Fes-deficient macrophages enhance CD8 <sup>+</sup> T cell priming and tumour control through increased pro-inflammatory cytokine production and localization
Bibliographic record
Abstract
Abstract Homeostatic immunoregulatory mechanisms that prevent adverse effects of immune overaction can serve as barriers to successful anti-cancer immunity, representing attractive targets to improve cancer immunotherapy. Here, we demonstrate a novel role of the Fes tyrosine kinase, abundantly expressed in immune cells, as an innate intracellular immune checkpoint. Host Fes-deficiency delays tumour onset in a gene dose-dependent manner and improves murine triple negative breast cancer and melanoma tumour control, survival, doxorubicin efficacy, and anti-PD-1 therapy sensitization. These effects were associated with a shift to an anti-tumourigenic tumour immune microenvironment. In vitro , we observed increased Toll-like receptor signaling, and proinflammatory cytokine production and presentation from antigen presenting cells, leading to increased T cell activation, cancer cell killing and tumour control. This study highlights Fes as a novel innate immune checkpoint with potential as a predictive biomarker for effective immune checkpoint blockade treatment, and a potential therapeutic target to improve anti-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 | 0.000 | 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".