CD73-dependent tumor resistance to agonistic anti-4-1BB therapy through TGF-β
Bibliographic record
Abstract
Abstract Agonist antibodies (Ab) directed against costimulatory molecules on the surface of antigen-primed T cells are in various stages of pre-clinical and clinical trials, albeit with limited therapeutic benefit as single agents. The underlying immune suppressive mechanisms remain incompletely understood. In our study, we demonstrate an inhibitory role of ecto-enzyme CD73 for agonistic anti-4-1BB (anti-CD137) Ab therapy. In particular, anti-4-1BB treatment can downregulate the expression of CD73 on T cells by preferentially driving CD73− effector T cell response and achieve tumor inhibition. Anti-CD73 neutralizing Ab further improves anti-4-1BB therapy associated with enhanced anti-tumor T cell immunity. Furthermore, we find the same observation in the combination of anti-CD73 and anti-GITR, another agonist antibody. However, the TGF-β-rich tumor milieu confers resistance to anti-4-1BB and anti-GITR therapy by sustaining CD73 expression primarily on infiltrating CD8+T cells across several tumor models. TGF-β blockade results in downregulation of CD73 expression on infiltrating T cells and sensitizes resistant tumors to agonistic anti-4-1BB therapy with increased number and function of T cells. Thus, our findings identify a mechanism of action for more effective clinical targeting of 4-1BB or likely 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".