Targeting the adenosine 2A receptor enhances chimeric antigen receptor T cell efficacy
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) T cells have been highly successful in treating hematological malignancies, including acute and chronic lymphoblastic leukemia. However, treatment of solid tumors using CAR T cells has been largely unsuccessful to date, partly because of tumor-induced immunosuppressive mechanisms, including adenosine production. Previous studies have shown that adenosine generated by tumor cells potently inhibits endogenous antitumor T cell responses through activation of adenosine 2A receptors (A2ARs). Herein, we have observed that CAR activation resulted in increased A2AR expression and suppression of both murine and human CAR T cells. This was reversible using either A2AR antagonists or genetic targeting of A2AR using shRNA. In 2 syngeneic HER2+ self-antigen tumor models, we found that either genetic or pharmacological targeting of the A2AR profoundly increased CAR T cell efficacy, particularly when combined with PD-1 blockade. Mechanistically, this was associated with increased cytokine production of CD8+ CAR T cells and increased activation of both CD8+ and CD4+ CAR T cells. Given the known clinical relevance of the CD73/adenosine pathway in several solid tumor types, and the initiation of phase I trials for A2AR antagonists in oncology, this approach has high translational potential to enhance CAR T cell efficacy in several cancer types.
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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) | 0.001 | 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 | 0.000 | 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".