Blockade of A <sub>2A</sub> receptors potently suppresses the metastasis of CD73 <sup>+</sup> tumors
Bibliographic record
Abstract
CD73 inhibits antitumor immunity through the activation of adenosine receptors expressed on multiple immune subsets. CD73 also enhances tumor metastasis, although the nature of the immune subsets and adenosine receptor subtypes involved in this process are largely unknown. In this study, we revealed that A2A/A2B receptor antagonists were effective in reducing the metastasis of tumors expressing CD73 endogenously (4T1.2 breast tumors) and when CD73 was ectopically expressed (B16F10 melanoma). A2A(-/-) mice were strongly protected against tumor metastasis, indicating that host A2A receptors enhanced tumor metastasis. A2A blockade enhanced natural killer (NK) cell maturation and cytotoxic function in vitro, reduced metastasis in a perforin-dependent manner, and enhanced NK cell expression of granzyme B in vivo, strongly suggesting that the antimetastatic effect of A2A blockade was due to enhanced NK cell function. Interestingly, A2B blockade had no effect on NK cell cytotoxicity, indicating that an NK cell-independent mechanism also contributed to the increased metastasis of CD73(+) tumors. Our results thus revealed that CD73 promotes tumor metastasis through multiple mechanisms, including suppression of NK cell function. Furthermore, our data strongly suggest that A2A or A2B antagonists may be useful for the treatment of metastatic disease. Overall, our study has potential therapeutic implications given that A2A/A2B receptor antagonists have already entered clinical trials in other therapeutic settings.
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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) | 0.001 | 0.001 |
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".