Adenosine Receptor 2A Blockade Increases the Efficacy of Anti–PD-1 through Enhanced Antitumor T-cell Responses
Bibliographic record
Abstract
Immunotherapy is rapidly emerging as a cancer treatment with high potential. Recent clinical trials with anti-CTLA-4 and anti-PD-1/PD-L1 antibodies (mAbs) suggest that targeting multiple immunosuppressive pathways may significantly improve patient survival. The generation of adenosine by CD73 also suppresses antitumor immune responses through the activation of A2A receptors on T cells and natural killer (NK) cells. We sought to determine whether blockade of A2A receptors could enhance the efficacy of anti-PD-1 mAb. The expression of CD73 by tumor cells limited the efficacy of anti-PD-1 mAb in two tumor models, and this was alleviated with concomitant treatment with an A2A adenosine receptor antagonist. The blockade of PD-1 enhanced A2A receptor expression on tumor-infiltrating CD8(+) T cells, making them more susceptible to A2A-mediated suppression. Thus, dual blockade of PD-1 and A2A significantly enhanced the expression of IFNγ and Granzyme B by tumor-infiltrating CD8(+) T cells and, accordingly, increased growth inhibition of CD73(+) tumors and survival of mice. The results of our study indicate that CD73 expression may constitute a potential biomarker for the efficacy of anti-PD-1 mAb in patients with cancer and that the efficacy of anti-PD-1 mAb can be significantly enhanced by A2A antagonists. We have therefore revealed a potentially novel biomarker for the efficacy of anti-PD-1 that warrants further investigation in patients. Because our studies used SYN-115, a drug that has already undergone phase IIb testing in Parkinson disease, our findings have immediate translational relevance for patients with cancer.
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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 | 0.000 |
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".