Abstract 958: Myeloid cell populations drive early Vaccinia-induced anti-tumor responses differentially based on tumor cell immunogenicity
Bibliographic record
Abstract
Abstract Cancer poses an enormous burden on the healthcare system, with approximately 1 in 5 people worldwide expected to develop cancer in their lifetime according to the World Health Organization. Given this, there is a significant need for new therapeutics that are highly selective and evade tumor resistance to therapy. Oncolytic viruses (OVs) have been shown to selectively target and kill cancer cells while simultaneously activating local and systemic immune responses. Among these, Vaccinia (VACV) virus is a potential OV candidate with a high safety profile and a large genome, providing opportunities for genetic modification. However, in clinical trials it has shown to have limited success. To improve its efficacy, we need a more thorough understanding of VACV’s immunomodulatory activity both within and across distinct tumor microenvironments (TME). In these studies, we treated weakly immunogenic (B16-F10) and highly immunogenic (MC38) tumors with a mouse-adapted wild-type VACV (3 intratumoral injections, 48 hours apart). Changes in immune cell infiltration and tumor associated macrophage (TAM) activation were assessed in tissues collected 24 hours after each injection and up to 21 days after completion of the treatment regimen. We found that VACV treatment significantly reduced tumor volume and was associated with an early influx of CD45+ cells across both models. This infiltration occurred earlier, was more pronounced, and lasted longer in MC38 vs. B16-F10 tumors. While both models were associated with a significant decrease in the relative frequency of immunosuppressive TAMs (24 hours after first injection), VACV treatment of MC38 tumors was associated with higher and more sustained levels of newly recruited inflammatory monocytes. Similarly, the MC38 TME maintained higher levels of activated TAMs (↑CD80/CD86 expression) and sustaining this phenotype for a longer period. Importantly, these changes were linked to increased infiltration of CD8 T cells, anti-tumor immune responses and a slower rebound of tumor growth (>2 weeks). Collectively, our results suggest that VACV treatment can help limit tumor growth in both MC38 and B16-F10 models but that the anti-tumor response is more pronounced and sustained in highly vs. weakly immunogenic tumors. This increased activity is not dependent on early loss of immunosuppressive TAMs but is dependent on the increased recruitment of inflammatory monocytes and the maintenance of activated TAMs in the TME. Further studies are required to assess the specific contribution of these cells in driving anti-tumor immune responses and whether sustained viral replication is required for these processes. Citation Format: Duale Ahmed, Robyn Skillings, Omar Abdo, Aroosha Fareghdeli, Zoya Versey, Alicia Boxma, Leila Mostaço-Guidolin, Edana Cassol. Myeloid cell populations drive early Vaccinia-induced anti-tumor responses differentially based on tumor cell immunogenicity [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 958.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".