The research progress of gastric cancer vaccines: a narrative review

Introduction

Gastric cancer (GC) is the fifth most commonly diagnosed cancer worldwide and the third leading cause of cancer-related death (1). Although chemotherapy, targeted therapy, and immunotherapy have improved systemic treatment, most patients are diagnosed at an advanced stage, with a median overall survival (mOS) of only approximately 8 months, and conventional treatments offer limited benefits (2). Immunotherapy has become one of the important therapeutic strategies for advanced GC. Among them, tumor vaccines, as a form of active immunotherapy, exert antitumor effects by stimulating host immune responses and exhibit favorable tolerability and low dose-limiting toxicity, making them a current research hotspot (3). Tumor vaccines can deliver tumor-associated antigens (TAAs) and activate immune cells to specifically eliminate tumor cells, enabling precise therapy. Their applications mainly include palliative treatment for advanced cancer, adjuvant therapy after surgery, and tumor prevention in high-risk populations (4). GC has a high incidence and poor prognosis at advanced stages, with limitations in conventional treatments. Meanwhile, GC expresses defined TAAs, providing a basis for targeted vaccine design (5). Furthermore, vaccines can overcome the immunosuppressive microenvironment by optimizing delivery systems, incorporating adjuvants, and implementing combination therapies, which is expected to compensate for the shortcomings of existing treatments.

This review summarizes the recent advancements in the development of various GC vaccines and provides an in-depth discussion on how to overcome the impact of the immunosuppressive microenvironment on GC vaccines, how to optimize the design of existing vaccines, and how to more scientifically evaluate vaccine efficacy. We present this article in accordance with the Narrative Review reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2299/rc).

Methods

A comprehensive narrative review of the relevant literature is presented in this article. We incorporated relevant English articles available in the PubMed database as at 31 December 2025. Search terms included ((Gastric Neoplasms[MH] OR Gastric Cancer[TW] OR Stomach Cancer[TW] OR Gastric Carcinoma[TW] OR Gastric Adenocarcinoma[TW] OR Stomach Neoplasm*[TW]) AND (Cancer Vaccines[MH] OR Therapeutic Vaccines[TW] OR Peptide Vaccine[TW] OR mRNA Vaccine[TW] OR DNA Vaccine[TW] OR Dendritic Cell Vaccine[TW] OR Viral Vector Vaccine[TW])) AND (Therapeutic[TW] OR Immunotherapy[TW]) NOT (Prophylactic[TW] OR Helicobacter Pylori Vaccine[TW] OR Prevention[TW] OR Prophylaxis[TW]). We reviewed all research papers, systematic reviews, and meta-analyses. Exclusions included case reports, letters to the editor, short communications, editorials, articles not written in English, studies with low relevance to the present literature review, and older articles with limited academic value. The search strategy is summarized in Table 1.

Results

Helicobacter pylori (H. pylori) vaccine

The presence of H. pylori is strongly associated with the development of GC

Infection with H. pylori is closely associated with gastric carcinogenesis. First, the helical morphology and flagella of H. pylori enable the bacterium to colonize the highly acidic environment of the stomach. By producing urease, H. pylori hydrolyzes urea into ammonia, which neutralizes gastric acid, damages the gastric mucosal barrier and creates a niche that supports persistent colonization. Second, H. pylori virulence factors, particularly cytotoxin-associated gene A (CagA), can be translocated into gastric epithelial cells, where they activate multiple intracellular signaling pathways and drive a sequence of precancerous alterations, including non-atrophic gastritis, atrophic gastritis, intestinal metaplasia and dysplasia. Ultimately leading to the development of GC (6-9).

Basic clinical evidence of H. pylori vaccine’s anti-GC effect

H. pylori vaccines are designed to deliver defined antigenic components of the bacterium to the host immune system, resulting in their activation and the generation of pathogen-specific antibodies and memory cells and conferring protection against primary infection and reinfection (10). Animal experiments have shown that H. pylori vaccination can promote bacterial clearance and reduce the risk of abnormal epithelial proliferation and subsequent carcinogenesis (6). Early-phase, small-scale clinical studies have reported that vaccinated individuals exhibit substantially lower H. pylori infection rates and improved gastric mucosal pathology. Moreover, longitudinal observations indicate a tendency toward reduced GC incidence in vaccinated cohorts, supporting the potential of H. pylori vaccines as a preventive strategy against gastric malignancy (11,12).

Progress in H. pylori vaccine development

In current clinical practice, H. pylori infection is predominantly managed with combination antibiotic regimens. However, this treatment regimen is often accompanied by a high recurrence rate and can lead to H. pylori resistance (8). In this context, the development of H. pylori vaccines offers a promising strategy for both the prevention and adjunctive treatment of H. pylori infection (13).

Third Military Medical University and Chongqing KangWei Biotechnology in China have developed an oral recombinant H. pylori vaccine. A randomized, double-blind, placebo-controlled phase 3 clinical trial enrolled 4,464 participants, of whom 4,403 (99%) completed the three-dose regimen and were included in the efficacy analysis. After 3 years of follow-up, the vaccine demonstrated an efficacy of 71.8% (95% CI: 48.2–85.6%) against H. pylori infection (14).

In an experimental study, Chehelgerdi et al. developed a DNA vaccine: pcDNA3.1(+)-cagW-CS-NPs. Mice vaccinated with pcDNA3.1(+)-cagW-CS-NPs showed marked protection against H. pylori infection at day 45 post-immunization (P<0.001) (15).

Accumulating evidence indicates that Salmonella can serve as an efficient live vector for delivering heterologous antigens from diverse pathogens (16). Using this approach, Ghasemi et al. applied a novel Protective Immunity Enhanced Salmonella Vaccine (PIESV) platform. In their study, sterile protection against H. pylori SS1 infection was achieved in 7 of 10 immunized mice, highlighting the strong protective potential of this multicomponent vaccine (17). In parallel, Ni et al. constructed two oral live-carrier vaccines, named LL-UreA and LL-LTB-UreA. Oral vaccination with LL-UreA or LL-LTB-UreA markedly enhanced the production of interferon-gamma (IFN-γ), interleukin (IL)-17A and IL-10, and increased the count of CD4⁺IFN-γ⁺ and CD4⁺IL-17A⁺ T cells. Notably, administration of LL-UreA led to an approximate 70% reduction in H. pylori colonization in mice, underscoring the promise of live bacterial vectors for H. pylori vaccine development (18).

More and more evidence indicates that H. pylori-induced cellular immune responses may, under certain conditions, exert inhibitory effects on tumor development (19). In one experimental study, fragments of the H. pylori virulence genes cagA, vacA and babA were cloned into a recombinant pcDNA3 plasmid, which was used to immunize BALB/c mice. Activated splenic CD3⁺ T cells were then purified from immunized animals. These CD3⁺ T cells significantly inhibited GC cell growth in vitro, and adoptive transfer of CD3⁺ T cells suppressed the progression of xenograft GC in vivo, with average tumor inhibition rates of 35.8–72.3% in vivo and 77.6%±4.7% in vitro (20). In addition, researchers have developed an engineered live bacterial near-infrared (NIR)-triggered system [H. pylori-Chlorin e6 (Ce6)]. In tumor-bearing mice, vaccination with this system resulted in an 80% survival rate at day 45 (21) (the principal mechanisms of action of GC vaccines are illustrated in Figure 1).

Figure 1 Mechanisms of action of major therapeutic GC vaccines. DCs, dendritic cells; GC, gastric cancer; IFN, interferon; IL, interleukin; NK, natural killer; TNF, tumor necrosis factor.

Recent advances in the development of H. pylori vaccines have led to substantial progress. Multi-epitope vaccines, DNA-based vaccines and formulations using Salmonella or Lactococcus as delivery vectors have elicited favorable immune responses (6). Notably, vaccines incorporating H. pylori antigens have also shown the potential to inhibit the growth of GC xenografts. Nevertheless, further studies are needed to optimize vaccine design and to enhance both immunogenicity and safety. Given the specific etiological association between H. pylori infection and gastric carcinogenesis, the development of H. pylori antigen-based vaccines to suppress GC progression may represent a novel and promising strategy for the prevention and treatment of GC in the future (22).

Dendritic cell (DC) vaccine

The primary function of DCs is to process and present TAAs to T lymphocytes. Moreover, DC vaccines loaded with allogeneic IgG and restricted by human leukocyte antigen (HLA) have been shown to exert potent antitumor effects in mouse models (23-25).

DC vaccines exploit the strong antigen-presenting capacity of DCs to initiate and amplify host immune responses against tumor cells or infectious agents. In most clinical protocols, monocytes are first isolated from peripheral blood and differentiated ex vivo into DCs using defined cytokine cocktails. These DCs are then loaded with TAAs and induced to fully mature through additional cytokine stimulation. After being reinfused into the patient, the antigen-pulsed DCs traffic to secondary lymphoid organs, including lymph nodes, where they prime and expand antigen-specific T-cell populations, thereby promoting effective tumor cell killing or pathogen eradication (26,27).

In a preclinical study, Bagheri et al. transfected DCs with total mRNA derived from spheroid-forming GC cells or from normal gastric tissue and used these DCs to prime T cells. T cells stimulated with DCs transfected with spheroid cell mRNA exhibited a marked increase in IFN-γ mRNA expression, with a 6.4- to 9.39-fold upregulation. The cytotoxic activity of T lymphocytes was also significantly higher than that of the other groups (P≤0.0001) (28).

Mao et al. designed an in situ DC vaccine platform, NLC/Ce6. In tumor-bearing mice, NLC/Ce6 treatment markedly suppressed both primary and distant tumors (primary tumor volume, 223 mm³; distant tumor volume, 81 mm³) compared with controls (796 mm³ and 522 mm³, respectively). These data indicate that this in situ DC vaccine strategy can effectively control primary gastric tumors and limit their distant metastatic growth in mice (29).

Zhu et al. developed a DC-based vaccine targeting MG-7Ag, a GC-associated antigen. This vaccine elicited a robust cytotoxic T lymphocyte (CTL) response. In vivo, adoptive transfer of CTLs primed by the MG-7Ag DC vaccine markedly suppressed xenograft tumor growth in nude mice, reducing tumor volumes to approximately one-third of those in animals receiving a conventional DC vaccine (30).

DC vaccines remain a dynamic and rapidly evolving area of GC immunotherapy. However, further refinement of vaccine design is needed to enhance immunogenicity while maintaining an acceptable safety profile. Recently explored complementary strategies include next-generation adjuvants to boost DC activation, chemical modulation to improve antigen uptake, loading DCs with tumor cell lysates, and generating DC-T-cell conjugates or fusion hybrids (31-34). As these technologies are translated and adapted to GC, DC vaccines are expected to play an increasingly important role in the overall strategy of GC vaccination and immunotherapy.

Nucleic acid vaccines

Nucleic acid vaccines constitute a novel vaccine platform that harnesses the host cell’s intrinsic transcriptional and translational machinery to synthesize target antigens, thereby mimicking the natural processes of antigen expression and presentation that occur during viral or other pathogenic infections. This strategy is designed to elicit robust humoral and cellular immune responses. In addition, nucleic acid vaccines possess the advantages of flexible design, rapid development, a favorable safety profile, strong immunogenicity, easy large-scale production, and cost-effective manufacturing (35,36).

Over the past three decades, studies have established the feasibility of direct in vivo transfection of mammalian cells using naked plasmid DNA (37,38). In this context, Afkhamipour et al. engineered a recombinant plasmid encoding the H. pylori urease accessory protein F gene (ureF) gene and used it to immunize BALB/c mice. This DNA vaccine achieved in vivo tumor inhibition rates of 35.8–72.3% and an in vitro inhibition rate of 77.6%±4.7%, underscoring its antitumor potential (39).

mRNA vaccines have emerged as a powerful alternative to conventional vaccine platforms, offering high efficacy, good safety profile and the capacity for rapid and cost-efficient large-scale manufacturing (40).

Wei et al. identified RAI14 and NREP as candidate TAAs that may be suitable targets for gastric adenocarcinoma (GAC)-directed mRNA vaccine development (41). You et al. identified ADAMTS18, COL10A1, PPEF1, and STRA6 as additional potential mRNA vaccine antigens for GAC (42). In a mouse experiment, researchers evaluated the efficacy of a neoantigen-mRNA lipid nanoparticles (LNPs) in combination with anti-programmed cell death protein 1 (PD-1) therapy in a mouse peritoneal metastasis model, and found that it exhibited beneficial activity in both the prevention and treatment of GC peritoneal metastasis in mice (43).

Traditionally, mRNA vaccines adopt a linear mRNA or self-amplifying mRNA (SAM) design (44). Subsequently, to extend the expression duration and stability of mRNA, circular mRNA (circ-RNA) vaccines have been further developed (45). At present, mRNA vaccines lack more effective vaccine design strategies. Tumor Open Reading Frames that are Unique mRNA (TOFU mRNA) is a novel linear mRNA vaccine design method, which can load multiple tumor-specific antigens, making mRNA vaccines a promising personalized vaccine (46). Combining circ-RNA profiling analysis with pharmacogenomic approaches helps optimize treatment models, overcome drug resistance, and ultimately improve patient prognosis (47). In addition, appropriate mRNA structural modifications (such as codon optimization, nucleoside modification, SAM, etc.) and formulation methods (such as LNPs, polymers, peptides, etc.) are also promising solutions (36).

Peptide-based vaccines

Peptide-based cancer vaccines aim to elicit effector adaptive immune responses and to establish durable, antigen-specific immunity against tumor antigens recognized as non-self (48).

Within this context, Wiedermann and colleagues developed a peptide-based B-cell epitope vaccine, IMU-131 (HER-Vaxx), and evaluated it in a clinical trial enrolling 14 patients with human epidermal growth factor receptor 2 (HER2)-overexpressing tumors. The treatment yielded one complete remission (CR), five partial remissions (PR), four cases of stable disease (SD) and one case of progressive disease (PD), indicating that IMU-131/HER-Vaxx is generally well tolerated and has an acceptable safety profile in patients with GC (49).

Peptide-based vaccines offer several advantages, including a precisely defined composition, a generally favorable safety profile and a low frequency of severe adverse events. However, their broader clinical application is limited by the difficulty of identifying peptide epitopes that are both highly immunogenic and selectively expressed in tumor tissue. OTSGC-A24 is an HLA-A*24:02-restricted multipeptide vaccine. In a phase I/Ib study by Sundar et al., patients treated with OTSGC-A24 achieved a median progression-free survival (mPFS) of 1.7 months (95% CI: 1.4–3.5) and a mOS of 5.7 months (95% CI: 3.8–8.6) (50). In a separate trial, Fujiwara et al. evaluated an HLA-A*24-restricted peptide cocktail plus an anti-angiogenic peptide targeting VEGFR1 in 35 patients with unresectable or recurrent GC that had progressed after standard chemotherapy. The median survival time (MST) was 155 days. Among evaluable patients, 10 (45%) achieved SD, whereas 12 (55%) had PD (51).

Wang et al. developed a peptide vaccine platform: T7-MG3. In BALB/c mice, vaccination with T7-MG3 significantly reduced tumor burden to 62.64%±5.55% of that in PBS-treated controls (P<0.01). Moreover, this vaccine enhanced CTL cytotoxicity (40.92%±4.38% vs. 16.29%±1.90%; P<0.01) (52). In another study, Jiang et al. used cryo-ultrasonic fragmentation to isolate GC stem cells and then prepared chaperone molecule-antigen peptide complexes derived from these cells. The resulting complexes showed strong immunogenicity and exhibited antitumor activity in vitro, highlighting their potential as immunotherapeutic candidates (53).

Peptide vaccines have multiple advantages, including precisely defined molecular composition, good safety with extremely low toxicity, ease of chemical synthesis, and the ability to elicit potent antigen-specific immune responses. Despite these advantages, clinical development remains constrained by the challenge of identifying epitopes that are both highly immunogenic and selectively expressed in tumors. With continued advances in tumor antigen discovery and epitope prediction, next-generation peptide vaccines incorporating novel targets may hold considerable promise for clinical translation.

Nanovaccines

Nanovaccines are a new generation of immunization agents that use nanoparticle (NP)-based carriers and/or adjuvants. They offer several advantages, including enhanced drainage to lymphoid organs, co-delivery of multiple antigens and immunostimulatory molecules, and the ability to elicit sustained antitumor immunity (54,55).

Liposomal and lipoprotein-like nanovaccines exhibit excellent biocompatibility, enable facile surface modification with targeting moieties, protect antigens from enzymatic degradation in vivo, and prolong antigen retention (56). Huang et al. developed the HPPS@RMn nanovaccine. In mice, tumor growth in the treatment group was significantly slower than in control groups, and final tumor volumes were markedly reduced. This effect was accompanied by a significantly higher proportion of CD44⁺CD62L⁺ central memory T cells among total CD8⁺ T cells (57).

PLGA polymer nanovaccines are based on poly(lactic-co-glycolic acid) (PLGA), a material with excellent biocompatibility, complete biodegradability, and minimal apparent toxicity. They also enable prolonged antigen delivery and extended immune stimulation (58). In one study, PLGA NPs were used as delivery vehicles. After treatment, the ability of the treatment group to promote T cells to secrete IFN-γ was more than twice that of the control group, the tumor volume and weight were significantly smaller than those in other control groups, and the MST of tumor-bearing mice was significantly prolonged (59).

Chitosan-based nanovaccines exhibit good biocompatibility, low toxicity, and degradability. They have pH responsiveness and can achieve targeted release of antigens under the acidic conditions of the tumor microenvironment (TME). In addition, chitosan itself can serve as a weak immune adjuvant (60). Researchers developed a NIR light-responsive chitosan-based nanovaccine, which showed excellent antiproliferative effects in HGC-27 and AGS cell models (IC₅₀ =0.096 µm for HGC-27 cells) and exhibited extremely good biocompatibility with normal cells (cell viability above 88.7%) (61).

Hydrogel nanovaccines are often peptide-based hydrogels (e.g., RADA32). After injection, they can form an in vivo gel-like depot that enables sustained, localized release of antigens and adjuvants, thereby extending the duration of immune activation (62,63). Yu et al. reported a dynamic covalent hydrogel-based vaccine (DCHVax) that can recruit DCs in situ, promote DCs uptake and maturation of GC-specific antigens, elicit potent tumor-specific immune responses, and inhibit residual tumor growth after GC surgery (64).

Metal-organic framework (MOF) nanovaccines enable efficient loading of antigens, chemotherapeutic agents, and immune adjuvants to support synergistic chemoimmunotherapy. MOFs can also degrade under the acidic conditions of the TME, allowing targeted release of encapsulated antigens and drugs (65). In an AGS human GC xenograft model, a copper-tetrahydroxybenzoquinone MOF nanovaccine (Cu-THBQ/AX) achieved a tumor inhibition rate of 81.2% and was reported to suppress tumor growth and distant metastasis (66).

Self-assembled (carrier-free) nanovaccines are formed through the self-assembly of antigens with immune adjuvants via electrostatic and hydrophobic interactions. They enable efficient co-delivery of antigens and adjuvants and can support synchronized release (67). In one study, researchers prepared a self-assembled nanovaccine. This formulation was efficiently delivered to DCs, promoted DCs maturation, reduced regulatory T-cell (Treg) infiltration, and enhanced T-cell responses. When combined with anti-PD-1 therapy, it was reported to overcome gemcitabine resistance in GC and induce long-term immune memory (68).

In addition to the aforementioned nanovaccines, biomimetic nanovaccines that utilize cell lysates, cell-derived nanovesicles, or extracted cell membranes as functional components have attracted extensive attention in the field (69). pH-responsive nanovaccines can release antigens at a specific pH, which helps improve the targeting of the vaccines (70). In addition, chiral nanomaterials are also have been proposed as candidates for the development of next-generation vaccines, relying on their unique optical, electronic, and catalytic properties (71).

Personalized vaccines

By analyzing tumor and matched normal tissues, accurately identifying tumor-specific neoantigens, and designing patient-specific vaccines, it is possible to elicit immune responses that selectively target cancer cells, thereby improving therapeutic efficacy (72). Although most personalized vaccines are still in the early stages of clinical development and face challenges such as high manufacturing costs and significant interpatient heterogeneity (73,74). However, with the advancement of science and technology, personalized vaccines are bound to become an important part of individualized treatment for GC.

Liu et al. developed a personalized neoantigen nanovaccine platform (PNVAC) and initiated a phase I clinical trial. The results showed that patients vaccinated with this platform achieved 1-year and 2-year disease-free survival (DFS) rates of 96.6% (28/29) and 82.4% (14/17), respectively (75). Go et al. designed a nanovaccine, CCM-MPLA-aCD28. This formulation elicited a more potent immune response offering the induction of tumor-specific CD8⁺ T cells and demonstrated superior antitumor efficacy in tumor-bearing mice. These findings all demonstrate the potential and broad application prospects of personalized vaccines in the treatment of GC (34).

Combination of vaccination with immune checkpoint inhibitors (ICIs)

ICIs have rapidly emerged as transformative agents within the field of cancer immunotherapy, substantially reshaping treatment modalities of multiple tumor types (76,77). While therapeutic cancer vaccines prime antigen-specific immune responses, ICIs counteract tumor-induced immunosuppression. When used together, these modalities act synergistically to amplify immune activation, diversify effector responses, overcome immune evasion mechanisms and mitigate therapeutic resistance, ultimately contributing to improved patient outcomes (78). Guo et al. used a neoantigen-loaded monocyte-derived dendritic cell vaccine (Neo-MoDC) administered in combination with the anti-PD-1 antibody nivolumab. Imaging demonstrated a near-complete metabolic response on positron emission tomography (PET) at day 231 after the initial vaccination, and complete disappearance of ovarian implant metastases on computed tomography (CT) at day 389 of combination therapy. Follow-up indicated a sustained radiologic complete response lasting 25 months (as of October 2021) (79).

The combined application of vaccines and ICIs has complementary advantages. However, there are relatively few relevant clinical studies. Future studies should expand the scope of clinical trials of combined treatment strategies to cover a broader population of GC patients with different disease stages and pathological subtypes, thereby achieving strict evaluation of efficacy, safety, and reproducibility.

Vaccination combined with chemotherapy

Chemotherapy remains a primary treatment modality for GC; however, despite its tumoricidal efficacy, substantial systemic toxicity often compromises patients’ quality of life. The combined application of GC vaccines and chemotherapy can enhance anti-tumor effects, reduce treatment-related toxicity, and improve patients’ quality of life (80).

In a phase I study in Japan, 18 patients received dose-escalating cyclophosphamide 4 days before vaccination, followed by weekly administration of a five-peptide cocktail over a 4-week course. This regimen was associated with a mOS of 13.5 months, suggesting that low-dose cyclophosphamide may enhance the efficacy of multipeptide cancer vaccines (81) (the major clinical trials discussed in this review are summarized in Table 2).

Qiao et al. evaluated the therapeutic efficacy of combining dendritic cell-cytokine-induced killer cell therapy (DC-CIK) with S-1 (tegafur/gimeracil/oteracil potassium) and cisplatin in patients with advanced gastric cancer (AGC). A total of 63 patients were enrolled. The 1-year overall survival (OS) rate in the DC-CIK + S-1 + cisplatin group was 87.5%, the 1-year progression-free survival (PFS) rate in the DC-CIK + S-1 + cisplatin group (76.9%) (82).

Zhang et al. conducted a phase II trial to evaluate the safety and efficacy of adjuvant vaccination with gp96, an autologous heat shock protein derived from tumor tissue, in patients with GC. Thirty-eight patients received gp96 vaccination concurrently with chemotherapy. The gp96 group had improved DFS compared with chemotherapy alone (P=0.045; HR =0.47; 95% CI: 0.23–0.96). The 2-year OS rates were 81.9% in the gp96 group (P=0.123; HR =0.42; 95% CI: 0.15–1.24) (83).

Although combining GC vaccines with chemotherapy shows promise for prolonging survival and improving quality of life, the clinical evidence remains limited and largely preliminary. Further studies are needed to define optimal vaccine-chemotherapy pairings, determine appropriate sequencing, and identify patient subsets most likely to benefit from multimodal approaches. Such insights will provide a scientific basis for refining combination regimens and guiding the design of next-generation GC vaccines.

Overcoming the immunosuppressive TME

In clinical practice, even when tumor vaccines generate detectable T-cell responses, robust antitumor efficacy is often difficult to achieve, largely because of the immunosuppressive TME (84). The immunosuppressive TME comprises immunosuppressive cell populations, inhibitory cytokines, and other factors in the local milieu surrounding tumor cells. It limits immune-mediated tumor killing by impairing the infiltration, function, survival, and antigen recognition of tumor-reactive T cells, thereby undermining vaccine efficacy (85). Key mechanisms include the following:

T cell infiltration disorder

TME hinders T cells from crossing vascular and stromal barriers to reach the tumor parenchyma through three major mechanisms. First, abnormal tumor vasculature: tumor cells, tumor-associated macrophages (TAMs), and cancer-associated fibroblasts (CAFs) secrete factors such as VEGF-A and ANGPT2, leading to endothelial disorganization and reduced expression of adhesion molecules (VCAM-1 and ICAM-1), thereby limiting T-cell extravasation (86,87). Second, stromal densification: CAFs deposit abundant extracellular matrix (ECM) components, including collagen and hyaluronic acid, creating a physical barrier that restricts T-cell migration (88,89). Third, chemokine imbalance: reduced levels of T-cell-recruiting chemokines (e.g., CXCL9 and CXCL10) and increased levels of chemokines associated with immune exclusion or suppression (e.g., CXCL12 and CCL22) diminish directional cues for T-cell trafficking (90-92).

T cell dysfunction

Immunosuppressive signals within the TME induce dysfunction or exhaustion in a subset of T cells. First, tumor and immune cells can overexpress ligands such as programmed death-ligand 1 (PD-L1) and galectin-9, which engage receptors including PD-1 and T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) on T cells, suppressing proliferation and cytokine production and promoting T-cell exhaustion (93,94). Second, regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), M2-polarized TAMs, and related subsets can impair T-cell function by secreting inhibitory mediators (e.g., IL-10 and TGF-β), competing for nutrients, and mediating contact-dependent suppression (95). Third, enrichment of inhibitory soluble factors [e.g., prostaglandin E2 (PGE2), adenosine, and VEGF] can directly dampen T-cell activation and support the expansion of immunosuppressive cells (96-98).

Metabolic immunosuppression

The Warburg effect in tumor cells contributes to a hypoxic, glucose-depleted, lactate-rich, and metabolically dysregulated TME. Hypoxia activates hypoxia-inducible factor 1α (HIF-1α) signaling, which suppresses T-cell activity and promotes apoptosis and exhaustion (99); the low-glucose environment results in insufficient energy supply for T cells, preventing their activation and proliferation (100,101). Finally, metabolic pathways such as IDO/TDO-mediated tryptophan catabolism to kynurenine and CD39/CD73-mediated adenosine production can inhibit T-cell function and promote apoptosis through nutrient depletion and accumulation of inhibitory metabolites (102,103).

Tumor immune escape

Under vaccine-driven immune selection pressure, tumor cells may evade T-cell recognition via antigen gene silencing, deletion, mutation, or antigen modulation (104). In addition, immunosuppressive cells such as TAMs and MDSCs secrete inhibitory cytokines (e.g., IL-10 and TGF-β). These factors interfere with the maturation and differentiation of DCs, ultimately impairing efficient T-cell recognition and promoting immune escape (95,105,106).

In addition to the immunosuppressive TME, microbial community structure and metabolites influence DCs maturation and antigen presentation, as well as T- and B-cell function. This may contribute to suboptimal vaccine efficacy in some patients (107,108).

To overcome these obstacles, multiple strategies can be adopted. Combining vaccines with metabolic modulators (e.g., butyrate and IDO1 inhibitors) may alleviate metabolic deprivation, enhance CTL effector function, and promote vascular normalization, thereby improving T-cell metabolism and intratumoral infiltration (109-112). In addition, combining microbiome-based interventions with tumor vaccines may improve antigen presentation and tumor immunogenicity by remodeling the gut-tumor immune axis (113-115). Finally, vascular normalization strategies (e.g., targeting ANGPT2/TIE2 or dual blockade of VEGF and ANGPT2) can restore vascular integrity and facilitate T-cell infiltration (116-119).

Optimization of the development pipeline for GC vaccines

Current screening strategies often prioritize candidates based on predicted HLA-binding affinity; however, ranking epitopes solely by binding affinity can yield substantial false-positive and false-negative results (120). By contrast, cancer vaccines based on whole-tumor lysates or neoepitopes have been reported to show higher major histocompatibility complex (MHC)-binding affinity (P<0.05) (121). These observations highlight the need to shift tumor vaccine antigen discovery from binding-centered screening toward function-oriented identification.

First, effective vaccine design requires integrative optimization to bridge the proteogenomic gap between mRNA transcription levels and functional protein antigens (122,123). High-resolution phosphoproteomic studies have demonstrated that the biological relevance and therapeutic susceptibility of antigens are determined not only by static protein expression but also by their active signaling status (124-126). This is particularly important for aberrant post-translational modifications, which contribute to immune evasion, cell adhesion, and metastasis and also represent actionable vaccine targets that are often missed by purely genomic approaches (127,128).

In addition, MHC II-restricted epitopes should be considered core components of vaccine design because they activate CD4⁺ T helper cells and synergize with CD8⁺ cytotoxic T cells primed by MHC I-restricted epitopes. This can help mitigate immune escape driven by loss of MHC I expression in some tumors (129,130). CD4⁺ T cells provide essential co-stimulatory signals for CD8⁺ cytotoxic T cells to enhance cellular immune efficacy (129), drive B cell activation and antibody maturation to improve humoral immunity, differentiate into effector subsets that directly mediate immune responses, and target tumor cell elimination (130). Notably, recent studies have reported that high CD4⁺ T-cell infiltration in GC tissue is associated with reduced 5-year postoperative survival and may serve as an independent adverse prognostic factor and an indirect indicator of benefit from immunotherapy (131), Together, these findings support incorporating MHC II-restricted epitopes as key elements in vaccine design.

For vaccine efficacy assessment, the proportions of PD-1-positive CD4⁺ T-cell subsets in peripheral blood have been reported to predict response rate, PFS, and OS in patients with advanced GC receiving combination chemoimmunotherapy, supporting their use as noninvasive biomarkers for patient selection (132). High intratumoral density of CD4⁺ tumor-infiltrating lymphocytes (TILs) has been identified as an independent risk factor for shorter OS and, together with high PD-L1 expression and increased CD163⁺ TAM infiltration, is associated with poor prognosis. By contrast, high CD8⁺ TIL infiltration predicts favorable outcomes, and combined assessment of these features may improve stratification of GC patients for immunotherapy benefit (133). Recently, advanced artificial intelligence (AI) models such as IEPAPI and TLimmuno2 have been used to predict MHC I and II immunogenicity by integrating peptide-HLA binding with T-cell receptor (TCR) recognition patterns, addressing the traditional emphasis on CD8⁺ T-cell epitopes alone (134,135).

Current studies have not fully accounted for tumor heterogeneity and molecular subtype characteristics when discussing vaccine efficacy. High-resolution proteomic and phosphoproteomic studies have demonstrated that the definition of clinically relevant tumor subtypes (such as immune-inflammatory, metabolism-dominant, or mesenchymal GC) depends not only on genomic alterations but also on active signaling states (136-138). Among these, phosphorylation-driven status directly impacts antigen presentation capacity, immune cell infiltration patterns, and sensitivity to immunotherapy (139). Therefore, vaccine efficacy should be evaluated based on tumor subtypes defined by phosphoproteomics, rather than treating GC as a single disease. Notably, integrated signaling pathway analysis can identify functionally essential antigens—antigens that are not only overexpressed but also directly drive tumor progression, thus holding higher priority. Integrin αv (CD51), β5 (ITGB5), and others have been validated as functionally essential antigens in GC (140-142).

In addition, studies have reported that GC patients with higher TCR repertoire diversity exhibit more durable vaccine-induced antigen-specific T-cell responses and improved 5-year survival compared with those with lower diversity (143). Recent deep learning-based frameworks, such as BertTCR and THLANet, enable systematic characterization of TCR repertoire features associated with clinical response, moving beyond simple T-cell counts to assess the functional fitness of adaptive immunity (144,145).

Predicting neoantigen immunogenicity should incorporate peptide-HLA-TCR ternary complex modeling rather than relying solely on HLA-binding predictions. This concept is supported by advanced AI methods that integrate structural and sequence information, such as NeoaPred and THLANet (145,146).

Tertiary lymphoid structures (TLSs) are organized ectopic lymphoid aggregates within the TME and have been associated with improved prognosis and enhanced responses to immunotherapy, particularly immune checkpoint blockade (147). Spatial characterization of TLSs may provide biomarkers for predicting and monitoring vaccine responses. For example, Wang et al. performed integrated single-cell and spatial transcriptomic analyses of 30 GC specimens and demonstrated the predictive value of TLS features for immunotherapy outcomes in GC (148).

The key to the future of GC vaccine development lies in population-scale validation. National whole-genome sequencing projects and multi-omics biobank resources have laid an important foundation for subtype-specific patient screening, target validation across genetic backgrounds, and scalable clinical trial design (149). This is particularly important for East Asian populations—its molecular characteristics also differ significantly from those of Western populations. Based on this, the development of GC vaccines should first distinguish between broadly applicable targets and population-specific targets. For example, TP53 is a core broad-spectrum target for GC, while CLDN18.2 is an East Asian-specific target, with a positive expression rate of 42% in East Asians compared to only 21% in Westerners (P<0.001) (150). For example, copy number deletions of FAT4 and ZFHX4 genes are only associated with peritoneal metastasis in East Asian GC patients [hazard ratio (HR) =2.3, P<0.01] (151). The cost of tumor vaccines has greatly limited their clinical application scale; therefore, vaccine cost should be included in the evaluation of vaccine efficacy before large-scale population validation. The integration of cost-effective production platforms such as plant-derived recombinant protein systems may become an effective way to achieve equitable access to vaccines in high-burden, resource-limited regions (152,153).

Evaluation of AI‑based epitope prediction tools

AI-based epitope prediction tools have enabled high-throughput screening with improved accuracy by leveraging deep learning, multi-omics integration, and structural modeling. For example, models such as MUNIS and GraphBepi can capture sequence and structural features of peptide-HLA-TCR ternary complexes, thereby improving the identification of T- and B-cell epitopes and tumor neoantigens (154,155). Despite these advances, important limitations remain, including population and disease biases in training datasets; incomplete modeling of tumor heterogeneity and the immune microenvironment; discordance between in vitro predictions and in vivo immune responses; reduced performance for low-frequency mutant epitopes and conformational epitopes; and inflated false-positive rates due to a paucity of high-quality negative validation data (146,156,157). These constraints hinder clinical translation. Accordingly, the effective use of AI epitope prediction tools requires an integrated framework that couples algorithm refinement with multidimensional experimental validation and clinical calibration. Incorporating population-specific datasets, improving interpretability, and combining in vitro assays (e.g., ELISpot and pMHC tetramer staining) with in vivo animal models may strengthen the link between computational predictions, experimental immunology, and clinical outcomes, thereby maximizing translational utility (146,158).

Before epitopes identified by AI can be advanced into clinical vaccine trials, the minimum required validation includes: in silico verification (MHC binding, antigen processing and presentation, population HLA coverage, conservation/safety), in vitro functional validation [peptide-MHC binding, confirmation of presentation by immunopeptidomics, detection of specific T-cell responses by ELISPOT/flow-based activation‑induced marker assay (AIM) assays], and in vivo validation in humanized animals (induction of functional immunity, preliminary protection/safety). Only upon establishing a closed-loop evidence chain from prediction to function can clinical applications be submitted (159,160).

Conclusions

Different types of GC vaccines exhibit varying degrees of antitumor potential. Among them, H. pylori vaccines provide an important direction for GC prevention, while personalized vaccines, nanovaccines, and combination therapeutic strategies have significantly enhanced the clinical application value of vaccines. Although AI-based epitope prediction tools have achieved high-throughput and high-precision breakthroughs in epitope screening, they still have limitations such as data bias and insufficient modeling of biological complexity, necessitating integration with in vitro validation and in vivo animal model verification. Furthermore, as a high-risk population for GC, East Asian populations possess unique molecular characteristics that require vaccine development to distinguish between broad-spectrum and population-specific targets. Overall, considerable room for optimization remains in GC vaccine research and development. In the future, with further advances in immunology, molecular biology, and AI technologies, as well as the implementation of large-scale population validation and low-cost production techniques, personalized vaccines and precise combination therapeutic strategies will continue to promote the clinical translation of GC immunoprophylaxis and treatment, bringing new breakthroughs for improving the prognosis of GC patients.

Acknowledgments

We would like to express our sincere gratitude to all those who offered valuable guidance during the writing of this review.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2299/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2299/prf

Funding: This project is supported by the National Natural Science Foundation of China (grant No. 82203267), the Gansu Health and Health Industry Research Project (No. GSWSKY2024-06), and the Cuiying Scientific and Technological Innovation Project of the Second Hospital of Lanzhou University (No. CY2024-MS-B18).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2299/coif). The authors have no conflicts of interest to declare.

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

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

References

1. Smyth EC, Nilsson M, Grabsch HI, et al. Gastric cancer. Lancet 2020;396:635-48. [Crossref] [PubMed]
2. Abderhalden LA, Wu P, Amonkar MM, et al. Clinical Outcomes for Previously Treated Patients with Advanced Gastric or Gastroesophageal Junction Cancer: A Systematic Literature Review and Meta-Analysis. J Gastrointest Cancer 2023;54:1031-45. [Crossref] [PubMed]
3. Christodoulidis G, Koumarelas KE, Kouliou MN. Revolutionizing gastric cancer treatment: The potential of immunotherapy. World J Gastroenterol 2024;30:286-9. [Crossref] [PubMed]
4. Hu Z, Ott PA, Wu CJ. Towards personalized, tumour-specific, therapeutic vaccines for cancer. Nat Rev Immunol 2018;18:168-82. [Crossref] [PubMed]
5. Coutzac C, Pernot S, Chaput N, et al. Immunotherapy in advanced gastric cancer, is it the future? Crit Rev Oncol Hematol 2019;133:25-32. [Crossref] [PubMed]
6. Yunle K, Tong W, Jiyang L, et al. Advances in Helicobacter pylori vaccine research: From candidate antigens to adjuvants-A review. Helicobacter 2024;29:e13034. [Crossref] [PubMed]
7. Cahill RJ, Kilgallen C, Beattie S, et al. Gastric epithelial cell kinetics in the progression from normal mucosa to gastric carcinoma. Gut 1996;38:177-81. [Crossref] [PubMed]
8. Malfertheiner P, Camargo MC, El-Omar E, et al. Helicobacter pylori infection. Nat Rev Dis Primers 2023;9:19. [Crossref] [PubMed]
9. Salvatori S, Marafini I, Laudisi F, et al. Helicobacter pylori and Gastric Cancer: Pathogenetic Mechanisms. Int J Mol Sci 2023;24:2895. [Crossref] [PubMed]
10. Gong J, Wang Q, Chen X, et al. Helicobacter pylori Vaccine: Mechanism of Pathogenesis, Immune Evasion and Analysis of Vaccine Types. Vaccines (Basel) 2025;13:526. [Crossref] [PubMed]
11. Ikuse T, Blanchard TG, Czinn SJ. Inflammation, Immunity, and Vaccine Development for the Gastric Pathogen Helicobacter pylori. Curr Top Microbiol Immunol 2019;421:1-19. [Crossref] [PubMed]
12. Yamaoka Y. Revolution of Helicobacter pylori treatment. J Gastroenterol Hepatol 2024;39:1016-26. [Crossref] [PubMed]
13. Li S, Zhao W, Xia L, et al. How Long Will It Take to Launch an Effective Helicobacter pylori Vaccine for Humans? Infect Drug Resist 2023;16:3787-805. [Crossref] [PubMed]
14. Zeng M, Mao XH, Li JX, et al. Efficacy, safety, and immunogenicity of an oral recombinant Helicobacter pylori vaccine in children in China: a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2015;386:1457-64. [Crossref] [PubMed]
15. Chehelgerdi M, Doosti A. Effect of the cagW-based gene vaccine on the immunologic properties of BALB/c mouse: an efficient candidate for Helicobacter pylori DNA vaccine. J Nanobiotechnology 2020;18:63. [Crossref] [PubMed]
16. Zhang XL, Jeza VT, Pan Q. Salmonella typhi: from a human pathogen to a vaccine vector. Cell Mol Immunol 2008;5:91-7. [Crossref] [PubMed]
17. Ghasemi A, Wang S, Sahay B, et al. Protective immunity enhanced Salmonella vaccine vectors delivering Helicobacter pylori antigens reduce H. pylori stomach colonization in mice. Front Immunol 2022;13:1034683.
18. Ni X, Liu Y, Sun M, et al. Oral Live-Carrier Vaccine of Recombinant Lactococcus lactis Inducing Prophylactic Protective Immunity Against Helicobacter pylori Infection. Probiotics Antimicrob Proteins 2025;17:4111-23. [Crossref] [PubMed]
19. Duan Y, Xu Y, Dou Y, et al. Helicobacter pylori and gastric cancer: mechanisms and new perspectives. J Hematol Oncol 2025;18:10. [Crossref] [PubMed]
20. Xue LJ, Mao XB, Liu XB, et al. Activation of CD3(+) T cells by Helicobacter pylori DNA vaccines in potential immunotherapy of gastric carcinoma. Cancer Biol Ther 2019;20:866-76. [Crossref] [PubMed]
21. Zeng Z, Sun Y, Jiang J, et al. Engineered low-pathogenic Helicobacter pylori as orally tumor immunomodulators for the stimulation of systemic immune response. Biomaterials 2024;311:122672. [Crossref] [PubMed]
22. Liu Z, Xu H, You W, et al. Helicobacter pylori eradication for primary prevention of gastric cancer: progresses and challenges. J Natl Cancer Cent 2024;4:299-310. [Crossref] [PubMed]
23. Wang Y, Xiang Y, Xin VW, et al. Dendritic cell biology and its role in tumor immunotherapy. J Hematol Oncol 2020;13:107. [Crossref] [PubMed]
24. Marciscano AE, Anandasabapathy N. The role of dendritic cells in cancer and anti-tumor immunity. Semin Immunol 2021;52:101481. [Crossref] [PubMed]
25. Rowley DA, Fitch FW. The road to the discovery of dendritic cells, a tribute to Ralph Steinman. Cell Immunol 2012;273:95-8. [Crossref] [PubMed]
26. Morisaki T, Morisaki T, Kubo M, et al. Lymph Nodes as Anti-Tumor Immunotherapeutic Tools: Intranodal-Tumor-Specific Antigen-Pulsed Dendritic Cell Vaccine Immunotherapy. Cancers (Basel) 2022;14:2438. [Crossref] [PubMed]
27. Papakonstantinou M, Chatzikomnitsa P, Gkaitatzi AD, et al. The Efficacy of Neoantigen-Loaded Dendritic Cell Vaccine Immunotherapy in Non-Metastatic Gastric Cancer. Med Sci (Basel) 2025;13:90. [Crossref] [PubMed]
28. Bagheri V, Abbaszadegan MR, Memar B, et al. Induction of T cell-mediated immune response by dendritic cells pulsed with mRNA of sphere-forming cells isolated from patients with gastric cancer. Life Sci 2019;219:136-43. [Crossref] [PubMed]
29. Mao M, Liu S, Zhou Y, et al. Nanostructured lipid carrier delivering chlorins e6 as in situ dendritic cell vaccine for immunotherapy of gastric cancer. J Mater Res 2020;35:3257-64. [Crossref] [PubMed]
30. Zhu B, Sun Y, Wei X, et al. Dendritic Cell Vaccine Loaded with MG-7 Antigen Induces Cytotoxic T Lymphocyte Responses against Gastric Cancer. J Healthc Eng 2022;2022:1964081. [Crossref] [PubMed]
31. Zhang H, Heng X, Yang H, et al. Metal-Free Atom Transfer Radical Polymerization to Prepare Recylable Micro-Adjuvants for Dendritic Cell Vaccine. Angew Chem Int Ed Engl 2024;63:e202402853. [Crossref] [PubMed]
32. Lv L, Zhang J, Wang Y, et al. Boron Neutron Capture Therapy-Derived Extracellular Vesicles via DNA Accumulation Boost Antitumor Dendritic Cell Vaccine Efficacy. Adv Sci (Weinh) 2024;11:e2405158. [Crossref] [PubMed]
33. Fujioka Y, Ueki H, A R, et al. The Improved Antigen Uptake and Presentation of Dendritic Cells Using Cell-Penetrating D-octaarginine-Linked PNVA-co-AA as a Novel Dendritic Cell-Based Vaccine. Int J Mol Sci 2024;25:5997. [Crossref] [PubMed]
34. Go S, Jung M, Lee S, et al. A Personalized Cancer Nanovaccine that Enhances T-Cell Responses and Efficacy Through Dual Interactions with Dendritic Cells and T Cells. Adv Mater 2023;35:e2303979. [Crossref] [PubMed]
35. Wang C, Yuan F. A comprehensive comparison of DNA and RNA vaccines. Adv Drug Deliv Rev 2024;210:115340. [Crossref] [PubMed]
36. Miao L, Zhang Y, Huang L. mRNA vaccine for cancer immunotherapy. Mol Cancer 2021;20:41. [Crossref] [PubMed]
37. Pagliari S, Dema B, Sanchez-Martinez A, et al. DNA Vaccines: History, Molecular Mechanisms and Future Perspectives. J Mol Biol 2023;435:168297. [Crossref] [PubMed]
38. Franck CO, Fanslau L, Bistrovic Popov A, et al. Biopolymer-based Carriers for DNA Vaccine Design. Angew Chem Int Ed Engl 2021;60:13225-43. [Crossref] [PubMed]
39. Afkhamipour M, Kaviani F, Dalali S, et al. Potential Gastric Cancer Immunotherapy: Stimulating the Immune System with Helicobacter pylori pIRES2-DsRed-Express-ureF DNA Vaccines. Arch Immunol Ther Exp (Warsz) 2024; [Crossref]
40. Gote V, Bolla PK, Kommineni N, et al. A Comprehensive Review of mRNA Vaccines. Int J Mol Sci 2023;24:2700. [Crossref] [PubMed]
41. Wei S, Sun Q, Chen J, et al. Bioinformatics analyses for the identification of tumor antigens and immune subtypes of gastric adenocarcinoma. Front Genet 2022;13:1068112. [Crossref] [PubMed]
42. You W, Ouyang J, Cai Z, et al. Comprehensive Analyses of Immune Subtypes of Stomach Adenocarcinoma for mRNA Vaccination. Front Immunol 2022;13:827506. [Crossref] [PubMed]
43. Nagaoka K, Nakanishi H, Tanaka H, et al. Neoantigen mRNA vaccines induce progenitor-exhausted T cells that support anti-PD-1 therapy in gastric cancer with peritoneal metastasis. Gastric Cancer 2025;28:825-36. [Crossref] [PubMed]
44. Singh ON, Berry U, Joshi G, et al. Comparison of immunogenicity and protection efficacy of self-amplifying and circular mRNA vaccines against SARS-CoV-2. iScience 2025;28:113498. [Crossref] [PubMed]
45. Savguira M, Chen DXW, Dong S, et al. RNA immunotherapy: revolutionizing cancer and autoimmune disease treatments. Trends Mol Med 2026;32:181-96. [Crossref] [PubMed]
46. Trivedi V, Yang C, Klippel K, et al. mRNA-based precision targeting of neoantigens and tumor-associated antigens in malignant brain tumors. Genome Med 2024;16:17. [Crossref] [PubMed]
47. Alqahtani S, Alqahtani T, Venkatesan K, et al. Unveiling Pharmacogenomics Insights into Circular RNAs: Toward Precision Medicine in Cancer Therapy. Biomolecules 2025;15:535. [Crossref] [PubMed]
48. Buonaguro L, Tagliamonte M. Peptide-based vaccine for cancer therapies. Front Immunol 2023;14:1210044. [Crossref] [PubMed]
49. Wiedermann U, Garner-Spitzer E, Chao Y, et al. Clinical and Immunologic Responses to a B-Cell Epitope Vaccine in Patients with HER2/neu-Overexpressing Advanced Gastric Cancer-Results from Phase Ib Trial IMU.ACS.001. Clin Cancer Res 2021;27:3649-60. [Crossref] [PubMed]
50. Sundar R, Rha SY, Yamaue H, et al. A phase I/Ib study of OTSGC-A24 combined peptide vaccine in advanced gastric cancer. BMC Cancer 2018;18:332. [Crossref] [PubMed]
51. Fujiwara Y, Okada K, Omori T, et al. Multiple therapeutic peptide vaccines for patients with advanced gastric cancer. Int J Oncol 2017;50:1655-62. [Crossref] [PubMed]
52. Wang XD, Gao NN, Diao YW, et al. Conjugation of toll-like receptor-7 agonist to gastric cancer antigen MG7-Ag exerts antitumor effects. World J Gastroenterol 2015;21:8052-60. [Crossref] [PubMed]
53. Jiang YQ, Guo QM, Xu XP, et al. Preparation of chaperone-antigen peptide vaccine derived from human gastric cancer stem cells and its immune function. Zhonghua Zhong Liu Za Zhi 2017;39:109-14. [Crossref] [PubMed]
54. Yao M, Liu X, Qian Z, et al. Research progress of nanovaccine in anti-tumor immunotherapy. Front Oncol 2023;13:1211262. [Crossref] [PubMed]
55. Liao Z, Huang J, Lo PC, et al. Self-adjuvanting cancer nanovaccines. J Nanobiotechnology 2022;20:345. [Crossref] [PubMed]
56. Hald Albertsen C, Kulkarni JA, Witzigmann D, et al. The role of lipid components in lipid nanoparticles for vaccines and gene therapy. Adv Drug Deliv Rev 2022;188:114416. [Crossref] [PubMed]
57. Huang X, Hong L, Lv Y, et al. Peptide hydrogel platform encapsulating manganese ions and high-density lipoprotein nanoparticle-mimicking nanovaccines for the prevention and treatment of gastric cancer. J Transl Med 2025;23:371. [Crossref] [PubMed]
58. Gao L, Li J, Song T. Poly lactic-co-glycolic acid-based nanoparticles as delivery systems for enhanced cancer immunotherapy. Front Chem 2022;10:973666. [Crossref] [PubMed]
59. Kohnepoushi C, Nejati V, Delirezh N, et al. Poly Lactic-co-Glycolic Acid Nanoparticles Containing Human Gastric Tumor Lysates as Antigen Delivery Vehicles for Dendritic Cell-Based Antitumor Immunotherapy. Immunol Invest 2019;48:794-808. [Crossref] [PubMed]
60. Gong X, Gao Y, Shu J, et al. Chitosan-Based Nanomaterial as Immune Adjuvant and Delivery Carrier for Vaccines. Vaccines (Basel) 2022;10:1906. [Crossref] [PubMed]
61. Fan Z, Shao Y, Jiang X, et al. Cytotoxic effects of NIR responsive chitosan-polymersome layer coated melatonin-upconversion nanoparticles on HGC27 and AGS gastric cancer cells: Role of the ROS/PI3K/Akt/mTOR signaling pathway. Int J Biol Macromol 2024;278:134187. [Crossref] [PubMed]
62. Zhong R, Talebian S, Mendes BB, et al. Hydrogels for RNA delivery. Nat Mater 2023;22:818-31. [Crossref] [PubMed]
63. Zhuo Y, Zeng H, Su C, et al. Tailoring biomaterials for vaccine delivery. J Nanobiotechnology 2024;22:480. [Crossref] [PubMed]
64. Yu Z, Xu Y, Yao H, et al. A simple and general strategy for postsurgical personalized cancer vaccine therapy based on an injectable dynamic covalent hydrogel. Biomater Sci 2021;9:6879-88. [Crossref] [PubMed]
65. Guo Y, Li Y, Zhou S, et al. Metal-Organic Framework-Based Composites for Protein Delivery and Therapeutics. ACS Biomater Sci Eng 2022;8:4028-38. [Crossref] [PubMed]
66. Liu Y, Niu R, Zhang X, et al. 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;18:12386-400. [Crossref] [PubMed]
67. Liu D, Deng B, Liu Z, et al. Enhanced Antitumor Immune Responses via a Self-Assembled Carrier-Free Nanovaccine. Nano Lett 2021;21:3965-73. [Crossref] [PubMed]
68. Pan W, Wang Y, Chen G, et al. A carrier-free nanovaccine combined with cancer immunotherapy overcomes gemcitabine resistance. Biomaterials 2025;313:122788. [Crossref] [PubMed]
69. Meng Z, Zhang Y, Zhou X, et al. Nanovaccines with cell-derived components for cancer immunotherapy. Adv Drug Deliv Rev 2022;182:114107. [Crossref] [PubMed]
70. Zhang J, Liu Y, Bai L, et al. pH Responsive Poly(Amino Acid) Nanoparticles as Potent Carrier Adjuvants for Enhancing Cellular Immunity. Macromol Biosci 2023;23:e2200520. [Crossref] [PubMed]
71. Wang K, Wang H, Wang X. Chiral nanomaterials as vaccine adjuvants: a new horizon in immunotherapy. Nanoscale 2025;17:1932-5. [Crossref] [PubMed]
72. Singh P, Khatib MN. Advancements and challenges in personalized neoantigen-based cancer vaccines. Oncol Rev 2025;19:1541326. [Crossref] [PubMed]
73. Katsikis PD, Ishii KJ, Schliehe C. Challenges in developing personalized neoantigen cancer vaccines. Nat Rev Immunol 2024;24:213-27. [Crossref] [PubMed]
74. Wu DW, Jia SP, Xing SJ, et al. Personalized neoantigen cancer vaccines: current progression, challenges and a bright future. Clin Exp Med 2024;24:229. [Crossref] [PubMed]
75. Liu Q, Chu Y, Shao J, et al. Benefits of an Immunogenic Personalized Neoantigen Nanovaccine in Patients with High-Risk Gastric/Gastroesophageal Junction Cancer. Adv Sci (Weinh) 2022;10:e2203298. [Crossref] [PubMed]
76. Naimi A, Mohammed RN, Raji A, et al. Tumor immunotherapies by immune checkpoint inhibitors (ICIs); the pros and cons. Cell Commun Signal 2022;20:44. [Crossref] [PubMed]
77. Li S, Xu Q, Dai X, et al. Neoadjuvant Therapy with Immune Checkpoint Inhibitors in Gastric Cancer: A Systematic Review and Meta-Analysis. Ann Surg Oncol 2023;30:3594-602. [Crossref] [PubMed]
78. Suzuki N, Shindo Y, Nakajima M, et al. Current status of vaccine immunotherapy for gastrointestinal cancers. Surg Today 2024;54:1279-91. [Crossref] [PubMed]
79. Guo Z, Yuan Y, Chen C, et al. Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer. NPJ Precis Oncol 2022;6:34. [Crossref] [PubMed]
80. Smith JP, Cao H, Chen W, et al. Gastrin Vaccine Alone and in Combination With an Immune Checkpoint Antibody Inhibits Growth and Metastases of Gastric Cancer. Front Oncol 2021;11:788875. [Crossref] [PubMed]
81. Murahashi M, Hijikata Y, Yamada K, et al. Phase I clinical trial of a five-peptide cancer vaccine combined with cyclophosphamide in advanced solid tumors. Clin Immunol 2016;166-167:48-58. [Crossref] [PubMed]
82. Qiao G, Wang X, Zhou L, et al. Autologous Dendritic Cell-Cytokine Induced Killer Cell Immunotherapy Combined with S-1 Plus Cisplatin in Patients with Advanced Gastric Cancer: A Prospective Study. Clin Cancer Res 2019;25:1494-504. [Crossref] [PubMed]
83. Zhang K, Peng Z, Huang X, et al. Phase II Trial of Adjuvant Immunotherapy with Autologous Tumor-derived Gp96 Vaccination in Patients with Gastric Cancer. J Cancer 2017;8:1826-32. [Crossref] [PubMed]
84. Zhou Y, Wei Y, Tian X, et al. Cancer vaccines: current status and future directions. J Hematol Oncol 2025;18:18. [Crossref] [PubMed]
85. Binnewies M, Roberts EW, Kersten K, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 2018;24:541-50. [Crossref] [PubMed]
86. Fukumura D, Kloepper J, Amoozgar Z, et al. Enhancing cancer immunotherapy using antiangiogenics: opportunities and challenges. Nat Rev Clin Oncol 2018;15:325-40. [Crossref] [PubMed]
87. Lanitis E, Irving M, Coukos G. Tumour-associated vasculature in T cell homing and immunity: opportunities for cancer therapy. Nat Rev Immunol 2025;25:831-46. [Crossref] [PubMed]
88. Mao X, Xu J, Wang W, et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer 2021;20:131. [Crossref] [PubMed]
89. Jensen C, Nissen NI, Von Arenstorff CS, et al. Serological assessment of collagen fragments and tumor fibrosis may guide immune checkpoint inhibitor therapy. J Exp Clin Cancer Res 2021;40:326. [Crossref] [PubMed]
90. Kim DJ. Molecular and Immune Mechanisms Governing Cancer Metastasis, Including Dormancy, Microenvironmental Niches, and Tumor-Specific Programs. Int J Mol Sci 2026;27:875. [Crossref] [PubMed]
91. Li J, Zhang L, Liu R, et al. CXCL12/CXCR4 axis governs Treg spatial dominance over CD8+ T cells via IL-2 sequestration: a dual therapeutic target in prostate cancer. Front Immunol 2025;16:1626708. [Crossref] [PubMed]
92. Wang Z, Moresco P, Yan R, et al. Carcinomas assemble a filamentous CXCL12-keratin-19 coating that suppresses T cell-mediated immune attack. Proc Natl Acad Sci U S A 2022;119:e2119463119. [Crossref] [PubMed]
93. Pauken KE, Sammons MA, Odorizzi PM, et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 2016;354:1160-5. [Crossref] [PubMed]
94. Yang R, Sun L, Li CF, et al. Galectin-9 interacts with PD-1 and TIM-3 to regulate T cell death and is a target for cancer immunotherapy. Nat Commun 2021;12:832. [Crossref] [PubMed]
95. Lv J, Zhong X, Wang L, et al. Plasticity of myeloid-derived suppressor cells in cancer and cancer therapy. Oncol Res 2025;33:1581-92. [Crossref] [PubMed]
96. Zhang Y, Huang H, Coleman M, et al. VEGFR2 activity on myeloid cells mediates immune suppression in the tumor microenvironment. JCI Insight 2021;6:e150735. [Crossref] [PubMed]
97. Allard D, Cormery J, Bricha S, et al. Adenosine Uptake through the Nucleoside Transporter ENT1 Suppresses Antitumor Immunity and T-cell Pyrimidine Synthesis. Cancer Res 2025;85:692-703. [Crossref] [PubMed]
98. Lacher SB, Dörr J, de Almeida GP, et al. PGE(2) limits effector expansion of tumour-infiltrating stem-like CD8(+) T cells. Nature 2024;629:417-25. [Crossref] [PubMed]
99. Wu H, Zhao X, Hochrein SM, et al. Mitochondrial dysfunction promotes the transition of precursor to terminally exhausted T cells through HIF-1α-mediated glycolytic reprogramming. Nat Commun 2023;14:6858. [Crossref] [PubMed]
100. Ferrari A, Filoni J, Di Dedda C, et al. Ketone bodies rescue T cell impairments induced by low glucose availability. Eur J Nutr 2024;63:2815-25. [Crossref] [PubMed]
101. Hu W, Li F, Liang Y, et al. Glut3 overexpression improves environmental glucose uptake and antitumor efficacy of CAR-T cells in solid tumors. J Immunother Cancer 2025;13:e010540. [Crossref] [PubMed]
102. Campesato LF, Budhu S, Tchaicha J, et al. Blockade of the AHR restricts a Treg-macrophage suppressive axis induced by L-Kynurenine. Nat Commun 2020;11:4011. [Crossref] [PubMed]
103. Xia C, Yin S, To KKW, et al. CD39/CD73/A2AR pathway and cancer immunotherapy. Mol Cancer 2023;22:44. [Crossref] [PubMed]
104. Huber F, Bassani-Sternberg M. Defects in antigen processing and presentation: mechanisms, immune evasion and implications for cancer vaccine development. Nat Rev Immunol 2026;26:23-34. [Crossref] [PubMed]
105. Saeed AF. Tumor-Associated Macrophages: Polarization, Immunoregulation, and Immunotherapy. Cells 2025;14:741. [Crossref] [PubMed]
106. Chen J, Duan Y, Che J, et al. Dysfunction of dendritic cells in tumor microenvironment and immunotherapy. Cancer Commun (Lond) 2024;44:1047-70. [Crossref] [PubMed]
107. Rossouw C, Ryan FJ, Lynn DJ. The role of the gut microbiota in regulating responses to vaccination: current knowledge and future directions. FEBS J 2025;292:1480-99. [Crossref] [PubMed]
108. Ardura-Garcia C, Curtis N, Zimmermann P. Systematic review of the impact of intestinal microbiota on vaccine responses. NPJ Vaccines 2024;9:254. [Crossref] [PubMed]
109. Yu X, Ou J, Wang L, et al. Gut microbiota modulate CD8(+) T cell immunity in gastric cancer through Butyrate/GPR109A/HOPX. Gut Microbes 2024;16:2307542. [Crossref] [PubMed]
110. Liang Y, Rao Z, Du D, et al. Butyrate prevents the migration and invasion, and aerobic glycolysis in gastric cancer via inhibiting Wnt/β-catenin/c-Myc signaling. Drug Dev Res 2023;84:532-41. [Crossref] [PubMed]
111. Xiang Z, Li J, Song S, et al. A positive feedback between IDO1 metabolite and COL12A1 via MAPK pathway to promote gastric cancer metastasis. J Exp Clin Cancer Res 2019;38:314. [Crossref] [PubMed]
112. Huang Z, Dong J, Guo T, et al. TRIM28 Regulates Proliferation of Gastric Cancer Cells Partly Through SRF/IDO1 Axis. J Cancer 2024;15:4417-29. [Crossref] [PubMed]
113. Decker V, Qureshi K, Roberts L, et al. The emerging role of the gut microbiota in vaccination responses. Gut Microbes 2025;17:2549585. [Crossref] [PubMed]
114. Kim CH. Functional regulation of cytotoxic T cells by gut microbial metabolites. Gut Microbes Rep 2025;2:1-16. [Crossref] [PubMed]
115. Khan AA, Chaudhary AA, Singh H, et al. Probiotics integrated to cancer vaccines and their potential for cancer management. Microb Pathog 2025;206:107742. [Crossref] [PubMed]
116. Park HR, Shiva A, Cummings P, et al. Angiopoietin-2-Dependent Spatial Vascular Destabilization Promotes T-cell Exclusion and Limits Immunotherapy in Melanoma. Cancer Res 2023;83:1968-83. [Crossref] [PubMed]
117. Cai Z, Meng K, Yu T, et al. IFN-γ-mediated suppression of ANGPT2-Tie2 in endothelial cells facilitates tumor vascular normalization during immunotherapy. Front Immunol 2025;16:1551322. [Crossref] [PubMed]
118. Di Tacchio M, Macas J, Weissenberger J, et al. Tumor Vessel Normalization, Immunostimulatory Reprogramming, and Improved Survival in Glioblastoma with Combined Inhibition of PD-1, Angiopoietin-2, and VEGF. Cancer Immunol Res 2019;7:1910-27. [Crossref] [PubMed]
119. Yang S, Zou X, Li J, et al. Immunoregulation and clinical significance of neutrophils/NETs-ANGPT2 in tumor microenvironment of gastric cancer. Front Immunol 2022;13:1010434. [Crossref] [PubMed]
120. Koşaloğlu-Yalçın Z, Lanka M, Frentzen A, et al. Predicting T cell recognition of MHC class I restricted neoepitopes. Oncoimmunology 2018;7:e1492508. [Crossref] [PubMed]
121. Fritah H, Graciotti M, Lai-Lai Chiang C, et al. Cancer vaccines based on whole-tumor lysate or neoepitopes with validated HLA binding outperform those with predicted HLA-binding affinity. iScience 2023;26:106288. [Crossref] [PubMed]
122. Leppek K, Byeon GW, Kladwang W, et al. Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics. Nat Commun 2022;13:1536. [Crossref] [PubMed]
123. Jin L, Zhou Y, Zhang S, et al. mRNA vaccine sequence and structure design and optimization: Advances and challenges. J Biol Chem 2025;301:108015. [Crossref] [PubMed]
124. Gallagher RI, Wulfkuhle J, Wolf DM, et al. Protein signaling and drug target activation signatures to guide therapy prioritization: Therapeutic resistance and sensitivity in the I-SPY 2 Trial. Cell Rep Med 2023;4:101312. [Crossref] [PubMed]
125. Casado P, Rio-Machin A, Miettinen JJ, et al. Integrative phosphoproteomics defines two biologically distinct groups of KMT2A rearranged acute myeloid leukaemia with different drug response phenotypes. Signal Transduct Target Ther 2023;8:80. [Crossref] [PubMed]
126. Casado P, Marfa S, Hadi MM, et al. Phosphoproteomics identifies determinants of PAK inhibitor sensitivity in leukaemia cells. Cell Commun Signal 2025;23:135. [Crossref] [PubMed]
127. Xu X, Peng Q, Jiang X, et al. Altered glycosylation in cancer: molecular functions and therapeutic potential. Cancer Commun (Lond) 2024;44:1316-36. [Crossref] [PubMed]
128. Ren X, Lin S, Guan F, et al. Glycosylation Targeting: A Paradigm Shift in Cancer Immunotherapy. Int J Biol Sci 2024;20:2607-21. [Crossref] [PubMed]
129. Shi Y, Liu Y, Huang J, et al. Optimized mobilization of MHC class I- and II- restricted immunity by dendritic cell vaccine potentiates cancer therapy. Theranostics 2022;12:3488-502. [Crossref] [PubMed]
130. Mao Q, Tian Y, Yu Q, et al. Enhanced efficiency of MHC class II tumor neoantigen vaccines with a novel CD4(+) T-cell helper epitope. J Pharmacol Exp Ther 2025;392:103570. [Crossref] [PubMed]
131. Li F, Sun Y, Huang J, et al. CD4/CD8 + T cells, DC subsets, Foxp3, and IDO expression are predictive indictors of gastric cancer prognosis. Cancer Med 2019;8:7330-44. [Crossref] [PubMed]
132. Gao D, Liu L, Liu J, et al. Predictive response and outcome of peripheral CD4(+) T cell subpopulations to combined immunotherapy and chemotherapy in advanced gastric cancer patients. Int Immunopharmacol 2024;129:111663. [Crossref] [PubMed]
133. Qi X, Guo Y, Xiao Y, et al. Nomograms for stratified prognosis prediction of gastric cancer by integrating programmed death ligand 1 and tumor-infiltrating immune cells including CD4+/CD8+ TILs and CD163+ TAMs. Front Oncol 2025;15:1530054. [Crossref] [PubMed]
134. Deng J, Zhou X, Zhang P, et al. IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity. Brief Bioinform 2023;24:bbad171. [Crossref] [PubMed]
135. Wang G, Wu T, Ning W, et al. TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning. Brief Bioinform 2023;24:bbad116. [Crossref] [PubMed]
136. Chang E, An JY. Whole-genome doubling is a double-edged sword: the heterogeneous role of whole-genome doubling in various cancer types. BMB Rep 2024;57:125-34. [Crossref] [PubMed]
137. Song KJ, Choi S, Kim K, et al. Proteogenomic analysis reveals non-small cell lung cancer subtypes predicting chromosome instability, and tumor microenvironment. Nat Commun 2024;15:10164. [Crossref] [PubMed]
138. Chang E, Kim SJ, Hwang HS, et al. Pan-cancer proteogenomic landscape of whole-genome doubling reveals putative therapeutic targets in various cancer types. Clin Transl Med 2024;14:e1796. [Crossref] [PubMed]
139. Shoji H, Hirano H, Nojima Y, et al. Phosphoproteomic subtyping of gastric cancer reveals dynamic transformation with chemotherapy and guides targeted cancer treatment. Cell Rep 2024;43:114774. [Crossref] [PubMed]
140. Wu S, Nasser B, Singab A, Lin G, et al. The regulatory role of integrin in gastric cancer tumor microenvironment and drug resistance. Prog Biophys Mol Biol 2025;195:130-6. [Crossref] [PubMed]
141. Peng J, Yin Y, Liu X, et al. CD51 promotes gastric cancer stemness via blocking Numb-mediated Notch1 degradation. Cancer Lett 2025;629:217886. [Crossref] [PubMed]
142. Liu D, Cao J, Hu K, et al. Integrin β5 interacts with G3BP1 through activating FAK/Src signaling pathway to promote gastric carcinogenesis. Sci Rep 2025;15:28633. [Crossref] [PubMed]
143. Cao X, Kang Y, Tai P, et al. Prognostic role of tumor-infiltrating lymphocytes in gastric cancer: A systematic review and meta-analysis. Clin Res Hepatol Gastroenterol 2025;49:102510. [Crossref] [PubMed]
144. Zhang M, Cheng Q, Wei Z, et al. BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire. Brief Bioinform 2024;25:bbae420. [Crossref] [PubMed]
145. Long X, Yang Q, Dong W, et al. THLANet: A deep learning framework for predicting TCR-pHLA binding in immunotherapy applications. PLoS Comput Biol 2025;21:e1013050. [Crossref] [PubMed]
146. Jiang D, Xi B, Tan W, et al. NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes. Bioinformatics 2024;40:btae547. [Crossref] [PubMed]
147. Peyraud F, Guegan JP, Vanhersecke L, et al. Tertiary lymphoid structures and cancer immunotherapy: From bench to bedside. Med 2025;6:100546. [Crossref] [PubMed]
148. Wang Y, Zhang G, Zhang X, et al. Single-cell and spatial transcriptomics implicate a prognostic function of tertiary lymphoid structures in gastric cancer. Nat Commun 2025;16:10435. [Crossref] [PubMed]
149. Lee H, Kim W, Kwon N, et al. Lessons from national biobank projects utilizing whole-genome sequencing for population-scale genomics. Genomics Inform 2025;23:8. [Crossref] [PubMed]
150. Mousavi SE, Ilaghi M, Elahi Vahed I, et al. Epidemiology and socioeconomic correlates of gastric cancer in Asia: results from the GLOBOCAN 2020 data and projections from 2020 to 2040. Sci Rep 2025;15:6529. [Crossref] [PubMed]
151. Peles S, Khalife R, Magliocco A. Molecular Insights into Gastric Cancer: A Comparative Analysis of Asian and White Populations. Cancer Diagn Progn 2025;5:429-36. [Crossref] [PubMed]
152. Kang YJ, Kim DS, Kim S, et al. Plant-derived PAP proteins fused to immunoglobulin A and M Fc domains induce anti-prostate cancer immune response in mice. BMB Rep 2023;56:392-7. [Crossref] [PubMed]
153. Knödler M, Opdensteinen P, Sankaranarayanan RA, et al. Simple plant-based production and purification of the assembled human ferritin heavy chain as a nanocarrier for tumor-targeted drug delivery and bioimaging in cancer therapy. Biotechnol Bioeng 2023;120:1038-54. [Crossref] [PubMed]
154. Wohlwend J, Nathan A, Shalon N, et al. Deep learning enhances the prediction of HLA class I-presented CD8(+) T cell epitopes in foreign pathogens. Nat Mach Intell 2025;7:232-43. [Crossref] [PubMed]
155. Zeng Y, Wei Z, Yuan Q, et al. Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model. Bioinformatics 2023;39:btad187. [Crossref] [PubMed]
156. Conev A, Fasoulis R, Hall-Swan S, et al. HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors. iScience 2024;27:108613. [Crossref] [PubMed]
157. Acar DD, Witkowski W, Wejda M, et al. Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted. EBioMedicine 2024;100:104960. [Crossref] [PubMed]
158. Grewal S, Hegde N, Yanow SK. Integrating machine learning to advance epitope mapping. Front Immunol 2024;15:1463931. [Crossref] [PubMed]
159. Federico L, Malone B, Tennøe S, et al. Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Front Immunol 2023;14:1265044. [Crossref] [PubMed]
160. Becker JP, Riemer AB. The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies. Front Immunol 2022;13:883989. [Crossref] [PubMed]