Macrophages Are More Potent Immune Suppressors Ex Vivo Than Immature Myeloid-Derived Suppressor Cells Induced by Metastatic Murine Mammary Carcinomas
Bibliographic record
Abstract
Myeloid-derived suppressor cells (MDSCs) are emerging as potential promoters of metastatic tumor growth, and there is interest in targeting immature MDSCs by inducing their differentiation into more mature myeloid cells. We used all-trans retinoic acid (ATRA) to differentiate MDSCs in mice bearing metastatic 4T1 or 4TO7 murine mammary tumors, and assessed the immune-suppressive mechanisms and potencies of different myeloid cell subpopulations. Metastatic mammary tumors induced the accumulation of distinct populations of immature CD11b(+)Gr1(+)F4/80(-)Ly6C(mid)Ly6G(+) MDSCs ("Gr1(+) cells") and mature CD11b(+)Gr1(-)F4/80(+) cells ("F4/80(+) cells") in metastatic target organs. ATRA triggered the differentiation of Gr1(+) cells into F4/80(+) cells in the lungs and, unexpectedly, enhanced pulmonary metastatic tumor growth. We found that F4/80(+)Ly6C(-)Ly6G(-) mature macrophages (Ms) were up to 30-fold more potent immune suppressors than Gr1(+) cells on a per-cell basis, which we postulate may contribute to the increased metastatic growth observed with ATRA treatment. F4/80(+) cells and Gr1(+) cells used different reactive oxygen species (ROS)-mediated mechanisms of immunosuppression ex vivo, with F4/80(+) cells producing higher levels of ROS, which is consistent with their superior immunosuppressive abilities. These data highlight the potent immunosuppressive functions of Ms, reveal that Ms can suppress T cell responses via ROS production, and suggest that ROS inhibitors may be useful in promoting antitumor immune responses. Our findings also caution against using ATRA to modulate myeloid cell differentiation and function to treat breast cancer metastases in the lung, and support the development of therapeutic strategies to enhance antitumor immunity by targeting myeloid cells as a collective group.
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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) | 0.001 | 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".