The impact of Foxp3 <sup>+</sup> regulatory T‐cells on CD8 <sup>+</sup> T‐cell dysfunction in tumour microenvironments and responses to immune checkpoint inhibitors
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have been a breakthrough in cancer therapy, inducing durable remissions in responding patients. However, they are associated with variable outcomes, spanning from disease hyperprogression to complete responses with the onset of immune‐related adverse events. The consequences of checkpoint inhibition on Foxp3 + regulatory T (T reg ) cells remain unclear but could provide key insights into these variable outcomes. In this review, we first cover the mechanisms that underlie the development of hot and cold tumour microenvironments, which determine the efficacy of immunotherapy. We then outline how differences in tumour‐intrinsic immunogenicity, T‐cell trafficking, local metabolic environments and inhibitory checkpoint signalling differentially impair CD8 + T‐cell function in tumour microenvironments, all the while promoting T reg ‐cell suppressive activity. Finally, we focus on the mechanisms that enable the induction of polyfunctional CD8 + T‐cells upon checkpoint blockade and discuss the role of ICI‐induced T reg ‐cell reactivation in acquired resistance to treatment. LINKED ARTICLES This article is part of a themed issue Immunotherapy in Cancer. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.6/issuetoc
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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".