Improvement of agonist cancer immunotherapy by CD73 blockade
Bibliographic record
Abstract
Abstract Agonist antibodies (Ab) can directly target costimulatory molecule on the surface of antigen-primed T-lymphocytes. Among those, tumor necrosis factor receptor superfamily (TNFRSF) agonists are attractive and some of them are currently in various stages of pre-clinical and clinical trials, including anti-4-1BB, anti-GITR and anti-OX40, 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/CD137 Ab therapy, as anti-4-1BB treatment preferentially drove CD73− effector T cell response within TGF-β-low tumor microenvironment. The anti-CD73/anti-4-1BB combination therapy induced tumor regression associated with enhanced antitumor CD8+ T cell responses and reduced CD4+Foxp3+ regulatory T cell (Treg) accumulation. However, the TGF-β-rich tumor milieu confered resistance to anti-4-1BB therapy with sustained CD73 expression on both infiltrating CD4+ and CD8+ T cells across several tumor models. Our study establishes a novel mechanistic link between the CD73 axis and engagement of 4-1BB and possibly other costimulatory molecules. Thus, CD73 blockade has great potential for more effective clinical targeting of TNFRSF agonists.
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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".