Antitumor Activity of Tumor‐Infiltrating Neutrophils Revealed by a Syngeneic Mouse Model of Cholangiocarcinoma
Bibliographic record
Abstract
The tumor immune microenvironment plays a key role in the regulation of cancer progression. Recent studies have suggested a relation between diverse tumor genotypes and tumor immune microenvironment phenotypes for cholangiocarcinoma (CCA). However, the contribution of tumor-infiltrating immune cells to CCA progression has remained unclear, underscoring the need for genetically defined CCA models in immunocompetent mice. We here aimed to generate genetically engineered and transplantable CCA organoids from C57BL/6 mice and to investigate the role of tumor-infiltrating immune cells in CCA progression with this model. CCA organoids were generated ex vivo with the use of the CRISPR/Cas9 system. Orthotopic transplantation of CCA organoids harboring mutations in Smad4, Trp53, and Kras into wild-type C57BL/6 mice resulted in tumor formation accompanied by distant metastasis. Selective depletion of immune cell types in the tumor-bearing mice revealed an antitumor action of tumor-infiltrating neutrophils (TINs) that was mediated by direct killing of cancer cells through the production of reactive oxygen species. Furthermore, administration of recombinant human granulocyte colony-stimulating factor (rhG-CSF) increased the number and cytotoxicity of TINs, suppressed tumor growth, and prolonged the survival of tumor-bearing mice. Finally, combination treatment with rhG-CSF and standard chemotherapy resulted in a synergistic attenuation of tumor growth. Our study therefore provides a syngeneic and genetically defined mouse model of CCA and highlights the therapeutic potential of targeting TINs with rhG-CSF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".