Bacterial-Mediated Knockdown of Tumor Resistance to an Oncolytic Virus Enhances Therapy
Bibliographic record
Abstract
Oncolytic viruses (OVs) and bacteria share the property of tumor-selective replication following systemic administration. In the case of nonpathogenic bacteria, tumor selectivity relates to their ability to grow extracellularly within tumor stroma and is therefore ideally suited to restricting the production of bacterially produced therapeutic agents to tumors. We have previously shown the ability of the type 1 interferon antagonist B18R to enhance the replication and spread of vesicular stomatitis virus (VSV) by overcoming related cellular innate immunity. In this study, we utilized nonpathogenic bacteria (E. coli) expressing B18R to facilitate tumor-specific production of B18R, resulting in a microenvironment depleted of bioactive antiviral cytokine, thus "preconditioning" the tumor to enhance subsequent tumor destruction by the OV. Both in vitro and in vivo infection by VSVΔ51 was greatly enhanced by B18R produced from E. coli. Moreover, a significant increase in therapeutic efficacy resulted from intravenous (i.v.) injection of bacteria to tumor-bearing mice 5 days prior to i.v. VSVΔ51 administration, as evidenced by a significant reduction in tumor growth and increased survival in mice. Our strategy is the first example where two such diverse microorganisms are rationally combined and demonstrates the feasibility of combining complementary microorganisms to improve therapeutic outcome.
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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".