NK-Cell Recruitment Is Necessary for Eradication of Peritoneal Carcinomatosis with an IL12-Expressing Maraba Virus Cellular Vaccine
Bibliographic record
Abstract
Abstract Despite improvements in chemotherapy and radical surgical debulking, peritoneal carcinomatosis (PC) remains among the most common causes of death from abdominal cancers. Immunotherapies have been effective for selected solid malignancies, but their potential in PC has been little explored. Here, we report that intraperitoneal injection of an infected cell vaccine (ICV), consisting of autologous tumor cells infected ex vivo with an oncolytic Maraba MG1 virus expressing IL12, promotes the migration of activated natural killer (NK) cells to the peritoneal cavity in response to the secretion of IFNγ-induced protein-10 (IP-10) from dendritic cells. The recruitment of cytotoxic, IFNγ-secreting NK cells was associated with reduced tumor burden and improved survival in a colon cancer model of PC. Even in mice with bulky PC (tumors > 8 mm), a complete radiologic response was demonstrated within 8 to14 weeks, associated with 100% long-term survival. The impact of MG1-IL12-ICV upon NK-cell recruitment and function observed in the murine system was recapitulated in human lymphocytes exposed to human tumor cell lines infected with MG1-IL12. These findings suggest that an MG1-IL12-ICV is a promising therapy that could provide benefit to the thousands of patients diagnosed with PC each year. Cancer Immunol Res; 5(3); 211–21. ©2017 AACR.
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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".