Neoantigen Prediction Algorithms in Cancer Immunotherapy

Molecular Mechanisms and Biological Basis of Neoantigen Formation

Introduction to Neoantigens

Neoantigens are tumor-specific antigens that arise due to mutations in tumor cells. They are not present in normal tissues, making them highly specific targets for cancer immunotherapy. The formation of neoantigens is a complex process that involves various molecular mechanisms, including genetic mutations, post-translational modifications, and aberrant protein processing. Understanding these mechanisms is crucial for the development of effective neoantigen prediction algorithms and cancer immunotherapies.

Genetic Mutations and Neoantigen Formation

The primary source of neoantigens is genetic mutations, which can be categorized into somatic mutations, insertions, deletions, and chromosomal rearrangements. Somatic mutations are alterations in the DNA sequence that occur after conception and are not inherited. These mutations can lead to the formation of novel peptide sequences that are presented on the surface of tumor cells by major histocompatibility complex (MHC) molecules.

Missense mutations, where a single nucleotide change results in a different amino acid, are a common source of neoantigens. These mutations can alter the protein structure, creating novel epitopes that can be recognized by the immune system. Nonsense mutations, which introduce a premature stop codon, can also lead to truncated proteins with unique neoantigenic properties. Additionally, frameshift mutations, caused by insertions or deletions, can result in entirely new peptide sequences due to the alteration of the reading frame.

Post-Translational Modifications

Post-translational modifications (PTMs) are chemical changes that occur to proteins after they are synthesized. These modifications can affect protein function, localization, and stability. In the context of neoantigen formation, PTMs can create novel epitopes that are not present in the unmodified protein. Common PTMs include phosphorylation, glycosylation, ubiquitination, and methylation.

Phosphorylation, the addition of a phosphate group to a protein, can create unique neoantigenic sites. This modification is often involved in signaling pathways and can be dysregulated in cancer, leading to the presentation of phosphorylated neoantigens. Glycosylation, the attachment of sugar moieties to proteins, can also generate neoantigens by altering the protein's structure and antigenic properties. Aberrant glycosylation patterns are frequently observed in tumors and can serve as neoantigenic targets.

Aberrant Protein Processing

The process of protein synthesis and degradation is tightly regulated in normal cells. However, in cancer cells, this regulation can be disrupted, leading to the production of aberrant proteins that can serve as neoantigens. The proteasome, a protein complex responsible for degrading damaged or misfolded proteins, plays a crucial role in generating peptide fragments that are presented by MHC molecules.

In cancer cells, mutations in genes encoding proteasomal subunits or regulatory proteins can lead to altered protein degradation pathways. This can result in the accumulation of abnormal protein fragments that are processed and presented as neoantigens. Additionally, defects in the endoplasmic reticulum-associated degradation (ERAD) pathway, which targets misfolded proteins for degradation, can contribute to neoantigen formation by allowing the accumulation of aberrant proteins.

Role of the Immune System

The immune system plays a critical role in recognizing and responding to neoantigens. The presentation of neoantigens by MHC molecules on the surface of tumor cells is a key step in the activation of T cells. CD8+ cytotoxic T lymphocytes (CTLs) can recognize and kill tumor cells presenting neoantigens, while CD4+ helper T cells can enhance the immune response by providing necessary cytokines and support to CTLs and other immune cells.

The effectiveness of the immune response to neoantigens depends on several factors, including the affinity of the T cell receptor (TCR) for the neoantigen-MHC complex, the expression level of the neoantigen, and the presence of immune checkpoints that can inhibit T cell activity. Immune checkpoint inhibitors, such as PD-1/PD-L1 and CTLA-4 blockers, have been developed to enhance the immune response to neoantigens by preventing the inhibition of T cell activity.

Challenges in Neoantigen Prediction

Despite the potential of neoantigens as targets for cancer immunotherapy, predicting which neoantigens will elicit a strong immune response remains a significant challenge. Several factors contribute to this complexity, including the high mutational burden of tumors, the variability in MHC allele expression among individuals, and the influence of the tumor microenvironment on immune recognition.

Current neoantigen prediction algorithms rely on computational models that integrate genomic, transcriptomic, and proteomic data to identify potential neoantigens. These algorithms assess the binding affinity of peptide-MHC complexes, the likelihood of peptide processing and presentation, and the immunogenicity of the neoantigen. However, the accuracy of these predictions is limited by the incomplete understanding of the molecular mechanisms underlying neoantigen formation and immune recognition.

Conclusion

The formation of neoantigens is a multifaceted process involving genetic mutations, post-translational modifications, and aberrant protein processing. These mechanisms contribute to the generation of novel peptide sequences that can be recognized by the immune system, making them attractive targets for cancer immunotherapy. However, the complexity of neoantigen formation and immune recognition poses significant challenges for the development of accurate prediction algorithms. Continued research into the molecular mechanisms and biological basis of neoantigen formation is essential for advancing cancer immunotherapy and improving patient outcomes.

Current Technologies and Methodologies for Neoantigen Identification

The identification of neoantigens, which are novel peptide sequences presented by cancer cells as a result of tumor-specific mutations, is a cornerstone of personalized cancer immunotherapy. These neoantigens are crucial because they can be recognized by the immune system as foreign, thereby eliciting a targeted immune response. The development and refinement of technologies and methodologies for neoantigen identification have been propelled by advances in genomics, bioinformatics, and immunology, each contributing to the precision and efficacy of cancer immunotherapies.

High-Throughput Sequencing Technologies

High-throughput sequencing technologies, particularly next-generation sequencing (NGS), have revolutionized the field of genomics and are pivotal in the identification of neoantigens. NGS allows for comprehensive profiling of tumor genomes and transcriptomes, providing the necessary data to identify mutations that give rise to neoantigens. The sequencing of tumor DNA and RNA enables the detection of single nucleotide variants (SNVs), insertions and deletions (indels), and other genomic alterations that can result in the production of novel peptide sequences.

The integration of NGS in neoantigen identification involves several key steps. Initially, tumor and normal tissue samples are sequenced to distinguish somatic mutations from germline variants. Bioinformatic pipelines then process these data to predict the impact of mutations on protein sequences. This step is crucial as it filters out mutations that do not result in altered peptides. The subsequent prediction of peptide-MHC binding affinities, using algorithms such as NetMHC and others, is essential to determine which mutated peptides are likely to be presented on the surface of cancer cells.

Bioinformatics and Computational Prediction

Bioinformatics tools play a critical role in the prediction and validation of neoantigens. These tools analyze sequencing data to predict which mutations result in peptides that can bind to major histocompatibility complex (MHC) molecules. The binding affinity of peptides to MHC molecules is a critical determinant of whether a neoantigen will be presented on the cell surface and recognized by T cells. Predictive algorithms, such as NetMHCpan, use machine learning models trained on experimental binding data to estimate these affinities with high accuracy.

Moreover, bioinformatics approaches are employed to assess the immunogenic potential of predicted neoantigens. This involves evaluating the likelihood that a neoantigen can be recognized by T cell receptors (TCRs), which is influenced by factors such as the peptide's expression level, its processing and presentation efficiency, and the presence of TCRs capable of recognizing the neoantigen. The integration of these predictive models into neoantigen identification pipelines enhances the specificity and sensitivity of neoantigen discovery.

Immunopeptidomics

Immunopeptidomics, the study of peptides presented by MHC molecules on the cell surface, provides direct evidence of neoantigen presentation. Mass spectrometry-based immunopeptidomics allows for the identification and characterization of the MHC-bound peptide repertoire, including neoantigens. This approach complements predictive algorithms by validating the physical presence of neoantigens on cancer cells. The combination of immunopeptidomics with NGS data enhances the confidence in neoantigen identification and provides a more comprehensive understanding of tumor immunogenicity.

Functional Validation and Experimental Approaches

Functional validation of predicted neoantigens is a critical step in confirming their potential as targets for immunotherapy. Experimental validation often involves the use of in vitro assays to test the ability of neoantigen-specific T cells to recognize and kill tumor cells. Techniques such as enzyme-linked immunospot (ELISpot) assays, flow cytometry, and cytotoxicity assays are employed to measure T cell responses against candidate neoantigens. These assays provide empirical evidence of neoantigen immunogenicity and are essential for the selection of neoantigens for therapeutic applications.

In addition to in vitro assays, in vivo models, such as humanized mouse models, are used to evaluate the efficacy of neoantigen-targeted therapies. These models allow for the assessment of neoantigen-specific immune responses in a more physiologically relevant context, providing insights into the potential clinical efficacy of neoantigen-based vaccines or adoptive T cell therapies.

Integration of Multi-Omics Data

The integration of multi-omics data, including genomics, transcriptomics, and proteomics, enhances the accuracy and reliability of neoantigen identification. By combining data from different molecular layers, researchers can gain a more comprehensive view of tumor biology and the neoantigen landscape. This holistic approach allows for the identification of neoantigens that are not only mutation-derived but also arise from alternative splicing, post-translational modifications, or other non-genetic alterations.

Furthermore, the integration of multi-omics data facilitates the identification of neoantigens that are shared across different patients or tumor types, which can be leveraged to develop more broadly applicable cancer vaccines. The use of systems biology approaches to analyze these complex datasets provides a deeper understanding of the tumor microenvironment and its influence on neoantigen presentation and immune evasion.

Challenges and Future Directions

Despite significant advancements, several challenges remain in the field of neoantigen identification. The heterogeneity of tumors, both within a single patient and across different patients, poses a challenge to the identification of universally applicable neoantigens. Additionally, the dynamic nature of the tumor microenvironment and the potential for immune escape mechanisms necessitate continuous refinement of neoantigen prediction and validation methodologies.

Future directions in neoantigen identification involve the development of more sophisticated algorithms that incorporate machine learning and artificial intelligence to improve prediction accuracy. Advances in single-cell sequencing technologies are also expected to provide deeper insights into the clonal architecture of tumors and the heterogeneity of neoantigen presentation. Moreover, the integration of neoantigen identification with personalized immunotherapy strategies, such as CAR-T cell therapy and neoantigen vaccines, holds promise for improving the efficacy of cancer treatments.

In conclusion, the identification of neoantigens is a rapidly evolving field that leverages cutting-edge technologies and methodologies to advance cancer immunotherapy. The integration of high-throughput sequencing, bioinformatics, immunopeptidomics, and functional validation approaches provides a robust framework for the discovery and validation of neoantigens. Continued innovation and collaboration across disciplines will be essential to overcome existing challenges and realize the full potential of neoantigen-based therapies in cancer treatment.

Algorithmic Approaches and Computational Models for Neoantigen Prediction

The prediction of neoantigens is a cornerstone of personalized cancer immunotherapy, offering the potential to tailor treatments to the unique mutational landscape of a patient's tumor. This section delves into the algorithmic approaches and computational models that underpin neoantigen prediction, examining the methodologies, biological mechanisms, and computational challenges involved.

Biological Context and Mechanisms

Neoantigens arise from non-synonymous mutations in tumor cells, leading to novel peptide sequences that are presented on the cell surface by Major Histocompatibility Complex (MHC) molecules. These neoantigens are recognized by T cells, initiating an immune response against the tumor [1]. The identification of these neoantigens is crucial for designing effective cancer vaccines and adoptive T-cell therapies. The process involves several key steps: identifying tumor-specific mutations, predicting peptide-MHC binding, assessing T-cell receptor (TCR) recognition, and evaluating the immunogenicity of the neoantigens [2].

Computational Frameworks and Methodologies

Machine Learning and Deep Learning Approaches

Machine learning (ML) and deep learning (DL) have revolutionized neoantigen prediction by enabling the analysis of complex, high-dimensional omics data. These methods facilitate the extraction of key neoantigen features, improving the accuracy of predictions [2]. Traditional ML algorithms such as ElasticNet, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest, and AdaBoost have been employed to predict immunogenic peptides, but they often struggle with the vast search space and complex feature interactions inherent in neoantigen prediction [3].

Deep learning models, particularly Convolutional Neural Networks (CNNs), Residual Networks (ResNets), and Graph Neural Networks (GNNs), have shown superior performance in handling large datasets and capturing intricate patterns in sequence data [3]. For instance, the DeepImmuno-CNN model outperformed traditional algorithms by accurately predicting which residues are critical for T-cell recognition [3]. Moreover, generative models like Generative Adversarial Networks (GANs) have been used to simulate immunogenic peptides, enhancing training datasets and improving prediction accuracy [3].

Multi-Task Learning Models

Multi-task learning models, such as NeoMUST, leverage shared knowledge across related tasks to enhance neoantigen prediction. These models capture task-specific information, leading to improved performance metrics and reduced training times [4]. NeoMUST, for example, efficiently predicts neoantigen presentation via MHC-I molecules, demonstrating competitive accuracy with significantly shorter training durations compared to existing algorithms [4].

Network-Based Approaches

Network-based deep learning methods, such as DeepNetBim, integrate binding and immunogenicity information to predict HLA-peptide interactions. By treating HLA-peptide pairs as nodes in a network, these models can detect essential interactive propensities and improve prediction accuracy [5]. DeepNetBim, which utilizes a convolutional neural network and an attention mechanism, achieved an AUC score of 93.74% in HLA-peptide binding prediction, outperforming several state-of-the-art models [5].

Challenges and Limitations

Despite advances in computational models, several challenges persist in neoantigen prediction. The accuracy of predictions is often hampered by incomplete integration of immune-regulatory processes and algorithmic limitations [6]. The vast search space for potential neoantigens and the need for precise feature engineering further complicate the task [7]. Moreover, the clinical applicability of these models is limited by the high variability in immunogenicity among predicted neoantigens, necessitating experimental validation to confirm their therapeutic potential [8].

Integration with Biological Data and Experimental Validation

The integration of multi-omics data, including genomic, transcriptomic, and proteomic information, is essential for accurate neoantigen prediction. Advances in DNA sequencing and TCR repertoire sequencing have facilitated the identification of candidate neoantigens, but the validation of these predictions remains a critical bottleneck [1]. Experimental methods, such as T-cell activation assays and MHC binding assays, are necessary to confirm the immunogenicity of predicted neoantigens [5].

Future Directions

To overcome current limitations, future research should focus on improving the integration of computational predictions with experimental validation. The development of hybrid models that combine various ML and DL approaches could enhance prediction accuracy and reduce computational costs [2]. Additionally, the incorporation of immune system simulations and advanced modeling techniques could bridge the gap between computational predictions and clinical outcomes [6].

Regulatory agencies, such as the World Health Organization (WHO) and the National Center for Biotechnology Information (NCBI), play a crucial role in standardizing methodologies and ensuring the reliability of computational predictions. Formal recognition of digital evidence by these agencies could accelerate the adoption of in silico approaches in clinical practice [6].

In conclusion, the field of neoantigen prediction is rapidly evolving, driven by advances in computational models and machine learning techniques. While significant challenges remain, the integration of multi-omics data, experimental validation, and regulatory support holds promise for the development of personalized cancer immunotherapies that are both effective and efficient.

Challenges and Limitations in Neoantigen Prediction Algorithms

The field of cancer immunotherapy has witnessed rapid advancements, particularly with the advent of neoantigen prediction algorithms. These algorithms are pivotal in identifying tumor-specific antigens that can be targeted by the immune system, offering a personalized approach to cancer treatment. However, despite their potential, several challenges and limitations hinder the efficacy and widespread adoption of these algorithms in clinical settings.

Methodological Challenges

Neoantigen prediction algorithms primarily rely on next-generation sequencing (NGS) data to identify mutations that give rise to neoantigens. The process involves several steps, including variant calling, HLA typing, peptide-MHC binding prediction, and immunogenicity assessment [9, 10]. Each of these steps presents unique challenges:

1. Variant Calling: Accurate identification of somatic mutations is the first step in neoantigen prediction. However, the presence of sequencing errors, tumor heterogeneity, and low tumor purity can lead to false-positive or false-negative variant calls [11]. Advanced bioinformatics tools and stringent filtering criteria are required to mitigate these issues, but they can also result in the loss of potentially relevant mutations.

2. HLA Typing: Human leukocyte antigen (HLA) typing is crucial for predicting peptide binding affinity to MHC molecules. Inaccuracies in HLA typing can significantly affect the downstream prediction of neoantigen binding. The complexity of the HLA locus, with its high polymorphism and linkage disequilibrium, adds to the challenge [12].

3. Peptide-MHC Binding Prediction: The binding affinity of peptides to MHC molecules is a critical determinant of neoantigen presentation. Current algorithms use machine learning models trained on experimental binding data to predict peptide-MHC interactions. However, these models often struggle with peptides that have rare or uncharacterized motifs, limiting their predictive accuracy [13].

4. Immunogenicity Assessment: Even if a peptide binds strongly to an MHC molecule, it may not elicit a robust immune response. Predicting the immunogenicity of neoantigens involves understanding complex interactions between the peptide, MHC, and T-cell receptors, as well as the broader immune context [14]. Current models do not fully capture these dynamics, leading to uncertainty in predicting which neoantigens will be effective targets.

Biological Mechanisms and Context

The biological complexity of tumors further complicates neoantigen prediction. Tumors are characterized by high heterogeneity, with different regions of the tumor harboring distinct mutations. This intratumoral heterogeneity poses a significant challenge for neoantigen prediction algorithms, which typically analyze bulk tumor samples [11]. As a result, potentially immunogenic neoantigens present in subclonal populations may be overlooked.

Moreover, the tumor microenvironment (TME) plays a crucial role in modulating immune responses. An immunosuppressive TME, characterized by the presence of regulatory T-cells, myeloid-derived suppressor cells, and inhibitory cytokines, can dampen the effectiveness of neoantigen-targeted therapies [9]. Neoantigen prediction algorithms do not currently account for the TME's influence, which can lead to overestimation of a neoantigen's therapeutic potential.

The biological context also includes the patient's immune repertoire. The diversity and specificity of T-cell receptors (TCRs) are critical for recognizing and responding to neoantigens. However, current prediction models do not incorporate TCR repertoire data, limiting their ability to predict patient-specific immune responses [12].

Technological and Computational Limitations

The computational demands of neoantigen prediction are non-trivial. High-throughput sequencing generates vast amounts of data that require significant computational resources for processing and analysis. The integration of multi-omics data, such as genomics, transcriptomics, and proteomics, can enhance prediction accuracy but also increases computational complexity [9].

Furthermore, the algorithms themselves are subject to limitations in their design and implementation. Many prediction tools are developed and validated using limited datasets, which may not capture the full diversity of tumor types and HLA alleles encountered in clinical practice [11]. The lack of standardized benchmarks and evaluation criteria further complicates the comparison and validation of different prediction algorithms.

Clinical and Practical Considerations

From a clinical perspective, the translation of neoantigen prediction into effective therapies faces several hurdles. The time-sensitive nature of cancer treatment requires rapid turnaround times for neoantigen identification and vaccine production. However, the current workflow, from sequencing to vaccine formulation, can be time-consuming, delaying treatment initiation [15].

Additionally, the high cost of personalized neoantigen-based therapies poses a barrier to widespread adoption. The need for individualized sequencing, computational analysis, and vaccine manufacturing contributes to the expense, making these therapies inaccessible for many patients [16].

Regulatory challenges also exist, as the integration of computational predictions into clinical decision-making requires rigorous validation and approval processes. The World Health Organization (WHO) and other regulatory bodies have yet to establish comprehensive guidelines for the use of digital evidence in clinical settings, which could facilitate the adoption of neoantigen prediction algorithms [17].

Future Directions and Solutions

Despite these challenges, ongoing research and technological advancements offer promising solutions. The integration of artificial intelligence and machine learning into neoantigen prediction pipelines holds the potential to improve accuracy and efficiency. Multi-omics approaches, combining genomic, transcriptomic, and proteomic data, can provide a more comprehensive view of tumor antigens and their immunogenic potential [13].

Efforts to model and modify the TME, such as combining neoantigen vaccines with immune checkpoint inhibitors or other synergistic therapies, are being explored to enhance therapeutic outcomes [14]. Additionally, the development of rapid, cost-effective sequencing technologies and streamlined manufacturing processes could reduce the time and cost associated with personalized therapies [15].

In conclusion, while neoantigen prediction algorithms face significant challenges, they remain a cornerstone of personalized cancer immunotherapy. Continued advancements in computational methods, biological understanding, and clinical integration are essential to overcoming current limitations and realizing the full potential of neoantigen-driven therapies.

Future Directions and Innovations in Neoantigen Prediction for Personalized Cancer Immunotherapy

Introduction to Neoantigen Prediction

The landscape of cancer immunotherapy is undergoing a transformative shift, with neoantigen prediction emerging as a cornerstone for personalized treatment strategies. Neoantigens, unique to tumor cells, arise from somatic mutations and are not present in normal tissues, making them ideal targets for immune system recognition and attack. The accurate prediction of these neoantigens is crucial for the development of personalized cancer vaccines and therapies. Recent advancements in computational biology, genomics, and immunology have significantly enhanced our ability to predict neoantigens, yet challenges remain in translating these predictions into effective clinical interventions.

Advances in Computational Methodologies

The prediction of neoantigens involves several computational steps, including the identification of tumor-specific mutations, prediction of peptide-MHC binding affinity, and assessment of peptide immunogenicity. Recent developments in machine learning and artificial intelligence (AI) have greatly improved the accuracy and speed of these predictions. AI algorithms can now integrate vast datasets, including genomic, transcriptomic, and proteomic data, to identify potential neoantigens with high precision [10].

Machine learning models have been particularly effective in predicting peptide-MHC binding, a critical step in neoantigen identification. These models leverage large-scale datasets of known peptide-MHC interactions to train algorithms that can predict binding affinities with remarkable accuracy. Furthermore, AI-driven approaches are being developed to predict the immunogenicity of neoantigens, a challenging task given the complex nature of immune responses [13].

Biological Mechanisms and Context

The biological mechanisms underlying neoantigen presentation and recognition are complex and involve multiple components of the immune system. Neoantigens are presented on the surface of tumor cells by major histocompatibility complex (MHC) molecules, where they can be recognized by T cells. This interaction is critical for the activation of cytotoxic T lymphocytes, which can target and destroy tumor cells.

Recent studies have highlighted the importance of the tumor microenvironment in modulating neoantigen presentation and immune recognition. The presence of immunosuppressive cells and factors within the tumor microenvironment can hinder the effectiveness of neoantigen-targeted therapies. Understanding the interplay between neoantigens, the tumor microenvironment, and the immune system is essential for developing effective immunotherapies [13].

Innovations in Personalized Cancer Vaccines

Personalized cancer vaccines represent a promising application of neoantigen prediction. These vaccines are designed to elicit a robust immune response against tumor-specific neoantigens, thereby enhancing the body's ability to target and eliminate cancer cells. Recent advances in mRNA vaccine technology have facilitated the rapid development and testing of personalized cancer vaccines.

mRNA vaccines offer several advantages, including rapid prototyping, the ability to encode multiple neoantigens, and the stimulation of both CD8+ and CD4+ T cell responses. The use of lipid nanoparticle (LNP) delivery systems has further enhanced the stability and in vivo uptake of mRNA vaccines, making them a viable option for personalized cancer immunotherapy [13].

Integration of Biomimetic Nanovaccines

Biomimetic nanovaccines are an emerging innovation in the field of cancer immunotherapy. These vaccines utilize biomimetic delivery systems, such as exosome- or immune cell membrane-coated nanoparticles, to enhance antigen presentation and immune activation. By mimicking natural biological interfaces, biomimetic nanovaccines can improve the targeting precision and clinical efficacy of neoantigen-based therapies [10].

The integration of AI-guided neoantigen prediction with biomimetic nanovaccine design holds significant promise for advancing personalized cancer immunotherapy. AI algorithms can identify optimal neoantigen candidates, which can then be incorporated into biomimetic platforms to enhance immune priming and adaptive immune responses [10].

Challenges and Future Directions

Despite significant progress, several challenges remain in the field of neoantigen prediction and personalized cancer immunotherapy. One major challenge is the variability of tumors and the heterogeneity of neoantigen expression within and between patients. This variability complicates the identification of universal neoantigen targets and necessitates highly personalized approaches [13].

The immunosuppressive tumor microenvironment also poses a significant barrier to effective neoantigen-based therapies. Strategies to overcome this challenge include the use of combination therapies, such as immune checkpoint inhibitors and stromal modulators, which can enhance the efficacy of neoantigen-targeted treatments [13].

Manufacturing scalability and cost are additional challenges that must be addressed to make personalized cancer vaccines widely accessible. Innovations in modular microfluidic manufacturing and scalable production processes are needed to reduce costs and increase the availability of these therapies [10].

Conclusion

The future of neoantigen prediction for personalized cancer immunotherapy is bright, with numerous innovations on the horizon. The integration of AI-guided prediction algorithms, biomimetic nanovaccine platforms, and combination therapy strategies holds the potential to revolutionize cancer treatment. As research continues to advance, it is crucial to address the remaining challenges and ensure that these promising therapies can be effectively translated into clinical practice. The collaboration between computational scientists, immunologists, and clinicians will be essential in achieving this goal and ultimately improving outcomes for cancer patients worldwide.

References

[1] Computational Prediction and Validation of Tumor-Associated Neoantigens. DOI: 10.3389/fimmu.2020.00027

[2] Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. DOI: 10.3389/fonc.2022.1054231

[3] DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity. DOI: 10.1101/2020.12.24.424262

[4] NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation. DOI: 10.26508/lsa.202302255

[5] DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information. DOI: 10.1186/s12859-021-04155-y

[6] In-silico epitope-based vaccines design: progress, challenges and the road ahead. DOI: 10.1080/17460441.2025.2599178

[7] A Deep Learning Approach for NeoAG-Specific Prediction Considering Both HLA-Peptide Binding and Immunogenicity: Finding Neoantigens to Making T-Cell Products More Personal. DOI: 10.1101/2021.12.22.473942

[8] Abstract B78: Co-potentiation of human T cells to identify subdominant tumor neoantigens from melanoma patients responding to immune checkpoint blockade. DOI: 10.1158/2326-6074.TUMIMM18-B78

[9] Computational immunogenomic approaches to predict response to cancer immunotherapies. DOI: 10.1038/s41571-023-00830-6

[10] Biomimetic and personalized nanovaccines in cancer immunotherapy: Design innovations, translational challenges, and future directions.. DOI: 10.1016/j.jare.2026.01.070

[11] Challenges targeting cancer neoantigens in 2021: a systematic literature review. DOI: 10.1080/14760584.2021.1935248

[12] Neoantigen vaccine platforms in clinical development: understanding the future of personalized immunotherapy. DOI: 10.1080/13543784.2021.1896702

[13] mRNA Cancer Vaccines: A New Paradigm for Personalized Immunotherapy. DOI: 10.5455/jppr.20250722060032

[14] Rapid‐Turnaround Co‐Administration of mRNA‐Based MHC‐I and MHC‐II‐Restricted Neoantigens Enhances Immune Responses of Antigen‐Specific CD8+ T Cells and Anti‐Cancer Efficacy in Colorectal Cancer. DOI: 10.1002/advs.202506426

[15] Personalized mRNA Cancer Vaccines: Advances, Limitations, and the Promise of mRNA-4157 (V940). DOI: 10.54097/dg93xg63

[16] Neoantigen-Driven Immunotherapy in Triple-Negative Breast Cancer: Emerging Strategies and Clinical Potential. DOI: 10.3390/biomedicines13092213

[17] In-silico epitope-based vaccines design: progress, challenges and the road ahead. DOI: 10.1080/17460441.2025.2599178

[18] Neoantigen-Driven Immunotherapy in Triple-Negative Breast Cancer: Emerging Strategies and Clinical Potential. DOI: 10.3390/biomedicines13092213

[19] Computational immunogenomic approaches to predict response to cancer immunotherapies. DOI: 10.1038/s41571-023-00830-6

[20] 143 Identification of Neoantigen-specific CD8+ T Cells in Two Murine Orthotopic Glioblastoma Models Using Cancer Immunogenomics.. DOI: 10.1227/01.neu.0000489713.52326.9a

[21] NeoMUST: an accurate and ef fi cient multi-task learning model for neoantigen presentation. DOI: No DOI