Cancer remains one of the most formidable challenges in global health, claiming millions of lives annually and placing immense strain on healthcare systems worldwide. The disease’s complexity is deeply rooted in its genetic basis, where mutations and alterations at various molecular levels result in malignant transformation. Among the most promising avenues for combating cancer is the exploitation of tumor-specific antigens—unique molecular signatures derived from cancer-associated genetic changes. These neoantigens represent critical targets for emerging personalized and generalized therapeutic strategies, such as cancer vaccines, adoptive T cell therapies, and sophisticated immunomonitoring methods.
At the heart of neoantigen-based immunotherapy lies the immune system’s remarkable ability to distinguish abnormal cells from healthy tissue. This recognition centrally involves the presentation of neoantigens on the surface of tumor cells via Major Histocompatibility Complex (MHC) molecules. When displayed effectively, these neoantigens enable cytotoxic T cells to identify and eliminate malignant cells. However, successful activation of T cells not only depends on antigen presentation but also requires intricate co-stimulatory signals delivered by antigen-presenting cells, notably dendritic cells. The interplay of these elements forms the basis of the immune system’s antitumor response, which researchers are keen to enhance through targeted interventions.
In recent years, the field of computational biology has witnessed rapid advancements that dramatically improve the prediction and identification of neoantigens. Bioinformatics tools leverage high-throughput sequencing data to detect somatic mutations—the genetic alterations specific to tumor cells—and subsequently predict the peptides capable of binding to a patient’s MHC molecules. These algorithms assess binding affinities and immunogenic potential, helping scientists prioritize neoantigens most likely to elicit strong immune responses. This methodical selection process is vital for designing effective personalized cancer vaccines and T cell therapies with maximal specificity and minimal off-target effects.
A cornerstone in neoantigen discovery is the accurate determination of an individual’s Human Leukocyte Antigen (HLA) haplotype, which dictates the MHC molecule repertoire. Traditional techniques are often costly and resource-intensive, but computational haplotyping methods offer a cost-effective alternative by analyzing sequencing data to infer HLA types. These advances democratize access to personalized immunotherapies by reducing logistical barriers and accelerating the timeline from sample collection to neoantigen identification. Improved HLA typing enhances the precision of neoantigen prediction pipelines, facilitating tailored immunotherapeutic interventions.
Nevertheless, the computational prediction of neoantigens alone is insufficient, as the immunopeptidome—the actual collection of peptides presented by MHC molecules on tumor cells—can differ from predicted sequences. This has driven the integration of proteogenomics, a multidisciplinary approach combining genomic, transcriptomic, and proteomic data to validate neoantigen presentation experimentally. Immunopeptidomics, which directly identifies MHC-bound peptides via mass spectrometry, confirms the natural processing and presentation of predicted neoantigens. This crucial step adds an empirical layer of confidence, ensuring that therapeutic strategies target epitopes genuinely displayed by cancer cells.
The synergy between computational algorithms and proteogenomic validation reshapes the landscape of neoantigen research. Using multiple layers of molecular data not only refines neoantigen selection but also enhances the overall reliability of personalized cancer vaccines and cellular therapies. This integrative approach addresses challenges such as tumor heterogeneity and immune evasion, which complicate treatment efficacy. It embodies a shift toward precision oncology, where therapies are custom-designed based on the unique molecular fingerprint of each patient’s tumor.
Clinical trials have begun to harness these technological advancements, translating neoantigen predictions into therapeutic realities. Early-phase studies on neoantigen vaccines demonstrate encouraging immunogenicity and safety profiles, underlining the potential to induce durable antitumor immunity. Adoptive T cell therapies, employing neoantigen-specific T cells expanded ex vivo, show promising results in eliminating otherwise resistant tumors. These clinical efforts reflect a growing commitment to bridging computational neoantigen predictions with patient-centered outcomes.
Despite remarkable progress, challenges persist in the neoantigen prediction arena. Variability in tumor mutation burden across cancer types influences the abundance of targetable neoantigens, with some tumors exhibiting low mutational loads that limit therapeutic targets. Additionally, accurate prediction of peptide-MHC binding remains computationally intensive and imperfect, partly due to the vast genetic diversity of HLA alleles. Immunosuppressive tumor microenvironments and antigen processing abnormalities can further impede the presentation and recognition of neoantigens, hindering immune activation.
Addressing these hurdles requires continuous refinement of bioinformatics pipelines, incorporating machine learning techniques that improve predictive accuracy by learning from experimental and clinical data. Multi-omics integration—combining genomics, transcriptomics, proteomics, and epigenomics—provides a holistic view of tumor biology, offering new layers of insight into antigen presentation and immunogenicity. Moreover, advances in single-cell sequencing and spatial transcriptomics promise to unravel the complexity of tumor-immune interactions, shedding light on the contextual factors influencing therapy response.
The future of neoantigen-based cancer immunotherapy is intertwined with innovations in computational biology and experimental validation. Open-access databases and collaborative networks accelerate data sharing, strengthening the knowledge base necessary for algorithm training and validation. Personalized medicine will benefit from streamlined pipelines that reduce turnaround times and costs, enabling real-time adaptation of immunotherapies based on tumor evolution and patient responses. This dynamic approach anticipates overcoming immune escape mechanisms and improving long-term treatment efficacy.
Notably, the development of neoantigen vaccines and T cell therapies underscores the importance of patient-specific approaches over conventional, broadly targeted treatments. By focusing on unique tumor antigens, these therapies minimize off-target effects and reduce collateral damage to normal tissues. The paradigm shift toward personalized immunotherapy exemplifies the cutting edge of oncology, representing a convergence of computational science, molecular biology, and clinical innovation.
Furthermore, neoantigen identification has broad implications beyond treatment, extending into cancer diagnostics and prognostics. Monitoring neoantigen-specific T cell responses can inform disease progression and therapy effectiveness, aiding clinicians in treatment decisions. As bioinformatics tools evolve, they may also assist in uncovering novel biomarkers predictive of immunotherapy response, facilitating patient stratification and clinical trial design.
Amid this promising landscape, ethical and regulatory considerations surrounding personalized immunotherapy require careful navigation. Data privacy, equitable access to cutting-edge treatments, and the management of treatment-related toxicities are critical factors influencing the clinical translation of neoantigen-based approaches. Multidisciplinary collaboration among bioinformaticians, immunologists, clinicians, and policymakers will be essential to ensure that technological innovations translate safely and effectively into patient care.
In summary, the confluence of evolving computational methods and experimental validation strategies marks a new era in cancer immunotherapy focused on neoantigen targeting. By bridging genomic insights with immune activation mechanisms, researchers and clinicians are forging a path toward highly tailored, effective, and enduring cancer treatments. Continued investment in bioinformatics tool development and integrated multi-omics approaches will be crucial to fully unlocking the therapeutic potential of neoantigens. This strategy holds promise not only for improving survival rates but also for fundamentally transforming the management of cancer worldwide.
Subject of Research: Computational prediction and validation of tumor-specific neoantigens for personalized cancer immunotherapy.
Article Title: Computational neoantigen prediction for cancer immunotherapy.
Article References:
Tejaswi, L., Ramesh, P., Aditya, S. et al. Computational neoantigen prediction for cancer immunotherapy. Genes Immun (2025). https://doi.org/10.1038/s41435-025-00365-z
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41435-025-00365-z
Tags: advancements in computational biology for cancercancer vaccines and adoptive T cell therapiescytotoxic T cell activation mechanismsdendritic cells in immune responseenhancing antitumor immune responsegenetic mutations and cancer progressionimmunomonitoring techniques in oncologyMajor Histocompatibility Complex role in cancerneoantigen prediction for cancer treatmentpersonalized cancer immunotherapy strategiestargeting cancer with neoantigenstumor-specific antigens in immunotherapy
