Aug 2026· Frontiers in Oncology· Vol 16· 0 citations· 85 references
Medicine
TL;DR
CEDAR’s current capabilities, report on progress in curation, database development, and tool availability, and outline the opportunities and challenges ahead for expanding its scope and utility to the cancer research community are described.
Abstract
Cancer epitopes, the molecular structures recognized by T and B cells at the tumor interface, are central to understanding antitumor immunity and developing immunotherapies. Yet despite the rapid growth of cancer immunology data, a comprehensive, continuously updated, and accessible resource for cancer epitope data has been lacking. The Cancer Epitope Database and Analysis Resource (CEDAR, cedar.iedb.org) was established in 2021 to fill this gap, providing curated experimental epitope data alongside a suite of cancer-specific computational tools for epitope prediction and analysis. Built on the validated infrastructure of the Immune Epitope Database (IEDB), CEDAR integrates cancer epitope data with biological, immunological, and clinical context, enabling researchers to explore immune recognition of tumors, identify candidate targets for immunotherapy, and benchmark prediction methods. Here we describe CEDAR’s current capabilities, report on progress in curation, database development, and tool availability, and outline the opportunities and challenges ahead for expanding its scope and utility to the cancer research community.
Selecting clinically relevant tumor antigens remains a major challenge in the development of therapeutic cancer vaccines. Although computational approaches have considerably improved neoantigen prediction, many candidate epitopes identified in silico are not ultimately presented on the tumor cell surface. The emergence of immunopeptidomics has provided direct access to naturally processed HLA-associated peptides and has offered new opportunities for antigen discovery. Increasing evidence has shown that information derived from the immunopeptidome becomes considerably more informative when interpreted alongside genomic, transcriptomic, and proteomic data. This integrative view has broadened the spectrum of targetable antigens and has also revealed important limitations related to peptide abundance, HLA diversity, tumor heterogeneity, and the imperfect relationship between antigen presentation and immunogenicity. These issues have renewed interest in multi-antigen vaccine strategies designed to better reflect the complexity of tumor antigen landscapes. Advances in bioinformatics and artificial intelligence are facilitating the interpretation of increasingly complex datasets and are beginning to support more systematic approaches to antigen prioritization. In this review, we discuss how immunopeptidomics is contributing to next-generation cancer vaccine development, summarize the major translational challenges, and highlight emerging concepts that may improve the clinical applicability of immunopeptidomics-guided immunotherapy.
Mohammad Mahdi Behzadifar, Mahshid Shadkam, Saba Kazemi et al.· Biomedicine & pharmacotherap...· 0 citations
Revising all immune receptor records to produce resolved, standardized, and analysis-ready receptor data will enable researchers to seamlessly query large-scale repertoires for receptors with experimentally verified specificity in the IEDB, link orphan sequences to known targets, and support cross-repository studies of receptor-epitope pairs and their relationship to health and disease.
Lonneke Scheffer, Eve Richardson, R. Vita et al.· Journal of Immunology· 0 citations
This study systematically analyzed the literature on tumor antigen-based cancer immunotherapy by integrating quantitative bibliometric analysis with knowledge network visualization. It aimed to provide an overview of the developmental trajectory of this field, identify major research hotspots and key scientific challenges, and explore the underlying drivers of its evolution, thereby providing references for future antigen-targeted immunotherapy strategies and clinical translation.
Literature related to tumor antigen-based cancer immunotherapy was retrieved from the Web of Science Core Collection, covering the period from September 15, 2005, to September 15, 2025. A total of 2,801 research articles were included. Bibliometric data were statistically analyzed using CiteSpace, VOSviewer, Charticulator, Scimago Graphica, and Bibliometrix, and visual scientific network maps were generated.
Bibliometric analysis showed that annual publication output in this field has remained relatively stable over the past six years, with approximately 170 publications per year and a peak of 188 publications in 2021. Regarding national contributions, the United States ranked first in publication output (1,282 publications), total citations (83,431 citations), and network centrality (0.46). Although China showed rapid growth in publication output and ranked second globally, its overall citation impact remained lower than that of several Western countries. In terms of academic influence, researchers such as Jeffrey Schlom from the National Cancer Institute and Chien-Fu Hung from Johns Hopkins University have maintained important positions through long-term research on tumor antigen recognition, immune regulation, and vaccine development. Temporal keyword analysis revealed an evolutionary transition from early adoptive immunotherapy-related studies, represented by terms such as “transfer therapy” and “adoptive immunotherapy”, to immune checkpoint regulatory strategies involving “nivolumab” and “immune checkpoint blockade”, and subsequently to emerging tumor antigen vaccine approaches characterized by “nanovaccine” and “mRNA vaccine”. Co-citation analysis further supported this evolutionary trajectory, indicating a gradual shift from broadly enhancing immune activation toward precise tumor antigen identification and optimization of antigen-driven immune responses.
Over the past two decades, tumor antigen-based cancer immunotherapy has evolved from adoptive immune cell therapy and immune checkpoint regulation to personalized neoantigen vaccine strategies, forming a multi-level therapeutic framework involving antigen recognition, immune activation, and targeted delivery. Future advances in antigen prediction, sequencing technologies, mRNA vaccine platforms, and immune regulatory strategies may further promote the development of more precise, effective, and personalized cancer immunotherapies.
The results show that current LLMs capture partial epitope-related signals but remain limited in antibody-specific sequence grounding, long-context residue localization, and biologically grounded reasoning, so EpiBench provides a diagnostic testbed for measuring and improving sequence-aware biomedical LLMs toward reliable LLM-assisted antibody discovery.
Zi-Rui Wang, Jiaqing Wang, Qinghan Wang et al.· 0 citations
Abstract Background Immune checkpoint inhibitors improve outcomes in clear cell renal cell carcinoma (ccRCC), yet most patients do not achieve durable responses due in part to insufficient tumor antigenicity. While neoantigen vaccines have shown promising early-phase results in ccRCC, their patient-specific design limits broad applicability and poses additional challenges in low mutational burden tumors, where targetable epitopes are scarce. Human endogenous retroviruses (hERVs), which comprise ∼8% of the human genome, can become aberrantly expressed in cancer and represent a source of shared, tumor-specific antigens; however, the extent to which they expand the limited antigenic landscape of not only ccRCC, but non-ccRCC subtypes, remains largely unexplored. We hypothesized that a subset of these aberrantly expressed hERVs provide a new public source of immunogenic peptides that can be prioritized as candidates for mRNA vaccine development. Methods We performed a pan-cancer (TCGA) analysis of 3,173 putative hERV loci from the Vargiu et al (Retrovirology, 2016) reference to identify hERVs upregulated across all cancer types, with a focus on kidney cancer subtypes. Candidates were prioritized using a scoring framework that enriched for hERVs with high tumor expression and minimal expression in normal tissues. From prioritized loci, six-frame translation was used to generate candidate open reading frames (ORFs), which were filtered and integrated with polysome sequencing data from RCC cell lines to identify translation-supported regions. Candidate peptides were evaluated for HLA binding using complimentary antigen prediction pipelines (e.g., HLAthena, netMHCpan) for various HLA alleles. To assess immunogenicity, we developed a multi-epitope hERV-targeting mRNA vaccine and evaluated antigen-specific immune responses in HLA-A11 transgenic mouse models. Results TCGA analysis combined with our scoring framework identified ≥25 unique hERV vaccine candidates per RCC subtype, with substantial overlap between ccRCC and papillary RCC (pRCC) and a distinct profile in chromophobe RCC (chRCC). Within ccRCC-prioritized hERV candidates, ORFs were generated via six-frame translation, filtered for canonical structure, and integrated with polysome sequencing data from three ccRCC cell lines to identify regions with strong translational support. This reduced 294,927 candidate ORFs to 805 high-confidence ORFs from 18 unique hERV loci, including recently described HIF-2α–regulated hERVs 4818 and 5875. HLA binding prediction identified numerous high-affinity candidate peptides across all HLA alleles (e.g., HLA-A*02, HLA-A*11), with some loci yielding >80 predicted binders for HLA-A*11 alone. Reanalysis of published immunopeptidomics datasets identified multiple peptides mapping to prioritized hERV loci, supporting endogenous processing and presentation. A pilot study using a multi-epitope mRNA vaccine encoding previously described hERV-derived peptides in a prophylactic setting elicited robust tetramer-positive, antigen-specific T cell responses in HLA-A11 transgenic mice. Compared with a peptide-based counterpart, mRNA vaccination generated superior responses, with approximately threefold and twofold higher frequencies of tetramer-positive CD8+ T cells in the spleen and vaccine-draining lymph nodes, respectively. Conclusions We present an integrative framework to identify translated hERV-derived antigens across RCC, expanding on prior work in ccRCC by identifying both known and novel immunogenic candidate peptides with evidence of translation and strong predicted HLA binding. mRNA vaccination using previously described hERV-derived peptides elicited robust antigen-specific CD8+ T cell responses, outperforming peptide vaccination. Our findings also provide initial insight into the hERV landscape in pRCC and chRCC, supporting further investigation. Future work will evaluate newly predicted peptides in multi-epitope mRNA vaccines and test their anti-tumor efficacy in vivo. DOD CDMRP Funding yes
F. Scallo, A. Dighe, Josephine Burdekin et al.· The Oncologist· 0 citations
Chimeric antigen receptor (CAR) cell therapy has achieved transformative clinical success through targeting of CD19 in refractory B cell malignancies, but extension of this strategy to solid tumors, other hematological malignancies, and autoimmune disease has exposed the complexity of target selection. Antigen abundance alone is not sufficient to define a suitable CAR target. Instead, therapeutic efficacy and safety are shaped by a broader set of molecular features, including isoform usage, subcellular localization, secretion, epitope stability, and the structural context in which antibody-derived binding domains engage their target. At the same time, advances in transcriptomics, structural biology, and artificial intelligence (AI)-enabled prediction now make it possible to assess many of these properties systematically. Here, we outline the principal molecular features that characterize effective and safe CAR targets and present a practical framework that integrates public datasets with computational and AI-based tools for their evaluation. Using HER2 as an illustrative case, we show how isoform-resolved expression, single-cell analyses, topology prediction, structure modelling, epitope mapping, and in silico binding analyses can reveal liabilities that are not captured by conventional target-expression screens alone. This framework provides a systematic strategy to prioritize targets and epitopes, guide preclinical investigation, and de-risk clinical translation. We anticipate that such integrative workflows will become increasingly important for moving CAR target discovery from descriptive expression analysis towards informed therapeutic design.
Giorgia Moranzoni, Lasse Vedel Jørgensen, Javier Herranz del Cerro et al.· bioRxiv· 0 citations
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