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Record W4407866505 · doi:10.1158/2326-6074.io2025-b016

Abstract B016: Prime-Boost: mRNA Tumor Vaccines in Combination with Oncolytic Virotherapy for Durable Anti-Tumor Immunity

2025· article· en· W4407866505 on OpenAlexaff

Bibliographic record

VenueCancer Immunology Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsOncolytic virusVirotherapyMedicineImmunityImmunotherapyImmunologyVirologyCancer researchImmune system

Abstract

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Abstract Tumor vaccines have shown clinical promise in certain cancers, but their long-term efficacy is limited by two key challenges: 1) the immune-suppressive tumor microenvironment (TME), and 2) the selective targeting of antigen-positive tumor cells by the vaccine, which can lead to the overgrowth of antigen-negative tumor cells and relapse. To overcome these barriers, we investigated a combination approach using mRNA tumor vaccines alongside oncolytic virotherapy in HPV and HER2 mouse tumor models. Our results indicate that while mRNA vaccines effectively eradicate antigen-positive tumors and inhibit their growth, resistance develops rapidly, allowing tumors to escape the vaccine's effects. However, when mRNA vaccination was followed by intratumoral treatment with an oncolytic virus, with or without the expression of HPV or HER2 antigens, we observed significantly more durable anti-tumor responses in both models. The oncolytic virus induced a broad and dynamic tumor antigen presentation through its lytic infection, effectively lysing tumor cells and releasing a wide variety of tumor-associated antigens. This process not only increased the antigenic load within the tumor but also reprogrammed the TME, shifting it from an immune-suppressive environment to one more conducive to anti-tumor immune activity. The combination of mRNA vaccination and oncolytic virotherapy resulted in a strong local immune response, driven by the enhanced antigen presentation from the viral infection. In addition, the alteration of the TME allowed the systemically established anti-tumor immunity—generated by the mRNA vaccine—to penetrate and act within the tumor microenvironment, overcoming the previous immune barriers. We also observed the generation of systemic antigen-specific T-cells, and animals showed resistance to subsequent tumor challenges, indicating the development of immune memory. These findings highlight the unique role of oncolytic virotherapy as an intratumoral “boost” agent, capable of inducing a broader range of tumor antigens through the lysis of tumor cells, while simultaneously altering the TME to support and amplify the action of the systemically delivered tumor vaccine. When combined in a prime-boost regimen, these two approaches synergize to generate a robust, durable anti-tumor immune response that is both localized within the tumor and systemically protective against recurrence. In summary, our study demonstrates that the combination of mRNA tumor vaccines and oncolytic virotherapy offers a powerful strategy to enhance tumor antigen presentation, reshape the TME, and overcome immune evasion mechanisms, providing a promising approach for achieving durable anti-tumor immunity. Citation Format: WILLIAM JIA. Prime-Boost: mRNA Tumor Vaccines in Combination with Oncolytic Virotherapy for Durable Anti-Tumor Immunity [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr B016.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.380
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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