TGF-β in T Cell Biology: Implications for Cancer Immunotherapy
Bibliographic record
Abstract
Transforming Growth Factor beta (TGF-β) is a pleiotropic cytokine produced in large amounts within cancer microenvironments that will ultimately promote neoplastic progression, notably by suppressing the host’s T-cell immunosurveillance. This effect is mostly due to the well-known inhibitory effect of TGF-β on T cell proliferation, activation, and effector functions. Moreover, TGF-β subverts T cell immunity by favoring regulatory T-cell differentiation, further reinforcing immunosuppression within tumor microenvironments. These findings stimulated the development of many strategies to block TGF-β or its signaling pathways, either as monotherapy or in combination with other therapies, to restore anti-cancer immunity. Paradoxically, recent studies provided evidence that TGF-β can also promote differentiation of certain inflammatory populations of T cells, such as Th17, Th9, and resident-memory T cells (Trm), which have been associated with improved tumor control in several models. Here, we review current advances in our understanding of the many roles of TGF-β in T cell biology in the context of tumor immunity and discuss the possibility to manipulate TGF-β signaling to improve cancer immunotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".