Natural killer T cell immunotherapy combined with IL-15-expressing oncolytic virotherapy and PD-1 blockade mediates pancreatic tumor regression
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer is one of the leading causes of cancer death, with a 5-year -year survival rate of less than 10%. This results from late detection, high rates of metastasis, and resistance to standard chemotherapies. Furthermore, chemotherapy and radiation are associated with significant morbidity, underscoring the need for novel therapies. Recent clinical studies have shown that immunotherapies can provide durable outcomes in cancer patients, but successes in pancreatic cancer have been limited. It is likely that novel and combined therapies will be needed to achieve clinical benefits. METHODS: Using experimental mouse models of pancreatic ductal adenocarcinoma, we examined natural killer T (NKT) cell activation therapy in combination with a recombinant oncolytic vesicular stomatitis virus (VSVΔM51) engineered to express the cytokine IL-15 (VSV-IL-15). Panc02 pancreatic ductal carcinoma cells were implanted subcutaneously or orthotopically into syngeneic C57BL/6 mice. Mice were then treated with VSV expressing green fluorescent protein (VSV-GFP) or VSV-IL-15 and/or NKT cell activation therapy via delivery of α-GalCer-loaded DCs. We further assessed whether the addition of PD-1 blockade could increase the therapeutic benefit of our combination treatment. Three days after NKT cell activation, some groups of mice were treated with anti-PD-1 antibodies weekly for 3 weeks. RESULTS: VSV-GFP and VSV-IL-15 mediated equal killing of human and mouse pancreatic cancer lines in vitro. In vivo, VSV-IL-15 combined with NKT cell activation therapy to enhance tumor regression and increase survival time over individual treatments, and was also superior to NKT cell therapy combined with VSV-GFP. Enhanced tumor control was associated with increased immune cell infiltration and anti-tumor effector functions (cytotoxicity and cytokine production). While ineffective as a monotherapy, the addition of blocking PD-1 antibodies to the combined protocol sustained immune cell activation and effector functions, resulting in prolonged tumor regression and complete tumor clearance in 20% of mice. Mice who cleared the initial tumor challenge exhibited reduced tumor growth uponon rechallenge, consistent with the formation of immune memory. CONCLUSION: TThese results demonstrate that NKT cell immunotherapy combined with oncolytic VSV-IL-15 virotherapy and PD-1 blockade enhances tumor control and presents a promising treatment strategy for targeting pancreatic cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".