Adenosine 2A Receptor Blockade as an Immunotherapy for Treatment-Refractory Renal Cell Cancer
Bibliographic record
Abstract
Abstract Adenosine mediates immunosuppression within the tumor microenvironment through triggering adenosine 2A receptors (A2AR) on immune cells. To determine whether this pathway could be targeted as an immunotherapy, we performed a phase I clinical trial with a small-molecule A2AR antagonist. We find that this molecule can safely block adenosine signaling in vivo. In a cohort of 68 patients with renal cell cancer (RCC), we also observe clinical responses alone and in combination with an anti–PD-L1 antibody, including subjects who had progressed on PD-1/PD-L1 inhibitors. Durable clinical benefit is associated with increased recruitment of CD8+ T cells into the tumor. Treatment can also broaden the circulating T-cell repertoire. Clinical responses are associated with an adenosine-regulated gene-expression signature in pretreatment tumor biopsies. A2AR signaling, therefore, represents a targetable immune checkpoint distinct from PD-1/PD-L1 that restricts antitumor immunity. Significance: This first-in-human study of an A2AR antagonist for cancer treatment establishes the safety and feasibility of targeting this pathway by demonstrating antitumor activity with single-agent and anti–PD-L1 combination therapy in patients with refractory RCC. Responding patients possess an adenosine-regulated gene-expression signature in pretreatment tumor biopsies. See related commentary by Sitkovsky, p. 16. This article is highlighted in the In This Issue feature, p. 1
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".