Oncolytic vaccinia virus immunotherapy antagonizes image-guided radiotherapy in mouse mammary tumor models
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".