Abstract 6701: CircRNA-derived neoantigen identification in cancer vaccine development

Abstract Immunotherapy has been a popular topic in combating cancer for its great performance in specificity, versatility and durability recently. Cancer vaccines targeting neoantigens have shown clinical efficacy against cancer, eliciting long-lasting immune response and offering a more selective therapeutic approach with less side effects. However, rare mutation rates and low expression levels of neoantigen make it challenging to identify suitable neoantigens. Traditionally, researchers focus on the canonical mRNA transcriptome and proteome to find potential targets. Recent studies suggest that exploring the non-canonical transcriptome and proteome, particularly circular RNAs (circRNAs), can expand the search. CircRNAs, endogenously formed by the unique back-splicing events during pre-RNA processing, can serve as templates for protein synthesis. Some of these non-canonical peptides are specifically expressed in cancer cells, making circRNAs a valid source for neoantigen discovery. We have developed high-throughput circRNA reporter screening methods and mass spectrometry-based peptidomic workflow to pinpoint translating circRNAs and their derived peptides. Using these techniques, we have identified circRNA-derived neoantigens in breast cancer (BC) cell lines that can be used as possible targets for cancer vaccines. These circRNA-derived neoantigens are immunogenic and can trigger dendritic cell (DC) maturation and T cell activation in vitro. Next, we will test these findings by transferring to in vivo mouse system. We will assess cancer vaccine efficacy by targeting the neoantigens using syngeneic mouse tumor models. Our long-term goal is to apply the circRNA neoantigen identification technology to BC patient samples to facilitate cancer vaccine development and improve cancer immunotherapy. Our work will demonstrate the potential of circRNA-derived non canonical neoantigens in cancer vaccines, serving as a crucial first step to prove their effectiveness in therapy. By expanding our search beyond the traditional transcriptome and proteome, we will explore new targets and transform the neoantigen-based treatment. This innovative effort shifts the current understanding of neoantigen sources and opens up new possibilities for targeted cancer therapy strategies. Citation Format: Yi-Cian Zheng, Chun-Kan Chen. CircRNA-derived neoantigen identification in cancer vaccine development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6701.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-6701
Primary Topic
Circular RNAs in diseases
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 6701: CircRNA-derived neoantigen identification in cancer vaccine development

Chun‐Kan Chen, Yangchun Zheng
Cancer Research
Circular RNAs in diseases
article

Abstract 6701: CircRNA-derived neoantigen identification in cancer vaccine development

Chun‐Kan Chen, Yangchun Zheng
article en

Abstract

Abstract Immunotherapy has been a popular topic in combating cancer for its great performance in specificity, versatility and durability recently. Cancer vaccines targeting neoantigens have shown clinical efficacy against cancer, eliciting long-lasting immune response and offering a more selective therapeutic approach with less side effects. However, rare mutation rates and low expression levels of neoantigen make it challenging to identify suitable neoantigens. Traditionally, researchers focus on the canonical mRNA transcriptome and proteome to find potential targets. Recent studies suggest that exploring the non-canonical transcriptome and proteome, particularly circular RNAs (circRNAs), can expand the search. CircRNAs, endogenously formed by the unique back-splicing events during pre-RNA processing, can serve as templates for protein synthesis. Some of these non-canonical peptides are specifically expressed in cancer cells, making circRNAs a valid source for neoantigen discovery. We have developed high-throughput circRNA reporter screening methods and mass spectrometry-based peptidomic workflow to pinpoint translating circRNAs and their derived peptides. Using these techniques, we have identified circRNA-derived neoantigens in breast cancer (BC) cell lines that can be used as possible targets for cancer vaccines. These circRNA-derived neoantigens are immunogenic and can trigger dendritic cell (DC) maturation and T cell activation in vitro. Next, we will test these findings by transferring to in vivo mouse system. We will assess cancer vaccine efficacy by targeting the neoantigens using syngeneic mouse tumor models. Our long-term goal is to apply the circRNA neoantigen identification technology to BC patient samples to facilitate cancer vaccine development and improve cancer immunotherapy. Our work will demonstrate the potential of circRNA-derived non canonical neoantigens in cancer vaccines, serving as a crucial first step to prove their effectiveness in therapy. By expanding our search beyond the traditional transcriptome and proteome, we will explore new targets and transform the neoantigen-based treatment. This innovative effort shifts the current understanding of neoantigen sources and opens up new possibilities for targeted cancer therapy strategies. Citation Format: Yi-Cian Zheng, Chun-Kan Chen. CircRNA-derived neoantigen identification in cancer vaccine development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6701.

Cancer ResearchVol. 85(8_Supplement_1)
Washington University in St. Louis (US)
Openalex Percentile: Top 93%
Circular RNAs in diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.