Senescent human fibroblasts have increased FasL expression and impair the tumor immune response
Bibliographic record
Abstract
Introduction: Syngeneic mouse tumor models have shown that senescence influences the tumor immune response in multiple ways, including the induction of an immunosuppressive microenvironment or the promotion of immune cell recruitment. Yet, the impact of senescence on the tumor immune response in a humanized setting remains largely unexplored. Methods: To address this question, we employed a combination cells co-culture models, tumor spheroids and mice bearing tumors immunogenic to human immune cells derived from the same donor. Results: We found that senescent fibroblasts exert a dual effect by enhancing the recruitment of immune cells into the tumor microenvironment while simultaneously promoting the apoptosis of T and NK cells. Mechanistically, we demonstrate that this apoptosis is primarily due to increased Fas ligand (FasL) expression on the surface of senescent fibroblasts. Increased FasL expression was observed on different human fibroblast cell lines in response to different senescence inducers with a particular robust effect in response to RAS-induced senescence. Deletion of FasL on fibroblasts was sufficient to prevent immune cell death and increase tumor cell killing in mice. Discussion: Our results identified the expression of FasL expression as a novel component of the senescent tumor microenvironment and highlight the importance of evaluating the impact of therapy-induced senescence in humanized models to understand and predict the outcome of cancer treatments.
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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 | 0.000 |
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".