Role of TGF-β in the creation of an immunosuppressive microenvironment during leukemia (P2025)
Bibliographic record
Abstract
Abstract Tumors develop a milieu that fosters their development and counteract the total efficacy of immune system through different mechanisms. TGF-β, a potent growth suppressor and immunosuppressive cytokine has the capacity to inhibit the anti-tumor immune response in several models. However, all the mechanisms underlying the effects of TGF-β are not well known. Specifically, the role of TGF-β in shaping the cancer microenvironment is ill-defined. Our objective is to characterize the role of TGF-β in shaping the neoplastic microenvironment using a leukemia model. Using the TGF-β producing EL4 cell line, we characterized the immune cell infiltration and the levels of several cytokines in the context of systemic TGF-β neutralization. When compared to tumor extracted from untreated animals, leukemic masses that had evolved in the context of TGF-β inhibition had increased levels of inflammatory cytokines and a tendency to accumulate more activated T cells and surprisingly more myeloid cells, unveiling a potential role for these cells in leukemia specific immunity. More precisely, secretion of cytokines involved in migration and activation of leucocytes, namely IL-2, GM-CSF, KC and MIP-1α was significantly induced in treated animals. Our data suggest that TGF-β blockade alters the leukemic microenvironment. Further studies will confirm the impact of TGF-β on the leukemic milieu including immune cells, stromal cells and angiogenesis and how this cytokine impedes anti-cancer immunity.
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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".