Immunosuppressive myeloid cells induce mesenchymal-like breast cancer stem cells by a mechanism involving membrane-bound TGF-β1
Bibliographic record
Abstract
Summary Suppressive myeloid cells play a central role in cancer escape from anti-tumor immunity. Beyond their immunosuppressive function, these cells are capable of exerting multiple other pro-tumoral activities, including the promotion of cancer cell survival, invasion and metastasis. The ability of some myeloid subsets to induce cancer stemness has recently emerged. Here we demonstrated that human immunosuppressive myeloid cells, generated in vitro or isolated from breast cancer patients, promoted the acquisition of mesenchymal-like breast cancer stemness properties. This cancer-stemness-inducing function was restricted to a myeloid subset expressing the glycoprotein CD52. Single cell transcriptomic- and surface proteome-based interactome analysis pointed towards membrane-bound TGF-β1 as a potential factor involved in cancer stemness induction. Functional inhibition of the TGF-β1 pathway blocked the emergence of cancer stem cells induced by suppressive myeloid cells. These results therefore identified the underlying mechanisms of a new tumor-promoting function of immunosuppressive myeloid cells, which may potentially be targeted. Highlights Immunosuppressive CD33 high CD52 + myeloid cells induce mesenchymal-like cancer stem cells Cancer stemness induction requires membrane bound TGF-β1 Blockade of the TGF-β1 pathway prevents cancer stemness induction
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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) | 0.002 | 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".