The antitumor function of anti-4-1BB therapy involves the suppression of CD73-A2BR axis
Bibliographic record
Abstract
Abstract Agonist antibodies (Ab) directed against costimulatory molecule CD137 (4-1BB) on the surface of antigen-primed T-lymphocytes are currently in various stages of pre-clinical and clinical trials, albeit with limited therapeutic benefit as single agents. Here, we examined the inhibitory role of ecto-enzyme CD73 (generating adenosine) on the antitumor efficacy of agonistic anti-4-1BB Ab therapy, as anti-4-1BB treatment can downregulate the expression of CD73 on T cells within tumor microenvironment. CD73-deificient mice treated with anti-4-1BB Ab completely rejected CD73-nonexpressing B16-SIY melanoma when compared to wild-type mice. To explore the therapeutic relevance of these observations, we combined anti-4-1BB and anti-CD73 Ab to treat mice with established B16-SIY melanoma. This combination therapy induced tumor regression associated with enhanced antitumor CD8+ T cell responses and reduced CD4+Foxp3+ regulatory T cell number and function. In contrast, neither anti-4-1BB nor anti-CD73 monotherapy is effective to control the growth of established melanoma. Notably, anti-4-1BB treatment was also able to downregulate specifically the expression of adenosine receptor A2BR. Moreover, anti-4-1BB treatment elicited potent antitumor effect in A2BR-deficient mice as that in CD-73-deficient hosts. Our study establishes a novel mechanistic link between the CD73-A2BR axis and 4-1BB engagement. Thus, the blockade of CD73-A2BR axis has potential implications for more effective clinical targeting of 4-1BB and possibly other costimulatory molecules.
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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 | 0.001 |
| 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".