M2-polarized and tumor-associated macrophages alter NK cell phenotype and function in a contact-dependent manner
Bibliographic record
Abstract
Abstract The crosstalk between NK cells and M1 macrophages has a vital role in the protection against infections and tumor development. However, macrophages in the tumor resemble an M2 phenotype, and, at present, their effect on NK cells is less clear. This study investigated whether tumor-associated macrophages (TAMs) have a role in altering NK cell function and phenotype using in vitro cocultures of murine NK cells with peritoneal or bone marrow-derived, M2-polarized macrophages or TAMs isolated from spontaneous mouse breast tumors. We report here that both peritoneal and bone marrow-derived M2 macrophages, as well as TAMs, substantially inhibit NK cell activation and concordant cytotoxicity against tumor cells. The mechanism for this inhibition was found to require contact between the respective cell types. Both M2 macrophages and TAMs are producers of the immunosuppressive cytokine TGF-β. The inhibition of TGF-β restored the cytotoxicity of NK cells in contact with M2 macrophages, implicating TGF-β in the mechanism for NK cell inhibition. In addition to affecting NK cell function, TAMs also induced a CD27lowCD11bhigh-exhausted NK cell phenotype, which corresponds with the reduced activation and cytotoxicity observed. This study reveals a novel implication of TAMs in the tumor-associated inhibition of NK cell function by demonstrating their capacity to directly alter NK cell cytotoxicity and phenotype in a contact-dependent mechanism involving TGF-β. These findings identify the interaction between NK cells and TAMs as a prospective therapeutic target to enhance NK cell effector function for effective NK cell cancer therapies.
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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 | 0.000 |
| 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".