Fusogenic vesicular stomatitis virus combined with natural killer T cell immunotherapy controls metastatic breast cancer
Bibliographic record
Abstract
BACKGROUND: Metastatic breast cancer is a leading cause of cancer death in woman. Current treatment options are often associated with adverse side effects and poor outcomes, demonstrating the need for effective new treatments. Immunotherapies can provide durable outcomes in many cancers; however, limited success has been achieved in metastatic triple negative breast cancer. We tested whether combining different immunotherapies can target metastatic triple negative breast cancer in pre-clinical models. METHODS: Using primary and metastatic 4T1 triple negative mammary carcinoma models, we examined the therapeutic effects of oncolytic vesicular stomatitis virus (VSVΔM51) engineered to express reovirus-derived fusion associated small transmembrane proteins p14 (VSV-p14) or p15 (VSV-p15). These viruses were delivered alone or in combination with natural killer T (NKT) cell activation therapy mediated by adoptive transfer of α-galactosylceramide-loaded dendritic cells. RESULTS: Treatment of primary 4T1 tumors with VSV-p14 or VSV-p15 alone increased immunogenic tumor cell death, attenuated tumor growth, and enhanced immune cell infiltration and activation compared to control oncolytic virus (VSV-GFP) treatments and untreated mice. When combined with NKT cell activation therapy, oncolytic VSV-p14 and VSV-p15 reduced metastatic lung burden to undetectable levels in all mice and generated immune memory as evidenced by enhanced in vitro recall responses (tumor killing and cytokine production) and impaired tumor growth upon rechallenge. CONCLUSION: Combining NKT cell immunotherapy with enhanced oncolytic virotherapy increased anti-tumor immune targeting of lung metastasis and presents a promising treatment strategy for metastatic breast cancer.
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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) | 0.001 | 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".