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Record W4392912974 · doi:10.1371/journal.pone.0298437

Oncolytic vaccinia virus immunotherapy antagonizes image-guided radiotherapy in mouse mammary tumor models

2024· article· en· W4392912974 on OpenAlexafffund
Brittany A. Umer, Ryan S. Noyce, Quinten Kieser, Nicole A. Favis, Mira M. Shenouda, Kim J. Rans, Jacqueline Middleton, Mary Hitt, David H. Evans

Bibliographic record

VenuePLoS ONE · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Alberta
FundersNational Institutes of HealthCanada Foundation for InnovationCanadian Cancer SocietyLi Ka Shing Institute of Virology, University of AlbertaAlberta Innovates - Health SolutionsUniversity of AlbertaCanadian Institutes of Health ResearchAlberta Cancer Foundation
KeywordsOncolytic virusVacciniaImmune systemIn vivoCancer researchVirusCancerImmunotherapyMammary tumorImmune checkpointRadiation therapyMedicineImmunopotentiatorBreast cancerImmunologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Ionizing radiation (IR) and oncolytic viruses are both used to treat cancer, and the effectiveness of both agents depends upon stimulating an immune response against the tumor. In this study we tested whether combining image guided ionizing radiation (IG-IR) with an oncolytic vaccinia virus (VACV) could yield a better therapeutic response than either treatment alone. ΔF4LΔJ2R VACV grew well on irradiated human and mouse breast cancer cells, and the virus can be combined with 4 or 8 Gy of IR to kill cells in an additive or weakly synergistic manner. To test efficacy in vivo we used immune competent mice bearing orthotopic TUBO mammary tumors. IG-IR worked well with 10 Gy producing 80% complete responses, but this was halved when the tumors were treated with VACV starting 2 days after IG-IR. VACV monotherapy was ineffective in this model. The antagonism was time dependent as waiting for 21 days after IG-IR eliminated the inhibitory effect but without yielding any further benefits over IR alone. In irradiated tumors, VACV replication was also lower, suggesting that irradiation created an environment that did not support infection as well in vivo as in vitro. A study of how four different treatment regimens affected the immune composition of the tumor microenvironment showed that treating irradiated tumors with VACV altered the immunological profiles in tumors exposed to IR or VACV alone. We detected more PD-1 and PD-L1 expression in tumors exposed to IR+VACV but adding an αPD-1 antibody to the protocol did not change the way VACV interferes with IG-IR therapy. VACV encodes many immunosuppressive gene products that may interfere with the ability of radiotherapy to induce an effective anti-tumor immune response through the release of danger-associated molecular patterns. These data suggest that infecting irradiated tumors with VACV, too soon after exposure, may interfere in the innate and linked adaptive immune responses that are triggered by radiotherapy to achieve a beneficial impact.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.300
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes2
Has abstractyes

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