Impact of viral sensitizer-enhanced oncolytic virus immunotherapy on the anti-tumor immune response
Bibliographic record
Abstract
Abstract Oncolytic viruses (OVs) have emerged as promising anticancer treatment platforms, able to specifically replicate in and kill cancer cells. OVs also have immunostimulatory effects, promoting antitumor immune responses, and several strategies have been developed in combination with OVs to stimulate cancer-specific immune responses. Viral sensitizers (VSes) are molecules that alter the tumor antiviral response in order to improve the efficacy of OVs. Such compounds can improve OV spread, transgene expression, and promote long-term antitumor immunity in various tumor models. A major objective of our study is to investigate the impact of VSes compounds on T-cells and other immune cells in the context of OV therapy and cancer vaccination strategies employing OVs. Results to date, VSes in combination with OVs significantly improve the therapeutic efficacy in syngeneic murine carcinoma tumor models. Additionally, we performed peptide-MHC-class I pentameric complexes staining to evaluate the kinetics of the T cell mediated anti-tumor immune response in vivo, our data illustrated that this combination therapy induced the production of antigen specific CD8+ T cells. We screened for chemokine and cytokine secretion using multiplex assay, and we found significant increases in IFN-γ, GM-CSF, and IL-12, 24 hour and 5 days post the treatment. We currently aim to phenotype the tumor microenvironment in order to understanding the mechanism that modulates the anti-tumor immunity using flow cytometry. We anticipate that unraveling the impact of VSes on the immunological response to OV therapy will lead to better understanding of mechanism contributing to its efficacy and improve our ability to successfully translate our findings to the clinic.
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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 | 0.000 |
| 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".