Local checkpoint inhibition of CTLA‐4 as a monotherapy or in combination with anti‐PD1 prevents the growth of murine bladder cancer
Bibliographic record
Abstract
Checkpoint blockade of CTLA-4 results in long-lasting survival benefits in metastatic cancer patients. However, patients treated with CTLA-4 blockade have suffered from immune-related adverse events, most likely due to the breadth of the induced T-cell activation. Here, we investigated the efficacy of a local low-dose anti-CTLA-4 administration for treatment of subcutaneous or orthotopic murine bladder 49 (MB49) bladder carcinoma in C57BL/6 mice. When MB49 tumors were grown s.c., peritumoral (p.t.) injection of anti-CTLA-4 treatment was equally effective as intravenous or s.c. (nontumor bearing flank) administration. Notably, p.t. injection was associated with lower circulating antibody levels and decreased IL-6 serum levels as compared to systemic treatment. Ultrasound-guided intratumoral anti-CTLA-4 antibody treatment of orthotopically growing MB49 tumors resulted in tumor regression, with more than tenfold reduction in systemic antibody levels as compared to i.v. or s.c. administration, in line with the compartmentally restrained nature of the bladder. Local anti-CTLA-4 therapy in combination with anti-PD-1 therapy resulted in complete responses, superior to each therapy alone. In addition, p.t. anti-CTLA-4 therapy was potentiated by depletion of regulatory T cells. Our results demonstrate that local anti-CTLA-4 antibody therapy is equally effective as systemic administration, but reduces systemic antibody levels and cytokine release, and enhances the response to anti-PD1 therapy.
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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) | 0.001 | 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 | 0.001 |
| 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".