CCR4 blockade leads to clinical activity and prolongs survival in a canine model of advanced prostate cancer
Bibliographic record
Abstract
Abstract Targeting regulatory T cell (Treg) infiltration is an emerging strategy for cancer immunotherapy. However, the efficacy of this strategy in advanced prostate cancer remains unclear. Here, we describe the therapeutic efficacy of this strategy in a canine model of advanced prostate cancer. We used dogs with naturally occurring prostate cancer to study the molecular mechanism underlying Treg infiltration into tumor tissues and the effect of anti-Treg treatment. We found that tumor-infiltrating Tregs were associated with poor prognosis in dogs bearing spontaneous prostate cancer. RNA sequencing and protein analyses showed that Treg infiltration was mediated by interaction between the tumor-producing chemokine, CCL17, and the receptor CCR4 expressed on Tregs. Dogs with advanced prostate cancer responded to mogamulizumab, a monoclonal antibody targeting CCR4, with improved survival and low incidence of clinically relevant adverse events. Exploratory analyses showed urinary CCL17 concentration and BRAF V595E mutation to be independently predictive of the response to mogamulizumab. Analysis of a publicly available transcriptomic dataset of human prostate cancer showed that the CCL17/CCR4 axis correlated with the Treg marker, Foxp3. In silico survival analyses showed that high expression of CCL17 was associated with poor prognosis. Immunohistochemistry confirmed that tumor-infiltrating Tregs expressed CCR4 in human patients with prostate cancer. These findings suggest that anti-Treg treatment through the blocking of CCR4 is a promising therapeutic approach for advanced prostate cancer. One Sentence Summary Targeting regulatory T cell infiltration by CCR4 blockade induces objective responses and improves survival in a canine model of prostate cancer.
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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) | 0.001 | 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 | 0.001 |
| 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".