The ectonucleotidases <scp>CD</scp>39 and <scp>CD</scp>73: Novel checkpoint inhibitor targets
Bibliographic record
Abstract
Cancers are able to grow by subverting immune suppressive pathways, to prevent the malignant cells as being recognized as dangerous or foreign. This mechanism prevents the cancer from being eliminated by the immune system and allows disease to progress from a very early stage to a lethal state. Immunotherapies are newly developing interventions that modify the patient's immune system to fight cancer, by either directly stimulating rejection-type processes or blocking suppressive pathways. Extracellular adenosine generated by the ectonucleotidases CD39 and CD73 is a newly recognized "immune checkpoint mediator" that interferes with anti-tumor immune responses. In this review, we focus on CD39 and CD73 ectoenzymes and encompass aspects of the biochemistry of these molecules as well as detailing the distribution and function on immune cells. Effects of CD39 and CD73 inhibition in preclinical and clinical studies are discussed. Finally, we provide insights into potential clinical application of adenosinergic and other purinergic-targeting therapies and forecast how these might develop in combination with other anti-cancer modalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".