; Alex, A.; Agarwal, N. Development of PROSTVAC Immunotherapy in Prostate Cancer. Future Oncol. 2015, 11, 2137–2148. [Google Scholar] [CrossRef] [PubMed]
  97. Cattaneo, R.; Miest, T.; Shashkova, E.V.; Barry, M.A. Reprogrammed Viruses as Cancer Therapeutics: Targeted, Armed and Shielded. Nat. Rev. Microbiol. 2008, 6, 529–540. [Google Scholar] [CrossRef] [PubMed]
  98. Liu, Z.; Lu, Z.; Jing, R.; Zuo, B.; Gao, X.; Han, G.; Qi, H.; Wu, L.; Liu, Y.; Yin, H. Alarmin Augments the Antitumor Immunity of Lentiviral Vaccine in Ectopic, Orthotopic and Autochthonous Hepatocellular Carcinoma Mice. Theranostics 2019, 9, 4006–4018. [Google Scholar] [CrossRef] [PubMed]
  99. Tada, T.; Norton, T.D.; Leibowitz, R.; Landau, N.R. Checkpoint Inhibitor-Expressing Lentiviral Vaccine Suppresses Tumor Growth in Preclinical Cancer Models. J. Immunother. Cancer 2024, 12, e008761. [Google Scholar] [CrossRef] [PubMed]
  100. Tian, C.; Liu, J.; Zhou, H.; Li, J.; Sun, C.; Zhu, W.; Yin, Y.; Li, X. Enhanced Anti-Tumor Response Elicited by a Novel Oncolytic HSV-1 Engineered with an Anti-PD-1 Antibody. Cancer Lett. 2021, 518, 49–58. [Google Scholar] [CrossRef] [PubMed]
  101. Qiu, Z.; Huang, H.; Grenier, J.M.; Perez, O.A.; Smilowitz, H.M.; Adler, B.; Khanna, K.M. Cytomegalovirus-Based Vaccine Expressing a Modified Tumor Antigen Induces Potent Tumor-Specific CD8(+) T-Cell Response and Protects Mice from Melanoma. Cancer Immunol. Res. 2015, 3, 536–546. [Google Scholar] [CrossRef] [PubMed]
  102. Gui, M.; Wu, C.; Qi, R.; Zeng, Y.; Huang, P.; Cao, J.; Chen, T.; Chen, K.; Lin, L.; Han, Q.; et al. Swine Pseudorabies Virus Attenuated Vaccine Reprograms the Kidney Cancer Tumor Microenvironment and Synergizes with PD-1 Blockade. J. Med. Virol. 2024, 96, e29568. [Google Scholar] [CrossRef] [PubMed]
  103. Iyer, R.F.; Verweij, M.C.; Nair, S.S.; Morrow, D.; Mansouri, M.; Chakravarty, D.; Beechwood, T.; Meyer, C.; Uebelhoer, L.; Lauron, E.J.; et al. CD8+ T Cell Targeting of Tumor Antigens Presented by HLA-E. Sci. Adv. 2024, 10, eadm7515. [Google Scholar] [CrossRef] [PubMed]
  104. Wu, Y.; He, J.; An, Y.; Wang, X.; Liu, Y.; Yan, S.; Ye, X.; Qi, J.; Zhu, S.; Yu, Q.; et al. Recombinant Newcastle Disease Virus (NDV/Anh-IL-2) Expressing Human IL-2 as a Potential Candidate for Suppresses Growth of Hepatoma Therapy. J. Pharmacol. Sci. 2016, 132, 24–30. [Google Scholar] [CrossRef] [PubMed]
  105. Pliquet, E.; Ruffie, C.; Escande, M.; Thalmensi, J.; Najburg, V.; Combredet, C.; Bestetti, T.; Julithe, M.; Liard, C.; Huet, T.; et al. Strong Antigen-Specific T-Cell Immunity Induced by a Recombinant Human TERT Measles Virus Vaccine and Amplified by a DNA/Viral Vector Prime Boost in IFNAR/CD46 Mice. Cancer Immunol. Immunother. CII 2019, 68, 533–544. [Google Scholar] [CrossRef] [PubMed]
  106. Leoni, V.; Vannini, A.; Gatta, V.; Rambaldi, J.; Sanapo, M.; Barboni, C.; Zaghini, A.; Nanni, P.; Lollini, P.-L.; Casiraghi, C.; et al. A Fully-Virulent Retargeted Oncolytic HSV Armed with IL-12 Elicits Local Immunity and Vaccine Therapy towards Distant Tumors. PLoS Pathog. 2018, 14, e1007209. [Google Scholar] [CrossRef] [PubMed]
  107. Somaiah, N.; Block, M.S.; Kim, J.W.; Shapiro, G.I.; Do, K.T.; Hwu, P.; Eder, J.P.; Jones, R.L.; Lu, H.; ter Meulen, J.H.; et al. First-in-Class, First-in-Human Study Evaluating LV305, a Dendritic-Cell Tropic Lentiviral Vector, in Sarcoma and Other Solid Tumors Expressing NY-ESO-1. Clin. Cancer Res. 2019, 25, 5808–5817. [Google Scholar] [CrossRef] [PubMed]
  108. Šustić, M.; Cokarić Brdovčak, M.; Lisnić, B.; Materljan, J.; Juranić Lisnić, V.; Rožmanić, C.; Indenbirken, D.; Hiršl, L.; Busch, D.H.; Brizić, I.; et al. Memory CD8 T Cells Generated by Cytomegalovirus Vaccine Vector Expressing NKG2D Ligand Have Effector-like Phenotype and Distinct Functional Features. Front. Immunol. 2021, 12, 681380. [Google Scholar] [CrossRef] [PubMed]
  109. Bryson, P.D.; Han, X.; Truong, N.; Wang, P. Breast Cancer Vaccines Delivered by Dendritic Cell-Targeted Lentivectors Induce Potent Antitumor Immune Responses and Protect Mice from Mammary Tumor Growth. Vaccine 2017, 35, 5842–5849. [Google Scholar] [CrossRef] [PubMed]
  110. Barasa, A.K.; Ye, P.; Phelps, M.; Arivudainambi, G.T.; Tison, T.; Ogembo, J.G. BALB/c Mice Immunized with a Combination of Virus-like Particles Incorporating Kaposi Sarcoma-Associated Herpesvirus (KSHV) Envelope Glycoproteins gpK8.1, gB, and gH/gL Induced Comparable Serum Neutralizing Antibody Activity to UV-Inactivated KSHV. Oncotarget 2017, 8, 34481–34497. [Google Scholar] [CrossRef] [PubMed]
  111. Abdelaziz, M.O.; Ossmann, S.; Kaufmann, A.M.; Leitner, J.; Steinberger, P.; Willimsky, G.; Raftery, M.J.; Schönrich, G. Development of a Human Cytomegalovirus (HCMV)-Based Therapeutic Cancer Vaccine Uncovers a Previously Unsuspected Viral Block of MHC Class I Antigen Presentation. Front. Immunol. 2019, 10, 1776. [Google Scholar] [CrossRef] [PubMed]
  112. Ramshaw, I.A.; Ramsay, A.J. The prime-boost strategy: Exciting prospects for improved vaccination. Immunol. Today 2000, 21, 163–165. [Google Scholar] [CrossRef] [PubMed]
  113. Rühl, J.; Citterio, C.; Engelmann, C.; Haigh, T.; Dzionek, A.; Dreyer, J.; Khanna, R.; Taylor, G.S.; Wilson, J.B.; Leung, C.S.; et al. Heterologous Prime-Boost Vaccination Protects against EBV Antigen–Expressing Lymphomas. J. Clin. Investig. 2019, 129, 2071–2087. [Google Scholar] [CrossRef] [PubMed]
  114. Nguyen, T.T.; Quach, T.H.T.; Tran, T.M.; Phuoc, H.N.; Nguyen, H.T.; Vo, T.K.; Vo, G.V. Reactogenicity and Immunogenicity of Heterologous Prime-Boost Immunization with COVID-19 Vaccine. Biomed. Pharmacother. Biomed. Pharmacother. 2022, 147, 112650. [Google Scholar] [CrossRef] [PubMed]
  115. Jin, K.-T.; Du, W.-L.; Liu, Y.-Y.; Lan, H.-R.; Si, J.-X.; Mou, X.-Z. Oncolytic Virotherapy in Solid Tumors: The Challenges and Achievements. Cancers 2021, 13, 588. [Google Scholar] [CrossRef] [PubMed]
  116. Lin, D.; Shen, Y.; Liang, T. Oncolytic Virotherapy: Basic Principles, Recent Advances and Future Directions. Signal Transduct. Target. Ther. 2023, 8, 156. [Google Scholar] [CrossRef]
  117. Zhang, Y.; Fang, Z.; Li, R.; Huang, X.; Liu, Q. Design of Outer Membrane Vesicles as Cancer Vaccines: A New Toolkit for Cancer Therapy. Cancers 2019, 11, 1314. [Google Scholar] [CrossRef] [PubMed]
  118. Zhong, Y.; Du, S.; Dong, Y. mRNA Delivery in Cancer Immunotherapy. Acta Pharm. Sin. B 2023, 13, 1348–1357. [Google Scholar] [CrossRef] [PubMed]
  119. Tan, D.; Li, G.; Fu, W.; Lei, C. Exosomes: The next Frontier in Vaccine Development and Delivery. Front. Immunol. 2024, 15, 1435426. [Google Scholar] [CrossRef] [PubMed]
  120. Huang, T.; Peng, L.; Han, Y.; Wang, D.; He, X.; Wang, J.; Ou, C. Lipid Nanoparticle-Based mRNA Vaccines in Cancers: Current Advances and Future Prospects. Front. Immunol. 2022, 13, 922301. [Google Scholar] [CrossRef] [PubMed]
  121. Cheng, Z.; Li, M.; Dey, R.; Chen, Y. Nanomaterials for Cancer Therapy: Current Progress and Perspectives. J. Hematol. Oncol. J. Hematol. Oncol. 2021, 14, 85. [Google Scholar] [CrossRef] [PubMed]
  122. Tenchov, R.; Bird, R.; Curtze, A.E.; Zhou, Q. Lipid Nanoparticles—From Liposomes to mRNA Vaccine Delivery, a Landscape of Research Diversity and Advancement. ACS Nano 2021, 15, 16982–17015. [Google Scholar] [CrossRef] [PubMed]
  123. Adams, D.; Gonzalez-Duarte, A.; O’Riordan, W.D.; Yang, C.-C.; Ueda, M.; Kristen, A.V.; Tournev, I.; Schmidt, H.H.; Coelho, T.; Berk, J.L.; et al. Patisiran, an RNAi Therapeutic, for Hereditary Transthyretin Amyloidosis. N. Engl. J. Med. 2018, 379, 11–21. [Google Scholar] [CrossRef] [PubMed]
  124. Hajj, K.A.; Whitehead, K.A. Tools for Translation: Non-Viral Materials for Therapeutic mRNA Delivery. Nat. Rev. Mater. 2017, 2, 17056. [Google Scholar] [CrossRef]
  125. Kon, E.; Elia, U.; Peer, D. Principles for Designing an Optimal mRNA Lipid Nanoparticle Vaccine. Curr. Opin. Biotechnol. 2022, 73, 329–336. [Google Scholar] [CrossRef] [PubMed]
  126. Tsoi, K.M.; MacParland, S.A.; Ma, X.-Z.; Spetzler, V.N.; Echeverri, J.; Ouyang, B.; Fadel, S.M.; Sykes, E.A.; Goldaracena, N.; Kaths, J.M.; et al. Mechanism of Hard Nanomaterial Clearance by the Liver. Nat. Mater. 2016, 15, 1212–1221. [Google Scholar] [CrossRef] [PubMed]
  127. Alavi, M.; Hamidi, M. Passive and Active Targeting in Cancer Therapy by Liposomes and Lipid Nanoparticles. Drug Metab. Pers. Ther. 2019, 34, 20180032. [Google Scholar] [CrossRef] [PubMed]
  128. Kon, E.; Ad-El, N.; Hazan-Halevy, I.; Stotsky-Oterin, L.; Peer, D. Targeting Cancer with mRNA–Lipid Nanoparticles: Key Considerations and Future Prospects. Nat. Rev. Clin. Oncol. 2023, 20, 739–754. [Google Scholar] [CrossRef] [PubMed]
  129. Xu, M.; Qi, Y.; Liu, G.; Song, Y.; Jiang, X.; Du, B. Size-Dependent In Vivo Transport of Nanoparticles: Implications for Delivery, Targeting, and Clearance. ACS Nano 2023, 17, 20825–20849. [Google Scholar] [CrossRef] [PubMed]
  130. Guorgui, J.; Wang, R.; Mattheolabakis, G.; Mackenzie, G.G. Curcumin Formulated in Solid Lipid Nanoparticles Has Enhanced Efficacy in Hodgkin’s Lymphoma in Mice. Arch. Biochem. Biophys. 2018, 648, 12–19. [Google Scholar] [CrossRef] [PubMed]
  131. Hamishehkar, H.; Bahadori, M.B.; Vandghanooni, S.; Eskandani, M.; Nakhlband, A.; Eskandani, M. Preparation, characterization and anti-proliferative effects of sclareol-loaded solid lipid nanoparticles on A549 human lung epithelial cancer cells. J. Drug Deliv. Sci. Technol. 2018, 45, 272–280. [Google Scholar] [CrossRef]
  132. Yoo, S.; Faisal, M.; Bae, S.-H.; Youn, K.; Park, H.-J.; Kwon, S.P.; Hwang, I.K.; Lee, J.; Kim, H.J.; Nam, J.-H.; et al. Novel Less Toxic, Lymphoid Tissue-Targeted Lipid Nanoparticles Containing a Vitamin B5-Derived Ionizable Lipid for mRNA Vaccine Delivery. Adv. Healthc. Mater. 2024, 14, 2403366. [Google Scholar] [CrossRef] [PubMed]
  133. Lokugamage, M.P.; Sago, C.D.; Gan, Z.; Krupzak, B.; Dahlman, J.E. Constrained Nanoparticles Deliver siRNA and sgRNA to T Cells In Vivo without Targeting Ligands. Adv. Mater. 2019, 31, e1902251. [Google Scholar] [CrossRef] [PubMed]
  134. Chen, J.; Ye, Z.; Huang, C.; Qiu, M.; Song, D.; Li, Y.; Xu, Q. Lipid Nanoparticle-Mediated Lymph Node–Targeting Delivery of mRNA Cancer Vaccine Elicits Robust CD8+T Cell Response. Proc. Natl. Acad. Sci. USA 2022, 119, e2207841119. [Google Scholar] [CrossRef] [PubMed]
  135. Zhao, Y.; Song, D.; Wang, Z.; Huang, Q.; Huang, F.; Ye, Z.; Wich, D.; Chen, M.; Khirallah, J.; Gao, S.; et al. Antitumour Vaccination via the Targeted Proteolysis of Antigens Isolated from Tumour Lysates. Nat. Biomed. Eng. 2025, 9, 234–248. [Google Scholar] [CrossRef] [PubMed]
  136. Xue, L.; Zhao, G.; Gong, N.; Han, X.; Shepherd, S.J.; Xiong, X.; Xiao, Z.; Palanki, R.; Xu, J.; Swingle, K.L.; et al. Combinatorial Design of Siloxane-Incorporated Lipid Nanoparticles Augments Intracellular Processing for Tissue-Specific mRNA Therapeutic Delivery. Nat. Nanotechnol. 2025, 20, 132–143. [Google Scholar] [CrossRef] [PubMed]
  137. Zhu, Y.; Ma, J.; Shen, R.; Lin, J.; Li, S.; Lu, X.; Stelzel, J.L.; Kong, J.; Cheng, L.; Vuong, I.; et al. Screening for Lipid Nanoparticles That Modulate the Immune Activity of Helper T Cells towards Enhanced Antitumour Activity. Nat. Biomed. Eng. 2024, 8, 544–560. [Google Scholar] [CrossRef] [PubMed]
  138. Rampado, R.; Naidu, G.S.; Karpov, O.; Goldsmith, M.; Sharma, P.; Ezra, A.; Stotsky, L.; Breier, D.; Peer, D. Lipid Nanoparticles with Fine-Tuned Composition Show Enhanced Colon Targeting as a Platform for mRNA Therapeutics. Adv. Sci. 2025, 12, 2408744. [Google Scholar] [CrossRef] [PubMed]
  139. Xue, L.; Hamilton, A.G.; Zhao, G.; Xiao, Z.; El-Mayta, R.; Han, X.; Gong, N.; Xiong, X.; Xu, J.; Figueroa-Espada, C.G.; et al. High-Throughput Barcoding of Nanoparticles Identifies Cationic, Degradable Lipid-like Materials for mRNA Delivery to the Lungs in Female Preclinical Models. Nat. Commun. 2024, 15, 1884. [Google Scholar] [CrossRef] [PubMed]
  140. Chen, J.; Xu, Y.; Zhou, M.; Xu, S.; Varley, A.J.; Golubovic, A.; Lu, R.X.Z.; Wang, K.C.; Yeganeh, M.; Vosoughi, D.; et al. Combinatorial Design of Ionizable Lipid Nanoparticles for Muscle-Selective mRNA Delivery with Minimized off-Target Effects. Proc. Natl. Acad. Sci. USA 2023, 120, e2309472120. [Google Scholar] [CrossRef] [PubMed]
  141. Bevers, S.; Kooijmans, S.A.A.; Van De Velde, E.; Evers, M.J.W.; Seghers, S.; Gitz-Francois, J.J.J.M.; Van Kronenburg, N.C.H.; Fens, M.H.A.M.; Mastrobattista, E.; Hassler, L.; et al. mRNA-LNP Vaccines Tuned for Systemic Immunization Induce Strong Antitumor Immunity by Engaging Splenic Immune Cells. Mol. Ther. 2022, 30, 3078–3094. [Google Scholar] [CrossRef] [PubMed]
  142. Liu, J.; Xiao, B.; Yang, Y.; Jiang, Y.; Wang, R.; Wei, Q.; Pan, Y.; Chen, Y.; Wang, H.; Fan, J.; et al. Low-Dose Mildronate-Derived Lipidoids for Efficient mRNA Vaccine Delivery with Minimal Inflammation Side Effects. ACS Nano 2024, 18, 23289–23300. [Google Scholar] [CrossRef] [PubMed]
  143. Lv, K.; Yu, Z.; Wang, J.; Li, N.; Wang, A.; Xue, T.; Wang, Q.; Shi, Y.; Han, L.; Qin, W.; et al. Discovery of Ketal-Ester Ionizable Lipid Nanoparticle with Reduced Hepatotoxicity, Enhanced Spleen Tropism for mRNA Vaccine Delivery. Adv. Sci. Weinh. Baden-Wurtt. Ger. 2024, 11, e2404684. [Google Scholar] [CrossRef] [PubMed]
  144. Shi, G.; Xu, Y.; Qiu, H.; Cao, F.; Xiao, Z.-X.; Zhang, C.; Zha, G.-F. Personalized membrane protein vaccine based on a lipid nanoparticle delivery system prevents postoperative recurrence in colorectal cancer models. Acta Biomater. 2025, 192, 315–327. [Google Scholar] [CrossRef] [PubMed]
  145. Tang, X.; Zhang, J.; Sui, D.; Yang, Q.; Wang, T.; Xu, Z.; Li, X.; Gao, X.; Yan, X.; Liu, X.; et al. Simultaneous dendritic cells targeting and effective endosomal escape enhance sialic acid-modified mRNA vaccine efficacy and reduce side effects. J. Control. Release 2023, 364, 529–545. [Google Scholar] [CrossRef] [PubMed]
  146. Billingsley, M.M.; Gong, N.; Mukalel, A.J.; Thatte, A.S.; El-Mayta, R.; Patel, S.K.; Metzloff, A.E.; Swingle, K.L.; Han, X.; Xue, L.; et al. In Vivo mRNA CAR T Cell Engineering via Targeted Ionizable Lipid Nanoparticles with Extrahepatic Tropism. Small 2024, 20, 2304378. [Google Scholar] [CrossRef] [PubMed]
  147. Lei, J.; Qi, S.; Yu, X.; Gao, X.; Yang, K.; Zhang, X.; Cheng, M.; Bai, B.; Feng, Y.; Lu, M.; et al. Development of Mannosylated Lipid Nanoparticles for mRNA Cancer Vaccine with High Antigen Presentation Efficiency and Immunomodulatory Capability. Angew. Chem. Int. Ed. 2024, 63, e202318515. [Google Scholar] [CrossRef] [PubMed]
  148. Parhiz, H.; Shuvaev, V.V.; Pardi, N.; Khoshnejad, M.; Kiseleva, R.Y.; Brenner, J.S.; Uhler, T.; Tuyishime, S.; Mui, B.L.; Tam, Y.K.; et al. PECAM-1 Directed Re-Targeting of Exogenous mRNA Providing Two Orders of Magnitude Enhancement of Vascular Delivery and Expression in Lungs Independent of Apolipoprotein E-Mediated Uptake. J. Control. Release Off. J. Control. Release Soc. 2018, 291, 106–115. [Google Scholar] [CrossRef] [PubMed]
  149. Tombácz, I.; Laczkó, D.; Shahnawaz, H.; Muramatsu, H.; Natesan, A.; Yadegari, A.; Papp, T.E.; Alameh, M.-G.; Shuvaev, V.; Mui, B.L.; et al. Highly Efficient CD4+ T Cell Targeting and Genetic Recombination Using Engineered CD4+ Cell-Homing mRNA-LNPs. Mol. Ther. 2021, 29, 3293–3304. [Google Scholar] [CrossRef] [PubMed]
  150. Rurik, J.G.; Tombácz, I.; Yadegari, A.; Méndez Fernández, P.O.; Shewale, S.V.; Li, L.; Kimura, T.; Soliman, O.Y.; Papp, T.E.; Tam, Y.K.; et al. CAR T Cells Produced In Vivo to Treat Cardiac Injury. Science 2022, 375, 91–96. [Google Scholar] [CrossRef] [PubMed]
  151. Park, W.; Choi, J.; Hwang, J.; Kim, S.; Kim, Y.; Shim, M.K.; Park, W.; Yu, S.; Jung, S.; Yang, Y.; et al. Apolipoprotein Fusion Enables Spontaneous Functionalization of mRNA Lipid Nanoparticles with Antibody for Targeted Cancer Therapy. ACS Nano 2025, 19, 6412–6425. [Google Scholar] [CrossRef] [PubMed]
  152. Liu, M.; Feng, Y.; Lu, Y.; Huang, R.; Zhang, Y.; Zhao, Y.; Mo, R. Lymph-Targeted High-Density Lipoprotein-Mimetic Nanovaccine for Multi-Antigenic Personalized Cancer Immunotherapy. Sci. Adv. 2024, 10, eadk2444. [Google Scholar] [CrossRef] [PubMed]
  153. Li, H.; Peng, K.; Yang, K.; Ma, W.; Qi, S.; Yu, X.; He, J.; Lin, X.; Yu, G. Circular RNA Cancer Vaccines Drive Immunity in Hard-to-Treat Malignancies. Theranostics 2022, 12, 6422–6436. [Google Scholar] [CrossRef] [PubMed]
  154. Ramos da Silva, J.; Bitencourt Rodrigues, K.; Formoso Pelegrin, G.; Silva Sales, N.; Muramatsu, H.; de Oliveira Silva, M.; Porchia, B.F.M.M.; Moreno, A.C.R.; Aps, L.R.M.M.; Venceslau-Carvalho, A.A.; et al. Single Immunizations of Self-Amplifying or Non-Replicating mRNA-LNP Vaccines Control HPV-Associated Tumors in Mice. Sci. Transl. Med. 2023, 15, eabn3464. [Google Scholar] [CrossRef] [PubMed]
  155. Hong, M.; Liu, M.; Zhu, F.; Zhao, D.; Liu, G.; Han, T.; Fei, C.; Zeng, W.; Chen, S.; Wu, Q.; et al. FcRn-Guided Antigen Trafficking Enhances Cancer Vaccine Efficacy. Cancer Immunol. Immunother. CII 2025, 74, 54. [Google Scholar] [CrossRef] [PubMed]
  156. Kozma, G.T.; Mészáros, T.; Vashegyi, I.; Fülöp, T.; Örfi, E.; Dézsi, L.; Rosivall, L.; Bavli, Y.; Urbanics, R.; Mollnes, T.E.; et al. Pseudo-Anaphylaxis to Polyethylene Glycol (PEG)-Coated Liposomes: Roles of Anti-PEG IgM and Complement Activation in a Porcine Model of Human Infusion Reactions. ACS Nano 2019, 13, 9315–9324. [Google Scholar] [CrossRef] [PubMed]
  157. 157Chatterjee, S.; Kon, E.; Sharma, P.; Peer, D. Endosomal Escape: A Bottleneck for LNP-Mediated Therapeutics. Proc. Natl. Acad. Sci. USA 2024, 121, e2307800120. [Google Scholar] [CrossRef] [PubMed]
  158. Jacob, E.M.; Huang, J.; Chen, M. Lipid Nanoparticle-Based mRNA Vaccines: A New Frontier in Precision Oncology. Precis. Clin. Med. 2024, 7, pbae017. [Google Scholar] [CrossRef] [PubMed]
  159. Zong, Y.; Lin, Y.; Wei, T.; Cheng, Q. Lipid Nanoparticle (LNP) Enables mRNA Delivery for Cancer Therapy. Adv. Materials 2023, 35, 2303261. [Google Scholar] [CrossRef] [PubMed]
  160. Su, K.; Shi, L.; Sheng, T.; Yan, X.; Lin, L.; Meng, C.; Wu, S.; Chen, Y.; Zhang, Y.; Wang, C.; et al. Reformulating Lipid Nanoparticles for Organ-Targeted mRNA Accumulation and Translation. Nat. Commun. 2024, 15, 5659. [Google Scholar] [CrossRef] [PubMed]
  161. Fei, Y.; Yu, X.; Liu, P.; Ren, H.; Wei, T.; Cheng, Q. Simplified Lipid Nanoparticles for Tissue- and Cell-Targeted mRNA Delivery Facilitate Precision Tumor Therapy in a Lung Metastasis Mouse Model. Adv. Mater. 2024, 36, 2409812. [Google Scholar] [CrossRef] [PubMed]
  162. Cheng, Q.; Wei, T.; Farbiak, L.; Johnson, L.T.; Dilliard, S.A.; Siegwart, D.J. Selective ORgan Targeting (SORT) Nanoparticles for Tissue Specific mRNA Delivery and CRISPR/Cas Gene Editing. Nat. Nanotechnol. 2020, 15, 313–320. [Google Scholar] [CrossRef] [PubMed]
  163. Luo, Z.; Lin, Y.; Meng, Y.; Li, M.; Ren, H.; Shi, H.; Cheng, Q.; Wei, T. Spleen-Targeted mRNA Vaccine Doped with Manganese Adjuvant for Robust Anticancer Immunity In Vivo. ACS Nano 2024, 18, 30701–30715. [Google Scholar] [CrossRef] [PubMed]
  164. Patel, S.K.; Billingsley, M.M.; Mukalel, A.J.; Thatte, A.S.; Hamilton, A.G.; Gong, N.; El-Mayta, R.; Safford, H.C.; Merolle, M.; Mitchell, M.J. Bile Acid-Containing Lipid Nanoparticles Enhance Extrahepatic mRNA Delivery. Theranostics 2024, 14, 1–16. [Google Scholar] [CrossRef] [PubMed]
  165. Xu, S.; Xu, Y.; Solek, N.C.; Chen, J.; Gong, F.; Varley, A.J.; Golubovic, A.; Pan, A.; Dong, S.; Zheng, G.; et al. Tumor-Tailored Ionizable Lipid Nanoparticles Facilitate IL-12 Circular RNA Delivery for Enhanced Lung Cancer Immunotherapy. Adv. Mater. 2024, 36, 2400307. [Google Scholar] [CrossRef] [PubMed]
  166. Yan, Y.; Liu, X.; Wang, L.; Wu, C.; Shuai, Q.; Zhang, Y.; Liu, S. Branched Hydrophobic Tails in Lipid Nanoparticles Enhance mRNA Delivery for Cancer Immunotherapy. Biomaterials 2023, 301, 122279. [Google Scholar] [CrossRef] [PubMed]
  167. Billingsley, M.M.; Hamilton, A.G.; Mai, D.; Patel, S.K.; Swingle, K.L.; Sheppard, N.C.; June, C.H.; Mitchell, M.J. Orthogonal Design of Experiments for Optimization of Lipid Nanoparticles for mRNA Engineering of CAR T Cells. Nano Lett. 2022, 22, 533–542. [Google Scholar] [CrossRef] [PubMed]
  168. Shobaki, N.; Sato, Y.; Suzuki, Y.; Okabe, N.; Harashima, H. Manipulating the Function of Tumor-Associated Macrophages by siRNA-Loaded Lipid Nanoparticles for Cancer Immunotherapy. J. Control. Release 2020, 325, 235–248. [Google Scholar] [CrossRef] [PubMed]
  169. Zamora, M.E.; Omo-Lamai, S.; Patel, M.N.; Wu, J.; Arguiri, E.; Muzykantov, V.R.; Myerson, J.W.; Marcos-Contreras, O.A.; Brenner, J.S. Combination of Physicochemical Tropism and Affinity Moiety Targeting of Lipid Nanoparticles Enhances Organ Targeting. Nano Lett. 2024, 24, 4774–4784. [Google Scholar] [CrossRef]
  170. Kim, Y.; Choi, J.; Kim, E.H.; Park, W.; Jang, H.; Jang, Y.; Chi, S.-G.; Kweon, D.-H.; Lee, K.; Kim, S.H.; et al. Design of PD-L1-Targeted Lipid Nanoparticles to Turn on PTEN for Efficient Cancer Therapy. Adv. Sci. 2024, 11, 2309917. [Google Scholar] [CrossRef] [PubMed]
  171. Liu, X.; Liu, Y.; Li, X.; Huang, J.; Guo, X.; Zhang, J.; Luo, Z.; Shi, Y.; Jiang, M.; Qin, B.; et al. ER-Targeting PDT Converts Tumors into In Situ Therapeutic Tumor Vaccines. ACS Nano 2022, 16, 9240–9253. [Google Scholar] [CrossRef] [PubMed]
  172. Affandi, A.J.; Grabowska, J.; Olesek, K.; Lopez Venegas, M.; Barbaria, A.; Rodríguez, E.; Mulder, P.P.G.; Pijffers, H.J.; Ambrosini, M.; Kalay, H.; et al. Selective Tumor Antigen Vaccine Delivery to Human CD169+ Antigen-Presenting Cells Using Ganglioside-Liposomes. Proc. Natl. Acad. Sci. USA 2020, 117, 27528–27539. [Google Scholar] [CrossRef] [PubMed]
  173. Kim, D.; Choi, J.; Jin, D.; Xu, E.; Lee, J.; Byun, J.; Oh, Y.-K. Hybrid Lipid Nanoparticles with Tumor Antigen-Primed Dendritic Cell Membranes for Post-Surgical Tumor Immunotherapy. J. Control. Release 2025, 379, 537–548. [Google Scholar] [CrossRef] [PubMed]
  174. Kumari, A.; Yadav, S.K.; Yadav, S.C. Biodegradable polymeric nanoparticles based drug delivery systems. Colloids Surf. B Biointerfaces 2010, 75, 1–18. [Google Scholar] [CrossRef] [PubMed]
  175. Pridgen, E.M.; Langer, R.; Farokhzad, O.C. Biodegradable, Polymeric Nanoparticle Delivery Systems for Cancer Therapy. Nanomed 2007, 2, 669–680. [Google Scholar] [CrossRef] [PubMed]
  176. Hassan, S.; Prakash, G.; Ozturk, A.; Saghazadeh, S.; Sohail, M.F.; Seo, J.; Dockmeci, M.; Zhang, Y.S.; Khademhosseini, A. Evolution and Clinical Translation of Drug Delivery Nanomaterials. Nano Today 2017, 15, 91–106. [Google Scholar] [CrossRef] [PubMed]
  177. Langer, R.; Folkman, J. Polymers for the Sustained Release of Proteins and Other Macromolecules. Nature 1976, 263, 797–800. [Google Scholar] [CrossRef] [PubMed]
  178. Xu, X.; Wang, R.; Li, D.; Xiang, J.; Zhang, W.; Shi, X.; Xu, H.; Yao, S.; Liu, J.; Shao, S.; et al. Guanidine-Modified Nanoparticles as Robust BTZ Delivery Carriers and Activators of Immune Responses. J. Control. Release 2023, 357, 310–318. [Google Scholar] [CrossRef] [PubMed]
  179. Wang, X.; Wilhelm, J.; Li, W.; Li, S.; Wang, Z.; Huang, G.; Wang, J.; Tang, H.; Khorsandi, S.; Sun, Z.; et al. Polycarbonate-Based Ultra-pH Sensitive Nanoparticles Improve Therapeutic Window. Nat. Commun. 2020, 11, 5828. [Google Scholar] [CrossRef] [PubMed]
  180. Zhu, Y.; Li, Y.; Li, X.; Yu, Y.; Zhang, L.; Zhang, H.; Chen, C.; Chen, D.; Wang, M.; Xing, N.; et al. Targeting Hypoxia and Autophagy Inhibition via Delivering Sonodynamic Nanoparticles with HIF-2α Inhibitor for Enhancing Immunotherapy in Renal Cell Carcinoma. Adv. Healthc. Mater. 2024, 13, 2402973. [Google Scholar] [CrossRef] [PubMed]
  181. Zhou, S.; Cheng, F.; Zhang, Y.; Su, T.; Zhu, G. Engineering and Delivery of cGAS-STING Immunomodulators for the Immunotherapy of Cancer and Autoimmune Diseases. Acc. Chem. Res. 2023, 56, 2933–2943. [Google Scholar] [CrossRef] [PubMed]
  182. Park, Y.; Moses, A.S.; Demessie, A.A.; Singh, P.; Lee, H.; Korzun, T.; Taratula, O.R.; Alani, A.G.; Taratula, O. Poly(Aspartic Acid)-Based Polymeric Nanoparticle for Local and Systemic mRNA Delivery. Mol. Pharm. 2022, 19, 4696–4704. [Google Scholar] [CrossRef] [PubMed]
  183. Wang, B.; Zhou, J.; Li, R.; Tang, D.; Cao, Z.; Xu, C.; Xiao, H. Activating CD8+ T Cells by Pt(IV) Prodrug-Based Nanomedicine and aPD-L1 Antibody for Enhanced Cancer Immunotherapy. Adv. Mater. 2024, 36, 2311640. [Google Scholar] [CrossRef] [PubMed]
  184. Zhang, X.; Yue, L.; Cao, L.; Liu, K.; Yang, S.; Liang, S.; Liu, L.; Zhao, C.; Wu, D.; Wang, Z.; et al. Tumor Microenvironment-Responsive Macrophage-Mediated Immunotherapeutic Drug Delivery. Acta Biomater. 2024, 186, 369–382. [Google Scholar] [CrossRef] [PubMed]
  185. Dang, B.-T.N.; Duwa, R.; Lee, S.; Kwon, T.K.; Chang, J.-H.; Jeong, J.-H.; Yook, S. Targeting Tumor-Associated Macrophages with Mannosylated Nanotherapeutics Delivering TLR7/8 Agonist Enhances Cancer Immunotherapy. J. Control. Release 2024, 372, 587–608. [Google Scholar] [CrossRef] [PubMed]
  186. Freitas, R.; Ferreira, E.; Miranda, A.; Ferreira, D.; Relvas-Santos, M.; Castro, F.; Santos, B.; Gonçalves, M.; Quintas, S.; Peixoto, A.; et al. Targeted and Self-Adjuvated Nanoglycovaccine Candidate for Cancer Immunotherapy. ACS Nano 2024, 18, 10088–10103. [Google Scholar] [CrossRef] [PubMed]
  187. Parayath, N.N.; Stephan, S.B.; Koehne, A.L.; Nelson, P.S.; Stephan, M.T. In Vitro-Transcribed Antigen Receptor mRNA Nanocarriers for Transient Expression in Circulating T Cells in Vivo. Nat. Commun. 2020, 11, 6080. [Google Scholar] [CrossRef] [PubMed]
  188. Hu, Y.; Nie, W.; Lyu, L.; Zhang, X.; Wang, W.; Zhang, Y.; He, S.; Guo, A.; Liu, F.; Wang, B.; et al. Tumor-Microenvironment-Activatable Nanoparticle Mediating Immunogene Therapy and M2 Macrophage-Targeted Inhibitor for Synergistic Cancer Immunotherapy. ACS Nano 2024, 18, 3295–3312. [Google Scholar] [CrossRef] [PubMed]
  189. Li, Y.; Liu, J.; Weichselbaum, R.R.; Lin, W. Mitochondria-targeted Multifunctional Nanoparticles Combine Cuproptosis and Programmed Cell Death-1 Downregulation for Cancer Immunotherapy. Adv. Sci. 2024, 11, 2403520. [Google Scholar] [CrossRef] [PubMed]
  190. Xing, Y.; Yang, J.; Peng, A.; Qian, Y.; Liu, Y.; Pan, P.; Liu, Q. Lysosome Targeted Nanoparticle Aggregation Reverses Immunosuppressive Tumor Microenvironment for Cancer Immunotherapy. Adv. Mater. 2024, 36, 2412730. [Google Scholar] [CrossRef] [PubMed]
  191. Chen, S.; He, Y.; Huang, X.; Shen, Y.; Zou, Q.; Yang, G.; Fu, L.; Liu, Q.; Luo, D. Photosensitive and Dual-Targeted Chromium Nanoparticle Delivering Small Interfering RNA YTHDF1 for Molecular-Targeted Immunotherapy in Liver Cancer. J. Nanobiotechnology 2024, 22, 348. [Google Scholar] [CrossRef] [PubMed]
  192. Guo, Y.; Li, Y.; Zhang, M.; Ma, R.; Wang, Y.; Weng, X.; Zhang, J.; Zhang, Z.; Chen, X.; Yang, W. Polymeric Nanocarrier via Metabolism Regulation Mediates Immunogenic Cell Death with Spatiotemporal Orchestration for Cancer Immunotherapy. Nat. Commun. 2024, 15, 8586. [Google Scholar] [CrossRef] [PubMed]
  193. Long, X.; Wang, H.; Yan, J.; Li, Y.; Dong, X.; Tian, S.; Sun, Y.; Luo, K.; He, B.; Liang, Y. Tailor-Made Autophagy Cascade Amplification Polymeric Nanoparticles for Enhanced Tumor Immunotherapy. Small 2023, 19, 2207898. [Google Scholar] [CrossRef] [PubMed]
  194. Chou, P.-Y.; Lin, S.-Y.; Wu, Y.-N.; Shen, C.-Y.; Sheu, M.-T.; Ho, H.-O. Glycosylation of OVA Antigen-Loaded PLGA Nanoparticles Enhances DC-Targeting for Cancer Vaccination. J. Control. Release 2022, 351, 970–988. [Google Scholar] [CrossRef] [PubMed]
  195. Zheng, J.; Wang, M.; Pang, L.; Wang, S.; Kong, Y.; Zhu, X.; Zhou, X.; Wang, X.; Chen, C.; Ning, H.; et al. Identification of a Novel DEC-205 Binding Peptide to Develop Dendritic Cell-Targeting Nanovaccine for Cancer Immunotherapy. J. Control. Release 2024, 373, 568–582. [Google Scholar] [CrossRef] [PubMed]
  196. Liu, Y.; Li, H.; Hao, Y.-Y.; Huang, L.-L.; Li, X.; Zou, J.; Zhang, S.-Y.; Yang, X.-Y.; Chen, H.-F.; Guo, Y.-X.; et al. Tumor-Selective Nano-Dispatcher Enforced Cancer Immunotherapeutic Effects via Regulating Lactate Metabolism and Activating Toll-like Receptors. Small Weinh. Bergstr. Ger. 2025, 21, e2406870. [Google Scholar] [CrossRef] [PubMed]
  197. Huang, H.; Fu, J.; Peng, H.; He, Y.; Chang, A.; Zhang, H.; Hao, Y.; Xu, X.; Li, S.; Zhao, J.; et al. Co-Delivery of Polyphyllin II and IR780 PLGA Nanoparticles Induced Pyroptosis Combined with Photothermal to Enhance Hepatocellular Carcinoma Immunotherapy. J. Nanobiotechnology 2024, 22, 647. [Google Scholar] [CrossRef] [PubMed]
  198. Babu, A.; Padmanaban, S.; Chahal, S.; Mohapatra, A.; Sundaram, A.; Cho, C.-S.; Park, I.-K. Targeted Nanoparticle Delivery Unleashes Synergistic Photothermal and Immunotherapeutic Effects against Hepatocellular Carcinoma. J. Nanobiotechnology 2024, 22, 778. [Google Scholar] [CrossRef] [PubMed]
  199. Wang, X.; Ye, L.; He, W.; Teng, C.; Sun, S.; Lu, H.; Li, S.; Lv, L.; Cao, X.; Yin, H.; et al. In Situ Targeting Nanoparticles-Hydrogel Hybrid System for Combined Chemo-Immunotherapy of Glioma. J. Control. Release 2022, 345, 786–797. [Google Scholar] [CrossRef] [PubMed]
  200. Ye, Q.-N.; Zhu, L.; Liang, J.; Zhao, D.-K.; Tian, T.-Y.; Fan, Y.-N.; Ye, S.-Y.; Liu, H.; Huang, X.-Y.; Cao, Z.-T.; et al. Orchestrating NK and T Cells via Tri-Specific Nano-Antibodies for Synergistic Antitumor Immunity. Nat. Commun. 2024, 15, 6211. [Google Scholar] [CrossRef] [PubMed]
  201. Liao, Y.; Zhao, C.; Pan, Y.; Guo, Y.; Liu, L.; Wu, J.; Zhang, Y.; Rao, L.; Li, Q. Genetically Engineered Cellular Nanoparticles Loaded with Curcuminoids for Cancer Immunotherapy. Theranostics 2024, 14, 6409–6425. [Google Scholar] [CrossRef] [PubMed]
  202. Ding, L.; Agrawal, P.; Singh, S.K.; Chhonker, Y.S.; Sun, J.; Murry, D.J. Polymer-Based Drug Delivery Systems for Cancer Therapeutics. Polymers 2024, 16, 843. [Google Scholar] [CrossRef] [PubMed]
  203. Yu, Z.; Shen, X.; Yu, H.; Tu, H.; Chittasupho, C.; Zhao, Y. Smart Polymeric Nanoparticles in Cancer Immunotherapy. Pharmaceutics 2023, 15, 775. [Google Scholar] [CrossRef] [PubMed]
  204. Niza, E.; Ocaña, A.; Castro-Osma, J.A.; Bravo, I.; Alonso-Moreno, C. Polyester Polymeric Nanoparticles as Platforms in the Development of Novel Nanomedicines for Cancer Treatment. Cancers 2021, 13, 3387. [Google Scholar] [CrossRef] [PubMed]
  205. Liu, Y.; Ghassemi, A.H.; Hennink, W.E.; Schwendeman, S.P. The Microclimate pH in Poly(D,L-Lactide-Co-Hydroxymethyl Glycolide) Microspheres during Biodegradation. Biomaterials 2012, 33, 10. [Google Scholar] [CrossRef] [PubMed]
  206. Yang, J.; Zeng, H.; Luo, Y.; Chen, Y.; Wang, M.; Wu, C.; Hu, P. Recent Applications of PLGA in Drug Delivery Systems. Polymers 2024, 16, 2606. [Google Scholar] [CrossRef] [PubMed]
  207. Suk, J.S.; Xu, Q.; Kim, N.; Hanes, J.; Ensign, L.M. PEGylation as a Strategy for Improving Nanoparticle-Based Drug and Gene Delivery. Adv. Drug Deliv. Rev. 2016, 99, 28–51. [Google Scholar] [CrossRef] [PubMed]
  208. Frigaard, J.; Jensen, J.L.; Galtung, H.K.; Hiorth, M. The Potential of Chitosan in Nanomedicine: An Overview of the Cytotoxicity of Chitosan Based Nanoparticles. Front. Pharmacol. 2022, 13, 880337. [Google Scholar] [CrossRef] [PubMed]
  209. Garg, U.; Chauhan, S.; Nagaich, U.; Jain, N. Current Advances in Chitosan Nanoparticles Based Drug Delivery and Targeting. Adv. Pharm. Bull. 2019, 9, 195–204. [Google Scholar] [CrossRef] [PubMed]
  210. Bhadran, A.; Shah, T.; Babanyinah, G.K.; Polara, H.; Taslimy, S.; Biewer, M.C.; Stefan, M.C. Recent Advances in Polycaprolactones for Anticancer Drug Delivery. Pharmaceutics 2023, 15, 1977. [Google Scholar] [CrossRef] [PubMed]
  211. Ji, Q.; Zhu, H.; Qin, Y.; Zhang, R.; Wang, L.; Zhang, E.; Zhou, X.; Meng, R. GP60 and SPARC as Albumin Receptors: Key Targeted Sites for the Delivery of Antitumor Drugs. Front. Pharmacol. 2024, 15, 1329636. [Google Scholar] [CrossRef] [PubMed]
  212. Li, C.L.; Li, Y.H.; Gao, Y.Q.; Wei, N.; Zhao, X.; Wang, C.X.; Li, Y.F.; Xiu, X.; Cui, J.X. Direct Comparison of Two Albumin-Based Paclitaxel-Loaded Nanoparticle Formulations: Is the Crosslinked Version More Advantageous? Int. J. Pharm. 2014, 468, 15–25. [Google Scholar] [CrossRef] [PubMed]
  213. Spada, A.; Emami, J.; Tuszynski, J.A.; Lavasanifar, A. The Uniqueness of Albumin as a Carrier in Nanodrug Delivery. Mol. Pharm. 2021, 18, 1862–1894. [Google Scholar] [CrossRef] [PubMed]
  214. Murphy, G.; Brayden, D.J.; Cheung, D.L.; Liew, A.; Fitzgerald, M.; Pandit, A. Albumin-Based Delivery Systems: Recent Advances, Challenges, and Opportunities. J. Control. Release 2025, 380, 375–395. [Google Scholar] [CrossRef] [PubMed]
  215. Hesemans, E.; Saffarzadeh, N.; Maksoudian, C.; Izci, M.; Chu, T.; Rios Luci, C.; Wang, Y.; Naatz, H.; Thieme, S.; Richter, C.; et al. Cu-Doped TiO2 Nanoparticles Improve Local Antitumor Immune Activation and Optimize Dendritic Cell Vaccine Strategies. J. Nanobiotechnology 2023, 21, 87. [Google Scholar] [CrossRef] [PubMed]
  216. Qu, C.; Yuan, H.; Tian, M.; Zhang, X.; Xia, P.; Shi, G.; Hou, R.; Li, J.; Jiang, H.; Yang, Z.; et al. Precise Photodynamic Therapy by Midkine Nanobody-Engineered Nanoparticles Remodels the Microenvironment of Pancreatic Ductal Adenocarcinoma and Potentiates the Immunotherapy. ACS Nano 2024, 18, 4019–4037. [Google Scholar] [CrossRef] [PubMed]
  217. Zhang, C.; Zeng, Z.; Cui, D.; He, S.; Jiang, Y.; Li, J.; Huang, J.; Pu, K. Semiconducting Polymer Nano-PROTACs for Activatable Photo-Immunometabolic Cancer Therapy. Nat. Commun. 2021, 12, 2934. [Google Scholar] [CrossRef] [PubMed]
  218. Liu, Y.; Niu, R.; Zhang, X.; Zhang, B.; Chen, X.; Guo, J.; Song, S.; Wang, Y.; Zhang, H.; Zhao, Y. Metal-Organic Framework-Based Nanovaccine for Relieving Immunosuppressive Tumors via Hindering Efferocytosis of Macrophages and Promoting Pyroptosis and Cuproptosis of Cancer Cells. ACS Nano 2024, 18, 12386–12400. [Google Scholar] [CrossRef] [PubMed]
  219. Lee, J.Y.; Kim, M.K.; Nguyen, T.L.; Kim, J. Hollow Mesoporous Silica Nanoparticles with Extra-Large Mesopores for Enhanced Cancer Vaccine. ACS Appl. Mater. Interfaces 2020, 12, 34658–34666. [Google Scholar] [CrossRef] [PubMed]
  220. Yang, C.; Ming, H.; Li, B.; Liu, S.; Chen, L.; Zhang, T.; Gao, Y.; He, T.; Huang, C.; Du, Z. A pH and Glutathione-Responsive Carbon Monoxide-Driven Nano-Herb Delivery System for Enhanced Immunotherapy in Colorectal Cancer. J. Control. Release 2024, 376, 659–677. [Google Scholar] [CrossRef] [PubMed]
  221. Feng, X.; Li, F.; Zhang, L.; Liu, W.; Wang, X.; Zhu, R.; Qiao, Z.-A.; Yu, B.; Yu, X. TRAIL-Modified, Doxorubicin-Embedded Periodic Mesoporous Organosilica Nanoparticles for Targeted Drug Delivery and Efficient Antitumor Immunotherapy. Acta Biomater. 2022, 143, 392–405. [Google Scholar] [CrossRef] [PubMed]
  222. Song, T.; Liao, Y.; Zuo, Q.; Liu, N.; Liu, Z. MnO2 Nanoparticles as a Minimalist Multimode Vaccine Adjuvant/Delivery System to Regulate Antigen Presenting Cells for Tumor Immunotherapy. J. Mater. Chem. B 2022, 10, 3474–3490. [Google Scholar] [CrossRef] [PubMed]
  223. Wang, J.; Wang, H.; Zou, F.; Gu, J.; Deng, S.; Cao, Y.; Cai, K. The Role of Inorganic Nanomaterials in Overcoming Challenges in Colorectal Cancer Diagnosis and Therapy. Pharmaceutics 2025, 17, 409. [Google Scholar] [CrossRef] [PubMed]
  224. Guan, C.; Han, Y.; Ling, Z.; Meng, X.; Zhang, B.; Dong, W.; Zhang, D.; Chen, K. Nanomaterials: Breaking the Bottleneck of Breast Cancer Drug Resistance. Front. Immunol. 2024, 15, 1492546. [Google Scholar] [CrossRef] [PubMed]
  225. Huang, H.; Liu, R.; Yang, J.; Dai, J.; Fan, S.; Pi, J.; Wei, Y.; Guo, X. Gold Nanoparticles: Construction for Drug Delivery and Application in Cancer Immunotherapy. Pharmaceutics 2023, 15, 1868. [Google Scholar] [CrossRef] [PubMed]
  226. Mitchell, M.J.; Billingsley, M.M.; Haley, R.M.; Wechsler, M.E.; Peppas, N.A.; Langer, R. Engineering Precision Nanoparticles for Drug Delivery. Nat. Rev. Drug Discov. 2021, 20, 101–124. [Google Scholar] [CrossRef] [PubMed]
  227. Gao, Y.; Gao, D.; Shen, J.; Wang, Q. A Review of Mesoporous Silica Nanoparticle Delivery Systems in Chemo-Based Combination Cancer Therapies. Front. Chem. 2020, 8, 598722. [Google Scholar] [CrossRef] [PubMed]
  228. Bharti, C.; Nagaich, U.; Pal, A.K.; Gulati, N. Mesoporous Silica Nanoparticles in Target Drug Delivery System: A Review. Int. J. Pharm. Investig. 2015, 5, 124–133. [Google Scholar] [CrossRef] [PubMed]
  229. Wang, P.; Guo, S.; Sun, M.; Wei, G.; Chen, C. Silica Nanoparticles as Versatile Carriers for Nanofertilizers and Nanopesticides: Design and Applications. J. Agric. Food Chem. 2025, 73, 14742–14759. [Google Scholar] [CrossRef] [PubMed]
  230. Fatima, R.; Katiyar, P.; Kushwaha, K. Recent Advances in Mesoporous Silica Nanoparticle: Synthesis, Drug Loading, Release Mechanisms, and Diverse Applications. Front. Nanotechnol. 2025, 7, 1564188. [Google Scholar] [CrossRef]
  231. Vasić, K.; Knez, Ž.; Leitgeb, M. Multifunctional Iron Oxide Nanoparticles as Promising Magnetic Biomaterials in Drug Delivery: A Review. J. Funct. Biomater. 2024, 15, 227. [Google Scholar] [CrossRef] [PubMed]
  232. Tehrani, S.F.; Bharadwaj, P.; Leblond Chain, J.; Roullin, V.G. Purification Processes of Polymeric Nanoparticles: How to Improve Their Clinical Translation? J. Control. Release 2023, 360, 591–612. [Google Scholar] [CrossRef] [PubMed]
  233. Jia, W.; Wu, Y.; Xie, Y.; Yu, M.; Chen, Y. Advanced Polymeric Nanoparticles for Cancer Immunotherapy: Materials Engineering, Immunotherapeutic Mechanism and Clinical Translation. Adv. Mater. 2025, 37, 2413603. [Google Scholar] [CrossRef] [PubMed]
  234. Rohner, E.; Yang, R.; Foo, K.S.; Goedel, A.; Chien, K.R. Unlocking the Promise of mRNA Therapeutics. Nat. Biotechnol. 2022, 40, 1586–1600. [Google Scholar] [CrossRef] [PubMed]
  235. Eisenbarth, S.C. Dendritic Cell Subsets in T Cell Programming: Location Dictates Function. Nat. Rev. Immunol. 2019, 19, 89–103. [Google Scholar] [CrossRef] [PubMed]
  236. Fukuda, K.; Okamura, K.; Riding, R.L.; Fan, X.; Afshari, K.; Haddadi, N.-S.; McCauley, S.M.; Guney, M.H.; Luban, J.; Funakoshi, T.; et al. AIM2 Regulates Anti-Tumor Immunity and Is a Viable Therapeutic Target for Melanoma. J. Exp. Med. 2021, 218, e20200962. [Google Scholar] [CrossRef] [PubMed]
  237. Yang, H.; Xiong, Z.; Heng, X.; Niu, X.; Wang, Y.; Yao, L.; Sun, L.; Liu, Z.; Chen, H. Click-Chemistry-Mediated Cell Membrane Glycopolymer Engineering to Potentiate Dendritic Cell Vaccines. Angew. Chem. 2024, 136, e202315782. [Google Scholar] [CrossRef]
  238. Lim, R.J.; Salehi-Rad, R.; Tran, L.M.; Oh, M.S.; Dumitras, C.; Crosson, W.P.; Li, R.; Patel, T.S.; Man, S.; Yean, C.E.; et al. CXCL9/10-Engineered Dendritic Cells Promote T Cell Activation and Enhance Immune Checkpoint Blockade for Lung Cancer. Cell Rep. Med. 2024, 5, 101479. [Google Scholar] [CrossRef] [PubMed]
  239. Ghasemi, A.; Martinez-Usatorre, A.; Li, L.; Hicham, M.; Guichard, A.; Marcone, R.; Fournier, N.; Torchia, B.; Martinez Bedoya, D.; Davanture, S.; et al. Cytokine-Armed Dendritic Cell Progenitors for Antigen-Agnostic Cancer Immunotherapy. Nat. Cancer 2024, 5, 240–261. [Google Scholar] [CrossRef] [PubMed]
  240. Huang, S.; Xing, F.; Dai, Y.; Zhang, Z.; Zhou, G.; Yang, S.; Liu, Y.-C.; Yuan, Z.; Luo, K.Q.; Ying, T.; et al. Navigating Chimeric Antigen Receptor-Engineered Natural Killer Cells as Drug Carriers via Three-Dimensional Mapping of the Tumor Microenvironment. J. Control. Release 2023, 362, 524–535. [Google Scholar] [CrossRef] [PubMed]
  241. Ning, P.; Du, F.; Wang, H.; Gong, X.; Xia, Y.; Zhang, X.; Deng, H.; Zhang, R.; Wang, Z. Genetically engineered macrophages as living cell drug carriers for targeted cancer therapy. J. Control. Release 2024, 367, 697–707. [Google Scholar] [CrossRef] [PubMed]
  242. Li, D.; Wang, R.; Liang, T.; Ren, H.; Park, C.; Tai, C.-H.; Ni, W.; Zhou, J.; Mackay, S.; Edmondson, E.; et al. Camel Nanobody-Based B7-H3 CAR-T Cells Show High Efficacy against Large Solid Tumours. Nat. Commun. 2023, 14, 5920. [Google Scholar] [CrossRef] [PubMed]
  243. Röder, J.; Alekseeva, T.; Kiefer, A.; Kühnel, I.; Prüfer, M.; Zhang, C.; Bodden, M.; Rosigkeit, S.; Waldmann, A.; Tonn, T.; et al. ErbB2/HER2-Targeted CAR-NK Cells Eliminate Breast Cancer Cells in an Organoid Model That Recapitulates Tumor Progression. Mol. Ther. 2025, 33, 8. [Google Scholar] [CrossRef] [PubMed]
  244. Wu, L.; Du, Z.; Li, L.; Qiao, L.; Zhang, S.; Yin, X.; Chang, X.; Li, C.; Hua, Z. Camouflaging Attenuated Salmonella by Cryo-Shocked Macrophages for Tumor-Targeted Therapy. Signal Transduct. Target. Ther. 2024, 9, 14. [Google Scholar] [CrossRef] [PubMed]
  245. Duan, R.; Milton, P.; Sittplangkoon, C.; Liu, X.; Sui, Z.; Boyce, B.F.; Yao, Z. Chimeric Antigen Receptor Dendritic Cells Targeted Delivery of a Single Tumoricidal Factor for Cancer Immunotherapy. Cancer Immunol. Immunother. CII 2024, 73, 203. [Google Scholar] [CrossRef] [PubMed]
  246. Zhang, Y.; Wang, Q.; Ma, T.; Zhu, D.; Liu, T.; Lv, F. Tumor Targeted Combination Therapy Mediated by Functional Macrophages under Fluorescence Imaging Guidance. J. Control. Release 2020, 328, 127–140. [Google Scholar] [CrossRef] [PubMed]
  247. Zhang, H.; Feng, Y.; Xie, X.; Song, T.; Yang, G.; Su, Q.; Li, T.; Li, S.; Wu, C.; You, F.; et al. Engineered Mesenchymal Stem Cells as a Biotherapy Platform for Targeted Photodynamic Immunotherapy of Breast Cancer. Adv. Healthc. Mater. 2022, 11, 2101375. [Google Scholar] [CrossRef] [PubMed]
  248. Costa-Garcia, M.; Moya-Borrego, L.; Alemany Bonastre, R.; Moreno Olié, R. Optimized Protocol for Culturing Menstrual Blood-Derived MSCs for Combination with Oncolytic Adenoviruses in Cancer Treatment. Mol. Ther. Oncol. 2024, 32, 200907. [Google Scholar] [CrossRef] [PubMed]
  249. Lan, T.; Luo, M.; Wei, X. Mesenchymal Stem/Stromal Cells in Cancer Therapy. J. Hematol. Oncol. 2021, 14, 195. [Google Scholar] [CrossRef] [PubMed]
  250. Takayama, Y.; Kosuke, K.; Nishikawa, M. Mesenchymal Stem/Stromal Cells as next-Generation Drug Delivery Vehicles for Cancer Therapeutics. Expert Opin. Drug Deliv. 2021, 18, 1627–1642. [Google Scholar] [CrossRef] [PubMed]
  251. Kamenšek, U.; Božič, T.; Čemažar, M.; Švajger, U. Antitumor Efficacy of Interleukin 12-Transfected Mesenchymal Stem Cells in B16-F10 Mouse Melanoma Tumor Model. Pharmaceutics 2025, 17, 278. [Google Scholar] [CrossRef] [PubMed]
  252. Adamus, T.; Hung, C.-Y.; Yu, C.; Kang, E.; Hammad, M.; Flores, L.; Nechaev, S.; Zhang, Q.; Gonzaga, J.M.; Muthaiyah, K.; et al. Glioma-Targeted Delivery of Exosome-Encapsulated Antisense Oligonucleotides Using Neural Stem Cells. Mol. Ther. Nucleic Acids 2021, 27, 611–620. [Google Scholar] [CrossRef] [PubMed]
  253. Kitadani, J.; Ojima, T.; Iwamoto, H.; Tabata, H.; Nakamori, M.; Nakamura, M.; Hayata, K.; Katsuda, M.; Miyajima, M.; Yamaue, H. Cancer Vaccine Therapy Using Carcinoembryonic Antigen—Expressing Dendritic Cells Generated from Induced Pluripotent Stem Cells. Sci. Rep. 2018, 8, 4569. [Google Scholar] [CrossRef] [PubMed]
  254. Jia, G.; Han, Y.; An, Y.; Ding, Y.; He, C.; Wang, X.; Tang, Q. NRP-1 Targeted and Cargo-Loaded Exosomes Facilitate Simultaneous Imaging and Therapy of Glioma in Vitro and in Vivo. Biomaterials 2018, 178, 302–316. [Google Scholar] [CrossRef] [PubMed]
  255. Zendedel, E.; Atkin, S.L.; Sahebkar, A. Use of Stem Cells as Carriers of Oncolytic Viruses for Cancer Treatment. J. Cell. Physiol. 2019, 234, 14906–14913. [Google Scholar] [CrossRef] [PubMed]
  256. Zhang, R.; Duan, X.; Liu, Y.; Xu, J.; Al-bashari, A.A.G.; Ye, P.; Ye, Q.; He, Y. The Application of Mesenchymal Stem Cells in Future Vaccine Synthesis. Vaccines 2023, 11, 1631. [Google Scholar] [CrossRef] [PubMed]
  257. Rahimian, S.; Mirkazemi, K.; Kamalinejad, A.; Doroudian, M. Exosome-Based Advances in Pancreatic Cancer: The Potential of Mesenchymal Stem Cells. Crit. Rev. Oncol. Hematol. 2025, 207, 104594. [Google Scholar] [CrossRef] [PubMed]
  258. Ghasempour, E.; Hesami, S.; Movahed, E.; Keshel, S.H.; Doroudian, M. Mesenchymal Stem Cell-Derived Exosomes as a New Therapeutic Strategy in the Brain Tumors. Stem Cell Res. Ther. 2022, 13, 527. [Google Scholar] [CrossRef] [PubMed]
  259. Huang, D.; Huang, W.; Liu, M.; Chen, J.; Xiao, D.; Peng, Z.; He, H.; Shen, H.; Jin, Q.; Chen, L.; et al. Progress of Mesenchymal Stem Cell-Derived Exosomes in Targeted Delivery of Antitumor Drugs. Cancer Cell Int. 2025, 25, 169. [Google Scholar] [CrossRef] [PubMed]
  260. Galland, S.; Stamenkovic, I. Mesenchymal Stromal Cells in Cancer: A Review of Their Immunomodulatory Functions and Dual Effects on Tumor Progression. J. Pathol. 2020, 250, 555–572. [Google Scholar] [CrossRef] [PubMed]
  261. Marar, C.; Starich, B.; Wirtz, D. Extracellular Vesicles in Immunomodulation and Tumor Progression. Nat. Immunol. 2021, 22, 560–570. [Google Scholar] [CrossRef] [PubMed]
  262. Ji, P.; Yang, Z.; Li, H.; Wei, M.; Yang, G.; Xing, H.; Li, Q. Smart Exosomes with Lymph Node Homing and Immune-Amplifying Capacities for Enhanced Immunotherapy of Metastatic Breast Cancer. Mol. Ther. Nucleic Acids 2021, 26, 987–996. [Google Scholar] [CrossRef] [PubMed]
  263. Phung, C.D.; Pham, T.T.; Nguyen, H.T.; Nguyen, T.T.; Ou, W.; Jeong, J.-H.; Choi, H.-G.; Ku, S.K.; Yong, C.S.; Kim, J.O. Anti-CTLA-4 Antibody-Functionalized Dendritic Cell-Derived Exosomes Targeting Tumor-Draining Lymph Nodes for Effective Induction of Antitumor T-Cell Responses. Acta Biomater. 2020, 115, 371–382. [Google Scholar] [CrossRef] [PubMed]
  264. Meng, Y.; Yao, Z.; Ke, X.; Hu, M.; Ren, H.; Gao, S.; Zhang, H. Extracellular Vesicles-Based Vaccines: Emerging Immunotherapies against Cancer. J. Control. Release 2025, 378, 438–459. [Google Scholar] [CrossRef] [PubMed]
  265. Peng, B.; Nguyen, T.M.; Jayasinghe, M.K.; Gao, C.; Pham, T.T.; Vu, L.T.; Yeo, E.Y.M.; Yap, G.; Wang, L.; Goh, B.C.; et al. Robust Delivery of RIG-I Agonists Using Extracellular Vesicles for Anti-cancer Immunotherapy. J. Extracell. Vesicles 2022, 11, e12187. [Google Scholar] [CrossRef] [PubMed]
  266. Wang, J.; Zhang, Z.; Zhuo, Y.; Zhang, Z.; Chen, R.; Liang, L.; Jiang, X.; Nie, D.; Liu, C.; Zou, Z.; et al. Endoplasmic Reticulum-Targeted Delivery of Celastrol and PD-L1 siRNA for Reinforcing Immunogenic Cell Death and Potentiating Cancer Immunotherapy. Acta Pharm. Sin. B 2024, 14, 3643–3660. [Google Scholar] [CrossRef]
  267. Xu, Z.; Zeng, S.; Gong, Z.; Yan, Y. Exosome-Based Immunotherapy: A Promising Approach for Cancer Treatment. Mol. Cancer 2020, 19, 160. [Google Scholar] [CrossRef] [PubMed]
  268. Tian, J.; Han, Z.; Song, D.; Peng, Y.; Xiong, M.; Chen, Z.; Duan, S.; Zhang, L. Engineered Exosome for Drug Delivery: Recent Development and Clinical Applications. Int. J. Nanomed. 2023, 18, 7923–7940. [Google Scholar] [CrossRef] [PubMed]
  269. Wu, J.; Jin, Z.; Fu, T.; Qian, Y.; Bian, X.; Zhang, X.; Zhang, J. Extracellular Vesicle-Based Drug Delivery Systems in Cancer Therapy. Int. J. Mol. Sci. 2025, 26, 4835. [Google Scholar] [CrossRef] [PubMed]
  270. Hu, C.-M.J.; Zhang, L.; Aryal, S.; Cheung, C.; Fang, R.H.; Zhang, L. Erythrocyte Membrane-Camouflaged Polymeric Nanoparticles as a Biomimetic Delivery Platform. Proc. Natl. Acad. Sci. USA 2011, 108, 10980–10985. [Google Scholar] [CrossRef] [PubMed]
  271. Qi, S.; Zhang, H.; Zhang, X.; Yu, X.; Wang, Y.; Meng, Q.-F.; Yang, K.; Bai, B.; Tian, R.; Zhu, S.; et al. Supramolecular Engineering of Cell Membrane Vesicles for Cancer Immunotherapy. Sci. Bull. 2022, 67, 1898–1909. [Google Scholar] [CrossRef] [PubMed]
  272. Li, Q.; Byun, J.; Kim, D.; Wu, Y.; Lee, J.; Oh, Y.-K. Cell Membrane-Coated mRNA Nanoparticles for Enhanced Delivery to Dendritic Cells and Immunotherapy. Asian J. Pharm. Sci. 2024, 19, 100968. [Google Scholar] [CrossRef] [PubMed]
  273. Wang, Z.; Miao, F.; Gu, L.; Zhang, R.; Ma, Y.; Li, Y.; Zheng, J.; Lin, Z.; Gao, Y.; Huang, L.; et al. Stimulator of Interferon Genes-Activated Biomimetic Dendritic Cell Nanovaccine as a Chemotherapeutic Booster to Enhance Systemic Fibrosarcoma Treatment. ACS Nano 2024, 18, 24219–24235. [Google Scholar] [CrossRef] [PubMed]
  274. Johnson, D.T.; Zhou, J.; Kroll, A.V.; Fang, R.H.; Yan, M.; Xiao, C.; Chen, X.; Kline, J.; Zhang, L.; Zhang, D.-E. Acute Myeloid Leukemia Cell Membrane-Coated Nanoparticles for Cancer Vaccination Immunotherapy. Leukemia 2021, 36, 994. [Google Scholar] [CrossRef] [PubMed]
  275. Li, J.; Wu, Y.; Wang, J.; Xu, X.; Zhang, A.; Li, Y.; Zhang, Z. Macrophage Membrane-Coated Nano-Gemcitabine Promotes Lymphocyte Infiltration and Synergizes AntiPD-L1 to Restore the Tumoricidal Function. ACS Nano 2023, 17, 322–336. [Google Scholar] [CrossRef] [PubMed]
  276. Zafar, H.; Zhang, J.; Raza, F.; Pan, X.; Hu, Z.; Feng, H.; Shen, Q. Biomimetic Gold Nanocages Incorporating Copper-Human Serum Albumin for Tumor Immunotherapy via Cuproptosis-Lactate Regulation. J. Control. Release 2024, 372, 446–466. [Google Scholar] [CrossRef] [PubMed]
  277. Du, T.; Wang, Y.; Luan, Z.; Zhao, C.; Yang, K. Tumor-Associated Macrophage Membrane-Camouflaged pH-Responsive Polymeric Micelles for Combined Cancer Chemotherapy-Sensitized Immunotherapy. Int. J. Pharm. 2022, 624, 121911. [Google Scholar] [CrossRef] [PubMed]
  278. Zeng, Y.; Li, S.; Zhang, S.; Wang, L.; Yuan, H.; Hu, F. Cell Membrane Coated-Nanoparticles for Cancer Immunotherapy. Acta Pharm. Sin. B 2022, 12, 3233–3254. [Google Scholar] [CrossRef] [PubMed]
  279. Yang, C.; Chen, Y.; Liu, J.; Zhang, W.; He, Y.; Chen, F.; Xie, X.; Tang, J.; Guan, S.; Shao, D.; et al. Leveraging Senescent Cancer Cell Membrane to Potentiate Cancer Immunotherapy through Biomimetic Nanovaccine. Adv. Sci. 2024, 11, 2400630. [Google Scholar] [CrossRef] [PubMed]
  280. Zheng, L.; Chang, R.; Liang, B.; Wang, Y.; Zhu, Y.; Jia, Z.; Fan, J.; Zhang, Z.; Du, B.; Kong, D. Overcoming Drug Resistance through Extracellular Vesicle-Based Drug Delivery System in Cancer Treatment. Cancer Drug Resist. 2024, 7, 50. [Google Scholar] [CrossRef] [PubMed]
  281. Pumpens, P.; Pushko, P. Virus-Like Particles: A Comprehensive Guide; CRC Press: Boca Raton, FL, USA, 2022; ISBN 978-1-000-56987-2. [Google Scholar]
  282. Nooraei, S.; Bahrulolum, H.; Hoseini, Z.S.; Katalani, C.; Hajizade, A.; Easton, A.J.; Ahmadian, G. Virus-like Particles: Preparation, Immunogenicity and Their Roles as Nanovaccines and Drug Nanocarriers. J. Nanobiotechnology 2021, 19, 59. [Google Scholar] [CrossRef] [PubMed]
  283. Kato, T.; Yui, M.; Deo, V.K.; Park, E.Y. Development of Rous Sarcoma Virus-like Particles Displaying hCC49 scFv for Specific Targeted Drug Delivery to Human Colon Carcinoma Cells. Pharm. Res. 2015, 32, 3699–3707. [Google Scholar] [CrossRef] [PubMed]
  284. Garg, A.; Dewangan, H.K. Nanoparticles as Adjuvants in Vaccine Delivery. Crit. Rev. Ther. Drug Carr. Syst. 2020, 37, 2. [Google Scholar] [CrossRef] [PubMed]
  285. Ruzzi, F.; Semprini, M.S.; Scalambra, L.; Palladini, A.; Angelicola, S.; Cappello, C.; Pittino, O.M.; Nanni, P.; Lollini, P.-L. Virus-like Particle (VLP) Vaccines for Cancer Immunotherapy. Int. J. Mol. Sci. 2023, 24, 12963. [Google Scholar] [CrossRef] [PubMed]
  286. Venkataraman, S.; Hefferon, K. Application of Plant Viruses in Biotechnology, Medicine, and Human Health. Viruses 2021, 13, 1697. [Google Scholar] [CrossRef] [PubMed]
  287. Peralta-Cuevas, E.; Garcia-Atutxa, I.; Huerta-Saquero, A.; Villanueva-Flores, F. The Role of Plant Virus-like Particles in Advanced Drug Delivery and Vaccine Development: Structural Attributes and Application Potential. Viruses 2025, 17, 148. [Google Scholar] [CrossRef] [PubMed]
  288. Zhao, Z.; Ledezma, D.K.; Affonso de Oliveira, J.F.; Omole, A.O.; Steinmetz, N.F. A Cowpea Mosaic Virus Adjuvant Conjugated to Liposomes Loaded with Tumor Cell Lysates as an Ovarian Cancer Vaccine. Nat. Commun. 2025, 16, 5047. [Google Scholar] [CrossRef] [PubMed]
  289. Gautam, A.; Beiss, V.; Wang, C.; Wang, L.; Steinmetz, N.F. Plant Viral Nanoparticle Conjugated with Anti-PD-1 Peptide for Ovarian Cancer Immunotherapy. Int. J. Mol. Sci. 2021, 22, 9733. [Google Scholar] [CrossRef] [PubMed]
  290. Shahgolzari, M.; Venkataraman, S.; Osano, A.; Akpa, P.A.; Hefferon, K. Plant Virus Nanoparticles Combat Cancer. Vaccines 2023, 11, 1278. [Google Scholar] [CrossRef] [PubMed]
  291. Chung, Y.H.; Park, J.; Cai, H.; Steinmetz, N.F. S100A9-targeted Cowpea Mosaic Virus as a Prophylactic and Therapeutic Immunotherapy against Metastatic Breast Cancer and Melanoma. Adv. Sci. 2021, 8, 2101796. [Google Scholar] [CrossRef] [PubMed]
  292. Beatty, P.H.; Lewis, J.D. Cowpea Mosaic Virus Nanoparticles for Cancer Imaging and Therapy. Adv. Drug Deliv. Rev. 2019, 145, 130–144. [Google Scholar] [CrossRef] [PubMed]
  293. Shahgolzari, M.; Pazhouhandeh, M.; Milani, M.; Yari Khosroushahi, A.; Fiering, S. Plant Viral Nanoparticles for Packaging and in Vivo Delivery of Bioactive Cargos. WIREs Nanomed. Nanobiotechnology 2020, 12, e1629. [Google Scholar] [CrossRef] [PubMed]
  294. Shahgolzari, M.; Dianat-Moghadam, H.; Yavari, A.; Fiering, S.N.; Hefferon, K. Multifunctional Plant Virus Nanoparticles for Targeting Breast Cancer Tumors. Vaccines 2022, 10, 1431. [Google Scholar] [CrossRef] [PubMed]
  295. Komane, M.D.; Kayoka-Kabongo, P.N.; Rutkowska, D.A. The Use of Plant Viral Nanoparticles in Cancer Biotherapy—A Review. Viruses 2025, 17, 218. [Google Scholar] [CrossRef] [PubMed]
  296. Shahgolzari, M.; Fiering, S. Emerging Potential of Plant Virus Nanoparticles (PVNPs) in Anticancer Immunotherapies. J. Cancer Immunol. 2022, 4, 22–29. [Google Scholar] [CrossRef] [PubMed]
  297. Stern, S.T.; Affonso de Oliveira, J.F.; Gatus, J.; Edmondson, E.; Neun, B.W.; Dobrovolskaia, M.A.; Steinmetz, N.F. Preclinical SC and IV Repeat-Dose Toxicology of a Cowpea Mosaic Virus—A Cancer Immunotherapy Candidate. Toxicol. Rep. 2025, 14, 102022. [Google Scholar] [CrossRef] [PubMed]
  298. Nkanga, C.I.; Steinmetz, N.F. The Pharmacology of Plant Virus Nanoparticles. Virology 2021, 556, 39–61. [Google Scholar] [CrossRef] [PubMed]
  299. Stark, J.C.; Jaroentomeechai, T.; Moeller, T.D.; Hershewe, J.M.; Warfel, K.F.; Moricz, B.S.; Martini, A.M.; Dubner, R.S.; Hsu, K.J.; Stevenson, T.C.; et al. On-Demand Biomanufacturing of Protective Conjugate Vaccines. Sci. Adv. 2021, 7, eabe9444. [Google Scholar] [CrossRef] [PubMed]
  300. Duong, M.T.-Q.; Qin, Y.; You, S.-H.; Min, J.-J. Bacteria-Cancer Interactions: Bacteria-Based Cancer Therapy. Exp. Mol. Med. 2019, 51, 152. [Google Scholar] [CrossRef] [PubMed]
  301. McCarthy, E.F. The Toxins of William B. Coley and the Treatment of Bone and Soft-Tissue Sarcomas. Iowa Orthop. J. 2006, 26, 154–158. [Google Scholar] [PubMed]
  302. Kalaora, S.; Nagler, A.; Nejman, D.; Alon, M.; Barbolin, C.; Barnea, E.; Ketelaars, S.L.C.; Cheng, K.; Vervier, K.; Shental, N.; et al. Identification of Bacteria-Derived HLA-Bound Peptides in Melanoma. Nature 2021, 592, 138–143. [Google Scholar] [CrossRef] [PubMed]
  303. Naghavian, R.; Faigle, W.; Oldrati, P.; Wang, J.; Toussaint, N.C.; Qiu, Y.; Medici, G.; Wacker, M.; Freudenmann, L.K.; Bonté, P.-E.; et al. Microbial Peptides Activate Tumour-Infiltrating Lymphocytes in Glioblastoma. Nature 2023, 617, 807–817. [Google Scholar] [CrossRef] [PubMed]
  304. Pawelek, J.M.; Low, K.B.; Bermudes, D. Tumor-Targeted Salmonella as a Novel Anticancer Vector. Cancer Res. 1997, 57, 4537–4544. [Google Scholar] [PubMed]
  305. Nejman, D.; Livyatan, I.; Fuks, G.; Gavert, N.; Zwang, Y.; Geller, L.T.; Rotter-Maskowitz, A.; Weiser, R.; Mallel, G.; Gigi, E.; et al. The Human Tumor Microbiome Is Composed of Tumor Type-Specific Intracellular Bacteria. Science 2020, 368, 973–980. [Google Scholar] [CrossRef] [PubMed]
  306. Redenti, A.; Im, J.; Redenti, B.; Li, F.; Rouanne, M.; Sheng, Z.; Sun, W.; Gurbatri, C.R.; Huang, S.; Komaranchath, M.; et al. Probiotic Neoantigen Delivery Vectors for Precision Cancer Immunotherapy. Nature 2024, 635, 453–461. [Google Scholar] [CrossRef] [PubMed]
  307. Nguyen, D.-H.; You, S.-H.; Ngo, H.T.-T.; Van Nguyen, K.; Tran, K.V.; Chu, T.-H.; Kim, S.; Ha, S.-J.; Hong, Y.; Min, J.-J. Reprogramming the Tumor Immune Microenvironment Using Engineered Dual-Drug Loaded Salmonella. Nat. Commun. 2024, 15, 6680. [Google Scholar] [CrossRef] [PubMed]
  308. Thomas, S.C.; Madaan, T.; Kamble, N.S.; Siddiqui, N.A.; Pauletti, G.M.; Kotagiri, N. Engineered Bacteria Enhance Immunotherapy and Targeted Therapy through Stromal Remodeling of Tumors. Adv. Healthc. Mater. 2022, 11, e2101487. [Google Scholar] [CrossRef] [PubMed]
  309. Ueki, H.; Kitagawa, K.; Kato, M.; Yanase, S.; Okamura, Y.; Bando, Y.; Hara, T.; Terakawa, T.; Furukawa, J.; Nakano, Y.; et al. An Oral Cancer Vaccine Using Bifidobacterium Vector Augments Combination of Anti-PD-1 and Anti-CTLA-4 Antibodies in Mouse Renal Cell Carcinoma Model. Sci. Rep. 2023, 13, 9994. [Google Scholar] [CrossRef] [PubMed]
  310. Kitagawa, K.; Tatsumi, M.; Kato, M.; Komai, S.; Doi, H.; Hashii, Y.; Katayama, T.; Fujisawa, M.; Shirakawa, T. An Oral Cancer Vaccine Using a Bifidobacterium Vector Suppresses Tumor Growth in a Syngeneic Mouse Bladder Cancer Model. Mol. Ther. Oncolytics 2021, 22, 592–603. [Google Scholar] [CrossRef] [PubMed]
  311. Zhang, Y.; Lei, Y.; Ou, Q.; Chen, M.; Tian, S.; Tang, J.; Li, R.; Liang, Q.; Chen, Z.; Wang, C. Listeria-Vectored Cervical Cancer Vaccine Candidate Strains Reduce MDSCs via the JAK-STAT Signaling Pathway. BMC Biol. 2024, 22, 88. [Google Scholar] [CrossRef] [PubMed]
  312. Guo, Y.; Song, M.; Liu, X.; Chen, Y.; Xun, Z.; Sun, Y.; Tan, W.; He, J.; Zheng, J.H. Photodynamic Therapy-Improved Oncolytic Bacterial Immunotherapy with FAP-Encoding S. Typhimurium. J. Control. Release 2022, 351, 860–871. [Google Scholar] [CrossRef] [PubMed]
  313. Han, D.; Wang, F.; Ma, Y.; Zhao, Y.; Zhang, W.; Zhang, Z.; Liu, H.; Yang, X.; Zhang, C.; Zhang, J.; et al. Redirecting Antigens by Engineered Photosynthetic Bacteria and Derived Outer Membrane Vesicles for Enhanced Cancer Immunotherapy. ACS Nano 2023, 17, 18716–18731. [Google Scholar] [CrossRef] [PubMed]
  314. Zheng, K.; Feng, Y.; Li, L.; Kong, F.; Gao, J.; Kong, X. Engineered Bacterial Outer Membrane Vesicles: A Versatile Bacteria-Based Weapon against Gastrointestinal Tumors. Theranostics 2024, 14, 761–787. [Google Scholar] [CrossRef] [PubMed]
  315. Qing, S.; Lyu, C.; Zhu, L.; Pan, C.; Wang, S.; Li, F.; Wang, J.; Yue, H.; Gao, X.; Jia, R.; et al. Biomineralized Bacterial Outer Membrane Vesicles Potentiate Safe and Efficient Tumor Microenvironment Reprogramming for Anticancer Therapy. Adv. Mater. 2020, 32, 2002085. [Google Scholar] [CrossRef] [PubMed]
  316. Park, K.; Svennerholm, K.; Crescitelli, R.; Lässer, C.; Gribonika, I.; Lötvall, J. Synthetic Bacterial Vesicles Combined with Tumour Extracellular Vesicles as Cancer Immunotherapy. J. Extracell. Vesicles 2021, 10, e12120. [Google Scholar] [CrossRef] [PubMed]
  317. Jahromi, L.P.; Fuhrmann, G. Bacterial Extracellular Vesicles: Understanding Biology Promotes Applications as Nanopharmaceuticals. Adv. Drug Deliv. Rev. 2021, 173, 125–140. [Google Scholar] [CrossRef] [PubMed]
  318. Hosseini-Giv, N.; Basas, A.; Hicks, C.; El-Omar, E.; El-Assaad, F.; Hosseini-Beheshti, E. Bacterial Extracellular Vesicles and Their Novel Therapeutic Applications in Health and Cancer. Front. Cell. Infect. Microbiol. 2022, 12, 962216. [Google Scholar] [CrossRef] [PubMed]
  319. Kang, S.-R.; Nguyen, D.-H.; Yoo, S.W.; Min, J.-J. Bacteria and Bacterial Derivatives as Delivery Carriers for Immunotherapy. Adv. Drug Deliv. Rev. 2022, 181, 114085. [Google Scholar] [CrossRef] [PubMed]
  320. Zhou, M.; Tang, Y.; Xu, W.; Hao, X.; Li, Y.; Huang, S.; Xiang, D.; Wu, J. Bacteria-Based Immunotherapy for Cancer: A Systematic Review of Preclinical Studies. Front. Immunol. 2023, 14, 1140463. [Google Scholar] [CrossRef] [PubMed]
  321. Dailey, K.M.; Small, J.M.; Pullan, J.E.; Winfree, S.; Vance, K.E.; Orr, M.; Mallik, S.; Bayles, K.W.; Hollingsworth, M.A.; Brooks, A.E. An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-Modified Clostridium Novyi-Non Toxic. PLoS ONE 2023, 18, e0289183. [Google Scholar] [CrossRef] [PubMed]
  322. Lin, F.; Yin, S.; Zhang, Z.; Yu, Y.; Fang, H.; Liang, Z.; Zhu, R.; Zhou, H.; Li, J.; Cao, K.; et al. Multimodal Targeting Chimeras Enable Integrated Immunotherapy Leveraging Tumor-Immune Microenvironment. Cell 2024, 187, 7470–7491. [Google Scholar] [CrossRef] [PubMed]
  323. Li, Y.; Feng, S.; Dai, P.; Liu, F.; Shang, Y.; Yang, Q.; Qin, J.; Yuchi, Z.; Wang, Z.; Zhao, Y. Tailored Trojan Horse Nanocarriers for Enhanced Redox-Responsive Drug Delivery. J. Control. Release 2022, 342, 201–209. [Google Scholar] [CrossRef] [PubMed]
  324. Guo, S.; Guan, T.; Ke, Y.; Lin, Y.; Tai, R.; Ye, J.; Deng, Z.; Deng, S.; Ou, C. Biologically Logic-Gated Trojan-Horse Strategy for Personalized Triple-Negative Breast Cancer Precise Therapy by Selective Ferroptosis and STING Pathway Provoking. Biomaterials 2025, 315, 122905. [Google Scholar] [CrossRef] [PubMed]
  325. Yasothamani, V.; Karthikeyan, L.; Sarathy, N.P.; Vivek, R. Targeted Designing of Multimodal Tumor-Seeking Nanomedicine for Breast Cancer-Specific Triple-Therapeutic Effects. ACS Appl. Bio Mater. 2021, 4, 6575–6588. [Google Scholar] [CrossRef] [PubMed]
  326. Xu, W.; Zeng, Z.; Tang, Y.; Tian, J.; Hao, X.; Sun, P.; Peng, Y.; Tian, T.; Xiang, D.; Wang, R.; et al. Spatiotemporal-Controllable ROS-Responsive Camptothecin Nano-Bomb for Chemo/Photo/Immunotherapy in Triple-Negative Breast Cancer. J. Nanobiotechnol. 2024, 22, 798. [Google Scholar] [CrossRef] [PubMed]
  327. Kumar, A.; Dixit, S.; Srinivasan, K.; M, D.; Vincent, P.M.D.R. Personalized Cancer Vaccine Design Using AI-Powered Technologies. Front. Immunol. 2024, 15, 1357217. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Schematic of vector targeting strategies, illustrating genetic engineering, surface modification, and biomembrane-based targeting approaches. Created in https://BioRender.com.
Figure 1. Schematic of vector targeting strategies, illustrating genetic engineering, surface modification, and biomembrane-based targeting approaches. Created in https://BioRender.com.
Ijms 26 06879 g001
Figure 2. Schematic of vector retargeting strategies and antitumor immune activation mechanisms. Using adenovirus as an example, the figure illustrates engineered tropism and the associated pathways of immune stimulation. Created in https://BioRender.com.
Figure 2. Schematic of vector retargeting strategies and antitumor immune activation mechanisms. Using adenovirus as an example, the figure illustrates engineered tropism and the associated pathways of immune stimulation. Created in https://BioRender.com.
Ijms 26 06879 g002
Figure 3. Schematic of nanoparticle carrier retargeting strategies and antitumor immune mechanisms. Using lipid nanoparticles (LNPs) as an example, the figure illustrates design modifications and their functional effects. Created in https://BioRender.com.
Figure 3. Schematic of nanoparticle carrier retargeting strategies and antitumor immune mechanisms. Using lipid nanoparticles (LNPs) as an example, the figure illustrates design modifications and their functional effects. Created in https://BioRender.com.
Ijms 26 06879 g003
Table 1. Targeting strategies for Ad/AAV vectors.
Table 1. Targeting strategies for Ad/AAV vectors.
Strategy TypeModificationTargetReference
Ad fiber engineeringAd35 fiber replacement to evade neutralization and improve uptakeGuanylyl cyclase C+ gastrointestinal tumors[69]
Ad5 genetic reprogrammingMelARV expression with ISD domain mutationTumors expressing endogenous retroviral antigens[70]
OAd + HDAd vector combinationCAdVEC platform for complex TME adaptationSolid tumors[71]
Heterologous Ad-based prime–boostChAd68 priming with VEE-samRNA boostTumors bearing personalized neoantigens[72]
AAV capsid S663 mutation + EV deliveryEV-mediated delivery to bypass immune memory and receptor bindingMelanoma[73]
Ad–PAMAM + hydrogel formulationLocalized release via hydrogel for enhanced regional accumulation[74]
Oncolytic Ad + tumor-derived EVsTrojan horse delivery using tumor membrane camouflage[75]
Multi-serotype AAV vectorCo-delivery of PD-1 and tumor antigens to dendritic cellsMesothelioma[76]
Table 3. Comparison of the advantages and disadvantages of common polymers and their common application ranges.
Table 3. Comparison of the advantages and disadvantages of common polymers and their common application ranges.
Polymer TypeAdvantageDisadvantageCommon ApplicationsReference
PLGABiodegradable; good biocompatibility; FDA-approved; controlled releaseAcidic degradation products acidify microenvironment; rapid clearance without modificationLaser-activated controlled drug delivery at targeted sites[204,205,206]
PEGReduces protein adsorption/opsonization; verified safety; improves nanoparticle stability and circulationNon-biodegradable, accumulates; antibody induction upon repeated use; cannot form nanoparticles aloneCopolymer to enhance carrier circulation and stability[206,207]
Chitosan (natural polycation)Biodegradable; mucosal absorption; strongly binds negatively charged drugsBatch-to-batch variability; high solubility only under acidic conditions, limiting systemic administrationMucosal vaccine delivery; systemic vaccine adjuvant[204,208,209]
Polycaprolactone (PCL)Biodegradable; good biocompatibility; FDA-approved; efficiently encapsulates hydrophobic drugsSlow degradation; poor hydrophilic drug encapsulation; forms semi-crystalline matrices, delaying releaseTargeted drug delivery (active/passive)[204,210]
Albumin (protein polymer nanoparticles)Biocompatible; biodegradable; clinically established; low immunogenicity; tumor-targeting capabilityInstability (easy dissociation); biological sourcing complicates purification; limited control over size/drug loadingDelivery of chemotherapeutics (e.g., paclitaxel)[211,212,213,214]
Table 4. Comparison of composition, size, morphology, key applications, and challenges of common inorganic nanoparticles.
Table 4. Comparison of composition, size, morphology, key applications, and challenges of common inorganic nanoparticles.
Nanoparticle TypeComposition, Size, MorphologyKey Applications ChallengesReference
Gold Nanoparticles (AuNPs)Gold core stabilized by surface modifications; spherical (5–100 nm) or tunable morphologies (rods, shells) with adjustable optical propertiesPhotothermal therapy, drug/gene delivery, imagingNon-degradable; immune activation; high production cost[225,226]
Mesoporous Silica Nanoparticles (MSNs)Amorphous silica with ordered nanopores; spherical (~50–200 nm), pore diameter 2–6 nm; usually surface-modifiedSustained-release drug delivery, combination therapyResidual additives; liver accumulation; poor biodegradability; mechanical brittleness[227,228,229]
Iron Oxide Nanoparticles (Magnetic NPs)Crystalline iron oxide stabilized by coatings; spherical (5–50 nm); ≤20 nm particles show superparamagnetism (no residual magnetization)Magnetic targeting, hyperthermia, MRI imaging contrast agentsCoating-dependent stability; limited targeting depth; dose-related toxicity[230,231]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wu, J.; Liang, J.; Zhang, Y.; Dong, C.; Tan, D.; Wang, H.; Zheng, Y.; He, Q. Strategic Advances in Targeted Delivery Carriers for Therapeutic Cancer Vaccines. Int. J. Mol. Sci. 2025, 26, 6879. https://doi.org/10.3390/ijms26146879

AMA Style

Wu J, Liang J, Zhang Y, Dong C, Tan D, Wang H, Zheng Y, He Q. Strategic Advances in Targeted Delivery Carriers for Therapeutic Cancer Vaccines. International Journal of Molecular Sciences. 2025; 26(14):6879. https://doi.org/10.3390/ijms26146879

Chicago/Turabian Style

Wu, Junxi, Jinghui Liang, Yuan Zhang, Chunyan Dong, Dejiang Tan, Hongyu Wang, Yiyang Zheng, and Qing He. 2025. "Strategic Advances in Targeted Delivery Carriers for Therapeutic Cancer Vaccines" International Journal of Molecular Sciences 26, no. 14: 6879. https://doi.org/10.3390/ijms26146879

APA Style

Wu, J., Liang, J., Zhang, Y., Dong, C., Tan, D., Wang, H., Zheng, Y., & He, Q. (2025). Strategic Advances in Targeted Delivery Carriers for Therapeutic Cancer Vaccines. International Journal of Molecular Sciences, 26(14), 6879. https://doi.org/10.3390/ijms26146879

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop