Fes-Deficient Macrophages Prime CD8+ T Cells to Stimulate Antitumor Immunity and Improve Immunotherapy Efficacy
Bibliographic record
Abstract
Homeostatic immunoregulatory mechanisms that prevent adverse effects of immune overaction can serve as barriers to successful anticancer immunity, representing attractive targets to improve cancer immunotherapy. Here, we demonstrated the role of the nonreceptor tyrosine kinase Fes, abundantly expressed in immune cells, as an innate intracellular immune checkpoint. Host Fes deficiency delayed tumor onset in a gene dose-dependent manner and improved tumor control, survival, doxorubicin efficacy, and sensitized tumors to anti-PD-1 therapy in murine triple-negative breast cancer and melanoma models. These effects were associated with a shift to an antitumorigenic immune microenvironment. Fes-deficient macrophages displayed increased Toll-like receptor signaling, proinflammatory cytokine production, and antigen presentation to and activation of T cells, leading to increased cancer cell killing in vitro and tumor control in vivo. This study highlights Fes as an innate immune checkpoint with potential as a therapeutic target and a predictive biomarker to guide immune checkpoint inhibitor treatment. SIGNIFICANCE: Fes activity modulates the inflammatory cytokine presentation and T-cell priming capabilities of macrophages, supporting the potential of Fes as a target for developing therapeutic and biomarker strategies 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 | 0.000 | 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".