Natural killer T cell activation overcomes immunosuppression to enhance clearance of postsurgical breast cancer metastasis in mice
Bibliographic record
Abstract
Metastatic lesions are responsible for over 90% of breast cancer associated deaths. Therefore, strategies that target metastasis are of particular interest. This study examined the efficacy of natural killer T (NKT) cell activation as a post-surgical immunotherapy in a mouse model of metastatic breast cancer. Following surgical resection of orthotopic 4T1 mammary carcinoma tumors, BALB/c mice were treated with NKT cell activating glycolipid antigens (α-GalCer, α-C-GalCer or OCH) or α-GalCer-loaded dendritic cells (DCs). Low doses of glycolipids transiently reduced metastasis but did not increase survival. A high dose of α-GalCer enhanced overall survival, but was associated with increased toxicity and mortality at early time points. Treatment with α-GalCer-loaded DCs limited tumor metastasis, prolonged survival, and provided curative outcomes in ∼45% of mice. However, survival was not increased further by additional DC treatments or co-transfer of expanded NKT cells. NKT cell activation via glycolipid-loaded DCs decreased the frequency and immunosuppressive activity of myeloid derived suppressor cells (MDSCs) in tumor-resected mice. In vitro, NKT cells were resistant to the immunosuppressive effects of MDSCs and were able to reverse the inhibitory effects of MDSCs on T cell proliferation. NKT cell activation enhanced antitumor immunity in tumor-resected mice, increasing 4T1-specific cytotoxic responses and IFNγ production from natural killer (NK) cells and CD8+ T cells. Consistent with increased tumor immunity, mice surviving to day 150 were resistant to a second tumor challenge. This work provides a clear rationale for manipulating NKT cells to target metastatic disease.
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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 | 0.000 | 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 | 0.002 |
| 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".