Targeting Cancer-Derived Adenosine:New Therapeutic Approaches
Bibliographic record
Abstract
UNLABELLED: CD73 generation of immunosuppressive adenosine within the hypoxic tumor microenvironment causes dysregulation of immune cell infiltrates, resulting in tumor progression, metastases, and poor disease outcomes. Therapies targeted toward the adenosinergic pathway, such as antibodies targeting CD73 and CD39, have proven efficacy in mouse tumor models; however, humanized versions are only in preliminary development. In contrast, A(2A) adenosine receptor antagonists have progressed to late-stage clinical trials in Parkinson disease, yet evidence of their role in oncology is limited. This review will compare the merits and challenges of these therapeutic approaches, identifying tumor indications and combinations that may be fruitful as they progress to the clinic. SIGNIFICANCE: High concentrations of immunosuppressive adenosine have been reported in cancers, and adenosine is implicated in the growth of tumors. This brief review delineates the current treatment strategies and tumor subtypes that will benefit from targeting adenosinergic pathways, alone or in combination with contemporary approaches to cancer treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".