Next-generation sequencing (NGS) technologies have significantly advanced the identification of somatic mutations from tumor biopsies, providing a comprehensive understanding of the genomic landscape of cancer. NGS has shown notable improvements in reliability, sequencing chemistry, data interpretation, and cost efficiency [32, 53]. These advancements have enabled precise profiling of both gene expression and the mutational landscape in liquid biopsies, including circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) [53].

NGS has facilitated high-resolution and large-scale detection of somatic mutations, as demonstrated in multiple studies [54, 55]. This technology enables the analysis of millions of DNA fragments, enhancing the efficiency of identifying targetable genetic mutations in metastatic colorectal cancer and directing the implementation of individualized treatment strategies. The integration of NGS into clinical practice has steadily increased, driven by advancements that have reduced costs, improved efficiency, and minimized tissue requirements [56].

NGS panels are widely utilized for characterizing tumor mutation profiles, published research has consistently demonstrated that accurately predicting patient outcomes and informing treatment strategies is a crucial element in the clinical decision-making process [53, 57]. These screening tools provide an exceptionally sensitive method for detecting mutations and have played a crucial role in uncovering new somatic mutations linked to various types of cancer [58]. Moreover, ultra-deep plasma NGS assays have facilitated the detection of targetable oncogenic drivers and resistance mutations in patients with non-small-cell lung cancer (NSCLC), especially in situations where tissue sampling fails to reveal targetable mutations [59]. The detection of somatic mutations in tumor samples has been greatly improved by next-generation sequencing (NGS) technologies, offering a more comprehensive understanding of the genetic changes that contribute to cancer development and progression [59]. Advancements in NGS not only improve the accuracy and sensitivity of mutation detection but also support the integration of personalized medicine by enabling genomic profiles of individual tumors.

NGS technologies are rapidly becoming a cornerstone of personalized cancer therapy, particularly for melanoma [60]. NGS plays a crucial role in unraveling the intricate landscape of somatic mutations, offering valuable insights for creating personalized neoantigen vaccines for individual patients [61]. NGS with a targeted approach hones in on specific regions of the genome known for harboring mutations, facilitating exceptionally sensitive and accurate detection. On the other hand, whole-exome sequencing examines all protein-coding regions of the genome for mutations, offering a more comprehensive perspective, though at a significantly higher expense. Whole-genome sequencing (WGS) takes this approach even further by analyzing the entire genome, including non-coding areas, to provide a complete picture of genetic variations [62]. From platform such as Illumina and Ion Torrent to Pacific Biosciences, coupled with sophisticated bioinformatics analysis tools including VarScan [63], MuTect, and GATK, advancements in microarrays and NGS have significantly enhanced the accuracy and speed of cancer genome analysis [64, 65]. These evolving technologies continue to drive innovation in melanoma treatment, facilitating personalized immunotherapeutic strategies based on the unique genetic profile on a patient’s tumor [66].

Neoantigens and tumor-associated antigens (TAAs) represent two distinct classes of tumor antigens that play critical roles in cancer immunotherapy [67]. Although both can be targeted by immune-based therapies, their distinct origins, immune-stimulating properties, therapeutic uses, and constraints shape their specific roles in treatment approaches [22]. Neoantigens are tumor-specific antigens that originate from somatic mutations, gene rearrangements, or alternative splicing in tumor cells. The absence of neoantigens in normal tissues contributes to their high tumor specificity, rendering them prime candidates for tailored cancer immunotherapy approaches [68]. In contrast, TAAs are self-antigens that are either overexpressed or re-expressed in tumor cells but are also present at lower levels in normal tissues. These distinctions influence their immunogenicity and therapeutic potential [68].

Neoantigens elicit robust immune responses as they are recognized as foreign (non-self) by the immune system. Due to their absence in normal tissues, they circumvent central and peripheral immune tolerance mechanisms, rendering them highly efficacious in eliciting anti-tumor immunity. Conversely, TAAs exhibit diminished immunogenicity as they originate from normal proteins subject to immune tolerance. Consequently, additional strategies, such as immune checkpoint inhibitors, are frequently requisite to augment immune recognition and response [69].

Neoantigens are highly suitable for individualized cancer immunotherapy, serving key targets for neoantigen-based vaccines and adoptive T cell therapies. Their high specificity enables precise tumor targeting while minimizing the risk of damage to normal tissues. In contrast, TAAs, are more applicable in broader immunotherapeutic approaches, including monoclonal antibodies and checkpoint inhibitors, which enhance the immune response against tumor cells expressing these antigens [52, 68].

Despite their advantages, both antigen classes present distinct challenges. Neoantigen-based therapies require complex computational challenges and approaches to identify patient-specific neoantigens using sequencing technologies. Additionally, the production of personalized therapies is labor-intensive, time-consuming, and costly. TAA-based therapies, while more broadly applicable, pose a risk of off-target effects and autoimmune toxicity due to TAAs expression in certain normal tissues, which can lead to unintended immune responses [51, 70]. The contrasting characteristics of neoantigens and TAAs underscore their unique functions in cancer immunotherapy. Neoantigens provide a highly individualized and cancer-specific approach to treatment, while TAAs offer broader therapeutic targets that necessitate additional measures to combat immune tolerance and reduce off-target effects. A thorough understanding of these distinctions is crucial for enhancing immunotherapeutic approaches and boosting treatment efficacy in individuals with cancer. The optimization of cancer immunotherapy strategies relies heavily on recognizing and leveraging these differences between neoantigens and TAAs [22].

Bioinformatics pipelines play a critical role in predicting neoantigens from tumor genomic and transcriptomic data, facilitating the identification of potential targets for personalized cancer immunotherapy. These pipelines integrate sophisticated algorithms and filtering criteria that account for key factors such as immunogenicity and HLA allele specificity [71, 72]. The field of neoantigen prediction has witnessed the development of multiple bioinformatics tools. Among these, ScanNeo2 and nextNEOpi stand out as advanced, fully automated systems. These pipelines excel in their ability to identify tumor neoantigens by directly processing raw DNA and RNA sequencing information [73–75]. Advanced algorithms are utilized in these instruments to scrutinize genomic changes and assess the immunogenic potential of anticipated neoantigens [76]. Another high-throughput neoantigen prediction pipeline, NeoPredPipe, employs NetMHCpan for HLA allele-specific neoantigen prediction. In addition to predicting neoantigens, NeoPredPipe evaluates their immunogenic potential, thereby enhancing the accuracy of neoantigen identification [75].

The establishment of guidelines for neoantigen bioinformatic characterization has delineated crucial parameters, encompassing variant identification, anticipated modifications in amino acid sequences, peptide sequence composition, predictions of binding affinity, and agretopicity values [72, 77]. Neoantigen prediction pipelines, such as TruNeo, incorporate multiple biological parameters, including peptide—MHC class I binding affinity, proteasomal cleavage efficiency, and expression abundance, to enhance prediction accuracy. Furthermore, these pipelines consider tumor heterogeneity, clonality, and HLA loss of heterozygosity (LOH), thereby refining neoantigen identification and improving the selection of clinically relevant targets [78, 79].

Despite advancements in neoantigen prediction, the accurate identification of highly immunogenic neoantigens remains a significant challenge. The limited precision of current bioinformatics pipelines constrains their capacity to reliably identify neoantigens capable of eliciting protective T cell responses, thus necessitating further refinement [51, 79]. Bioinformatic neoantigen prediction pipelines play a crucial role in neoantigen-based cancer immunotherapy. Through the utilization of advanced algorithms and HLA allele-specific predictions, these tools facilitate the identification of candidate neoantigens for personalized therapeutic strategies. Continued advancements in these pipelines are essential for the development of more efficacious and targeted cancer treatments.

Tumor heterogeneity and clonal pose significant challenges in cancer research and treatment, particularly in neoantigen prediction and immunotherapy [80]. Tumors consist of genetically diverse, resulting in the development of evolving subclonal neoantigens, which impact immune system monitoring and responses to treatment over time [81]. The presence of subclonal neoantigens complicates the identification of immunogenic targets for cancer immunotherapy [22]. Current research emphasizes the significance of neoantigen heterogeneity in immune surveillance, revealing that an elevated subclonal neoantigen load is linked to tumor development and immune avoidance tactics. By integrating subclonal neoantigens into prediction models, the accuracy of neoantigen identification could be substantially enhanced, potentially leading to improved immunotherapeutic strategies [82, 83].

Tumor evolution introduces an additional layer of complexity to neoantigen prediction and immunotherapy [84]. Clonal evolution drives intratumoral heterogeneity, wherein distinct tumor subclones possess diverse genetic profiles, resulting in heterogeneous antigenic landscapes. Continuous genetic alterations influence the efficacy of neoantigen-targeted immunotherapies, as tumor antigens evolve concomitantly with immune system responses throughout the course of treatment [81, 85].

Such a bioinformatics pipeline is urgently needed, capable of predicting neoantigens while considering of tumor evolution dynamics to overcome tumor heterogeneity and clonal evolution. Various works underlined that multi-region sequencing, in association with the analysis of clonal architecture, may lead to the disclosure of spatial and temporal patterns of tumor evolution and enable insights into intratumor heterogeneity and clonal dynamics [71, 75].

Tumor heterogeneity and clonal evolution represent a significant challenge, and advanced bioinformatics tools and deep analyses that consider subclonal neoantigens and tumor evolution over time are essential. These factors need to be integrated into neoantigen prediction pipelines and immunotherapeutic strategies to increase prediction accuracy and improve the efficacy of personalized cancer treatments [81, 86].

Neoantigen vaccines are being actively investigated for several types of cancer, including melanoma, glioblastoma, non-small cell lung cancer (NSCLC), and pancreatic cancer. These vaccines target tumor-specific mutations, eliciting strong immune responses through cytotoxic T cell activation. Their ability to generate highly tumor-specific antitumor activity has made them promising candidates for personalized cancer immunotherapy [87, 88].

Up to late 2022, 199 clinical trials were reported, of which Phase I studies represented the majority (59.8%) according to the Trialtrove database. Peptide-based vaccines are still the most extensively explored, while mRNA and DC-based platforms have gained popularity owing to their potential for tailored and adaptive immunotherapy. The appeal of mRNA-based neoantigen vaccines has been enhanced by the development of RNA stability, delivery technologies, and large-scale manufacturing [1, 88].

Early phase clinical trials have evaluated the safety and immunogenicity of neoantigen vaccines. For instance, in glioblastoma, enhanced intratumoral T cell infiltration has been observed following vaccination, implying an intensified local immune response. Furthermore, combination therapies with immune checkpoint inhibitors, such as nivolumab and ipilimumab, or adjuvants like poly-ICLC, have enhanced vaccine activity by promoting T cell activation and contracting tumor immune evasion mechanisms [89].

Neoantigen vaccines are being explored for a variety of cancer types, with NSCLC, melanoma, glioma, and pancreatic cancer being the most common indications. Triple-negative breast cancer (TNBC) has also emerged as an area of interest because it carries a high mutational burden, making it an attractive candidate for neoantigen-based immunotherapy [1]. In terms of vaccine delivery systems, peptide vaccines were the most common, accounting for 41% of neoantigen vaccine trials. Nevertheless, mRNA-based vaccines and liposomal delivery systems are rapidly emerging as promising options considering their flexibility in encoding patient-specific neoantigens. DC-based platforms, accounting for 16.1% of trials, have the advantage of direct antigen presentation to T cells, but pose challenges related to ex vivo manipulation and scalability [90, 91].

Neoantigen vaccines are frequently applied in combination with standard cancer treatments, including chemotherapy, radiation therapy, and immune checkpoint inhibitors. These combination regimens aim to enhance therapeutic efficacy by modulating the tumor microenvironment and promoting long-term antitumor immunity [4]. Despite these promising advances, several challenges remain in the widespread clinical implementation of neoantigen vaccines. One of the most significant obstacles is the computational complexity involved in identifying patient-specific neoantigens. This process requires high-throughput sequencing, advanced bioinformatics pipelines, and machine learning algorithms to accurately predict immunogenic epitopes. Additionally, the manufacturing of personalized vaccines is still labor-intensive and costly, necessitating advancements in automation and scalability. Patient recruitment also presents a challenge, as the stringent inclusion criteria for neoantigen vaccine trials limit enrollment, sometimes leading to the early termination of studies [15, 51, 92].

It is anticipated that ongoing technological advancements in sequencing and bioinformatics pipelines will facilitate neoantigen discovery, enhancing both accuracy and efficiency in vaccine development. Moreover, innovative adjuvants and delivery systems may improve vaccine efficacy by optimizing antigen presentation and immune stimulation. Expanding clinical trial designs to encompass more diverse patient populations will be essential for increasing the generalizability of neoantigen vaccine strategies and demonstrating broader clinical utility.

Neoantigen vaccines represent one of the most promising approaches in personalized cancer immunotherapy, and current clinical trials continue to elucidate their potential to transform treatment strategies for various cancer indications. However, further refinement in vaccine design, patient stratification, and manufacturing processes is necessary to enhance their clinical utility and facilitate their integration into mainstream oncology practice [22, 87].

Once candidate neoantigens have been defined, an appropriate vaccine platform is required to present these neoantigens to the immune system. Several vaccine platforms are available, each with its advantages and disadvantages [2, 17].

Peptide vaccines consist of synthetic peptides that mimic neoantigens. Well-defined epitopes and their ease of production make them attractive candidates. The well-defined epitopes and efficient production methods of these entities render them promising candidates for immunotherapeutic applications. However, their efficacy is significantly limited by HLA restriction, as peptides are presented exclusively by specific HLA alleles. Moreover, it is not yet clear whether off-target effects may occur, where T cells that recognize neoantigens could also target related sequences in healthy cells [93, 94].

The principle of DC-based vaccines involves the isolation of DCs from patients and neoantigen loading in vitro neoantigen loading before introducing them to the same patient [95, 96]. DCs are highly potent professional antigen-presenting cells capable of eliciting robust T cell-mediated immune responses [97]. Although DC vaccines provide strong antigen presentation and, broader immune activation, their widespread clinical applications are hindered by complex manufacturing, high costs, and logistical challenges [98].

DNA/RNA vaccines are based on DNA- or RNA-encoding neoantigens. After administration, nucleic acids are translated into proteins, thus allowing the in vivo expression and processing of neoantigens [99]. Furthermore, the high interest in vaccines is based on their fast and scalable production processes and their ability to encode many neoantigens on one vaccine construct. A major challenge is the efficient delivery of nucleic acids to target cells; specialized delivery systems such as nanoparticles or viral vectors are typically required [100]. For DNA vaccines, the safety considerations related to the risk of insertional mutagenesis are controversial. Generally, the following neoantigen delivery platforms can be compared: neoantigen/nucleic acid-based, peptide-based, and DC-based [101].

Neoantigen-specific, peptide-based vaccines offer well-defined epitopes and are easy to synthesize, making them a straightforward approach for targeting neoantigens. However, their fundamental limitations are HLA restriction, which may limit their applicability in different patient cohorts, and off-target toxicity [102]. DCs-based vaccines have strong antigen presenting abilities, leading to a robust immune response and wider immune system activation. But their clinical use is still limited by complex manufacturing processes and costs [13, 95, 97]. As schematically depicted in Figure 2, DC-based immunotherapy utilizes an ex vivo or in vivo approach to activate T cell, aiming to enhance the specificity of the immune response against tumor cells. This strategy is an integral component of modern cellular therapies cancer treatment.

Recent improvements in neoantigen vaccine delivery platforms have focused on leveraging innovative methodologies to enhance both immunogenicity and efficacy [2, 27, 71]. For example, structure-based immunogenicity and antitumor activity in preclinical models. Nanoparticles-based vaccine delivery systems also allow for improving pharmacokinetics of the administered drugs and facilitate co-delivery of antigens and adjuvants also improving immune activation. All these strategies synergistically improve delivery of neoantigens into target cells, enhancing immune responses for tumor-specific neoantigens [103, 104].

Taking this into consideration, although each of these platforms offers distinct advantages and disadvantages, current research efforts are focused on optimizing of delivery systems for maximal immunogenicity and therapeutic benefits in neoantigen vaccines for personalized cancer immunotherapy [27, 49, 105]. By overcoming the limits of each individual platform and combining the advantages of both, a better and more broadly applicable neoantigen vaccine can be developed for use in cancer patients [2, 4, 13, 71]. Table 2 summarizes neoantigen vaccine modalities with their mechanism of action, their advantages, and disadvantages clinically. The table also summarizes current efforts focused on optimizing peptide-based, DC-based, and RNA-based vaccine platforms against melanoma and glioblastoma.

Adjuvants actively increase neoantigen vaccine immunogenicity by enhancing innate and adaptive immunity [14]. By stimulating the innate immune response, adjuvants activate mature APCs, including DCs, which present neoantigenic peptides to T cells and ultimately elicit a strong adaptive immune response [106]. The application of adjuvants improves the efficiency of neoantigen vaccines through multiple mechanisms [106].

Improved antigen presentation: Adjuvants stimulate APCs in the uptake and processing of antigens more effectively to increase the presentation of neoantigens to T cells [106].

Enhancement of immune cell activation: In addition, adjuvants are used to activate immune cells, such as DCs and macrophages, which, in turn, activate T cells for a more effective response [106–108].

Modulation of the immune response: Adjuvants may modulate the balance between pro- and anti-inflammatory cytokines, thus further contributing to minimizing side effects [106, 109].

Promising adjuvant candidates have demonstrated promising results in enhancing neoantigen vaccine immunogenicity. Adjuvant use is widespread, especially for those that engage Toll-like receptors including several molecules, including poly-ICLC. These factors have been associated with enhanced neoantigen vaccine immunogenicity in numerous clinical trials [109].

STING agonists: STING agonists trigger the STING pathway, elicit innate immune responses, and enhance immunogenicity. The combination of STING agonists with other therapies is possible [110, 111].

Cytokine-based adjuvants: These include many cytokines, including IL-12 and IL-15, which have been shown to activate T cells and natural killer cells and enhance neoantigen vaccine activity in preclinical models [112, 113].

Combination with other immunotherapies: Several adjuvant systems can also collaborate with other immunotherapies to enhance the overall therapeutic activity of neoantigen vaccines [114, 115].

Combination with immune checkpoint blockade: Neoantigen vaccine immunogenicity enhanced through adjuvant therapy may be further augmented by the addition of immune checkpoint inhibitors. In this vein, the combination of adjuvants with checkpoint inhibitors, such as anti-PD-1 therapy, has thus far shown tremendous promise in enhancing immune responses across a wide variety of malignancies, including melanoma and renal cell carcinoma [116].

Combination of chemotherapy and radiation therapy: Adjuvants can also improve neoantigen vaccine immunogenicity upon administration in combination with chemotherapy and radiation therapy, thereby enhancing cancer disease outcomes [117].

The improvement of vaccine immunogenicity using immunological adjuvants is one of the most critical vaccine development strategies [17, 24, 44, 100]. Adjuvants are substances that enhance host immune responses, including cytokines, agonists of TLRs, and host defense peptides. Several studies have underlined the potential of using agonists of costimulatory molecules and modulating pre-existing inflammation to improve vaccine responsiveness. Knowledge of the molecular mechanisms of adjuvant action, such as their interaction with TLR4 receptors, is an important issue in formulating potent and safe vaccine compositions [9, 15, 17, 100].

The molecular and cellular mechanisms triggered by TLR4-targeted combination adjuvants have been studied [24, 44]. For example, the importance of understanding DC cross-presentation in the quest for effective vaccines has been stressed because adjuvants modulate DC cross-presentation toward balanced antibody and T cell-based immunity induction [8, 96, 98]. The combination of adjuvants that target cross-presenting pathways with innate immune signaling is expected to give rise to powerful cytotoxic T lymphocyte (CTL) immunity and long-lasting CTL memory [44, 65, 76, 102].

In summary, adjuvants play a fundamental role in enhancing neoantigen vaccine immunogenicity by activating the innate immune pathways and inducing an adaptive immune response. The most promising adjuvant candidates are TLR agonist-based, STING agonists, and cytokine-based adjuvants [110, 111]. The combination of adjuvants with immune checkpoint inhibitors and other immunotherapies has great potential for synergistic effects, ensuring better therapeutic outcomes in patients with cancer. Ongoing efforts focus on optimizing the conditions of adjuvant formulations to maximize neoantigen vaccine efficacy and promote their clinical use in personalized cancer immunotherapy.

Neoantigen vaccines, in concert with immune checkpoint inhibitors, represent one of the most effective strategies in cancer immunotherapies to maximize synergistic effects for improved therapeutic outcomes [116]. Several studies have emphasized the synergy between neoantigen vaccines and immune checkpoint inhibitors, including PD-1/PD-L1 inhibitors, in the generation and maintenance of neoantigen-specific immune responses [116]. The blockade of immune checkpoints is expected to act synergistically with both cell-mediated and vaccine-induced neoantigen-specific immune responses [118].

A combination of neoantigen vaccination with immune checkpoint inhibitors has shown promising clinical efficacy in patients with cancer. This combination treatment is expected to enhance the general efficacy of cancer immunotherapy by targeting patient-specific neoantigens, thereby improving the antitumor immune response. Preclinical and clinical investigations have reported encouraging results for the combination of a neoantigen vaccine with immune checkpoint inhibitors in melanoma, lung cancer, and gastrointestinal cancers. Neoantigen vaccines when used alongside immune checkpoint inhibitors, are being actively investigated in personalized cancer immunotherapy to enhance both treatment specificity and efficacy.

Perhaps combining neoantigen vaccines with strategies targeting different levels of the immune response will be the future of treatment [33, 45, 66]. Neoantigen vaccines can elicit strong, specific immune responses against tumor-specific antigens, whereas checkpoint inhibitors remove the breaks in the immune system and further potentiate the attack against cancerous cells [16, 41, 49]. This approach could overcome some of the mechanisms of resistance in cancer therapy and thus improve patient outcomes. Several studies have reported adjuvant use in combination with immune checkpoint inhibitors, such as anti-PD-1 therapy, to improve immune response and clinical outcomes in various malignancies [119].

Several such combinations have shown promise for non-small cell lung carcinoma, urothelial carcinoma, head and neck, gastric, esophageal, triple-negative breast, and pancreatic malignancies [120]. The proposed mechanisms of synergy include immunogenic tumor cell death, anti-angiogenesis, selective depletion of myeloid-derived immunosuppressive cells, and lymphopenia, which leads to the proliferation of effector T cells [40, 44, 48]. Although clinical benefits have been observed with some of these combinations, further studies are needed to optimize treatment strategies and to identify better predictors of response to immune checkpoint inhibitors [27, 116, 119].

Neoantigen vaccines combined with immune checkpoint inhibitors are emerging as promising new modalities for cancer immunotherapy [32, 114]. Preclinical and translational studies are underway using combinations of neoantigen vaccines with immune checkpoint inhibitors to further enhance antitumor immune responses against tumor-specific antigens, potentially providing better therapeutic benefits and more personalized treatment options for patients with cancer [10, 40]. Optimizing neoantigen vaccine-based combination optimization and expanding clinical translation across tumor types should further improve outcomes in patients with cancer.

Neoantigen vaccines are generally well-tolerated. The most common adverse events are usually mild-to-moderate grade and include injection-site reactions, fatigue, and flu-like symptoms, which contrast with the often serious toxicities of conventional chemotherapy or targeted therapies [17].

Neoantigen vaccines demonstrate robust immune responses against tumor-specific neoantigens [2]. This is largely supported by neoantigen-specific T cell responses often measured in phase I/II trials using techniques such as enzyme-linked immunospot (ELISpot), intracellular cytokine staining, and tetramer staining [123]. Moreover, the magnitude of T cell responses has been observed to parallel clinical outcomes in a manner that strongly suggested that neoantigen-specific responses play a role in antitumor activity [44, 45].

Although early-phase trials are not powered to definitively assess clinical efficacy, they have reported encouraging results [9, 27, 121]. Findings include prolonged progression-free survival (PFS), improved overall survival (OS), and complete tumor regression. For instance, a phase I trial combining a personalized neoantigen vaccine with anti-PD-1 therapy in advanced melanoma reported a 6-month PFS rate of 62% and an OS rate of 80% [123, 124].

Successful neoantigen vaccines depend on careful patient selection, optimized treatment regimens, and identification of predictive biomarkers. Patients with a high tumor mutational burden (TMB) and a favorable HLA genotype are more likely to benefit [51]. Several biomarkers are being explored for their potential to predict vaccine response, including tumor mutation burden, neoantigen clonality, and pre-existing T cell responses against neoantigens. A trial was initiated to improve neoantigen-based anticipation of a response to immune checkpoint blockade (ICB) therapy in melanoma patients by integrating neoantigen burden with immune-related resistance mechanisms [22, 125].

Tumors from patients with late-stage melanoma were comprehensively analyzed using an immune-enhanced exome and transcriptome platform to develop a predictive model. The neoantigen burden score, which integrates both exome and transcriptome features, effectively stratified patients into responders and non-responders, demonstrating superior predictive performance compared to TMB alone [126]. By incorporating immune-related resistance mechanisms, such as HLA allele-specific LOH, researchers have devised a composite neoantigen presentation score (NEOPS), which shows a strong correlation with therapy response [126]. NEOPS has emerged as a more reliable biomarker compared to single-gene biomarkers and expression signatures, positioning it as a novel predictor of ICB response in melanoma. Its validation in an independent patient cohort further reinforced its robustness and reliability as a predictive biomarker for ICB therapy response [126].

Building on the promising results of early phase trials, several phase III clinical trials are underway to rigorously assess the efficacy of neoantigen vaccines in melanoma by directly comparing them with standard-of-care therapies. Examples of ongoing phase III trials include [127]. NCT04990479, which evaluates the combination of pembrolizumab and a personalized neoantigen vaccine (NOUS-PEV) in patients with metastatic melanoma [128, 129].

The Nous-PEV vaccine is specifically designed to target patient-specific neoantigens, aiming to enhance the immune response against melanoma cells [130]. Pembrolizumab, an immune checkpoint inhibitor targeting PD-1, is administered in combination with neoantigen vaccines to improve the antitumor immune response and optimize clinical outcomes in patients with metastatic melanoma [131]. Studies have demonstrated that the development of a personalized vaccine, NOUS-PEV, incorporates 60 patient-specific neoantigens [130]. Currently, this is being investigated in a clinical trial involving patients with metastatic melanoma and non-small cell lung cancer in combination with pembrolizumab. This study aims to evaluate the safety, immunogenicity, and efficacy of this neoantigen vaccine in enhancing the antitumor immune response in patients with advanced tumors [132].

The combination of pembrolizumab with a neoantigen vaccine represents a promising approach in cancer immunotherapy, where, the synergy between immune checkpoint inhibition and neoantigen specificity enhances tumor elimination. These personalized strategies have the potential to improve patient outcomes and advance precision medical strategies for metastatic melanoma [132, 133].

This trial highlights the rising interest in neoantigen-targeting personalized cancer vaccines and the potential synergistic benefits of their combination with immune checkpoint inhibitors in the enhancement of immune responses against cancer cells. The results of the trial will be crucial for understanding the efficacy of neoantigen vaccines when used in combination with checkpoint inhibitors and their interaction with clinical outcomes in patients with metastatic melanoma [134].

The clinical trial investigating the efficacy of the EVX-01 vaccine in patients with advanced melanoma compared pembrolizumab alone [134, 135]. This study (NCT05309421) evaluated the efficacy of the neoepitope vaccine EVX-01 in patients with advanced melanoma relative to pembrolizumab alone. EVX-01 is a personalized neoepitope vaccine that targets neoantigens in patients, whereas pembrolizumab is an immune checkpoint inhibitor that targets the PD-1 receptor. This trial assessed the safety, immunogenicity, and efficacy of the EVX-01 vaccine in combination with pembrolizumab in patients with metastatic melanoma [136].

The KEYNOTE-D36 trial: personalized immunotherapy with the EVX-01 neoepitope vaccine and pembrolizumab in patients with advanced melanoma. In this study, the efficacy and safety of EVX-01 neoepitope vaccine combined with pembrolizumab were tested in patients with metastatic or unresectable melanoma [137]. The trial aims to evaluate clinical outcomes and immune responses generated by the EVX-01 vaccine in combination with pembrolizumab will follow.

Personalized immunotherapy against metastatic melanoma was conducted using the neoantigen peptide-based vaccine EVX-01 coupled with the adjuvant CAF^®09b in five patients. Neoantigens were precisely identified by analyzing blood and tumor samples using the PIONEERTM AI-powered platform. The vaccine consisted of six infusions containing 5–10 predicted neoantigens as synthetic peptides. Interim analysis confirmed the safety profile of EVX-01 in eliciting long-lasting, neoantigen-specific, T cell responses targeting tumor-associated neoantigens [138].

In addition, early signs of clinical efficacy were observed, indicating the potential of EVX-01 as one of the most promising strategies for further investigation and advancement of personalized immunotherapy for metastatic melanoma [138]. Further attention has been directed toward the development of the next-generation neoantigen vaccine-based personalized therapy, EVX-02, includes a novel adjuvant, CAF^®09b, developed for patients with melanoma [1]. Neoantigen selection for EVX-02 was performed using the AI-driven platform called PIONEER platform, enabling the development of targeted neoantigen vaccine in melanoma.

Thus, EVX-02 has demonstrated safety in patients with resected melanoma, eliciting only low-grade side effects while inducing neoantigen-specific immune responses driven by activated CD4+ and CD8+ T cells [139]. More importantly, this underscores the accuracy and prognostic potential of the PIONEER platform, as DNA-encoded neoantigen delivered via gene-based technology induce strong neoantigen-specific immune responses [140]. Collectively, these findings highlight the promise of neoantigen vaccines as therapeutic modalities in cancer immunotherapy. The results demonstrated the accuracy and efficiency of the PIONEER platform in predicting neoantigens capable of inducing potent immune responses, reinforcing the feasibility of personalized neoantigen vaccines in cancer immunotherapy [10].

Another published research [10] described a novel platform for neoantigen identification. The combination of the EVX-01 vaccine and pembrolizumab represents an unusual approach in cancer immunotherapy because it leverages the specificity of neoantigens and the immune-modulating effects of checkpoint inhibitors to augment antitumor immune responses [10]. The results of this trial may give insight into the neoepitope vaccine efficacy, personalized in combination with immune checkpoint inhibitors, and associated clinical outcomes of patients with advanced melanoma [137]. These are generally randomized controlled trials (RCTs) with primary endpoints of OS, progression-free survival, and objective response rates. These trials are intended to assess whether neoantigen vaccines confer long-term clinical benefits that will eventually define their position in (clinical) practice [87, 88, 140].

Compelling case studies involving individual patients have further accelerated the development of neoantigen vaccines [16, 102, 132]. Two notable examples demonstrate the transformative potential of personalized immunotherapy in treating melanoma. The first case involved a patient with stage IIIB/C melanoma who was treated with NeoVax vaccine [16, 141]. The patient achieved a complete response within 25 months and showed no signs of tumor progression at four years. The second case describes a patient with advanced-stage melanoma who had been treated with EVX-01 vaccine [16, 138]. The patient also achieved a complete response within 12 months and was free of tumor progression at two years. These two cases highlight the potential of neoantigen vaccines to induce durable responses, further emphasizing that the personalization is a key factor in treatment success [8, 135].

These cases further illustrate the impact of personalized immunotherapy, in which treatment is tailored to the unique tumor profile of each patient. Neoantigen vaccines exploit neoantigen specificity and develop individualized treatment strategies, highlighting a promising path forward in personalized cancer immunotherapy [135, 142].

Early-phase clinical trials, ongoing phase III trials, and compelling case studies collectively underscore the transformative potential of neoantigen vaccines in melanoma treatment. These studies provide important insights into safety, immunogenicity, and clinical outcome outcomes associated with from neoantigen vaccines, thereby paving the way for personalized, effective immunotherapeutic strategies in cancer care [143].

The efficacy of neoantigen-based cancer immunotherapy relies on a series of immunological events that trigger potent antitumor activity [22]. Following vaccination, APCs, particularly DCs, engulf and process neoantigens, and subsequently present them via MHC molecules to naïve CD8+ and CD4+ T cells. This activation leads to the expansion of tumor-specific CTLs, which recognize and eliminate neoantigen-presenting tumor cells [144].

The effectiveness of neoantigen vaccines relies on several variables, including antigen presentation quality, T cell priming efficiency, and longevity of T cell memory responses. Strong CD8+ T cell activation is required for the direct killing of tumor cells, while CD4+ helper T cells play an important role in providing cytokine support to sustain the cytotoxic response. Additionally, neoantigens with high MHC-binding affinity and low sequence homology to self-proteins elicit stronger intense immune responses, with a lower probability of immune tolerance [2, 4].

Optimal responses also necessitate overcoming the immunosuppressive tumor microenvironment. The immunosuppressive activities of regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and inhibitory cytokines can diminish vaccine-induced immune responses [88]. Strategies that combine neoantigen vaccines with immune checkpoint inhibitors (e.g., anti-PD-1 and anti-CTLA-4 antibodies) help restore T cell function by blocking suppressive signaling pathways. Furthermore, adjuvants, including poly-ICLC, CpG oligodeoxynucleotides, and STING agonists, enhance vaccine activity by inducing innate immune activation and boosting DC function [116, 145].

The combination of personalized vaccine strategies that consider tumor heterogeneity and clonal evolution is the cornerstone for ensuring long-term immunity [146]. Multi-epitope neoantigen vaccines targeting multiple mutations within a tumor increase the likelihood preventing immune-evasion mechanisms. In addition, optimizing vaccine delivery systems, such as mRNA-based systems, nanoparticle carriers, and DC-based vaccines, can increase antigen stability and presentation, ultimately leading to enhanced therapeutic outcomes. A deeper understanding of these mechanisms is critical for optimizing neoantigen vaccine approaches and develping more potent and persistent cancer immunotherapies [87, 147, 148].

Further refinement of neoantigen prediction algorithms is needed. Currently, neoantigen algorithms have low power for predicting immunogenicity, resulting in high false-positive and false-negative rates are high. To improve predictive accuracy, advancements in algorithms are required to integrate machine learning with multi-omics data. In addition, manufacturing needs to be harmonized across processes to produce vaccines that are consistent, scalable, and affordable [76]. Automation in production and collaboration with industrial partners can facilitate streamlining and economic growth. Optimized neoantigen selection can be achieved by prioritizing highly immunogenic neoantigens, enhancing antigen presentation, and improving T cell trafficking through state-of-the-art bioinformatics and novel delivery platforms [22].

The heterogeneity of the tumor and its clonal evolution require multi-epitope approach and adaptive vaccine designs that consider immune escape mechanisms for further improvement in efficacy [81]. Several combination strategies have been developed to overcome immunosuppressive conditions within the tumor microenvironment, including neoantigen vaccination in combination with checkpoint blockade and cell therapy, which remains one of the most promising approaches [149]. To establish long-lasting immunological memory, the factors affecting the generation and maintenance of memory T cells need to be understood; hence, studies on specific adjuvants and cytokines are essential to induce durable protection [150].

From a fundamental viewpoint, access to personalized therapies such as neoantigen vaccines is crucial to reducing disparities in cancer care. The industry must take proactive measures to ensure that it is accessible to all patients [9]. Informed consent must be sought in advance and privacy must be safeguarded through appropriate data governance structures. Long-term safety monitoring is essential to address potential off-target effects such as autoimmune reactions and late-onset toxicities. Therefore, continuous surveillance and evaluation should be performed.

However, neoantigen vaccine development presents major challenges related to the management of off-target effects and long-term safety [2, 87, 130, 132]. In this context, monitoring autoimmune reactions with stringent protocols can help detect immune responses against unintentionally targeted non-neoplastic tissues long-term toxicity monitoring will help assess toxicities that have adverse effects that may develop over time. Overall, risk-benefit analyses derived from these studies will continue to provide critical insights into overall safety and therapeutic benefits, including evaluating the risk versus benefit to the patient [151].