Antimetastatic Effects of Blocking PD-1 and the Adenosine A2A Receptor
Bibliographic record
Abstract
Adenosine targeting is an attractive new approach to cancer treatment, but no clinical study has yet examined adenosine inhibition in oncology despite the safe clinical profile of adenosine A2A receptor inhibitors (A2ARi) in Parkinson disease. Metastasis is the main cause of cancer-related deaths worldwide, and therefore we have studied experimental and spontaneous mouse models of melanoma and breast cancer metastasis to demonstrate the efficacy and mechanism of a combination of A2ARi in combination with anti-PD-1 monoclonal antibody (mAb). This combination significantly reduces metastatic burden and prolongs the life of mice compared with either monotherapy alone. Importantly, the combination was only effective when the tumor expressed high levels of CD73, suggesting a tumor biomarker that at a minimum could be used to stratify patients that might receive this combination. The mechanism of the combination therapy was critically dependent on NK cells and IFNγ, and to a lesser extent, CD8(+) T cells and the effector molecule, perforin. Overall, these results provide a strong rationale to use A2ARi with anti-PD-1 mAb for the treatment of minimal residual and metastatic disease.
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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) | 0.002 | 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".